diff --git a/.coveragerc b/.coveragerc deleted file mode 100644 index fe8d42dd..00000000 --- a/.coveragerc +++ /dev/null @@ -1,13 +0,0 @@ -# .coveragerc to control coverage.py -[report] -# regrexes for lines to exclude from consideration -exclude_lines = - if __name__ == .__main__.: - ValueError - TypeError - NotImplementedError -omit = - matchzoo/__init__.py - matchzoo/version.py - matchzoo/models/parameter_readme_generator.py - matchzoo/*/__init__.py diff --git a/.flake8 b/.flake8 deleted file mode 100644 index ce6588e6..00000000 --- a/.flake8 +++ /dev/null @@ -1,17 +0,0 @@ -[flake8] -ignore = - # D401 First line should be in imperative mood - D401, - # D202 No blank lines allowed after function docstring - D202, - - # For doctests: - # D207 Docstring is under-indented - D207, - # D301 Use r""" if any backslashes in a docstring - D301, - # F401 'blah blah' imported but unused - F401, - - # D100 Missing docstring in public module - D100, \ No newline at end of file diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index 037a5516..00000000 --- a/.gitattributes +++ /dev/null @@ -1 +0,0 @@ -tutorials/* linguist-vendored \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/bug-report.md b/.github/ISSUE_TEMPLATE/bug-report.md deleted file mode 100644 index b9f11b95..00000000 --- a/.github/ISSUE_TEMPLATE/bug-report.md +++ /dev/null @@ -1,29 +0,0 @@ ---- -name: Bug Report -about: Create a report to help us improve -title: '' -labels: bug -assignees: '' - ---- - -### Describe the bug -Please provide a clear and concise description of what the bug is. If applicable, add screenshots to help explain your problem, especially for visualization related problems. - -### To Reproduce -Please provide a [Minimal, Complete, and Verifiable example](https://stackoverflow.com/help/mcve) here. We hope we can simply copy/paste/run it. It is also nice to share a hosted runnable script (e.g. Google Colab), especially for hardware-related problems. - -### Describe your attempts -- [ ] I checked the documentation and found no answer -- [ ] I checked to make sure that this is not a duplicate issue - -You should also provide code snippets you tried as a workaround, StackOverflow solution that you have walked through, or your best guess of the cause that you can't locate (e.g. cosmic radiation). - -### Context - - **OS** [e.g. Windows 10, macOS 10.14]: - - **Hardware** [e.g. CPU only, GTX 1080 Ti]: - -In addition, figure out your MatchZoo version by running `import matchzoo; matchzoo.__version__`. If this gives you an error, then you're probably using `1.0`, and `1.0` is no longer supported. Then attach the corresponding label on the issue. - -### Additional Information -Other things you want the developers to know. diff --git a/.github/ISSUE_TEMPLATE/feature-request.md b/.github/ISSUE_TEMPLATE/feature-request.md deleted file mode 100644 index c419bd63..00000000 --- a/.github/ISSUE_TEMPLATE/feature-request.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -name: Feature Request -about: Suggest an idea for this project -title: '' -labels: enhancement -assignees: '' - ---- - -- [ ] I checked to make sure that this is not a duplicate issue -- [ ] I'm submitting the request to the correct repository (for model requests, see [here](https://github.com/NTMC-Community/awaresome-neural-models-for-semantic-match)) - -### Is your feature request related to a problem? Please describe. -A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] - -### Describe the solution you'd like -A clear and concise description of what you want to happen. - -### Describe alternatives you've considered -A clear and concise description of any alternative solutions or features you've considered. - -### Additional Information -Other things you want the developers to know. diff --git a/.github/ISSUE_TEMPLATE/usage-question.md b/.github/ISSUE_TEMPLATE/usage-question.md deleted file mode 100644 index 8604cf5b..00000000 --- a/.github/ISSUE_TEMPLATE/usage-question.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -name: Usage Question -about: Ask a question about MatchZoo usage -title: '' -labels: question -assignees: '' - ---- - -### Describe the Question -Please provide a clear and concise description of what the question is. - -### Describe your attempts -- [ ] I walked through the tutorials -- [ ] I checked the documentation -- [ ] I checked to make sure that this is not a duplicate question - -You may also provide a [Minimal, Complete, and Verifiable example](https://stackoverflow.com/help/mcve) you tried as a workaround, or StackOverflow solution that you have walked through. (e.g. cosmic radiation). - -In addition, figure out your MatchZoo version by running `import matchzoo; matchzoo.__version__`. If this gives you an error, then you're probably using `1.0`, and `1.0` is no longer supported. Then attach the corresponding label on the issue. diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 6e078af4..00000000 --- a/.gitignore +++ /dev/null @@ -1,27 +0,0 @@ -*.pyc -*.log -*.swp -*.bak -*.weights -*.trec -*.ranklist -*.DS_Store -.vscode -.coverage -.ipynb_checkpoints/ -predict.* -build/ -dist/ -data/ -log/* -.ipynb_checkpoints/ -matchzoo/log/* -matchzoo/querydecision/ -log/* -.idea/ -.pytest_cache/ -MatchZoo.egg-info/ -notebooks/wikiqa/.ipynb_checkpoints/* -.cache -.tmpdir -htmlcov/ \ No newline at end of file diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index e3911359..00000000 --- a/.travis.yml +++ /dev/null @@ -1,38 +0,0 @@ -language: python - -cache: pip - -sudo: true - -env: - global: - - PYTHONPATH=$PYTHONPATH:$TRAVIS_BUILD_DIR/tests:$TRAVIS_BUILD_DIR/matchzoo - -matrix: - allow_failures: - - os: osx - include: - - os: linux - dist: trusty - python: 3.6 - - os: osx - osx_image: xcode10.2 - language: shell - -install: - - pip3 install -U pip - - pip3 install -r requirements.txt - - python3 -m nltk.downloader punkt - - python3 -m nltk.downloader wordnet - - python3 -m nltk.downloader stopwords - -script: - - stty cols 80 - - export COLUMNS=80 - - if [ "$TRAVIS_EVENT_TYPE" == "pull_request" ]; then make push; fi - - if [ "$TRAVIS_EVENT_TYPE" == "push" ]; then make push; fi - - if [ "$TRAVIS_EVENT_TYPE" == "cron" ]; then make cron; fi - - -after_success: - - codecov diff --git a/CODEOWNERS b/CODEOWNERS deleted file mode 100644 index 3859aab1..00000000 --- a/CODEOWNERS +++ /dev/null @@ -1,49 +0,0 @@ -# Watchers and contributors to MatchZoo repo directories/packages/files -# Please see documentation of use of CODEOWNERS file at -# https://help.github.com/articles/about-codeowners/ and -# https://github.com/blog/2392-introducing-code-owners -# -# Anybody can add themselves or a team as additional watcher or contributor -# to get notified about changes in a specific package. -# See https://help.github.com/articles/about-teams how to setup teams. - -# Define individuals or teams that are responsible for code in a repository. - -# global owner. -* @faneshion - -# third-party & project configuration -.coveragerc @bwanglzu -.gitignore @faneshion -.travis.yml @bwanglzu -CONTRIBUTING.MD @bwanglzu -Makefile @uduse @bwanglzu -README.md @faneshion @pl8787 -readthedocs.yml @wqh17101 @bwanglzu -requirements.txt @faneshion @pl8787 -setup.py @faneshion @pl8787 - -# artworks -/artworks/ @faneshion - -# tutorials -/tutorials/ @uduse @faneshion - -# docs -/docs/ @wqh17101 @bwanglzu - -# tests -/tests/ @faneshion @uduse @bwanglzu - -# matchzoo -/matchzoo/engine/ @faneshion @bwanglzu @uduse @pl8787 -/matchzoo/auto/ @uduse @bwanglzu -/matchzoo/models/ @faneshion @pl8787 @bwanglzu @uduse -/matchzoo/preprocessor/ @uduse @faneshion @pl8787 -/matchzoo/tasks/ @uduse @bwanglzu -/matchzoo/data_generator/ @faneshion @uduse @pl8787 -/matchzoo/data_pack/ @faneshion @uduse -/matchzoo/metrics/ @faneshion @pl8787 @uduse -/matchzoo/losses/ @faneshion @pl8787 @bwanglzu -/matchzoo/layers/ @uduse @yangliuy -/matchzoo/* @faneshion @uduse @bwanglzu diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index 02685178..00000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,66 +0,0 @@ -Contributing to Matchzoo ----------- - -> Note: Matchzoo is developed under Python 3.6. - -Welcome! Matchzoo is a community project that aims to work for a wide range of NLP and IR tasks such as Question Answering, Information Retrieval, Paraphrase identification etc. Your experience and what you can contribute are important to the project's success. - -Discussion ----------- - -If you've run into behavior in Matchzoo you don't understand, or you're having trouble working out a good way to apply it to your code, or you've found a bug or would like a feature it doesn't have, we want to hear from you! - -Our main forum for discussion is the project's [GitHub issue tracker](https://github.com/NTMC-Community/MatchZoo/issues). This is the right place to start a discussion of any of the above or most any other topic concerning the project. - -For less formal discussion we have a chat room on Wechat (mostly Chinese speakers). Matchzoo core developers are almost always present; feel free to find us there and we're happy to chat. Please add *CLJ_Keep* as your wechat friend, she will invite you to join the chat room. - -First Time Contributors ------------------------ - -Matchzoo appreciates your contribution! If you are interested in helping improve Matchzoo, there are several ways to get started: - -* Work on [new models](https://github.com/NTMC-Community/awaresome-neural-models-for-semantic-match). -* Work on [tutorials](https://github.com/NTMC-Community/MatchZoo/tree/master/tutorials). -* Work on [documentation](https://github.com/NTMC-Community/MatchZoo/tree/master/docs). -* Try to answer questions on [the issue tracker](https://github.com/NTMC-Community/MatchZoo/issues). - -Submitting Changes ------------------- - -Even more excellent than a good bug report is a fix for a bug, or the implementation of a much-needed new model. - -(*) We'd love to have your contributions. - -(*) If your new feature will be a lot of work, we recommend talking to us early -- see below. - -We use the usual GitHub pull-request flow, which may be familiar to you if you've contributed to other projects on GitHub -- see blow. - -Anyone interested in Matchzoo may review your code. One of the Matchzoo core developers will merge your pull request when they think it's ready. -For every pull request, we aim to promptly either merge it or say why it's not yet ready; if you go a few days without a reply, please feel -free to ping the thread by adding a new comment. - -For a list of Matchzoo core developers, see [Readme](https://github.com/NTMC-Community/MatchZoo/blob/master/README.md). - -Contributing Flow ------------------- - -1. Fork the latest version of [MatchZoo](https://github.com/NTMC-Community/MatchZoo) into your repo. -2. Create an issue under [NTMC-Community/Matchzoo](https://github.com/NTMC-Community/MatchZoo/issues), write description about the bug/enhancement. -3. Clone your forked MatchZoo into your machine, add your changes together with associated tests. -4. Run `make test` with terminal, ensure all unit tests & integration tests passed on your computer. -5. Push to your forked repo, then send the pull request to the official repo. In pull request, you need to create a link to the issue you created using `#[issue_id]`, and describe what has been changed. -6. Wait [continuous integration](https://travis-ci.org/faneshion/MatchZoo/) passed. -7. Wait [Codecov](https://codecov.io/gh/faneshion/MatchZoo) generate the coverage report. -8. We'll assign reviewers to review your code. - - -Your PR will be merged if: -- Funcitonally benefit for the project. -- Passed Countinuous Integration (all unit tests, integration tests and [PEP8](https://www.python.org/dev/peps/pep-0008/) check passed). -- Test coverage didn't decreased, we use [pytest](https://docs.pytest.org/en/latest/). -- With proper docstrings, see codebase as examples. -- With type hints, see [typing](https://docs.python.org/3/library/typing.html). -- All reviewers approved your changes. - - -**Thanks and let's improve MatchZoo together!** \ No newline at end of file diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 11a9abcb..00000000 --- a/LICENSE +++ /dev/null @@ -1,203 +0,0 @@ -Copyright 2017 The MatchZoo Authors. All rights reserved. - - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. Definitions. - - "License" shall mean the terms and conditions for use, reproduction, - and distribution as defined by Sections 1 through 9 of this document. - - "Licensor" shall mean the copyright owner or entity authorized by - the copyright owner that is granting the License. - - "Legal Entity" shall mean the union of the acting entity and all - other entities that control, are controlled by, or are under common - control with that entity. For the purposes of this definition, - "control" means (i) the power, direct or indirect, to cause the - direction or management of such entity, whether by contract or - otherwise, or (ii) ownership of fifty percent (50%) or more of the - outstanding shares, or (iii) beneficial ownership of such entity. - - "You" (or "Your") shall mean an individual or Legal Entity - exercising permissions granted by this License. - - "Source" form shall mean the preferred form for making modifications, - including but not limited to software source code, documentation - source, and configuration files. - - "Object" form shall mean any form resulting from mechanical - transformation or translation of a Source form, including but - not limited to compiled object code, generated documentation, - and conversions to other media types. - - "Work" shall mean the work of authorship, whether in Source or - Object form, made available under the License, as indicated by a - copyright notice that is included in or attached to the work - (an example is provided in the Appendix below). - - "Derivative Works" shall mean any work, whether in Source or Object - form, that is based on (or derived from) the Work and for which the - editorial revisions, annotations, elaborations, or other modifications - represent, as a whole, an original work of authorship. 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For the purposes of this definition, "submitted" - means any form of electronic, verbal, or written communication sent - to the Licensor or its representatives, including but not limited to - communication on electronic mailing lists, source code control systems, - and issue tracking systems that are managed by, or on behalf of, the - Licensor for the purpose of discussing and improving the Work, but - excluding communication that is conspicuously marked or otherwise - designated in writing by the copyright owner as "Not a Contribution." - - "Contributor" shall mean Licensor and any individual or Legal Entity - on behalf of whom a Contribution has been received by Licensor and - subsequently incorporated within the Work. - - 2. Grant of Copyright License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - copyright license to reproduce, prepare Derivative Works of, - publicly display, publicly perform, sublicense, and distribute the - Work and such Derivative Works in Source or Object form. - - 3. Grant of Patent License. Subject to the terms and conditions of - this License, each Contributor hereby grants to You a perpetual, - worldwide, non-exclusive, no-charge, royalty-free, irrevocable - (except as stated in this section) patent license to make, have made, - use, offer to sell, sell, import, and otherwise transfer the Work, - where such license applies only to those patent claims licensable - by such Contributor that are necessarily infringed by their - Contribution(s) alone or by combination of their Contribution(s) - with the Work to which such Contribution(s) was submitted. If You - institute patent litigation against any entity (including a - cross-claim or counterclaim in a lawsuit) alleging that the Work - or a Contribution incorporated within the Work constitutes direct - or contributory patent infringement, then any patent licenses - granted to You under this License for that Work shall terminate - as of the date such litigation is filed. - - 4. Redistribution. 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The contents - of the NOTICE file are for informational purposes only and - do not modify the License. You may add Your own attribution - notices within Derivative Works that You distribute, alongside - or as an addendum to the NOTICE text from the Work, provided - that such additional attribution notices cannot be construed - as modifying the License. - - You may add Your own copyright statement to Your modifications and - may provide additional or different license terms and conditions - for use, reproduction, or distribution of Your modifications, or - for any such Derivative Works as a whole, provided Your use, - reproduction, and distribution of the Work otherwise complies with - the conditions stated in this License. - - 5. Submission of Contributions. Unless You explicitly state otherwise, - any Contribution intentionally submitted for inclusion in the Work - by You to the Licensor shall be under the terms and conditions of - this License, without any additional terms or conditions. - Notwithstanding the above, nothing herein shall supersede or modify - the terms of any separate license agreement you may have executed - with Licensor regarding such Contributions. - - 6. Trademarks. This License does not grant permission to use the trade - names, trademarks, service marks, or product names of the Licensor, - except as required for reasonable and customary use in describing the - origin of the Work and reproducing the content of the NOTICE file. - - 7. Disclaimer of Warranty. Unless required by applicable law or - agreed to in writing, Licensor provides the Work (and each - Contributor provides its Contributions) on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or - implied, including, without limitation, any warranties or conditions - of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A - PARTICULAR PURPOSE. You are solely responsible for determining the - appropriateness of using or redistributing the Work and assume any - risks associated with Your exercise of permissions under this License. - - 8. Limitation of Liability. In no event and under no legal theory, - whether in tort (including negligence), contract, or otherwise, - unless required by applicable law (such as deliberate and grossly - negligent acts) or agreed to in writing, shall any Contributor be - liable to You for damages, including any direct, indirect, special, - incidental, or consequential damages of any character arising as a - result of this License or out of the use or inability to use the - Work (including but not limited to damages for loss of goodwill, - work stoppage, computer failure or malfunction, or any and all - other commercial damages or losses), even if such Contributor - has been advised of the possibility of such damages. - - 9. Accepting Warranty or Additional Liability. While redistributing - the Work or Derivative Works thereof, You may choose to offer, - and charge a fee for, acceptance of support, warranty, indemnity, - or other liability obligations and/or rights consistent with this - License. However, in accepting such obligations, You may act only - on Your own behalf and on Your sole responsibility, not on behalf - of any other Contributor, and only if You agree to indemnify, - defend, and hold each Contributor harmless for any liability - incurred by, or claims asserted against, such Contributor by reason - of your accepting any such warranty or additional liability. - - END OF TERMS AND CONDITIONS - - APPENDIX: How to apply the Apache License to your work. - - To apply the Apache License to your work, attach the following - boilerplate notice, with the fields enclosed by brackets "[]" - replaced with your own identifying information. (Don't include - the brackets!) The text should be enclosed in the appropriate - comment syntax for the file format. We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright 2017, The MatchZoo Authors. - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. diff --git a/Makefile b/Makefile deleted file mode 100644 index df5408cd..00000000 --- a/Makefile +++ /dev/null @@ -1,70 +0,0 @@ -# Usages: -# -# to install matchzoo dependencies: -# $ make init -# -# to run all matchzoo tests, recommended for big PRs and new versions: -# $ make test -# -# there are three kinds of tests: -# -# 1. "quick" tests -# - run in seconds -# - include all unit tests without marks and all doctests -# - for rapid prototyping -# - CI run this for all PRs -# -# 2. "slow" tests -# - run in minutes -# - include all unit tests marked "slow" -# - CI run this for all PRs -# -# 3. "cron" tests -# - run in minutes -# - involves underministic behavoirs (e.g. network connection) -# - include all unit tests marked "cron" -# - CI run this on a daily basis -# -# to run quick tests, excluding time consuming tests and crons: -# $ make quick -# -# to run slow tests, excluding normal tests and crons: -# $ make slow -# -# to run crons: -# $ make cron -# -# to run all tests: -# $ make test -# -# to run CI push/PR tests: -# $ make push -# -# to run docstring style check: -# $ make flake - -init: - pip install -r requirements.txt - -TEST_ARGS = -v --full-trace -l --doctest-modules --doctest-continue-on-failure --cov matchzoo/ --cov-report term-missing --cov-report html --cov-config .coveragerc matchzoo/ tests/ -W ignore::DeprecationWarning --ignore=matchzoo/contrib -FLAKE_ARGS = ./matchzoo --exclude=__init__.py,matchzoo/contrib - -test: - pytest $(TEST_ARGS) - flake8 $(FLAKE_ARGS) - -push: - pytest -m 'not cron' $(TEST_ARGS) ${ARGS} - flake8 $(FLAKE_ARGS) - -quick: - pytest -m 'not slow and not cron' $(TEST_ARGS) ${ARGS} - -slow: - pytest -m 'slow and not cron' $(TEST_ARGS) ${ARGS} - -cron: - pytest -m 'cron' $(TEST_ARGS) ${ARGS} - -flake: - flake8 $(FLAKE_ARGS) ${ARGS} diff --git a/README.md b/README.md deleted file mode 100644 index c4844d34..00000000 --- a/README.md +++ /dev/null @@ -1,291 +0,0 @@ -
-logo -
- -# MatchZoo [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=MatchZoo:%20deep%20learning%20for%20semantic%20matching&url=https://github.com/NTMC-Community/MatchZoo) - -> Facilitating the design, comparison and sharing of deep text matching models.
-> MatchZoo 是一个通用的文本匹配工具包,它旨在方便大家快速的实现、比较、以及分享最新的深度文本匹配模型。 - -[![Python 3.6](https://img.shields.io/badge/python-3.6%20%7C%203.7-blue.svg)](https://www.python.org/downloads/release/python-360/) -[![Pypi Downloads](https://img.shields.io/pypi/dm/matchzoo.svg?label=pypi)](https://pypi.org/project/MatchZoo/) -[![Documentation Status](https://readthedocs.org/projects/matchzoo/badge/?version=master)](https://matchzoo.readthedocs.io/en/master/?badge=master) -[![Build Status](https://travis-ci.org/NTMC-Community/MatchZoo.svg?branch=master)](https://travis-ci.org/NTMC-Community/MatchZoo/) -[![codecov](https://codecov.io/gh/NTMC-Community/MatchZoo/branch/master/graph/badge.svg)](https://codecov.io/gh/NTMC-Community/MatchZoo) -[![License](https://img.shields.io/badge/License-Apache%202.0-yellowgreen.svg)](https://opensource.org/licenses/Apache-2.0) -[![Requirements Status](https://requires.io/github/NTMC-Community/MatchZoo/requirements.svg?branch=master)](https://requires.io/github/NTMC-Community/MatchZoo/requirements/?branch=master) ---- -🔥**News: [MatchZoo-py](https://github.com/NTMC-Community/MatchZoo-py) (PyTorch version of MatchZoo) is ready now.** - -The goal of MatchZoo is to provide a high-quality codebase for deep text matching research, such as document retrieval, question answering, conversational response ranking, and paraphrase identification. With the unified data processing pipeline, simplified model configuration and automatic hyper-parameters tunning features equipped, MatchZoo is flexible and easy to use. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
TasksText 1Text 2Objective
Paraphrase Identification string 1 string 2 classification
Textual Entailment text hypothesis classification
Question Answer question answer classification/ranking
Conversation dialog response classification/ranking
Information Retrieval query document ranking
- -## Get Started in 60 Seconds - -To train a [Deep Semantic Structured Model](https://www.microsoft.com/en-us/research/project/dssm/), import matchzoo and prepare input data. - -```python -import matchzoo as mz - -train_pack = mz.datasets.wiki_qa.load_data('train', task='ranking') -valid_pack = mz.datasets.wiki_qa.load_data('dev', task='ranking') -``` - -Preprocess your input data in three lines of code, keep track parameters to be passed into the model. - -```python -preprocessor = mz.preprocessors.DSSMPreprocessor() -train_processed = preprocessor.fit_transform(train_pack) -valid_processed = preprocessor.transform(valid_pack) -``` - -Make use of MatchZoo customized loss functions and evaluation metrics: - -```python -ranking_task = mz.tasks.Ranking(loss=mz.losses.RankCrossEntropyLoss(num_neg=4)) -ranking_task.metrics = [ - mz.metrics.NormalizedDiscountedCumulativeGain(k=3), - mz.metrics.MeanAveragePrecision() -] -``` - -Initialize the model, fine-tune the hyper-parameters. - -```python -model = mz.models.DSSM() -model.params['input_shapes'] = preprocessor.context['input_shapes'] -model.params['task'] = ranking_task -model.guess_and_fill_missing_params() -model.build() -model.compile() -``` - -Generate pair-wise training data on-the-fly, evaluate model performance using customized callbacks on validation data. - -```python -train_generator = mz.PairDataGenerator(train_processed, num_dup=1, num_neg=4, batch_size=64, shuffle=True) -valid_x, valid_y = valid_processed.unpack() -evaluate = mz.callbacks.EvaluateAllMetrics(model, x=valid_x, y=valid_y, batch_size=len(valid_x)) -history = model.fit_generator(train_generator, epochs=20, callbacks=[evaluate], workers=5, use_multiprocessing=False) -``` - -## References -[Tutorials](https://github.com/NTMC-Community/MatchZoo/tree/master/tutorials) - -[English Documentation](https://matchzoo.readthedocs.io/en/master/) - -[中文文档](https://matchzoo.readthedocs.io/zh/latest/) - -If you're interested in the cutting-edge research progress, please take a look at [awaresome neural models for semantic match](https://github.com/NTMC-Community/awaresome-neural-models-for-semantic-match). - -## Install - -MatchZoo is dependent on [Keras](https://github.com/keras-team/keras) and [Tensorflow](https://github.com/tensorflow/tensorflow). Two ways to install MatchZoo: - -**Install MatchZoo from Pypi:** - -```python -pip install matchzoo -``` - -**Install MatchZoo from the Github source:** - -``` -git clone https://github.com/NTMC-Community/MatchZoo.git -cd MatchZoo -python setup.py install -``` - - -## Models - -1. [DRMM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/drmm.py): this model is an implementation of A Deep Relevance Matching Model for Ad-hoc Retrieval. - -2. [MatchPyramid](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/match_pyramid.py): this model is an implementation of Text Matching as Image Recognition - -3. [ARC-I](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/arci.py): this model is an implementation of Convolutional Neural Network Architectures for Matching Natural Language Sentences - -4. [DSSM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/dssm.py): this model is an implementation of Learning Deep Structured Semantic Models for Web Search using Clickthrough Data - -5. [CDSSM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/cdssm.py): this model is an implementation of Learning Semantic Representations Using Convolutional Neural Networks for Web Search - -6. [ARC-II](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/arcii.py): this model is an implementation of Convolutional Neural Network Architectures for Matching Natural Language Sentences - -7. [MV-LSTM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/mvlstm.py):this model is an implementation of A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations - -8. [aNMM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/anmm.py): this model is an implementation of aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model - -9. [DUET](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/duet.py): this model is an implementation of Learning to Match Using Local and Distributed Representations of Text for Web Search - -10. [K-NRM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/knrm.py): this model is an implementation of End-to-End Neural Ad-hoc Ranking with Kernel Pooling - -11. [CONV-KNRM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/conv_knrm.py): this model is an implementation of Convolutional neural networks for soft-matching n-grams in ad-hoc search - -12. models under development: Match-SRNN, DeepRank, BiMPM .... - - -## Citation - -If you use MatchZoo in your research, please use the following BibTex entry. - -``` -@inproceedings{Guo:2019:MLP:3331184.3331403, - author = {Guo, Jiafeng and Fan, Yixing and Ji, Xiang and Cheng, Xueqi}, - title = {MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching}, - booktitle = {Proceedings of the 42Nd International ACM SIGIR Conference on Research and Development in Information Retrieval}, - series = {SIGIR'19}, - year = {2019}, - isbn = {978-1-4503-6172-9}, - location = {Paris, France}, - pages = {1297--1300}, - numpages = {4}, - url = {http://doi.acm.org/10.1145/3331184.3331403}, - doi = {10.1145/3331184.3331403}, - acmid = {3331403}, - publisher = {ACM}, - address = {New York, NY, USA}, - keywords = {matchzoo, neural network, text matching}, -} -``` - - -## Development Team - - ​ ​ ​ ​ - - - - - - - - - - - - - - - - - -
- ​ faneshion
- ​ Fan Yixing ​ -

Core Dev
- ASST PROF, ICT

​ -
- bwanglzu
- Wang Bo ​ -

Core Dev
M.S. TU Delft

​ -
- ​ uduse
- Wang Zeyi -

Core Dev
B.S. UC Davis

​ -
- ​ pl8787
- ​ Pang Liang -

Core Dev
- ASST PROF, ICT

​ -
- ​ yangliuy
- ​ Yang Liu -

Core Dev
- PhD. UMASS

​ -
- ​ wqh17101
- ​ Wang Qinghua ​ -

Documentation
- B.S. Shandong Univ.

​ -
- ​ ZizhenWang
- ​ Wang Zizhen ​ -

Dev
- M.S. UCAS

​ -
- ​ lixinsu
- ​ Su Lixin -

Dev
- PhD. UCAS

​ -
- ​ zhouzhouyang520
- ​ Yang Zhou ​ -

Dev
- M.S. CQUT

​ -
- ​ rgtjf
- ​ Tian Junfeng ​ -

Dev
- M.S. ECNU

​ -
- - - -## Contribution - -Please make sure to read the [Contributing Guide](./CONTRIBUTING.md) before creating a pull request. If you have a MatchZoo-related paper/project/compnent/tool, send a pull request to [this awesome list](https://github.com/NTMC-Community/awaresome-neural-models-for-semantic-match)! - -Thank you to all the people who already contributed to MatchZoo! - -[Jianpeng Hou](https://github.com/HouJP), [Lijuan Chen](https://github.com/githubclj), [Yukun Zheng](https://github.com/zhengyk11), [Niuguo Cheng](https://github.com/niuox), [Dai Zhuyun](https://github.com/AdeDZY), [Aneesh Joshi](https://github.com/aneesh-joshi), [Zeno Gantner](https://github.com/zenogantner), [Kai Huang](https://github.com/hkvision), [stanpcf](https://github.com/stanpcf), [ChangQF](https://github.com/ChangQF), [Mike Kellogg -](https://github.com/wordreference) - - - - -## Project Organizers - -- Jiafeng Guo - * Institute of Computing Technology, Chinese Academy of Sciences - * [Homepage](http://www.bigdatalab.ac.cn/~gjf/) -- Yanyan Lan - * Institute of Computing Technology, Chinese Academy of Sciences - * [Homepage](http://www.bigdatalab.ac.cn/~lanyanyan/) -- Xueqi Cheng - * Institute of Computing Technology, Chinese Academy of Sciences - * [Homepage](http://www.bigdatalab.ac.cn/~cxq/) - - -## License - -[Apache-2.0](https://opensource.org/licenses/Apache-2.0) - -Copyright (c) 2015-present, Yixing Fan (faneshion) diff --git a/artworks/matchzoo-logo.png b/artworks/matchzoo-logo.png deleted file mode 100644 index 781bc2d7..00000000 Binary files a/artworks/matchzoo-logo.png and /dev/null differ diff --git a/artworks/matchzoo_github_qr.png b/artworks/matchzoo_github_qr.png deleted file mode 100644 index 650b57e6..00000000 Binary files a/artworks/matchzoo_github_qr.png and /dev/null differ diff --git a/artworks/matchzoo_github_qr_black.png b/artworks/matchzoo_github_qr_black.png deleted file mode 100644 index 91c9ee85..00000000 Binary files a/artworks/matchzoo_github_qr_black.png and /dev/null differ diff --git a/docs/DOCCHECK.md b/docs/DOCCHECK.md deleted file mode 100644 index cdb5752c..00000000 --- a/docs/DOCCHECK.md +++ /dev/null @@ -1,29 +0,0 @@ -Documentation Checking Process(Only for the developers) -========================================================== - -# Why - -It is necessary for all the developers to generate the rst files which can help us check the documents. - -# When - -1. You add a new function to one of the scripts in the {MatchZoo/matchzoo} or its subdirs -1. You add a new script to {MatchZoo/matchzoo} or its subdirs -1. You add a new directory to {MatchZoo/matchzoo} or its subdirs - -# How -## Make sure you have installed sphinx - -1. Enter the docs directory - -``` -cd {MatchZoo/docs} -``` - -2. Generate the rst files - -``` -sphinx-apidoc -f -o source ../matchzoo -``` - -3. Commit diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index b29e8b0a..00000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -SPHINXPROJ = MatchZoo -SOURCEDIR = source -BUILDDIR = _build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/Readme.md b/docs/Readme.md deleted file mode 100644 index 7b1b43c7..00000000 --- a/docs/Readme.md +++ /dev/null @@ -1,25 +0,0 @@ -## Build Documentation: - - - -#### Install Requirements - -```python -pip install -r requirements.txt -``` - - - -#### Build Documentation - -```python -# Enter docs folder. -cd docs -# Use sphinx autodoc to generate rst. -# usage: sphinx-apidoc [OPTIONS] -o [EXCLUDE_PATTERN,...] -sphinx-apidoc -o source/ ../matchzoo/ ../matchzoo/contrib -# Generate html from rst -make clean -make html -``` - diff --git a/docs/_build/doctrees/environment.pickle b/docs/_build/doctrees/environment.pickle deleted file mode 100644 index abd3413f..00000000 Binary files a/docs/_build/doctrees/environment.pickle and /dev/null differ diff --git a/docs/_build/doctrees/index.doctree b/docs/_build/doctrees/index.doctree deleted file mode 100644 index 0cea69ad..00000000 Binary files a/docs/_build/doctrees/index.doctree and /dev/null differ diff --git a/docs/_build/doctrees/matchzoo.doctree b/docs/_build/doctrees/matchzoo.doctree deleted file mode 100644 index 10e6179b..00000000 Binary files a/docs/_build/doctrees/matchzoo.doctree and /dev/null differ diff --git a/docs/_build/doctrees/matchzoo.engine.doctree b/docs/_build/doctrees/matchzoo.engine.doctree deleted file mode 100644 index 2e417f01..00000000 Binary files a/docs/_build/doctrees/matchzoo.engine.doctree and /dev/null differ diff --git a/docs/_build/doctrees/matchzoo.models.doctree b/docs/_build/doctrees/matchzoo.models.doctree deleted file mode 100644 index 34f6c4d6..00000000 Binary files a/docs/_build/doctrees/matchzoo.models.doctree and /dev/null differ diff --git a/docs/_build/doctrees/matchzoo.tasks.doctree b/docs/_build/doctrees/matchzoo.tasks.doctree deleted file mode 100644 index 75463090..00000000 Binary files a/docs/_build/doctrees/matchzoo.tasks.doctree and /dev/null differ diff --git a/docs/_build/doctrees/modules.doctree b/docs/_build/doctrees/modules.doctree deleted file mode 100644 index 8af80f8d..00000000 Binary files a/docs/_build/doctrees/modules.doctree and /dev/null differ diff --git a/docs/_build/html/.buildinfo b/docs/_build/html/.buildinfo deleted file mode 100644 index 7f4d1e9f..00000000 --- a/docs/_build/html/.buildinfo +++ /dev/null @@ -1,4 +0,0 @@ -# Sphinx build info version 1 -# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 14607cdb85fbc503df4ae80dc1192ccd -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/_images/matchzoo-logo.png b/docs/_build/html/_images/matchzoo-logo.png deleted file mode 100644 index 781bc2d7..00000000 Binary files a/docs/_build/html/_images/matchzoo-logo.png and /dev/null differ diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt deleted file mode 100644 index a29aff78..00000000 --- a/docs/_build/html/_sources/index.rst.txt +++ /dev/null @@ -1,36 +0,0 @@ -.. MatchZoo documentation master file, created by - sphinx-quickstart on Mon May 28 16:40:41 2018. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to MatchZoo's documentation! -==================================== - - -.. image:: https://travis-ci.org/faneshion/MatchZoo.svg?branch=master - :alt: ci - :target: https://travis-ci.org/faneshion/MatchZoo/ - - -.. image:: ../../artworks/matchzoo-logo.png - :alt: logo - :align: center - - -MatchZoo is a toolkit for text matching. It was developed with a focus on facilitating the designing, comparing and sharing of deep text matching models. There are a number of deep matching methods, such as DRMM, MatchPyramid, MV-LSTM, aNMM, DUET, ARC-I, ARC-II, DSSM, and CDSSM, designed with a unified interface. Potential tasks related to MatchZoo include document retrieval, question answering, conversational response ranking, paraphrase identification, etc. We are always happy to receive any code contributions, suggestions, comments from all our MatchZoo users. - - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - modules - model_reference - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/_build/html/_sources/matchzoo.engine.rst.txt b/docs/_build/html/_sources/matchzoo.engine.rst.txt deleted file mode 100644 index 0beb96b7..00000000 --- a/docs/_build/html/_sources/matchzoo.engine.rst.txt +++ /dev/null @@ -1,78 +0,0 @@ -matchzoo.engine package -======================= - -Submodules ----------- - -matchzoo.engine.base\_metric module ------------------------------------ - -.. automodule:: matchzoo.engine.base_metric - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.base\_model module ----------------------------------- - -.. automodule:: matchzoo.engine.base_model - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.base\_preprocessor module ------------------------------------------ - -.. automodule:: matchzoo.engine.base_preprocessor - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.base\_task module ---------------------------------- - -.. automodule:: matchzoo.engine.base_task - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.callbacks module --------------------------------- - -.. automodule:: matchzoo.engine.callbacks - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.hyper\_spaces module ------------------------------------- - -.. automodule:: matchzoo.engine.hyper_spaces - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.param module ----------------------------- - -.. automodule:: matchzoo.engine.param - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.param\_table module ------------------------------------ - -.. automodule:: matchzoo.engine.param_table - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.engine - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_sources/matchzoo.models.rst.txt b/docs/_build/html/_sources/matchzoo.models.rst.txt deleted file mode 100644 index 590688ff..00000000 --- a/docs/_build/html/_sources/matchzoo.models.rst.txt +++ /dev/null @@ -1,134 +0,0 @@ -matchzoo.models package -======================= - -Submodules ----------- - -matchzoo.models.anmm module ---------------------------- - -.. automodule:: matchzoo.models.anmm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.arci module ---------------------------- - -.. automodule:: matchzoo.models.arci - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.arcii module ----------------------------- - -.. automodule:: matchzoo.models.arcii - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.cdssm module ----------------------------- - -.. automodule:: matchzoo.models.cdssm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.conv\_knrm module ---------------------------------- - -.. automodule:: matchzoo.models.conv_knrm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.dense\_baseline\_model module ---------------------------------------------- - -.. automodule:: matchzoo.models.dense_baseline_model - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.drmm module ---------------------------- - -.. automodule:: matchzoo.models.drmm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.drmmtks module ------------------------------- - -.. automodule:: matchzoo.models.drmmtks - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.dssm module ---------------------------- - -.. automodule:: matchzoo.models.dssm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.duet module ---------------------------- - -.. automodule:: matchzoo.models.duet - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.knrm module ---------------------------- - -.. automodule:: matchzoo.models.knrm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.match\_pyramid module -------------------------------------- - -.. automodule:: matchzoo.models.match_pyramid - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.mvlstm module ------------------------------ - -.. automodule:: matchzoo.models.mvlstm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.naive\_model module ------------------------------------ - -.. automodule:: matchzoo.models.naive_model - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.parameter\_readme\_generator module ---------------------------------------------------- - -.. automodule:: matchzoo.models.parameter_readme_generator - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.models - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_sources/matchzoo.rst.txt b/docs/_build/html/_sources/matchzoo.rst.txt deleted file mode 100644 index c6b49b52..00000000 --- a/docs/_build/html/_sources/matchzoo.rst.txt +++ /dev/null @@ -1,58 +0,0 @@ -matchzoo package -================ - -Subpackages ------------ - -.. toctree:: - - matchzoo.auto - matchzoo.contrib - matchzoo.data_generator - matchzoo.data_pack - matchzoo.datasets - matchzoo.engine - matchzoo.layers - matchzoo.losses - matchzoo.metrics - matchzoo.models - matchzoo.preprocessors - matchzoo.processor_units - matchzoo.tasks - matchzoo.utils - -Submodules ----------- - -matchzoo.embedding module -------------------------- - -.. automodule:: matchzoo.embedding - :members: - :undoc-members: - :show-inheritance: - -matchzoo.logger module ----------------------- - -.. automodule:: matchzoo.logger - :members: - :undoc-members: - :show-inheritance: - -matchzoo.version module ------------------------ - -.. automodule:: matchzoo.version - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_sources/matchzoo.tasks.rst.txt b/docs/_build/html/_sources/matchzoo.tasks.rst.txt deleted file mode 100644 index a0582360..00000000 --- a/docs/_build/html/_sources/matchzoo.tasks.rst.txt +++ /dev/null @@ -1,38 +0,0 @@ -matchzoo.tasks package -====================== - -Submodules ----------- - -matchzoo.tasks.classification module ------------------------------------- - -.. automodule:: matchzoo.tasks.classification - :members: - :undoc-members: - :show-inheritance: - -matchzoo.tasks.ranking module ------------------------------ - -.. automodule:: matchzoo.tasks.ranking - :members: - :undoc-members: - :show-inheritance: - -matchzoo.tasks.utils module ---------------------------- - -.. automodule:: matchzoo.tasks.utils - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.tasks - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_sources/modules.rst.txt b/docs/_build/html/_sources/modules.rst.txt deleted file mode 100644 index 123f26e7..00000000 --- a/docs/_build/html/_sources/modules.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -matchzoo -======== - -.. toctree:: - :maxdepth: 4 - - matchzoo diff --git a/docs/_build/html/_static/ajax-loader.gif b/docs/_build/html/_static/ajax-loader.gif deleted file mode 100644 index 61faf8ca..00000000 Binary files a/docs/_build/html/_static/ajax-loader.gif and /dev/null differ diff --git a/docs/_build/html/_static/basic.css b/docs/_build/html/_static/basic.css deleted file mode 100644 index 104f076a..00000000 --- a/docs/_build/html/_static/basic.css +++ /dev/null @@ -1,676 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/* -- main layout ----------------------------------------------------------- */ - -div.clearer { - clear: both; -} - -/* -- relbar ---------------------------------------------------------------- */ - -div.related { - width: 100%; - font-size: 90%; -} - -div.related h3 { - display: none; -} - -div.related ul { - margin: 0; - padding: 0 0 0 10px; - list-style: none; -} - -div.related li { - display: inline; -} - -div.related li.right { - float: right; - margin-right: 5px; -} - -/* -- sidebar --------------------------------------------------------------- */ - -div.sphinxsidebarwrapper { - padding: 10px 5px 0 10px; -} - -div.sphinxsidebar { - float: left; - width: 230px; - margin-left: -100%; - font-size: 90%; - 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the documentation. - */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { - this.initOnKeyListeners(); - } - }, - - /** - * i18n support - */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, - LOCALE : 'unknown', - - // gettext and ngettext don't access this so that the functions - // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') - return string; - return (typeof translated === 'string') ? translated : translated[0]; - }, - - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') - return (n == 1) ? singular : plural; - return translated[Documentation.PLURALEXPR(n)]; - }, - - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; - }, - - /** - * add context elements like header anchor links - */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); - }, - - /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 - */ - fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) - window.setTimeout(function() { - document.location.href += ''; - }, 10); - }, - - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); - } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('') - .appendTo($('#searchbox')); - } - }, - - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - }, - - /** - * make the url absolute - */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; - }, - - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); - }, - - initOnKeyListeners: function() { - $(document).keyup(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box or textarea - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') { - switch (event.keyCode) { - case 37: // left - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; - } - case 39: // right - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; - } - } - } - }); - } -}; - -// quick alias for translations -_ = Documentation.gettext; - -$(document).ready(function() { - Documentation.init(); -}); diff --git a/docs/_build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js deleted file mode 100644 index 03c78838..00000000 --- a/docs/_build/html/_static/documentation_options.js +++ /dev/null @@ -1,10 +0,0 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '2.0', - LANGUAGE: 'None', - COLLAPSE_INDEX: false, - FILE_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '.txt', - NAVIGATION_WITH_KEYS: false, -}; \ No newline at end of file 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[ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !jQuery.isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - src = target[ name ]; - copy = options[ name ]; - - // Prevent never-ending loop - if ( target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - - if ( copyIsArray ) { - copyIsArray = false; - clone = src && Array.isArray( src ) ? src : []; - - } else { - clone = src && jQuery.isPlainObject( src ) ? src : {}; - } - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isFunction: function( obj ) { - return jQuery.type( obj ) === "function"; - }, - - isWindow: function( obj ) { - return obj != null && obj === obj.window; - }, - - isNumeric: function( obj ) { - - // As of jQuery 3.0, isNumeric is limited to - // strings and numbers (primitives or objects) - // that can be coerced to finite numbers (gh-2662) - var type = jQuery.type( obj ); - return ( type === "number" || type === "string" ) && - - // parseFloat NaNs numeric-cast false positives ("") - // ...but misinterprets leading-number strings, particularly hex literals ("0x...") - // subtraction forces infinities to NaN - !isNaN( obj - parseFloat( obj ) ); - }, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - - /* eslint-disable no-unused-vars */ - // See https://github.com/eslint/eslint/issues/6125 - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - type: function( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; - }, - - // Evaluates a script in a global context - globalEval: function( code ) { - DOMEval( code ); - }, - - // Convert dashed to camelCase; used by the css and data modules - // Support: IE <=9 - 11, Edge 12 - 13 - // Microsoft forgot to hump their vendor prefix (#9572) - camelCase: function( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // Support: Android <=4.0 only - trim: function( text ) { - return text == null ? - "" : - ( text + "" ).replace( rtrim, "" ); - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return concat.apply( [], ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // Bind a function to a context, optionally partially applying any - // arguments. - proxy: function( fn, context ) { - var tmp, args, proxy; - - if ( typeof context === "string" ) { - tmp = fn[ context ]; - context = fn; - fn = tmp; - } - - // Quick check to determine if target is callable, in the spec - // this throws a TypeError, but we will just return undefined. - if ( !jQuery.isFunction( fn ) ) { - return undefined; - } - - // Simulated bind - args = slice.call( arguments, 2 ); - proxy = function() { - return fn.apply( context || this, args.concat( slice.call( arguments ) ) ); - }; - - // Set the guid of unique handler to the same of original handler, so it can be removed - proxy.guid = fn.guid = fn.guid || jQuery.guid++; - - return proxy; - }, - - now: Date.now, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = jQuery.type( obj ); - - if ( type === "function" || jQuery.isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.3 - * https://sizzlejs.com/ - * - * Copyright jQuery Foundation and other contributors - * Released under the MIT license - * http://jquery.org/license - * - * Date: 2016-08-08 - */ -(function( window ) { - -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ({}).hasOwnProperty, - arr = [], - pop = arr.pop, - push_native = arr.push, - push = arr.push, - slice = arr.slice, - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[i] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier - identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace + - "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ), - - rattributeQuotes = new RegExp( "=" + whitespace + "*([^\\]'\"]*?)" + whitespace + "*\\]", "g" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace + - "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace + - "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + - whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ), - funescape = function( _, escaped, escapedWhitespace ) { - var high = "0x" + escaped - 0x10000; - // NaN means non-codepoint - // Support: Firefox<24 - // Workaround erroneous numeric interpretation of +"0x" - return high !== high || escapedWhitespace ? - escaped : - high < 0 ? - // BMP codepoint - String.fromCharCode( high + 0x10000 ) : - // Supplemental Plane codepoint (surrogate pair) - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - disabledAncestor = addCombinator( - function( elem ) { - return elem.disabled === true && ("form" in elem || "label" in elem); - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - (arr = slice.call( preferredDoc.childNodes )), - preferredDoc.childNodes - ); - // Support: Android<4.0 - // Detect silently failing push.apply - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - push_native.apply( target, slice.call(els) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - // Can't trust NodeList.length - while ( (target[j++] = els[i++]) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - - if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) { - setDocument( context ); - } - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) { - - // ID selector - if ( (m = match[1]) ) { - - // Document context - if ( nodeType === 9 ) { - if ( (elem = context.getElementById( m )) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && (elem = newContext.getElementById( m )) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[2] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( (m = match[3]) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !compilerCache[ selector + " " ] && - (!rbuggyQSA || !rbuggyQSA.test( selector )) ) { - - if ( nodeType !== 1 ) { - newContext = context; - newSelector = selector; - - // qSA looks outside Element context, which is not what we want - // Thanks to Andrew Dupont for this workaround technique - // Support: IE <=8 - // Exclude object elements - } else if ( context.nodeName.toLowerCase() !== "object" ) { - - // Capture the context ID, setting it first if necessary - if ( (nid = context.getAttribute( "id" )) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", (nid = expando) ); - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[i] = "#" + nid + " " + toSelector( groups[i] ); - } - newSelector = groups.join( "," ); - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - } - - if ( newSelector ) { - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return (cache[ key + " " ] = value); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement("fieldset"); - - try { - return !!fn( el ); - } catch (e) { - return false; - } finally { - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split("|"), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[i] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( (cur = cur.nextSibling) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return (name === "input" || name === "button") && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - disabledAncestor( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction(function( argument ) { - argument = +argument; - return markFunction(function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ (j = matchIndexes[i]) ] ) { - seed[j] = !(matches[j] = seed[j]); - } - } - }); - }); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - // documentElement is verified for cases where it doesn't yet exist - // (such as loading iframes in IE - #4833) - var documentElement = elem && (elem.ownerDocument || elem).documentElement; - return documentElement ? documentElement.nodeName !== "HTML" : false; -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9-11, Edge - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - if ( preferredDoc !== document && - (subWindow = document.defaultView) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert(function( el ) { - el.className = "i"; - return !el.getAttribute("className"); - }); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert(function( el ) { - el.appendChild( document.createComment("") ); - return !el.getElementsByTagName("*").length; - }); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert(function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - }); - - // ID filter and find - if ( support.getById ) { - Expr.filter["ID"] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute("id") === attrId; - }; - }; - Expr.find["ID"] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter["ID"] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode("id"); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find["ID"] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode("id"); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( (elem = elems[i++]) ) { - node = elem.getAttributeNode("id"); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find["TAG"] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( (elem = results[i++]) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( (support.qsa = rnative.test( document.querySelectorAll )) ) { - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert(function( el ) { - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll("[msallowcapture^='']").length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll("[selected]").length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push("~="); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll(":checked").length ) { - rbuggyQSA.push(":checked"); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push(".#.+[+~]"); - } - }); - - assert(function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement("input"); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll("[name=d]").length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll(":enabled").length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll(":disabled").length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll("*,:x"); - rbuggyQSA.push(",.*:"); - }); - } - - if ( (support.matchesSelector = rnative.test( (matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector) )) ) { - - assert(function( el ) { - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - }); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - )); - } : - function( a, b ) { - if ( b ) { - while ( (b = b.parentNode) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - (!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) { - - // Choose the first element that is related to our preferred document - if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) { - return -1; - } - if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - return a === document ? -1 : - b === document ? 1 : - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( (cur = cur.parentNode) ) { - ap.unshift( cur ); - } - cur = b; - while ( (cur = cur.parentNode) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[i] === bp[i] ) { - i++; - } - - return i ? - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[i], bp[i] ) : - - // Otherwise nodes in our document sort first - ap[i] === preferredDoc ? -1 : - bp[i] === preferredDoc ? 1 : - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - // Set document vars if needed - if ( ( elem.ownerDocument || elem ) !== document ) { - setDocument( elem ); - } - - // Make sure that attribute selectors are quoted - expr = expr.replace( rattributeQuotes, "='$1']" ); - - if ( support.matchesSelector && documentIsHTML && - !compilerCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch (e) {} - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - // Set document vars if needed - if ( ( context.ownerDocument || context ) !== document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - // Set document vars if needed - if ( ( elem.ownerDocument || elem ) !== document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - (val = elem.getAttributeNode(name)) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return (sel + "").replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( (elem = results[i++]) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - // If no nodeType, this is expected to be an array - while ( (node = elem[i++]) ) { - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[1] = match[1].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape ); - - if ( match[2] === "~=" ) { - match[3] = " " + match[3] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[1] = match[1].toLowerCase(); - - if ( match[1].slice( 0, 3 ) === "nth" ) { - // nth-* requires argument - if ( !match[3] ) { - Sizzle.error( match[0] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) ); - match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" ); - - // other types prohibit arguments - } else if ( match[3] ) { - Sizzle.error( match[0] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[6] && match[2]; - - if ( matchExpr["CHILD"].test( match[0] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[3] ) { - match[2] = match[4] || match[5] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - // Get excess from tokenize (recursively) - (excess = tokenize( unquoted, true )) && - // advance to the next closing parenthesis - (excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) { - - // excess is a negative index - match[0] = match[0].slice( 0, excess ); - match[2] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { return true; } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - (pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) && - classCache( className, function( elem ) { - return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" ); - }); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - }; - }, - - "CHILD": function( type, what, argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( (node = node[ dir ]) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( (node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - (diff = nodeIndex = 0) || start.pop()) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - // Use previously-cached element index if available - if ( useCache ) { - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - // Use the same loop as above to seek `elem` from the start - while ( (node = ++nodeIndex && node && node[ dir ] || - (diff = nodeIndex = 0) || start.pop()) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || (node[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - (outerCache[ node.uniqueID ] = {}); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction(function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[i] ); - seed[ idx ] = !( matches[ idx ] = matched[i] ); - } - }) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - // Potentially complex pseudos - "not": markFunction(function( selector ) { - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction(function( seed, matches, context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( (elem = unmatched[i]) ) { - seed[i] = !(matches[i] = elem); - } - } - }) : - function( elem, context, xml ) { - input[0] = elem; - matcher( input, null, xml, results ); - // Don't keep the element (issue #299) - input[0] = null; - return !results.pop(); - }; - }), - - "has": markFunction(function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - }), - - "contains": markFunction(function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || elem.innerText || getText( elem ) ).indexOf( text ) > -1; - }; - }), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - // lang value must be a valid identifier - if ( !ridentifier.test(lang || "") ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( (elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( (elem = elem.parentNode) && elem.nodeType === 1 ); - return false; - }; - }), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected); - }, - - "selected": function( elem ) { - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos["empty"]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo(function() { - return [ 0 ]; - }), - - "last": createPositionalPseudo(function( matchIndexes, length ) { - return [ length - 1 ]; - }), - - "eq": createPositionalPseudo(function( matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - }), - - "even": createPositionalPseudo(function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "odd": createPositionalPseudo(function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "lt": createPositionalPseudo(function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - }), - - "gt": createPositionalPseudo(function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - }) - } -}; - -Expr.pseudos["nth"] = Expr.pseudos["eq"]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || (match = rcomma.exec( soFar )) ) { - if ( match ) { - // Don't consume trailing commas as valid - soFar = soFar.slice( match[0].length ) || soFar; - } - groups.push( (tokens = []) ); - } - - matched = false; - - // Combinators - if ( (match = rcombinators.exec( soFar )) ) { - matched = match.shift(); - tokens.push({ - value: matched, - // Cast descendant combinators to space - type: match[0].replace( rtrim, " " ) - }); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] || - (match = preFilters[ type ]( match ))) ) { - matched = match.shift(); - tokens.push({ - value: matched, - type: type, - matches: match - }); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[i].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( (elem = elem[ dir ]) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || (elem[ expando ] = {}); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {}); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( (oldCache = uniqueCache[ key ]) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return (newCache[ 2 ] = oldCache[ 2 ]); - } else { - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[i]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[0]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[i], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( (elem = unmatched[i]) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction(function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( (elem = temp[i]) ) { - matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( (elem = matcherOut[i]) ) { - // Restore matcherIn since elem is not yet a final match - temp.push( (matcherIn[i] = elem) ); - } - } - postFinder( null, (matcherOut = []), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( (elem = matcherOut[i]) && - (temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) { - - seed[temp] = !(results[temp] = elem); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - }); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[0].type ], - implicitRelative = leadingRelative || Expr.relative[" "], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - (checkContext = context).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( (matcher = Expr.relative[ tokens[i].type ]) ) { - matchers = [ addCombinator(elementMatcher( matchers ), matcher) ]; - } else { - matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[j].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" }) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( (tokens = tokens.slice( j )) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find["TAG"]( "*", outermost ), - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1), - len = elems.length; - - if ( outermost ) { - outermostContext = context === document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && (elem = elems[i]) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - if ( !context && elem.ownerDocument !== document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( (matcher = elementMatchers[j++]) ) { - if ( matcher( elem, context || document, xml) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - // They will have gone through all possible matchers - if ( (elem = !matcher && elem) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( (matcher = setMatchers[j++]) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !(unmatched[i] || setMatched[i]) ) { - setMatched[i] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[i] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( (selector = compiled.selector || selector) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[0] = match[0].slice( 0 ); - if ( tokens.length > 2 && (token = tokens[0]).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[1].type ] ) { - - context = ( Expr.find["ID"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[i]; - - // Abort if we hit a combinator - if ( Expr.relative[ (type = token.type) ] ) { - break; - } - if ( (find = Expr.find[ type ]) ) { - // Search, expanding context for leading sibling combinators - if ( (seed = find( - token.matches[0].replace( runescape, funescape ), - rsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context - )) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split("").sort( sortOrder ).join("") === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert(function( el ) { - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement("fieldset") ) & 1; -}); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert(function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute("href") === "#" ; -}) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - }); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert(function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -}) ) { - addHandle( "value", function( elem, name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - }); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert(function( el ) { - return el.getAttribute("disabled") == null; -}) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - (val = elem.getAttributeNode( name )) && val.specified ? - val.value : - null; - } - }); -} - -return Sizzle; - -})( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -}; -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -var risSimple = /^.[^:#\[\.,]*$/; - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( jQuery.isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Simple selector that can be filtered directly, removing non-Elements - if ( risSimple.test( qualifier ) ) { - return jQuery.filter( qualifier, elements, not ); - } - - // Complex selector, compare the two sets, removing non-Elements - qualifier = jQuery.filter( qualifier, elements ); - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not && elem.nodeType === 1; - } ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( jQuery.isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( jQuery.isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( nodeName( elem, "iframe" ) ) { - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( jQuery.isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && jQuery.type( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && jQuery.isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && jQuery.isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = jQuery.isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && jQuery.isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( jQuery.isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - jQuery.isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - jQuery.isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - jQuery.isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the master Deferred - master = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - master.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( master.state() === "pending" || - jQuery.isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return master.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); - } - - return master.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( jQuery.type( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !jQuery.isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ jQuery.camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ jQuery.camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ jQuery.camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( jQuery.camelCase ); - } else { - key = jQuery.camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = jQuery.camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - jQuery.contains( elem.ownerDocument, elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - -var swap = function( elem, options, callback, args ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.apply( elem, args || [] ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, - scale = 1, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - do { - - // If previous iteration zeroed out, double until we get *something*. - // Use string for doubling so we don't accidentally see scale as unchanged below - scale = scale || ".5"; - - // Adjust and apply - initialInUnit = initialInUnit / scale; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Update scale, tolerating zero or NaN from tween.cur() - // Break the loop if scale is unchanged or perfect, or if we've just had enough. - } while ( - scale !== ( scale = currentValue() / initial ) && scale !== 1 && --maxIterations - ); - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]+)/i ); - -var rscriptType = ( /^$|\/(?:java|ecma)script/i ); - - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // Support: IE <=9 only - option: [ 1, "" ], - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
" ], - col: [ 2, "", "
" ], - tr: [ 2, "", "
" ], - td: [ 3, "", "
" ], - - _default: [ 0, "", "" ] -}; - -// Support: IE <=9 only -wrapMap.optgroup = wrapMap.option; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, contains, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( jQuery.type( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - contains = jQuery.contains( elem.ownerDocument, elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( contains ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; -} )(); -var documentElement = document.documentElement; - - - -var - rkeyEvent = /^key/, - rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, - rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 only -// See #13393 for more info -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Don't attach events to noData or text/comment nodes (but allow plain objects) - if ( !elemData ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = {}; - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - // Make a writable jQuery.Event from the native event object - var event = jQuery.event.fix( nativeEvent ); - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - handlers = ( dataPriv.get( this, "events" ) || {} )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // Triggered event must either 1) have no namespace, or 2) have namespace(s) - // a subset or equal to those in the bound event (both can have no namespace). - if ( !event.rnamespace || event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: jQuery.isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - focus: { - - // Fire native event if possible so blur/focus sequence is correct - trigger: function() { - if ( this !== safeActiveElement() && this.focus ) { - this.focus(); - return false; - } - }, - delegateType: "focusin" - }, - blur: { - trigger: function() { - if ( this === safeActiveElement() && this.blur ) { - this.blur(); - return false; - } - }, - delegateType: "focusout" - }, - click: { - - // For checkbox, fire native event so checked state will be right - trigger: function() { - if ( this.type === "checkbox" && this.click && nodeName( this, "input" ) ) { - this.click(); - return false; - } - }, - - // For cross-browser consistency, don't fire native .click() on links - _default: function( event ) { - return nodeName( event.target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || jQuery.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - - which: function( event ) { - var button = event.button; - - // Add which for key events - if ( event.which == null && rkeyEvent.test( event.type ) ) { - return event.charCode != null ? event.charCode : event.keyCode; - } - - // Add which for click: 1 === left; 2 === middle; 3 === right - if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { - if ( button & 1 ) { - return 1; - } - - if ( button & 2 ) { - return 3; - } - - if ( button & 4 ) { - return 2; - } - - return 0; - } - - return event.which; - } -}, jQuery.event.addProp ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - /* eslint-disable max-len */ - - // See https://github.com/eslint/eslint/issues/3229 - rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi, - - /* eslint-enable */ - - // Support: IE <=10 - 11, Edge 12 - 13 - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( ">tbody", elem )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - var match = rscriptTypeMasked.exec( elem.type ); - - if ( match ) { - elem.type = match[ 1 ]; - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, pdataCur, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.access( src ); - pdataCur = dataPriv.set( dest, pdataOld ); - events = pdataOld.events; - - if ( events ) { - delete pdataCur.handle; - pdataCur.events = {}; - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = concat.apply( [], args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - isFunction = jQuery.isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( isFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( isFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl ) { - jQuery._evalUrl( node.src ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && jQuery.contains( node.ownerDocument, node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html.replace( rxhtmlTag, "<$1>" ); - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = jQuery.contains( elem.ownerDocument, elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rmargin = ( /^margin/ ); - -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - div.style.cssText = - "box-sizing:border-box;" + - "position:relative;display:block;" + - "margin:auto;border:1px;padding:1px;" + - "top:1%;width:50%"; - div.innerHTML = ""; - documentElement.appendChild( container ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = divStyle.marginLeft === "2px"; - boxSizingReliableVal = divStyle.width === "4px"; - - // Support: Android 4.0 - 4.3 only - // Some styles come back with percentage values, even though they shouldn't - div.style.marginRight = "50%"; - pixelMarginRightVal = divStyle.marginRight === "4px"; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - var pixelPositionVal, boxSizingReliableVal, pixelMarginRightVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - container.style.cssText = "border:0;width:8px;height:0;top:0;left:-9999px;" + - "padding:0;margin-top:1px;position:absolute"; - container.appendChild( div ); - - jQuery.extend( support, { - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelMarginRight: function() { - computeStyleTests(); - return pixelMarginRightVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !jQuery.contains( elem.ownerDocument, elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelMarginRight() && rnumnonpx.test( ret ) && rmargin.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }, - - cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style; - -// Return a css property mapped to a potentially vendor prefixed property -function vendorPropName( name ) { - - // Shortcut for names that are not vendor prefixed - if ( name in emptyStyle ) { - return name; - } - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a property mapped along what jQuery.cssProps suggests or to -// a vendor prefixed property. -function finalPropName( name ) { - var ret = jQuery.cssProps[ name ]; - if ( !ret ) { - ret = jQuery.cssProps[ name ] = vendorPropName( name ) || name; - } - return ret; -} - -function setPositiveNumber( elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function augmentWidthOrHeight( elem, name, extra, isBorderBox, styles ) { - var i, - val = 0; - - // If we already have the right measurement, avoid augmentation - if ( extra === ( isBorderBox ? "border" : "content" ) ) { - i = 4; - - // Otherwise initialize for horizontal or vertical properties - } else { - i = name === "width" ? 1 : 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin, so add it if we want it - if ( extra === "margin" ) { - val += jQuery.css( elem, extra + cssExpand[ i ], true, styles ); - } - - if ( isBorderBox ) { - - // border-box includes padding, so remove it if we want content - if ( extra === "content" ) { - val -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // At this point, extra isn't border nor margin, so remove border - if ( extra !== "margin" ) { - val -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } else { - - // At this point, extra isn't content, so add padding - val += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // At this point, extra isn't content nor padding, so add border - if ( extra !== "padding" ) { - val += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - return val; -} - -function getWidthOrHeight( elem, name, extra ) { - - // Start with computed style - var valueIsBorderBox, - styles = getStyles( elem ), - val = curCSS( elem, name, styles ), - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Computed unit is not pixels. Stop here and return. - if ( rnumnonpx.test( val ) ) { - return val; - } - - // Check for style in case a browser which returns unreliable values - // for getComputedStyle silently falls back to the reliable elem.style - valueIsBorderBox = isBorderBox && - ( support.boxSizingReliable() || val === elem.style[ name ] ); - - // Fall back to offsetWidth/Height when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - if ( val === "auto" ) { - val = elem[ "offset" + name[ 0 ].toUpperCase() + name.slice( 1 ) ]; - } - - // Normalize "", auto, and prepare for extra - val = parseFloat( val ) || 0; - - // Use the active box-sizing model to add/subtract irrelevant styles - return ( val + - augmentWidthOrHeight( - elem, - name, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: { - "float": "cssFloat" - }, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = jQuery.camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - if ( type === "number" ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = jQuery.camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( i, name ) { - jQuery.cssHooks[ name ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, name, extra ); - } ) : - getWidthOrHeight( elem, name, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = extra && getStyles( elem ), - subtract = extra && augmentWidthOrHeight( - elem, - name, - extra, - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - styles - ); - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ name ] = value; - value = jQuery.css( elem, name ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( !rmargin.test( prefix ) ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && - ( tween.elem.style[ jQuery.cssProps[ tween.prop ] ] != null || - jQuery.cssHooks[ tween.prop ] ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = jQuery.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 13 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = jQuery.camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( jQuery.isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - jQuery.proxy( result.stop, result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( jQuery.isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( jQuery.isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - jQuery.isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !jQuery.isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( jQuery.isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue && type !== false ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = jQuery.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://html.spec.whatwg.org/multipage/infrastructure.html#strip-and-collapse-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( jQuery.isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( typeof value === "string" && value ) { - classes = value.match( rnothtmlwhite ) || []; - - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( jQuery.isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - if ( typeof value === "string" && value ) { - classes = value.match( rnothtmlwhite ) || []; - - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value; - - if ( typeof stateVal === "boolean" && type === "string" ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( jQuery.isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( type === "string" ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = value.match( rnothtmlwhite ) || []; - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, isFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - isFunction = jQuery.isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( isFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !jQuery.isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( dataPriv.get( cur, "events" ) || {} )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && jQuery.isFunction( elem[ type ] ) && !jQuery.isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - elem[ type ](); - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " + - "mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " + - "change select submit keydown keypress keyup contextmenu" ).split( " " ), - function( i, name ) { - - // Handle event binding - jQuery.fn[ name ] = function( data, fn ) { - return arguments.length > 0 ? - this.on( name, null, data, fn ) : - this.trigger( name ); - }; -} ); - -jQuery.fn.extend( { - hover: function( fnOver, fnOut ) { - return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver ); - } -} ); - - - - -support.focusin = "onfocusin" in window; - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - var doc = this.ownerDocument || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = jQuery.now(); - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) { - xml = undefined; - } - - if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { - jQuery.error( "Invalid XML: " + data ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && jQuery.type( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = jQuery.isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ) - .filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ) - .map( function( i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( jQuery.isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() ] = match[ 2 ]; - } - } - match = responseHeaders[ key.toLowerCase() ]; - } - return match == null ? null : match; - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 13 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available, append data to url - if ( s.data ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce++ ) + uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( jQuery.isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - - -jQuery._evalUrl = function( url ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - "throws": true - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( jQuery.isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( jQuery.isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var isFunction = jQuery.isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( isFunction ? html.call( this, i ) : html ); - } ); - }, - - unwrap: function( selector ) { - this.parent( selector ).not( "body" ).each( function() { - jQuery( this ).replaceWith( this.childNodes ); - } ); - return this; - } -} ); - - -jQuery.expr.pseudos.hidden = function( elem ) { - return !jQuery.expr.pseudos.visible( elem ); -}; -jQuery.expr.pseudos.visible = function( elem ) { - return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); -}; - - - - -jQuery.ajaxSettings.xhr = function() { - try { - return new window.XMLHttpRequest(); - } catch ( e ) {} -}; - -var xhrSuccessStatus = { - - // File protocol always yields status code 0, assume 200 - 0: 200, - - // Support: IE <=9 only - // #1450: sometimes IE returns 1223 when it should be 204 - 1223: 204 - }, - xhrSupported = jQuery.ajaxSettings.xhr(); - -support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); -support.ajax = xhrSupported = !!xhrSupported; - -jQuery.ajaxTransport( function( options ) { - var callback, errorCallback; - - // Cross domain only allowed if supported through XMLHttpRequest - if ( support.cors || xhrSupported && !options.crossDomain ) { - return { - send: function( headers, complete ) { - var i, - xhr = options.xhr(); - - xhr.open( - options.type, - options.url, - options.async, - options.username, - options.password - ); - - // Apply custom fields if provided - if ( options.xhrFields ) { - for ( i in options.xhrFields ) { - xhr[ i ] = options.xhrFields[ i ]; - } - } - - // Override mime type if needed - if ( options.mimeType && xhr.overrideMimeType ) { - xhr.overrideMimeType( options.mimeType ); - } - - // X-Requested-With header - // For cross-domain requests, seeing as conditions for a preflight are - // akin to a jigsaw puzzle, we simply never set it to be sure. - // (it can always be set on a per-request basis or even using ajaxSetup) - // For same-domain requests, won't change header if already provided. - if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { - headers[ "X-Requested-With" ] = "XMLHttpRequest"; - } - - // Set headers - for ( i in headers ) { - xhr.setRequestHeader( i, headers[ i ] ); - } - - // Callback - callback = function( type ) { - return function() { - if ( callback ) { - callback = errorCallback = xhr.onload = - xhr.onerror = xhr.onabort = xhr.onreadystatechange = null; - - if ( type === "abort" ) { - xhr.abort(); - } else if ( type === "error" ) { - - // Support: IE <=9 only - // On a manual native abort, IE9 throws - // errors on any property access that is not readyState - if ( typeof xhr.status !== "number" ) { - complete( 0, "error" ); - } else { - complete( - - // File: protocol always yields status 0; see #8605, #14207 - xhr.status, - xhr.statusText - ); - } - } else { - complete( - xhrSuccessStatus[ xhr.status ] || xhr.status, - xhr.statusText, - - // Support: IE <=9 only - // IE9 has no XHR2 but throws on binary (trac-11426) - // For XHR2 non-text, let the caller handle it (gh-2498) - ( xhr.responseType || "text" ) !== "text" || - typeof xhr.responseText !== "string" ? 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- - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html deleted file mode 100644 index d3a1e1a7..00000000 --- a/docs/_build/html/index.html +++ /dev/null @@ -1,253 +0,0 @@ - - - - - - - - - - - Welcome to MatchZoo’s documentation! — MatchZoo 2.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Welcome to MatchZoo’s documentation!

-ci -logo -

MatchZoo is a toolkit for text matching. It was developed with a focus on facilitating the designing, comparing and sharing of deep text matching models. There are a number of deep matching methods, such as DRMM, MatchPyramid, MV-LSTM, aNMM, DUET, ARC-I, ARC-II, DSSM, and CDSSM, designed with a unified interface. Potential tasks related to MatchZoo include document retrieval, question answering, conversational response ranking, paraphrase identification, etc. We are always happy to receive any code contributions, suggestions, comments from all our MatchZoo users.

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Indices and tables

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- - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/matchzoo.engine.html b/docs/_build/html/matchzoo.engine.html deleted file mode 100644 index 57df6091..00000000 --- a/docs/_build/html/matchzoo.engine.html +++ /dev/null @@ -1,1417 +0,0 @@ - - - - - - - - - - - matchzoo.engine package — MatchZoo 2.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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matchzoo.engine package

-
-

Submodules

-
-
-

matchzoo.engine.base_metric module

-

Metric base class and some related utilities.

-
-
-class matchzoo.engine.base_metric.BaseMetric
-

Bases: abc.ABC

-

Metric base class.

-
-
-ALIAS = 'base_metric'
-
- -
- -
-
-matchzoo.engine.base_metric.parse_metric(metric)
-

Parse input metric in any form into a BaseMetric instance.

- --- - - - - - -
Parameters:metric (Union[str, Type[BaseMetric], BaseMetric]) – Input metric in any form.
Returns:A BaseMetric instance
-
-
Examples::
-
>>> from matchzoo import engine, metrics
-
-
-
-
Use str as keras native metrics:
-
>>> engine.parse_metric('mse')
-'mse'
-
-
-
-
Use str as MatchZoo metrics:
-
>>> mz_metric = engine.parse_metric('map')
->>> type(mz_metric)
-<class 'matchzoo.metrics.mean_average_precision.MeanAveragePrecision'>
-
-
-
-
Use matchzoo.engine.BaseMetric subclasses as MatchZoo metrics:
-
>>> type(engine.parse_metric(metrics.AveragePrecision))
-<class 'matchzoo.metrics.average_precision.AveragePrecision'>
-
-
-
-
Use matchzoo.engine.BaseMetric instances as MatchZoo metrics:
-
>>> type(engine.parse_metric(metrics.AveragePrecision()))
-<class 'matchzoo.metrics.average_precision.AveragePrecision'>
-
-
-
-
-
- -
-
-matchzoo.engine.base_metric.sort_and_couple(labels, scores)
-

Zip the labels with scores into a single list.

- --- - - - -
Return type:<built-in function array>
-
- -
-
-

matchzoo.engine.base_model module

-

Base Model.

-
-
-class matchzoo.engine.base_model.BaseModel(params=None, backend=None)
-

Bases: abc.ABC

-

Abstract base class of all matchzoo models.

-
-
-BACKEND_WEIGHTS_FILENAME = 'backend_weights.h5'
-
- -
-
-PARAMS_FILENAME = 'params.dill'
-
- -
-
-backend
-

return model backend, a keras model instance.

- --- - - - -
Return type:Model
-
- -
-
-build()
-

Build model, each sub class need to impelemnt this method.

-

Example

-
>>> BaseModel()  # doctest: +ELLIPSIS
-Traceback (most recent call last):
-...
-TypeError: Can't instantiate abstract class BaseModel ...
->>> class MyModel(BaseModel):
-...     def build(self):
-...         pass
->>> assert MyModel()
-
-
-
- -
-
-compile()
-

Compile model for training.

-

Only keras native metrics are compiled together with backend. -MatchZoo metrics are evaluated only through evaluate(). -Notice that keras count loss as one of the metrics while MatchZoo -matchzoo.engine.BaseTask does not.

-

Examples

-
>>> from matchzoo import models
->>> model = models.NaiveModel()
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.params['task'].metrics = ['mse', 'map']
->>> model.params['task'].metrics
-['mse', mean_average_precision(0)]
->>> model.build()
->>> model.compile()
-
-
-
- -
-
-evaluate(x, y, batch_size=128, verbose=1)
-

Evaluate the model.

-

See keras.models.Model.evaluate() for more details.

- --- - - - - - - - -
Parameters:
    -
  • x (Union[ndarray, List[ndarray], Dict[str, ndarray]]) – input data
  • -
  • y (ndarray) – labels
  • -
  • batch_size (int) – number of samples per gradient update
  • -
  • verbose (int) – verbosity mode, 0 or 1
  • -
-
Return type:

Dict[str, float]

-
Returns:

scalar test loss (if the model has a single output and no -metrics) or list of scalars (if the model has multiple outputs -and/or metrics). The attribute model.backend.metrics_names will -give you the display labels for the scalar outputs.

-
-
-
Examples::
-
>>> import matchzoo as mz
->>> data_pack = mz.datasets.toy.load_data()
->>> preprocessor = mz.preprocessors.NaivePreprocessor()
->>> data_pack = preprocessor.fit_transform(data_pack)
->>> m = mz.models.DenseBaselineModel()
->>> m.params['task'] = mz.tasks.Ranking()
->>> m.params['task'].metrics = [
-...     'acc', 'mse', 'mae', 'ce',
-...     'average_precision', 'precision', 'dcg', 'ndcg',
-...     'mean_reciprocal_rank', 'mean_average_precision', 'mrr',
-...     'map', 'MAP',
-...     mz.metrics.AveragePrecision(threshold=1),
-...     mz.metrics.Precision(k=2, threshold=2),
-...     mz.metrics.DiscountedCumulativeGain(k=2),
-...     mz.metrics.NormalizedDiscountedCumulativeGain(
-...         k=3, threshold=-1),
-...     mz.metrics.MeanReciprocalRank(threshold=2),
-...     mz.metrics.MeanAveragePrecision(threshold=3)
-... ]
->>> m.guess_and_fill_missing_params(verbose=0)
->>> m.build()
->>> m.compile()
->>> x, y = data_pack.unpack()
->>> evals = m.evaluate(x, y, verbose=0)
->>> type(evals)
-<class 'dict'>
-
-
-
-
-
- -
-
-fit(x, y, batch_size=128, epochs=1, verbose=1, **kwargs)
-

Fit the model.

-

See keras.models.Model.fit() for more details.

- --- - - - -
Parameters:
    -
  • x (Union[ndarray, List[ndarray]]) – input data.
  • -
  • y (ndarray) – labels.
  • -
  • batch_size (int) – number of samples per gradient update.
  • -
  • epochs (int) – number of epochs to train the model.
  • -
  • verbose (int) – 0, 1, or 2. Verbosity mode. 0 = silent, 1 = verbose, -2 = one log line per epoch.
  • -
-
-

Key word arguments not listed above will be propagated to keras’s fit.

- --- - - - - - -
Return type:History
Returns:A keras.callbacks.History instance. Its history attribute -contains all information collected during training.
-
- -
-
-fit_generator(generator, epochs=1, verbose=1, **kwargs)
-

Fit the model with matchzoo generator.

-

See keras.models.Model.fit_generator() for more details.

- --- - - - - - - - -
Parameters:
    -
  • generator (DataGenerator) – A generator, an instance of -engine.DataGenerator.
  • -
  • epochs (int) – Number of epochs to train the model.
  • -
  • verbose (int) – 0, 1, or 2. Verbosity mode. 0 = silent, 1 = verbose, -2 = one log line per epoch.
  • -
-
Return type:

History

-
Returns:

A keras.callbacks.History instance. Its history attribute -contains all information collected during training.

-
-
- -
-
-classmethod get_default_params(with_embedding=False, with_multi_layer_perceptron=False)
-

Model default parameters.

-
-
The common usage is to instantiate matchzoo.engine.ModelParams
-
first, then set the model specific parametrs.
-
-

Examples

-
>>> class MyModel(BaseModel):
-...     def build(self):
-...         print(self._params['num_eggs'], 'eggs')
-...         print('and', self._params['ham_type'])
-...
-...     @classmethod
-...     def get_default_params(cls):
-...         params = engine.ParamTable()
-...         params.add(engine.Param('num_eggs', 512))
-...         params.add(engine.Param('ham_type', 'Parma Ham'))
-...         return params
->>> my_model = MyModel()
->>> my_model.build()
-512 eggs
-and Parma Ham
-
-
-

Notice that all parameters must be serialisable for the entire model -to be serialisable. Therefore, it’s strongly recommended to use python -native data types to store parameters.

- --- - - - - - -
Return type:ParamTable
Returns:model parameters
-
- -
-
-classmethod get_default_preprocessor()
-

Model default preprocessor.

-

The preprocessor’s transform should produce a correctly shaped data -pack that can be used for training. Some extra configuration (e.g. -setting input_shapes in matchzoo.models.DSSMModel may be -required on the user’s end.

- --- - - - - - -
Return type:BasePreprocessor
Returns:Default preprocessor.
-
- -
-
-guess_and_fill_missing_params(verbose=1)
-

Guess and fill missing parameters in params.

-

Use this method to automatically fill-in hyper parameters. -This involves some guessing so the parameter it fills could be -wrong. For example, the default task is Ranking, and if we do not -set it to Classification manaully for data packs prepared for -classification, then the shape of the model output and the data will -mismatch.

- --- - - - -
Parameters:verbose – Verbosity.
-
- -
-
-load_embedding_matrix(embedding_matrix, name='embedding')
-

Load an embedding matrix.

-

Load an embedding matrix into the model’s embedding layer. The name -of the embedding layer is specified by name. For models with only -one embedding layer, set name=’embedding’ when creating the keras -layer, and use the default name when load the matrix. For models -with more than one embedding layers, initialize keras layer with -different layer names, and set name accordingly to load a matrix -to a chosen layer.

- --- - - - -
Parameters:
    -
  • embedding_matrix (ndarray) – Embedding matrix to be loaded.
  • -
  • name (str) – Name of the layer. (default: ‘embedding’)
  • -
-
-
- -
-
-params
-

model parameters.

- --- - - - - - -
Type:return
Return type:ParamTable
-
- -
-
-predict(x, batch_size=128)
-

Generate output predictions for the input samples.

-

See keras.models.Model.predict() for more details.

- --- - - - - - - - -
Parameters:
    -
  • x (Union[ndarray, List[ndarray]]) – input data
  • -
  • batch_size – number of samples per gradient update
  • -
-
Return type:

ndarray

-
Returns:

numpy array(s) of predictions

-
-
- -
-
-save(dirpath)
-

Save the model.

-

A saved model is represented as a directory with two files. One is a -model parameters file saved by pickle, and the other one is a model -h5 file saved by keras.

- --- - - - -
Parameters:dirpath (Union[str, Path]) – directory path of the saved model
-
- -
- -
-
-matchzoo.engine.base_model.load_model(dirpath)
-

Load a model. The reverse function of BaseModel.save().

- --- - - - - - - - -
Parameters:dirpath (Union[str, Path]) – directory path of the saved model
Return type:BaseModel
Returns:a BaseModel instance
-
- -
-
-

matchzoo.engine.base_preprocessor module

-

BasePreprocessor define input and ouutput for processors.

-
-
-class matchzoo.engine.base_preprocessor.BasePreprocessor
-

Bases: object

-

BasePreprocessor to input handle data.

-
-
-DATA_FILENAME = 'preprocessor.dill'
-
- -
-
-context
-

Return context.

-
- -
-
-fit(data_pack, verbose=1)
-

Fit parameters on input data.

-

This method is an abstract base method, need to be -implemented in the child class.

-

This method is expected to return itself as a callable -object.

- --- - - - - - -
Parameters:
    -
  • data_pack (DataPack) – Datapack object to be fitted.
  • -
  • verbose – Verbosity.
  • -
-
Return type:

BasePreprocessor

-
-
- -
-
-fit_transform(data_pack, verbose=1)
-

Call fit-transform.

- --- - - - - - -
Parameters:data_pack (DataPack) – DataPack object to be processed.
Return type:DataPack
-
- -
-
-save(dirpath)
-

Save the DSSMPreprocessor object.

-

A saved DSSMPreprocessor is represented as a directory with -the context object (fitted parameters on training data), it will -be saved by pickle.

- --- - - - -
Parameters:dirpath (Union[str, Path]) – directory path of the saved DSSMPreprocessor.
-
- -
-
-transform(data_pack, verbose=1)
-

Transform input data to expected manner.

-

This method is an abstract base method, need to be -implemented in the child class.

- --- - - - - - -
Parameters:
    -
  • data_pack (DataPack) – DataPack object to be transformed.
  • -
  • verbose – Verbosity. -or list of text-left, text-right tuples.
  • -
-
Return type:

DataPack

-
-
- -
- -
-
-matchzoo.engine.base_preprocessor.load_preprocessor(dirpath)
-

Load the fitted context. The reverse function of save().

- --- - - - - - - - -
Parameters:dirpath (Union[str, Path]) – directory path of the saved model.
Return type:DataPack
Returns:a DSSMPreprocessor instance.
-
- -
-
-matchzoo.engine.base_preprocessor.validate_context(func)
-

Validate context in the preprocessor.

-
- -
-
-

matchzoo.engine.base_task module

-

Base task.

-
-
-class matchzoo.engine.base_task.BaseTask(loss=None, metrics=None)
-

Bases: abc.ABC

-

Base Task, shouldn’t be used directly.

-
-
-classmethod convert_metrics(metrics)
-

Convert metrics into properly formed list of metrics.

-

Examples

-
>>> BaseTask.convert_metrics(['mse'])
-['mse']
->>> BaseTask.convert_metrics('map')
-[mean_average_precision(0)]
-
-
-
- -
-
-classmethod list_available_losses()
-
--- - - - - - -
Return type:list
Returns:a list of available losses.
-
- -
-
-classmethod list_available_metrics()
-
--- - - - - - -
Return type:list
Returns:a list of available metrics.
-
- -
-
-loss
-

Loss used in the task.

- --- - - - -
Type:return
-
- -
-
-metrics
-

Metrics used in the task.

- --- - - - -
Type:return
-
- -
-
-output_dtype
-

output data type for specific task.

- --- - - - -
Type:return
-
- -
-
-output_shape
-

output shape of a single sample of the task.

- --- - - - - - -
Type:return
Return type:tuple
-
- -
- -
-
-matchzoo.engine.base_task.list_available_tasks(base=<class 'matchzoo.engine.base_task.BaseTask'>)
-
--- - - - - - -
Return type:List[Type[BaseTask]]
Returns:a list of available task types.
-
- -
-
-

matchzoo.engine.callbacks module

-

Callbacks.

-
-
-class matchzoo.engine.callbacks.EvaluateAllMetrics(model, x, y, once_every=1, batch_size=32, model_save_path=None, verbose=1)
-

Bases: keras.callbacks.Callback

-

Callback to evaluate all metrics.

-

MatchZoo metrics can not be evaluated batch-wise since they require -dataset-level information. As a result, MatchZoo metrics are not -evaluated automatically when a Model fit. When this callback is used, -all metrics, including MatchZoo metrics and Keras metrics, are evluated -once every once_every epochs.

- --- - - - -
Parameters:
    -
  • model (BaseModel) – Model to evaluate.
  • -
  • x (Union[ndarray, List[ndarray]]) –
      -
    1. -
    -
  • -
  • y (ndarray) –
      -
    1. -
    -
  • -
  • once_every (int) – Evaluation only triggers when epoch % once_every == 0. -(default: 1, i.e. evaluate on every epoch’s end)
  • -
  • batch_size (int) – Number of samples per evaluation. This only affects the -evaluation of Keras metrics, since MatchZoo metrics are always -evaluated using the full data.
  • -
  • model_save_path (Optional[str]) – Directory path to save the model after each -evaluate callback, (default: None, i.e., no saving.)
  • -
  • verbose – Verbosity.
  • -
-
-
-
-on_epoch_end(epoch, logs=None)
-

Called at the end of en epoch.

- --- - - - - - -
Parameters:
    -
  • epoch – integer, index of epoch.
  • -
  • logs – dictionary of logs.
  • -
-
Returns:

dictionary of logs.

-
-
- -
- -
-
-

matchzoo.engine.hyper_spaces module

-

Hyper parameter search spaces wrapping hyperopt.

-
-
-class matchzoo.engine.hyper_spaces.HyperoptProxy(hyperopt_func, **kwargs)
-

Bases: object

-

Hyperopt proxy class.

-

See hyperopt’s documentation for more details: -https://github.com/hyperopt/hyperopt/wiki/FMin

-

Reason of these wrappers:

-
-
A hyper space in hyperopt requires a label to instantiate. This -label is used later as a reference to original hyper space that is -sampled. In matchzoo, hyper spaces are used in -matchzoo.engine.Param. Only if a hyper space’s label -matches its parent matchzoo.engine.Param’s name, matchzoo -can correctly back-refrenced the parameter got sampled. This can be -done by asking the user always use the same name for a parameter and -its hyper space, but typos can occur. As a result, these wrappers -are created to hide hyper spaces’ label, and always correctly -bind them with its parameter’s name.
-
-
Examples::
-
>>> import matchzoo as mz
->>> from hyperopt.pyll.stochastic import sample
-
-
-
-
Basic Usage:
-
>>> model = mz.models.DenseBaselineModel()
->>> sample(model.params.hyper_space)  # doctest: +SKIP
- {'mlp_num_layers': 1.0, 'mlp_num_units': 274.0}
-
-
-
-
Arithmetic Operations:
-
>>> new_space = 2 ** mz.hyper_spaces.quniform(2, 6)
->>> model.params.get('mlp_num_layers').hyper_space = new_space
->>> sample(model.params.hyper_space)  # doctest: +SKIP
-{'mlp_num_layers': 8.0, 'mlp_num_units': 292.0}
-
-
-
-
-
-
-convert(name)
-

Attach name as hyperopt.hp’s label.

- --- - - - - - - - -
Parameters:name (str) –
Return type:Apply
Returns:a hyperopt ready search space
-
- -
- -
-
-class matchzoo.engine.hyper_spaces.choice(options)
-

Bases: matchzoo.engine.hyper_spaces.HyperoptProxy

-

hyperopt.hp.choice() proxy.

-
- -
-
-class matchzoo.engine.hyper_spaces.quniform(low, high, q=1)
-

Bases: matchzoo.engine.hyper_spaces.HyperoptProxy

-

hyperopt.hp.quniform() proxy.

-
- -
-
-class matchzoo.engine.hyper_spaces.uniform(low, high)
-

Bases: matchzoo.engine.hyper_spaces.HyperoptProxy

-

hyperopt.hp.uniform() proxy.

-
- -
-
-

matchzoo.engine.param module

-

Parameter class.

-
-
-class matchzoo.engine.param.Param(name, value=None, hyper_space=None, validator=None, desc=None)
-

Bases: object

-

Parameter class.

-

Basic usages with a name and value:

-
>>> param = Param('my_param', 10)
->>> param.name
-'my_param'
->>> param.value
-10
-
-
-

Use with a validator to make sure the parameter always keeps a valid -value.

-
>>> param = Param(
-...     name='my_param',
-...     value=5,
-...     validator=lambda x: 0 < x < 20
-... )
->>> param.validator  # doctest: +ELLIPSIS
-<function <lambda> at 0x...>
->>> param.value
-5
->>> param.value = 10
->>> param.value
-10
->>> param.value = -1
-Traceback (most recent call last):
-    ...
-ValueError: Validator not satifised.
-The validator's definition is as follows:
-validator=lambda x: 0 < x < 20
-
-
-

Use with a hyper space. Setting up a hyper space for a parameter makes the -parameter tunable in a matchzoo.engine.Tuner.

-
>>> from matchzoo.engine.hyper_spaces import quniform
->>> param = Param(
-...     name='positive_num',
-...     value=1,
-...     hyper_space=quniform(low=1, high=5)
-... )
->>> param.hyper_space  # doctest: +ELLIPSIS
-<matchzoo.engine.hyper_spaces.quniform object at ...>
->>> from hyperopt.pyll.stochastic import sample
->>> hyperopt_space = param.hyper_space.convert(param.name)
->>> samples = [sample(hyperopt_space) for _ in range(64)]
->>> set(samples) == {1, 2, 3, 4, 5}
-True
-
-
-

The boolean value of a Param instance is only True -when the value is not None. This is because some default falsy values -like zero or an empty list are valid parameter values. In other words, -the boolean value means to be “if the parameter value is filled”.

-
>>> param = Param('dropout')
->>> if param:
-...     print('OK')
->>> param = Param('dropout', 0)
->>> if param:
-...     print('OK')
-OK
-
-
-

A _pre_assignment_hook is initialized as a data type convertor if the -value is set as a number to keep data type consistency of the parameter. -This conversion supports python built-in numbers, numpy numbers, and -any number that inherits numbers.Number.

-
>>> param = Param('float_param', 0.5)
->>> param.value = 10
->>> param.value
-10.0
->>> type(param.value)
-<class 'float'>
-
-
-
-
-desc
-

Parameter description.

- --- - - - -
Type:return
-
- -
-
-hyper_space
-

Hyper space of the parameter.

- --- - - - -
Type:return
-
- -
-
-name
-

Name of the parameter.

- --- - - - - - -
Type:return
Return type:str
-
- -
-
-set_default(val, verbose=1)
-

Set default value, has no effect if already has a value.

- --- - - - -
Parameters:
    -
  • val – Default value to set.
  • -
  • verbose – Verbosity.
  • -
-
-
- -
-
-validator
-

Validator of the parameter.

- --- - - - - - -
Type:return
Return type:Callable[[Any], bool]
-
- -
-
-value
-

Value of the parameter.

- --- - - - - - -
Type:return
Return type:Any
-
- -
- -
-
-

matchzoo.engine.param_table module

-

Parameters table class.

-
-
-class matchzoo.engine.param_table.ParamTable
-

Bases: object

-

Parameter table class.

-

Example

-
>>> params = ParamTable()
->>> params.add(Param('ham', 'Parma Ham'))
->>> params.add(Param('egg', 'Over Easy'))
->>> params['ham']
-'Parma Ham'
->>> params['egg']
-'Over Easy'
->>> print(params)
-ham                           Parma Ham
-egg                           Over Easy
->>> params.add(Param('egg', 'Sunny side Up'))
-Traceback (most recent call last):
-    ...
-ValueError: Parameter named egg already exists.
-To re-assign parameter egg value, use `params["egg"] = value` instead.
-
-
-
-
-add(param)
-
--- - - - -
Parameters:param (Param) – parameter to add.
-
- -
-
-completed()
-
--- - - - - - -
Return type:bool
Returns:True if all params are filled, False otherwise.
-

Example

-
>>> import matchzoo
->>> model = matchzoo.models.NaiveModel()
->>> model.params.completed()
-False
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.params.completed()
-True
-
-
-
- -
-
-get(key)
-
--- - - - - - -
Return type:Param
Returns:The parameter in the table named key.
-
- -
-
-hyper_space
-

Hyper space of the table, a valid hyperopt graph.

- --- - - - - - -
Type:return
Return type:dict
-
- -
-
-keys()
-
--- - - - - - -
Return type:Keysview[~KT]
Returns:Parameter table keys.
-
- -
-
-set(key, param)
-

Set key to parameter param.

-
- -
- -
-
-

Module contents

-
-
- - -
- -
- - -
-
- -
- -
- - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/matchzoo.html b/docs/_build/html/matchzoo.html deleted file mode 100644 index 54c033b1..00000000 --- a/docs/_build/html/matchzoo.html +++ /dev/null @@ -1,550 +0,0 @@ - - - - - - - - - - - matchzoo package — MatchZoo 2.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -
-

matchzoo package

-
-

Subpackages

-
- -
-
-
-

Submodules

-
-
-

matchzoo.embedding module

-

Matchzoo toolkit for token embedding.

-
-
-class matchzoo.embedding.Embedding(data)
-

Bases: object

-

Embedding class.

-
-
Examples::
-
>>> import matchzoo as mz
->>> data_pack = mz.datasets.toy.load_data()
->>> pp = mz.preprocessors.NaivePreprocessor()
->>> vocab_unit = mz.build_vocab_unit(pp.fit_transform(data_pack),
-...                                  verbose=0)
->>> term_index = vocab_unit.state['term_index']
->>> embed_path = mz.datasets.embeddings.EMBED_RANK
-
-
-
-
To load from a file:
-
>>> embedding = mz.embedding.load_from_file(embed_path)
->>> matrix = embedding.build_matrix(term_index)
->>> matrix.shape[0] == len(term_index) + 1
-True
-
-
-
-
To build your own:
-
>>> data = pd.DataFrame(data=[[0, 1], [2, 3]], index=['A', 'B'])
->>> embedding = mz.embedding.Embedding(data)
->>> matrix = embedding.build_matrix({'A': 2, 'B': 1})
->>> matrix.shape == (3, 2)
-True
-
-
-
-
-
-
-build_matrix(term_index, initializer=<function Embedding.<lambda>>)
-

Build a matrix using term_index.

- --- - - - - - - - -
Parameters:
    -
  • term_index (dict) – A dict or TermIndex to build with.
  • -
  • initializer – A callable that returns a default value for missing -terms in data. (default: a random uniform distribution in range) -(-0.2, 0.2)).
  • -
-
Return type:

ndarray

-
Returns:

A matrix.

-
-
- -
-
-input_dim
-

return Embedding input dimension.

- --- - - - -
Return type:int
-
- -
-
-output_dim
-

return Embedding output dimension.

- --- - - - -
Return type:int
-
- -
- -
-
-matchzoo.embedding.load_from_file(file_path, mode='word2vec')
-

Load embedding from file_path.

- --- - - - - - - - -
Parameters:
    -
  • file_path (str) – Path to file.
  • -
  • mode (str) – Embedding file format mode, one of ‘word2vec’ or ‘glove’. -(default: ‘word2vec’)
  • -
-
Return type:

Embedding

-
Returns:

An matchzoo.embedding.Embedding instance.

-
-
- -
-
-

matchzoo.logger module

-

MatchZoo Logging module.

-
-
-

matchzoo.version module

-

Matchzoo version file.

-
-
-

Module contents

-
-
- - -
- -
- - -
-
- -
- -
- - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/matchzoo.models.html b/docs/_build/html/matchzoo.models.html deleted file mode 100644 index 44868240..00000000 --- a/docs/_build/html/matchzoo.models.html +++ /dev/null @@ -1,932 +0,0 @@ - - - - - - - - - - - matchzoo.models package — MatchZoo 2.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -
-

matchzoo.models package

-
-

Submodules

-
-
-

matchzoo.models.anmm module

-

An implementation of aNMM Model.

-
-
-class matchzoo.models.anmm.ANMM(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

ANMM Model.

-

Examples

-
>>> model = ANMM()
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
-
-
-
-
-build()
-

Build model structure.

-

aNMM model based on bin weighting and query term attentions

-
- -
-
-classmethod get_default_params()
-
--- - - - - - -
Return type:ParamTable
Returns:model default parameters.
-
- -
- -
-
-

matchzoo.models.arci module

-

An implementation of ArcI Model.

-
-
-class matchzoo.models.arci.ArcI(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

ArcI Model.

-

Examples

-
>>> model = ArcI()
->>> model.params['num_blocks'] = 1
->>> model.params['left_filters'] = [32]
->>> model.params['right_filters'] = [32]
->>> model.params['left_kernel_sizes'] = [3]
->>> model.params['right_kernel_sizes'] = [3]
->>> model.params['left_pool_sizes'] = [2]
->>> model.params['right_pool_sizes'] = [4]
->>> model.params['conv_activation_func'] = 'relu'
->>> model.params['mlp_num_layers'] = 1
->>> model.params['mlp_num_units'] = 64
->>> model.params['mlp_num_fan_out'] = 32
->>> model.params['mlp_activation_func'] = 'relu'
->>> model.params['dropout_rate'] = 0.5
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
-
-
-
-
-build()
-

Build model structure.

-

ArcI use Siamese arthitecture.

-
- -
-
-classmethod get_default_params()
-
--- - - - - - -
Return type:ParamTable
Returns:model default parameters.
-
- -
- -
-
-

matchzoo.models.arcii module

-

An implementation of ArcII Model.

-
-
-class matchzoo.models.arcii.ArcII(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

ArcII Model.

-

Examples

-
>>> model = ArcII()
->>> model.params['embedding_output_dim'] = 300
->>> model.params['num_blocks'] = 2
->>> model.params['kernel_1d_count'] = 32
->>> model.params['kernel_1d_size'] = 3
->>> model.params['kernel_2d_count'] = [16, 32]
->>> model.params['kernel_2d_size'] = [[3, 3], [3, 3]]
->>> model.params['pool_2d_size'] = [[2, 2], [2, 2]]
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
-
-
-
-
-build()
-

Build model structure.

-

ArcII has the desirable property of letting two sentences meet before -their own high-level representations mature.

-
- -
-
-classmethod get_default_params()
-
--- - - - - - -
Return type:ParamTable
Returns:model default parameters.
-
- -
- -
-
-

matchzoo.models.cdssm module

-

An implementation of CDSSM (CLSM) model.

-
-
-class matchzoo.models.cdssm.CDSSM(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

CDSSM Model implementation.

-

Learning Semantic Representations Using Convolutional Neural Networks -for Web Search. (2014a) -A Latent Semantic Model with Convolutional-Pooling Structure for -Information Retrieval. (2014b)

-

Examples

-
>>> model = CDSSM()
->>> model.params['optimizer'] = 'adam'
->>> model.params['filters'] =  32
->>> model.params['kernel_size'] = 3
->>> model.params['conv_activation_func'] = 'relu'
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
-
-
-
-
-build()
-

Build model structure.

-

CDSSM use Siamese architecture.

-
- -
-
-classmethod get_default_params()
-
--- - - - - - -
Return type:ParamTable
Returns:model default parameters.
-
- -
-
-classmethod get_default_preprocessor()
-
--- - - - -
Returns:Default preprocessor.
-
- -
-
-guess_and_fill_missing_params(verbose=1)
-

Guess and fill missing parameters in params.

-

Use this method to automatically fill-in hyper parameters. -This involves some guessing so the parameter it fills could be -wrong. For example, the default task is Ranking, and if we do not -set it to Classification manually for data packs prepared for -classification, then the shape of the model output and the data will -mismatch.

- --- - - - -
Parameters:verbose – Verbosity.
-
- -
- -
-
-

matchzoo.models.conv_knrm module

-

ConvKNRM model.

-
-
-class matchzoo.models.conv_knrm.ConvKNRM(params=None, backend=None)
-

Bases: matchzoo.models.knrm.KNRM

-

ConvKNRM model.

-

Examples

-
>>> model = ConvKNRM()
->>> model.params['embedding_input_dim'] = 10000
->>> model.params['embedding_output_dim'] = 300
->>> model.params['embedding_trainable'] = True
->>> model.params['filters'] = 128
->>> model.params['conv_activation_func'] = 'tanh'
->>> model.params['max_ngram'] = 3
->>> model.params['use_crossmatch'] = True
->>> model.params['kernel_num'] = 11
->>> model.params['sigma'] = 0.1
->>> model.params['exact_sigma'] = 0.001
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
-
-
-
-
-build()
-

Build model.

-
- -
-
-get_default_params()
-

Get default parameters.

-
- -
- -
-
-

matchzoo.models.dense_baseline_model module

-

A simple densely connected baseline model.

-
-
-class matchzoo.models.dense_baseline_model.DenseBaselineModel(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

A simple densely connected baseline model.

-

Examples

-
>>> model = DenseBaselineModel()
->>> model.params['mlp_num_layers'] = 2
->>> model.params['mlp_num_units'] = 300
->>> model.params['mlp_num_fan_out'] = 128
->>> model.params['mlp_activation_func'] = 'relu'
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
->>> model.compile()
-
-
-
-
-build()
-

Model structure.

-
- -
-
-classmethod get_default_params()
-
--- - - - - - -
Return type:ParamTable
Returns:model default parameters.
-
- -
- -
-
-

matchzoo.models.drmm module

-

An implementation of DRMM Model.

-
-
-class matchzoo.models.drmm.DRMM(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

DRMM Model.

-

Examples

-
>>> model = DRMM()
->>> model.params['mlp_num_layers'] = 1
->>> model.params['mlp_num_units'] = 5
->>> model.params['mlp_num_fan_out'] = 1
->>> model.params['mlp_activation_func'] = 'tanh'
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
->>> model.compile()
-
-
-
-
-classmethod attention_layer(attention_input, attention_mask=None)
-

Performs attention on the input.

- --- - - - - - -
Parameters:
    -
  • attention_input (Any) – The input tensor for attention layer.
  • -
  • attention_mask (Optional[Any]) – A tensor to mask the invalid values.
  • -
-
Returns:

The masked output tensor.

-
-
- -
-
-build()
-

Build model structure.

-
- -
-
-classmethod get_default_params()
-
--- - - - - - -
Return type:ParamTable
Returns:model default parameters.
-
- -
- -
-
-

matchzoo.models.drmmtks module

-

An implementation of DRMMTKS Model.

-
-
-class matchzoo.models.drmmtks.DRMMTKS(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

DRMMTKS Model.

-

Examples

-
>>> model = DRMMTKS()
->>> model.params['embedding_input_dim'] = 10000
->>> model.params['embedding_output_dim'] = 100
->>> model.params['top_k'] = 20
->>> model.params['mlp_num_layers'] = 1
->>> model.params['mlp_num_units'] = 5
->>> model.params['mlp_num_fan_out'] = 1
->>> model.params['mlp_activation_func'] = 'tanh'
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
-
-
-
-
-classmethod attention_layer(attention_input, attention_mask=None)
-

Performs attention on the input.

- --- - - - - - -
Parameters:
    -
  • attention_input (Any) – The input tensor for attention layer.
  • -
  • attention_mask (Optional[Any]) – A tensor to mask the invalid values.
  • -
-
Returns:

The masked output tensor.

-
-
- -
-
-build()
-

Build model structure.

-
- -
-
-classmethod get_default_params()
-
--- - - - - - -
Return type:ParamTable
Returns:model default parameters.
-
- -
- -
-
-

matchzoo.models.dssm module

-

An implementation of DSSM, Deep Structured Semantic Model.

-
-
-class matchzoo.models.dssm.DSSM(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

Deep structured semantic model.

-

Examples

-
>>> model = DSSM()
->>> model.params['mlp_num_layers'] = 3
->>> model.params['mlp_num_units'] = 300
->>> model.params['mlp_num_fan_out'] = 128
->>> model.params['mlp_activation_func'] = 'relu'
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
-
-
-
-
-build()
-

Build model structure.

-

DSSM use Siamese arthitecture.

-
- -
-
-classmethod get_default_params()
-
--- - - - - - -
Return type:ParamTable
Returns:model default parameters.
-
- -
-
-classmethod get_default_preprocessor()
-
--- - - - -
Returns:Default preprocessor.
-
- -
- -
-
-

matchzoo.models.duet module

-

DUET Model.

-
-
-class matchzoo.models.duet.DUET(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

DUET Model.

-

Examples

-
>>> model = DUET()
->>> model.params['embedding_input_dim'] = 1000
->>> model.params['embedding_output_dim'] = 300
->>> model.params['lm_filters'] = 32
->>> model.params['lm_hidden_sizes'] = [64, 32]
->>> model.params['dropout_rate'] = 0.5
->>> model.params['dm_filters'] = 32
->>> model.params['dm_kernel_size'] = 3
->>> model.params['dm_d_mpool'] = 4
->>> model.params['dm_hidden_sizes'] = [64, 32]
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
-
-
-
-
-build()
-

Build model.

-
- -
-
-classmethod get_default_params()
-

Get default parameters.

-
- -
- -
-
-

matchzoo.models.knrm module

-

KNRM model.

-
-
-class matchzoo.models.knrm.KNRM(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

KNRM model.

-

Examples

-
>>> model = KNRM()
->>> model.params['embedding_input_dim'] =  10000
->>> model.params['embedding_output_dim'] =  10
->>> model.params['embedding_trainable'] = True
->>> model.params['kernel_num'] = 11
->>> model.params['sigma'] = 0.1
->>> model.params['exact_sigma'] = 0.001
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
-
-
-
-
-build()
-

Build model.

-
- -
-
-classmethod get_default_params()
-

Get default parameters.

-
- -
- -
-
-

matchzoo.models.match_pyramid module

-

An implementation of MatchPyramid Model.

-
-
-class matchzoo.models.match_pyramid.MatchPyramid(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

MatchPyramid Model.

-

Examples

-
>>> model = MatchPyramid()
->>> model.params['embedding_output_dim'] = 300
->>> model.params['num_blocks'] = 2
->>> model.params['kernel_count'] = [16, 32]
->>> model.params['kernel_size'] = [[3, 3], [3, 3]]
->>> model.params['dpool_size'] = [3, 10]
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
-
-
-
-
-build()
-

Build model structure.

-

MatchPyramid text matching as image recognition.

-
- -
-
-classmethod get_default_params()
-
--- - - - - - -
Return type:ParamTable
Returns:model default parameters.
-
- -
- -
-
-

matchzoo.models.mvlstm module

-

An implementation of MVLSTM Model.

-
-
-class matchzoo.models.mvlstm.MVLSTM(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

MVLSTM Model.

-

Examples

-
>>> model = MVLSTM()
->>> model.params['lstm_units'] = 32
->>> model.params['top_k'] = 50
->>> model.params['mlp_num_layers'] = 2
->>> model.params['mlp_num_units'] = 20
->>> model.params['mlp_num_fan_out'] = 10
->>> model.params['mlp_activation_func'] = 'relu'
->>> model.params['dropout_rate'] = 0.5
->>> model.guess_and_fill_missing_params(verbose=0)
->>> model.build()
-
-
-
-
-build()
-

Build model structure.

-
- -
-
-classmethod get_default_params()
-
--- - - - - - -
Return type:ParamTable
Returns:model default parameters.
-
- -
- -
-
-

matchzoo.models.naive_model module

-

Naive model with a simplest structure for testing purposes.

-
-
-class matchzoo.models.naive_model.NaiveModel(params=None, backend=None)
-

Bases: matchzoo.engine.base_model.BaseModel

-

Naive model with a simplest structure for testing purposes.

-

Bare minimum functioning model. The best choice to get things rolling. -The worst choice to fit and evaluate performance.

-
-
-build()
-

Build.

-
- -
- -
-
-

matchzoo.models.parameter_readme_generator module

-

matchzoo/models/README.md generater.

-
-
-

Module contents

-
-
-matchzoo.models.list_available()
-
- -
-
- - -
- -
- - -
-
- -
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- - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/matchzoo.tasks.html b/docs/_build/html/matchzoo.tasks.html deleted file mode 100644 index 0eca341e..00000000 --- a/docs/_build/html/matchzoo.tasks.html +++ /dev/null @@ -1,456 +0,0 @@ - - - - - - - - - - - matchzoo.tasks package — MatchZoo 2.0 documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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matchzoo.tasks package

-
-

Submodules

-
-
-

matchzoo.tasks.classification module

-

Classification task.

-
-
-class matchzoo.tasks.classification.Classification(num_classes=2)
-

Bases: matchzoo.engine.base_task.BaseTask

-

Classification task.

-

Examples

-
>>> classification_task = Classification(num_classes=2)
->>> classification_task.metrics = ['precision']
->>> classification_task.num_classes
-2
->>> classification_task.output_shape
-(2,)
->>> classification_task.output_dtype
-<class 'int'>
->>> print(classification_task)
-Classification Task with 2 classes
-
-
-
-
-classmethod list_available_losses()
-
--- - - - - - -
Return type:list
Returns:a list of available losses.
-
- -
-
-classmethod list_available_metrics()
-
--- - - - - - -
Return type:list
Returns:a list of available metrics.
-
- -
-
-num_classes
-

number of classes to classify.

- --- - - - - - -
Type:return
Return type:int
-
- -
-
-output_dtype
-

target data type, expect int as output.

- --- - - - -
Type:return
-
- -
-
-output_shape
-

output shape of a single sample of the task.

- --- - - - - - -
Type:return
Return type:tuple
-
- -
- -
-
-

matchzoo.tasks.ranking module

-

Ranking task.

-
-
-class matchzoo.tasks.ranking.Ranking(loss=None, metrics=None)
-

Bases: matchzoo.engine.base_task.BaseTask

-

Ranking Task.

-

Examples

-
>>> ranking_task = Ranking()
->>> ranking_task.metrics = ['map', 'ndcg']
->>> ranking_task.output_shape
-(1,)
->>> ranking_task.output_dtype
-<class 'float'>
->>> print(ranking_task)
-Ranking Task
-
-
-
-
-classmethod list_available_losses()
-
--- - - - - - -
Return type:list
Returns:a list of available losses.
-
- -
-
-classmethod list_available_metrics()
-
--- - - - - - -
Return type:list
Returns:a list of available metrics.
-
- -
-
-output_dtype
-

target data type, expect float as output.

- --- - - - -
Type:return
-
- -
-
-output_shape
-

output shape of a single sample of the task.

- --- - - - - - -
Type:return
Return type:tuple
-
- -
- -
-
-

matchzoo.tasks.utils module

-

Task utilities.

-
-
-matchzoo.tasks.utils.list_available_task_types()
-

Return a list of task type class objects.

- --- - - - -
Return type:List[Type[BaseTask]]
-
- -
-
-

Module contents

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- -
- - - - - - - - - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js deleted file mode 100644 index c1e9a54c..00000000 --- a/docs/_build/html/searchindex.js +++ /dev/null @@ -1 +0,0 @@ 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to MatchZoo\u2019s documentation!","matchzoo package","matchzoo.auto package","matchzoo.contrib package","matchzoo.contrib.layers package","matchzoo.contrib.models package","matchzoo.data_generator package","matchzoo.data_pack package","matchzoo.datasets package","matchzoo.datasets.embeddings package","matchzoo.datasets.snli package","matchzoo.datasets.toy package","matchzoo.datasets.wiki_qa package","matchzoo.engine package","matchzoo.layers package","matchzoo.losses package","matchzoo.metrics package","matchzoo.models package","matchzoo.preprocessors package","matchzoo.processor_units package","matchzoo.tasks package","matchzoo.utils package","MatchZoo Model 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\ No newline at end of file diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index f4f9e467..00000000 --- a/docs/make.bat +++ /dev/null @@ -1,36 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=_build -set SPHINXPROJ=MatchZoo - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% - -:end -popd diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index 4a7dc71d..00000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,13 +0,0 @@ -Sphinx >= 1.7.5 -sphinx_rtd_theme >= 0.4.0 -keras >= 2.0.5 -nltk >= 3.2.3 -numpy >= 1.12.1 -h5py >= 2.7.0 -dill >= 0.2.7.1 -hyperopt >= 0.1 -pandas >= 0.23.1 -sphinx_autodoc_typehints>=1.6.0 -tensorflow -tabulate >= 0.8.2 -nbsphinx \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py deleted file mode 100644 index 55981b7d..00000000 --- a/docs/source/conf.py +++ /dev/null @@ -1,180 +0,0 @@ -# -*- coding: utf-8 -*- -# -# Configuration file for the Sphinx documentation builder. -# -# This file does only contain a selection of the most common options. For a -# full list see the documentation: -# http://www.sphinx-doc.org/en/master/config - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -import os -import sys -sys.path.insert(0, os.path.abspath('../..')) -sys.path.insert(0, os.path.abspath('../../matchzoo')) -sys.path.insert(0, os.path.abspath('../../matchzoo/auto')) -sys.path.insert(0, os.path.abspath('../../matchzoo/data_generator')) -sys.path.insert(0, os.path.abspath('../../matchzoo/data_pack')) -sys.path.insert(0, os.path.abspath('../../matchzoo/datasets')) -sys.path.insert(0, os.path.abspath('../../matchzoo/embedding')) -sys.path.insert(0, os.path.abspath('../../matchzoo/engine')) -sys.path.insert(0, os.path.abspath('../../matchzoo/layers')) -sys.path.insert(0, os.path.abspath('../../matchzoo/losses')) -sys.path.insert(0, os.path.abspath('../../matchzoo/metrics')) -sys.path.insert(0, os.path.abspath('../../matchzoo/models')) -sys.path.insert(0, os.path.abspath('../../matchzoo/preprocessors')) -sys.path.insert(0, os.path.abspath('../../matchzoo/tasks')) -sys.path.insert(0, os.path.abspath('../../matchzoo/utils')) - - -# -- Project information ----------------------------------------------------- - -project = 'MatchZoo' -copyright = '2018, MatchZoo' -author = 'MatchZoo' - -# The short X.Y version -version = '' -# The full version, including alpha/beta/rc tags -release = '2.1' - - -# -- General configuration --------------------------------------------------- - -# If your documentation needs a minimal Sphinx version, state it here. -# -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.mathjax', - 'sphinx.ext.napoleon', - 'sphinx_autodoc_typehints', - 'nbsphinx', -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' -# from recommonmark.parser import CommonMarkParser -# source_parsers = { -# '.md':CommonMarkParser -# } -# The master toctree document. -master_doc = 'index' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path . -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store','**.ipynb_checkpoints'] - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = 'sphinx_rtd_theme' - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# -# html_theme_options = {} - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -# Custom sidebar templates, must be a dictionary that maps document names -# to template names. -# -# The default sidebars (for documents that don't match any pattern) are -# defined by theme itself. Builtin themes are using these templates by -# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', -# 'searchbox.html']``. -# -# html_sidebars = {} - - -# -- Options for HTMLHelp output --------------------------------------------- - -# Output file base name for HTML help builder. -htmlhelp_basename = 'MatchZoodoc' - - -# -- Options for LaTeX output ------------------------------------------------ - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # - # 'papersize': 'letterpaper', - - # The font size ('10pt', '11pt' or '12pt'). - # - # 'pointsize': '10pt', - - # Additional stuff for the LaTeX preamble. - # - # 'preamble': '', - - # Latex figure (float) alignment - # - # 'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - (master_doc, 'MatchZoo.tex', 'MatchZoo Documentation', - 'MatchZoo', 'manual'), -] - - -# -- Options for manual page output ------------------------------------------ - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'matchzoo', 'MatchZoo Documentation', - [author], 1) -] - - -# -- Options for Texinfo output ---------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - (master_doc, 'MatchZoo', 'MatchZoo Documentation', - author, 'MatchZoo', 'One line description of project.', - 'Miscellaneous'), -] - - -# -- Extension configuration ------------------------------------------------- diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index a29aff78..00000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,36 +0,0 @@ -.. MatchZoo documentation master file, created by - sphinx-quickstart on Mon May 28 16:40:41 2018. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to MatchZoo's documentation! -==================================== - - -.. image:: https://travis-ci.org/faneshion/MatchZoo.svg?branch=master - :alt: ci - :target: https://travis-ci.org/faneshion/MatchZoo/ - - -.. image:: ../../artworks/matchzoo-logo.png - :alt: logo - :align: center - - -MatchZoo is a toolkit for text matching. It was developed with a focus on facilitating the designing, comparing and sharing of deep text matching models. There are a number of deep matching methods, such as DRMM, MatchPyramid, MV-LSTM, aNMM, DUET, ARC-I, ARC-II, DSSM, and CDSSM, designed with a unified interface. Potential tasks related to MatchZoo include document retrieval, question answering, conversational response ranking, paraphrase identification, etc. We are always happy to receive any code contributions, suggestions, comments from all our MatchZoo users. - - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - modules - model_reference - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/source/matchzoo.auto.preparer.rst b/docs/source/matchzoo.auto.preparer.rst deleted file mode 100644 index 3291f9fe..00000000 --- a/docs/source/matchzoo.auto.preparer.rst +++ /dev/null @@ -1,30 +0,0 @@ -matchzoo.auto.preparer package -============================== - -Submodules ----------- - -matchzoo.auto.preparer.prepare module -------------------------------------- - -.. automodule:: matchzoo.auto.preparer.prepare - :members: - :undoc-members: - :show-inheritance: - -matchzoo.auto.preparer.preparer module --------------------------------------- - -.. automodule:: matchzoo.auto.preparer.preparer - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.auto.preparer - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.auto.rst b/docs/source/matchzoo.auto.rst deleted file mode 100644 index 1272de0a..00000000 --- a/docs/source/matchzoo.auto.rst +++ /dev/null @@ -1,18 +0,0 @@ -matchzoo.auto package -===================== - -Subpackages ------------ - -.. toctree:: - - matchzoo.auto.preparer - matchzoo.auto.tuner - -Module contents ---------------- - -.. automodule:: matchzoo.auto - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.auto.tuner.callbacks.rst b/docs/source/matchzoo.auto.tuner.callbacks.rst deleted file mode 100644 index 1671dc48..00000000 --- a/docs/source/matchzoo.auto.tuner.callbacks.rst +++ /dev/null @@ -1,46 +0,0 @@ -matchzoo.auto.tuner.callbacks package -===================================== - -Submodules ----------- - -matchzoo.auto.tuner.callbacks.callback module ---------------------------------------------- - -.. automodule:: matchzoo.auto.tuner.callbacks.callback - :members: - :undoc-members: - :show-inheritance: - -matchzoo.auto.tuner.callbacks.lambda\_callback module ------------------------------------------------------ - -.. automodule:: matchzoo.auto.tuner.callbacks.lambda_callback - :members: - :undoc-members: - :show-inheritance: - -matchzoo.auto.tuner.callbacks.load\_embedding\_matrix module ------------------------------------------------------------- - -.. automodule:: matchzoo.auto.tuner.callbacks.load_embedding_matrix - :members: - :undoc-members: - :show-inheritance: - -matchzoo.auto.tuner.callbacks.save\_model module ------------------------------------------------- - -.. automodule:: matchzoo.auto.tuner.callbacks.save_model - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.auto.tuner.callbacks - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.auto.tuner.rst b/docs/source/matchzoo.auto.tuner.rst deleted file mode 100644 index 024d1e83..00000000 --- a/docs/source/matchzoo.auto.tuner.rst +++ /dev/null @@ -1,37 +0,0 @@ -matchzoo.auto.tuner package -=========================== - -Subpackages ------------ - -.. toctree:: - - matchzoo.auto.tuner.callbacks - -Submodules ----------- - -matchzoo.auto.tuner.tune module -------------------------------- - -.. automodule:: matchzoo.auto.tuner.tune - :members: - :undoc-members: - :show-inheritance: - -matchzoo.auto.tuner.tuner module --------------------------------- - -.. automodule:: matchzoo.auto.tuner.tuner - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.auto.tuner - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.contrib.layers.rst b/docs/source/matchzoo.contrib.layers.rst deleted file mode 100644 index 4045c1b5..00000000 --- a/docs/source/matchzoo.contrib.layers.rst +++ /dev/null @@ -1,10 +0,0 @@ -matchzoo.contrib.layers package -=============================== - -Module contents ---------------- - -.. automodule:: matchzoo.contrib.layers - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.contrib.models.rst b/docs/source/matchzoo.contrib.models.rst deleted file mode 100644 index dfc7dcbf..00000000 --- a/docs/source/matchzoo.contrib.models.rst +++ /dev/null @@ -1,22 +0,0 @@ -matchzoo.contrib.models package -=============================== - -Submodules ----------- - -matchzoo.contrib.models.match\_lstm module ------------------------------------------- - -.. automodule:: matchzoo.contrib.models.match_lstm - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.contrib.models - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.contrib.rst b/docs/source/matchzoo.contrib.rst deleted file mode 100644 index 2c05d186..00000000 --- a/docs/source/matchzoo.contrib.rst +++ /dev/null @@ -1,18 +0,0 @@ -matchzoo.contrib package -======================== - -Subpackages ------------ - -.. toctree:: - - matchzoo.contrib.layers - matchzoo.contrib.models - -Module contents ---------------- - -.. automodule:: matchzoo.contrib - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.data_generator.callbacks.rst b/docs/source/matchzoo.data_generator.callbacks.rst deleted file mode 100644 index 995f34ba..00000000 --- a/docs/source/matchzoo.data_generator.callbacks.rst +++ /dev/null @@ -1,46 +0,0 @@ -matchzoo.data\_generator.callbacks package -========================================== - -Submodules ----------- - -matchzoo.data\_generator.callbacks.callback module --------------------------------------------------- - -.. automodule:: matchzoo.data_generator.callbacks.callback - :members: - :undoc-members: - :show-inheritance: - -matchzoo.data\_generator.callbacks.dynamic\_pooling module ----------------------------------------------------------- - -.. automodule:: matchzoo.data_generator.callbacks.dynamic_pooling - :members: - :undoc-members: - :show-inheritance: - -matchzoo.data\_generator.callbacks.histogram module ---------------------------------------------------- - -.. automodule:: matchzoo.data_generator.callbacks.histogram - :members: - :undoc-members: - :show-inheritance: - -matchzoo.data\_generator.callbacks.lambda\_callback module ----------------------------------------------------------- - -.. automodule:: matchzoo.data_generator.callbacks.lambda_callback - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.data_generator.callbacks - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.data_generator.rst b/docs/source/matchzoo.data_generator.rst deleted file mode 100644 index dd1206d5..00000000 --- a/docs/source/matchzoo.data_generator.rst +++ /dev/null @@ -1,37 +0,0 @@ -matchzoo.data\_generator package -================================ - -Subpackages ------------ - -.. toctree:: - - matchzoo.data_generator.callbacks - -Submodules ----------- - -matchzoo.data\_generator.data\_generator module ------------------------------------------------ - -.. automodule:: matchzoo.data_generator.data_generator - :members: - :undoc-members: - :show-inheritance: - -matchzoo.data\_generator.data\_generator\_builder module --------------------------------------------------------- - -.. automodule:: matchzoo.data_generator.data_generator_builder - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.data_generator - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.data_pack.rst b/docs/source/matchzoo.data_pack.rst deleted file mode 100644 index e59fde28..00000000 --- a/docs/source/matchzoo.data_pack.rst +++ /dev/null @@ -1,30 +0,0 @@ -matchzoo.data\_pack package -=========================== - -Submodules ----------- - -matchzoo.data\_pack.data\_pack module -------------------------------------- - -.. automodule:: matchzoo.data_pack.data_pack - :members: - :undoc-members: - :show-inheritance: - -matchzoo.data\_pack.pack module -------------------------------- - -.. automodule:: matchzoo.data_pack.pack - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.data_pack - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.datasets.embeddings.rst b/docs/source/matchzoo.datasets.embeddings.rst deleted file mode 100644 index e57c460b..00000000 --- a/docs/source/matchzoo.datasets.embeddings.rst +++ /dev/null @@ -1,22 +0,0 @@ -matchzoo.datasets.embeddings package -==================================== - -Submodules ----------- - -matchzoo.datasets.embeddings.load\_glove\_embedding module ----------------------------------------------------------- - -.. automodule:: matchzoo.datasets.embeddings.load_glove_embedding - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.datasets.embeddings - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.datasets.quora_qp.rst b/docs/source/matchzoo.datasets.quora_qp.rst deleted file mode 100644 index e0eb3d83..00000000 --- a/docs/source/matchzoo.datasets.quora_qp.rst +++ /dev/null @@ -1,22 +0,0 @@ -matchzoo.datasets.quora\_qp package -=================================== - -Submodules ----------- - -matchzoo.datasets.quora\_qp.load\_data module ---------------------------------------------- - -.. automodule:: matchzoo.datasets.quora_qp.load_data - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.datasets.quora_qp - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.datasets.rst b/docs/source/matchzoo.datasets.rst deleted file mode 100644 index d559538d..00000000 --- a/docs/source/matchzoo.datasets.rst +++ /dev/null @@ -1,21 +0,0 @@ -matchzoo.datasets package -========================= - -Subpackages ------------ - -.. toctree:: - - matchzoo.datasets.embeddings - matchzoo.datasets.quora_qp - matchzoo.datasets.snli - matchzoo.datasets.toy - matchzoo.datasets.wiki_qa - -Module contents ---------------- - -.. automodule:: matchzoo.datasets - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.datasets.snli.rst b/docs/source/matchzoo.datasets.snli.rst deleted file mode 100644 index 41cef656..00000000 --- a/docs/source/matchzoo.datasets.snli.rst +++ /dev/null @@ -1,22 +0,0 @@ -matchzoo.datasets.snli package -============================== - -Submodules ----------- - -matchzoo.datasets.snli.load\_data module ----------------------------------------- - -.. automodule:: matchzoo.datasets.snli.load_data - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.datasets.snli - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.datasets.toy.rst b/docs/source/matchzoo.datasets.toy.rst deleted file mode 100644 index 6e718e04..00000000 --- a/docs/source/matchzoo.datasets.toy.rst +++ /dev/null @@ -1,10 +0,0 @@ -matchzoo.datasets.toy package -============================= - -Module contents ---------------- - -.. automodule:: matchzoo.datasets.toy - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.datasets.wiki_qa.rst b/docs/source/matchzoo.datasets.wiki_qa.rst deleted file mode 100644 index dcaaac57..00000000 --- a/docs/source/matchzoo.datasets.wiki_qa.rst +++ /dev/null @@ -1,22 +0,0 @@ -matchzoo.datasets.wiki\_qa package -================================== - -Submodules ----------- - -matchzoo.datasets.wiki\_qa.load\_data module --------------------------------------------- - -.. automodule:: matchzoo.datasets.wiki_qa.load_data - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.datasets.wiki_qa - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.embedding.rst b/docs/source/matchzoo.embedding.rst deleted file mode 100644 index f1b0265b..00000000 --- a/docs/source/matchzoo.embedding.rst +++ /dev/null @@ -1,22 +0,0 @@ -matchzoo.embedding package -========================== - -Submodules ----------- - -matchzoo.embedding.embedding module ------------------------------------ - -.. automodule:: matchzoo.embedding.embedding - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.embedding - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.engine.rst b/docs/source/matchzoo.engine.rst deleted file mode 100644 index 01a5b19b..00000000 --- a/docs/source/matchzoo.engine.rst +++ /dev/null @@ -1,86 +0,0 @@ -matchzoo.engine package -======================= - -Submodules ----------- - -matchzoo.engine.base\_metric module ------------------------------------ - -.. automodule:: matchzoo.engine.base_metric - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.base\_model module ----------------------------------- - -.. automodule:: matchzoo.engine.base_model - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.base\_preprocessor module ------------------------------------------ - -.. automodule:: matchzoo.engine.base_preprocessor - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.base\_task module ---------------------------------- - -.. automodule:: matchzoo.engine.base_task - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.callbacks module --------------------------------- - -.. automodule:: matchzoo.engine.callbacks - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.hyper\_spaces module ------------------------------------- - -.. automodule:: matchzoo.engine.hyper_spaces - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.param module ----------------------------- - -.. automodule:: matchzoo.engine.param - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.param\_table module ------------------------------------ - -.. automodule:: matchzoo.engine.param_table - :members: - :undoc-members: - :show-inheritance: - -matchzoo.engine.parse\_metric module ------------------------------------- - -.. automodule:: matchzoo.engine.parse_metric - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.engine - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.layers.rst b/docs/source/matchzoo.layers.rst deleted file mode 100644 index 15b5e8ac..00000000 --- a/docs/source/matchzoo.layers.rst +++ /dev/null @@ -1,30 +0,0 @@ -matchzoo.layers package -======================= - -Submodules ----------- - -matchzoo.layers.dynamic\_pooling\_layer module ----------------------------------------------- - -.. automodule:: matchzoo.layers.dynamic_pooling_layer - :members: - :undoc-members: - :show-inheritance: - -matchzoo.layers.matching\_layer module --------------------------------------- - -.. automodule:: matchzoo.layers.matching_layer - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.layers - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.losses.rst b/docs/source/matchzoo.losses.rst deleted file mode 100644 index 724b3332..00000000 --- a/docs/source/matchzoo.losses.rst +++ /dev/null @@ -1,30 +0,0 @@ -matchzoo.losses package -======================= - -Submodules ----------- - -matchzoo.losses.rank\_cross\_entropy\_loss module -------------------------------------------------- - -.. automodule:: matchzoo.losses.rank_cross_entropy_loss - :members: - :undoc-members: - :show-inheritance: - -matchzoo.losses.rank\_hinge\_loss module ----------------------------------------- - -.. automodule:: matchzoo.losses.rank_hinge_loss - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.losses - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.metrics.rst b/docs/source/matchzoo.metrics.rst deleted file mode 100644 index 28db8f63..00000000 --- a/docs/source/matchzoo.metrics.rst +++ /dev/null @@ -1,62 +0,0 @@ -matchzoo.metrics package -======================== - -Submodules ----------- - -matchzoo.metrics.average\_precision module ------------------------------------------- - -.. automodule:: matchzoo.metrics.average_precision - :members: - :undoc-members: - :show-inheritance: - -matchzoo.metrics.discounted\_cumulative\_gain module ----------------------------------------------------- - -.. automodule:: matchzoo.metrics.discounted_cumulative_gain - :members: - :undoc-members: - :show-inheritance: - -matchzoo.metrics.mean\_average\_precision module ------------------------------------------------- - -.. automodule:: matchzoo.metrics.mean_average_precision - :members: - :undoc-members: - :show-inheritance: - -matchzoo.metrics.mean\_reciprocal\_rank module ----------------------------------------------- - -.. automodule:: matchzoo.metrics.mean_reciprocal_rank - :members: - :undoc-members: - :show-inheritance: - -matchzoo.metrics.normalized\_discounted\_cumulative\_gain module ----------------------------------------------------------------- - -.. automodule:: matchzoo.metrics.normalized_discounted_cumulative_gain - :members: - :undoc-members: - :show-inheritance: - -matchzoo.metrics.precision module ---------------------------------- - -.. automodule:: matchzoo.metrics.precision - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.metrics - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.models.rst b/docs/source/matchzoo.models.rst deleted file mode 100644 index 0d359c33..00000000 --- a/docs/source/matchzoo.models.rst +++ /dev/null @@ -1,134 +0,0 @@ -matchzoo.models package -======================= - -Submodules ----------- - -matchzoo.models.anmm module ---------------------------- - -.. automodule:: matchzoo.models.anmm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.arci module ---------------------------- - -.. automodule:: matchzoo.models.arci - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.arcii module ----------------------------- - -.. automodule:: matchzoo.models.arcii - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.cdssm module ----------------------------- - -.. automodule:: matchzoo.models.cdssm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.conv\_knrm module ---------------------------------- - -.. automodule:: matchzoo.models.conv_knrm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.dense\_baseline module --------------------------------------- - -.. automodule:: matchzoo.models.dense_baseline - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.drmm module ---------------------------- - -.. automodule:: matchzoo.models.drmm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.drmmtks module ------------------------------- - -.. automodule:: matchzoo.models.drmmtks - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.dssm module ---------------------------- - -.. automodule:: matchzoo.models.dssm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.duet module ---------------------------- - -.. automodule:: matchzoo.models.duet - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.knrm module ---------------------------- - -.. automodule:: matchzoo.models.knrm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.match\_pyramid module -------------------------------------- - -.. automodule:: matchzoo.models.match_pyramid - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.mvlstm module ------------------------------ - -.. automodule:: matchzoo.models.mvlstm - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.naive module ----------------------------- - -.. automodule:: matchzoo.models.naive - :members: - :undoc-members: - :show-inheritance: - -matchzoo.models.parameter\_readme\_generator module ---------------------------------------------------- - -.. automodule:: matchzoo.models.parameter_readme_generator - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.models - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.preprocessors.rst b/docs/source/matchzoo.preprocessors.rst deleted file mode 100644 index fc624ea4..00000000 --- a/docs/source/matchzoo.preprocessors.rst +++ /dev/null @@ -1,77 +0,0 @@ -matchzoo.preprocessors package -============================== - -Subpackages ------------ - -.. toctree:: - - matchzoo.preprocessors.units - -Submodules ----------- - -matchzoo.preprocessors.basic\_preprocessor module -------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.basic_preprocessor - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.build\_unit\_from\_data\_pack module ------------------------------------------------------------ - -.. automodule:: matchzoo.preprocessors.build_unit_from_data_pack - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.build\_vocab\_unit module ------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.build_vocab_unit - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.cdssm\_preprocessor module -------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.cdssm_preprocessor - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.chain\_transform module ----------------------------------------------- - -.. automodule:: matchzoo.preprocessors.chain_transform - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.dssm\_preprocessor module ------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.dssm_preprocessor - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.naive\_preprocessor module -------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.naive_preprocessor - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.preprocessors - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.preprocessors.units.rst b/docs/source/matchzoo.preprocessors.units.rst deleted file mode 100644 index dc501f4a..00000000 --- a/docs/source/matchzoo.preprocessors.units.rst +++ /dev/null @@ -1,134 +0,0 @@ -matchzoo.preprocessors.units package -==================================== - -Submodules ----------- - -matchzoo.preprocessors.units.digit\_removal module --------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.digit_removal - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.fixed\_length module -------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.fixed_length - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.frequency\_filter module ------------------------------------------------------ - -.. automodule:: matchzoo.preprocessors.units.frequency_filter - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.lemmatization module -------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.lemmatization - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.lowercase module ---------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.lowercase - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.matching\_histogram module -------------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.matching_histogram - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.ngram\_letter module -------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.ngram_letter - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.punc\_removal module -------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.punc_removal - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.stateful\_unit module --------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.stateful_unit - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.stemming module --------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.stemming - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.stop\_removal module -------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.stop_removal - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.tokenize module --------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.tokenize - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.unit module ----------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.unit - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.vocabulary module ----------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.vocabulary - :members: - :undoc-members: - :show-inheritance: - -matchzoo.preprocessors.units.word\_hashing module -------------------------------------------------- - -.. automodule:: matchzoo.preprocessors.units.word_hashing - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.preprocessors.units - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.processor_units.rst b/docs/source/matchzoo.processor_units.rst deleted file mode 100644 index 34a82a10..00000000 --- a/docs/source/matchzoo.processor_units.rst +++ /dev/null @@ -1,30 +0,0 @@ -matchzoo.processor\_units package -================================= - -Submodules ----------- - -matchzoo.processor\_units.chain\_transform module -------------------------------------------------- - -.. automodule:: matchzoo.processor_units.chain_transform - :members: - :undoc-members: - :show-inheritance: - -matchzoo.processor\_units.processor\_units module -------------------------------------------------- - -.. automodule:: matchzoo.processor_units.processor_units - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.processor_units - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.rst b/docs/source/matchzoo.rst deleted file mode 100644 index 56ee8b77..00000000 --- a/docs/source/matchzoo.rst +++ /dev/null @@ -1,41 +0,0 @@ -matchzoo package -================ - -Subpackages ------------ - -.. toctree:: - - matchzoo.auto - matchzoo.data_generator - matchzoo.data_pack - matchzoo.datasets - matchzoo.embedding - matchzoo.engine - matchzoo.layers - matchzoo.losses - matchzoo.metrics - matchzoo.models - matchzoo.preprocessors - matchzoo.tasks - matchzoo.utils - -Submodules ----------- - -matchzoo.version module ------------------------ - -.. automodule:: matchzoo.version - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.tasks.rst b/docs/source/matchzoo.tasks.rst deleted file mode 100644 index 6466ae9b..00000000 --- a/docs/source/matchzoo.tasks.rst +++ /dev/null @@ -1,30 +0,0 @@ -matchzoo.tasks package -====================== - -Submodules ----------- - -matchzoo.tasks.classification module ------------------------------------- - -.. automodule:: matchzoo.tasks.classification - :members: - :undoc-members: - :show-inheritance: - -matchzoo.tasks.ranking module ------------------------------ - -.. automodule:: matchzoo.tasks.ranking - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.tasks - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/matchzoo.utils.rst b/docs/source/matchzoo.utils.rst deleted file mode 100644 index c679d9fd..00000000 --- a/docs/source/matchzoo.utils.rst +++ /dev/null @@ -1,46 +0,0 @@ -matchzoo.utils package -====================== - -Submodules ----------- - -matchzoo.utils.list\_recursive\_subclasses module -------------------------------------------------- - -.. automodule:: matchzoo.utils.list_recursive_subclasses - :members: - :undoc-members: - :show-inheritance: - -matchzoo.utils.make\_keras\_optimizer\_picklable module -------------------------------------------------------- - -.. automodule:: matchzoo.utils.make_keras_optimizer_picklable - :members: - :undoc-members: - :show-inheritance: - -matchzoo.utils.one\_hot module ------------------------------- - -.. automodule:: matchzoo.utils.one_hot - :members: - :undoc-members: - :show-inheritance: - -matchzoo.utils.tensor\_type module ----------------------------------- - -.. automodule:: matchzoo.utils.tensor_type - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: matchzoo.utils - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/model_reference.rst b/docs/source/model_reference.rst deleted file mode 100644 index ee069670..00000000 --- a/docs/source/model_reference.rst +++ /dev/null @@ -1,615 +0,0 @@ -************************ -MatchZoo Model Reference -************************ - -Naive -##### - -Model Documentation -******************* - -Naive model with a simplest structure for testing purposes. - -Bare minimum functioning model. The best choice to get things rolling. -The worst choice to fit and evaluate performance. - -Model Hyper Parameters -********************** - -==== ============ ========================================================================================= ===================================== ======================================== - .. Name Description Default Value Default Hyper-Space -==== ============ ========================================================================================= ===================================== ======================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam choice in ['adam', 'adagrad', 'rmsprop'] -==== ============ ========================================================================================= ===================================== ======================================== - -DSSM -#### - -Model Documentation -******************* - -Deep structured semantic model. - -Examples: - >>> model = DSSM() - >>> model.params['mlp_num_layers'] = 3 - >>> model.params['mlp_num_units'] = 300 - >>> model.params['mlp_num_fan_out'] = 128 - >>> model.params['mlp_activation_func'] = 'relu' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== =========================== ========================================================================================= =================================== ===================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ========================================================================================= =================================== ===================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 5 mlp_num_units Number of units in first `mlp_num_layers` layers. 128 quantitative uniform distribution in [8, 256), with a step size of 8 - 6 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 6), with a step size of 1 - 7 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 8 mlp_activation_func Activation function used in the multiple layer perceptron. relu -==== =========================== ========================================================================================= =================================== ===================================================================== - -CDSSM -##### - -Model Documentation -******************* - -CDSSM Model implementation. - -Learning Semantic Representations Using Convolutional Neural Networks -for Web Search. (2014a) -A Latent Semantic Model with Convolutional-Pooling Structure for -Information Retrieval. (2014b) - -Examples: - >>> model = CDSSM() - >>> model.params['optimizer'] = 'adam' - >>> model.params['filters'] = 32 - >>> model.params['kernel_size'] = 3 - >>> model.params['conv_activation_func'] = 'relu' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== =========================== ============================================================================================= ===================================== ===================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ============================================================================================= ===================================== ===================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 5 mlp_num_units Number of units in first `mlp_num_layers` layers. 128 quantitative uniform distribution in [8, 256), with a step size of 8 - 6 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 6), with a step size of 1 - 7 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 8 mlp_activation_func Activation function used in the multiple layer perceptron. relu - 9 filters Number of filters in the 1D convolution layer. 32 - 10 kernel_size Number of kernel size in the 1D convolution layer. 3 - 11 strides Strides in the 1D convolution layer. 1 - 12 padding The padding mode in the convolution layer. It should be one of `same`, `valid`, and `causal`. same - 13 conv_activation_func Activation function in the convolution layer. relu - 14 w_initializer glorot_normal - 15 b_initializer zeros - 16 dropout_rate The dropout rate. 0.3 -==== =========================== ============================================================================================= ===================================== ===================================================================== - -DenseBaseline -############# - -Model Documentation -******************* - -A simple densely connected baseline model. - -Examples: - >>> model = DenseBaseline() - >>> model.params['mlp_num_layers'] = 2 - >>> model.params['mlp_num_units'] = 300 - >>> model.params['mlp_num_fan_out'] = 128 - >>> model.params['mlp_activation_func'] = 'relu' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - >>> model.compile() - -Model Hyper Parameters -********************** - -==== =========================== ========================================================================================= ====================================================== ====================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ========================================================================================= ====================================================== ====================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 5 mlp_num_units Number of units in first `mlp_num_layers` layers. 256 quantitative uniform distribution in [16, 512), with a step size of 1 - 6 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 5), with a step size of 1 - 7 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 8 mlp_activation_func Activation function used in the multiple layer perceptron. relu -==== =========================== ========================================================================================= ====================================================== ====================================================================== - -ArcI -#### - -Model Documentation -******************* - -ArcI Model. - -Examples: - >>> model = ArcI() - >>> model.params['num_blocks'] = 1 - >>> model.params['left_filters'] = [32] - >>> model.params['right_filters'] = [32] - >>> model.params['left_kernel_sizes'] = [3] - >>> model.params['right_kernel_sizes'] = [3] - >>> model.params['left_pool_sizes'] = [2] - >>> model.params['right_pool_sizes'] = [4] - >>> model.params['conv_activation_func'] = 'relu' - >>> model.params['mlp_num_layers'] = 1 - >>> model.params['mlp_num_units'] = 64 - >>> model.params['mlp_num_fan_out'] = 32 - >>> model.params['mlp_activation_func'] = 'relu' - >>> model.params['dropout_rate'] = 0.5 - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== =========================== ============================================================================================ =================================== ========================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ============================================================================================ =================================== ========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 9 mlp_num_units Number of units in first `mlp_num_layers` layers. 128 quantitative uniform distribution in [8, 256), with a step size of 8 - 10 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 6), with a step size of 1 - 11 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 12 mlp_activation_func Activation function used in the multiple layer perceptron. relu - 13 num_blocks Number of convolution blocks. 1 - 14 left_filters The filter size of each convolution blocks for the left input. [32] - 15 left_kernel_sizes The kernel size of each convolution blocks for the left input. [3] - 16 right_filters The filter size of each convolution blocks for the right input. [32] - 17 right_kernel_sizes The kernel size of each convolution blocks for the right input. [3] - 18 conv_activation_func The activation function in the convolution layer. relu - 19 left_pool_sizes The pooling size of each convolution blocks for the left input. [2] - 20 right_pool_sizes The pooling size of each convolution blocks for the right input. [2] - 21 padding The padding mode in the convolution layer. It should be oneof `same`, `valid`, and `causal`. same choice in ['same', 'valid', 'causal'] - 22 dropout_rate The dropout rate. 0.0 quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 -==== =========================== ============================================================================================ =================================== ========================================================================== - -ArcII -##### - -Model Documentation -******************* - -ArcII Model. - -Examples: - >>> model = ArcII() - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['num_blocks'] = 2 - >>> model.params['kernel_1d_count'] = 32 - >>> model.params['kernel_1d_size'] = 3 - >>> model.params['kernel_2d_count'] = [16, 32] - >>> model.params['kernel_2d_size'] = [[3, 3], [3, 3]] - >>> model.params['pool_2d_size'] = [[2, 2], [2, 2]] - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ========================================================================================= ===================================== ========================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ========================================================================================= ===================================== ========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam choice in ['adam', 'rmsprop', 'adagrad'] - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 num_blocks Number of 2D convolution blocks. 1 - 9 kernel_1d_count Kernel count of 1D convolution layer. 32 - 10 kernel_1d_size Kernel size of 1D convolution layer. 3 - 11 kernel_2d_count Kernel count of 2D convolution layer ineach block [32] - 12 kernel_2d_size Kernel size of 2D convolution layer in each block. [[3, 3]] - 13 activation Activation function. relu - 14 pool_2d_size Size of pooling layer in each block. [[2, 2]] - 15 padding The padding mode in the convolution layer. It should be oneof `same`, `valid`. same choice in ['same', 'valid'] - 16 dropout_rate The dropout rate. 0.0 quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 -==== ==================== ========================================================================================= ===================================== ========================================================================== - -MatchPyramid -############ - -Model Documentation -******************* - -MatchPyramid Model. - -Examples: - >>> model = MatchPyramid() - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['num_blocks'] = 2 - >>> model.params['kernel_count'] = [16, 32] - >>> model.params['kernel_size'] = [[3, 3], [3, 3]] - >>> model.params['dpool_size'] = [3, 10] - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ========================================================================================= ==================================================== ========================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ========================================================================================= ==================================================== ========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 num_blocks Number of convolution blocks. 1 - 9 kernel_count The kernel count of the 2D convolution of each block. [32] - 10 kernel_size The kernel size of the 2D convolution of each block. [[3, 3]] - 11 activation The activation function. relu - 12 dpool_size The max-pooling size of each block. [3, 10] - 13 padding The padding mode in the convolution layer. same - 14 dropout_rate The dropout rate. 0.0 quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 -==== ==================== ========================================================================================= ==================================================== ========================================================================== - -KNRM -#### - -Model Documentation -******************* - -KNRM model. - -Examples: - >>> model = KNRM() - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 10 - >>> model.params['embedding_trainable'] = True - >>> model.params['kernel_num'] = 11 - >>> model.params['sigma'] = 0.1 - >>> model.params['exact_sigma'] = 0.001 - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ========================================================================================= =================================== =========================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ========================================================================================= =================================== =========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 kernel_num The number of RBF kernels. 11 quantitative uniform distribution in [5, 20), with a step size of 1 - 9 sigma The `sigma` defines the kernel width. 0.1 quantitative uniform distribution in [0.01, 0.2), with a step size of 0.01 - 10 exact_sigma The `exact_sigma` denotes the `sigma` for exact match. 0.001 -==== ==================== ========================================================================================= =================================== =========================================================================== - -DUET -#### - -Model Documentation -******************* - -DUET Model. - -Examples: - >>> model = DUET() - >>> model.params['embedding_input_dim'] = 1000 - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['lm_filters'] = 32 - >>> model.params['lm_hidden_sizes'] = [64, 32] - >>> model.params['dropout_rate'] = 0.5 - >>> model.params['dm_filters'] = 32 - >>> model.params['dm_kernel_size'] = 3 - >>> model.params['dm_d_mpool'] = 4 - >>> model.params['dm_hidden_sizes'] = [64, 32] - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ============================================================================================= =================================== ========================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ============================================================================================= =================================== ========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 lm_filters Filter size of 1D convolution layer in the local model. 32 - 9 lm_hidden_sizes A list of hidden size of the MLP layer in the local model. [32] - 10 dm_filters Filter size of 1D convolution layer in the distributed model. 32 - 11 dm_kernel_size Kernel size of 1D convolution layer in the distributed model. 3 - 12 dm_q_hidden_size Hidden size of the MLP layer for the left text in the distributed model. 32 - 13 dm_d_mpool Max pooling size for the right text in the distributed model. 3 - 14 dm_hidden_sizes A list of hidden size of the MLP layer in the distributed model. [32] - 15 padding The padding mode in the convolution layer. It should be one of `same`, `valid`, and `causal`. same - 16 activation_func Activation function in the convolution layer. relu - 17 dropout_rate The dropout rate. 0.5 quantitative uniform distribution in [0.0, 0.8), with a step size of 0.02 -==== ==================== ============================================================================================= =================================== ========================================================================== - -DRMMTKS -####### - -Model Documentation -******************* - -DRMMTKS Model. - -Examples: - >>> model = DRMMTKS() - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 100 - >>> model.params['top_k'] = 20 - >>> model.params['mlp_num_layers'] = 1 - >>> model.params['mlp_num_units'] = 5 - >>> model.params['mlp_num_fan_out'] = 1 - >>> model.params['mlp_activation_func'] = 'tanh' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== =========================== ========================================================================================= ========================================= ===================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ========================================================================================= ========================================= ===================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. [(5,), (300,)] - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 9 mlp_num_units Number of units in first `mlp_num_layers` layers. 128 quantitative uniform distribution in [8, 256), with a step size of 8 - 10 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 6), with a step size of 1 - 11 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 12 mlp_activation_func Activation function used in the multiple layer perceptron. relu - 13 mask_value The value to be masked from inputs. -1 - 14 top_k Size of top-k pooling layer. 10 quantitative uniform distribution in [2, 100), with a step size of 1 -==== =========================== ========================================================================================= ========================================= ===================================================================== - -DRMM -#### - -Model Documentation -******************* - -DRMM Model. - -Examples: - >>> model = DRMM() - >>> model.params['mlp_num_layers'] = 1 - >>> model.params['mlp_num_units'] = 5 - >>> model.params['mlp_num_fan_out'] = 1 - >>> model.params['mlp_activation_func'] = 'tanh' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - >>> model.compile() - -Model Hyper Parameters -********************** - -==== =========================== ========================================================================================= =================================== ===================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ========================================================================================= =================================== ===================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. [(5,), (5, 30)] - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 9 mlp_num_units Number of units in first `mlp_num_layers` layers. 128 quantitative uniform distribution in [8, 256), with a step size of 8 - 10 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 6), with a step size of 1 - 11 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 12 mlp_activation_func Activation function used in the multiple layer perceptron. relu - 13 mask_value The value to be masked from inputs. -1 -==== =========================== ========================================================================================= =================================== ===================================================================== - -ANMM -#### - -Model Documentation -******************* - -ANMM Model. - -Examples: - >>> model = ANMM() - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ========================================================================================= =================================== ====================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ========================================================================================= =================================== ====================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 dropout_rate The dropout rate. 0.1 quantitative uniform distribution in [0, 1), with a step size of 0.05 - 9 num_layers Number of hidden layers in the MLP layer. 2 - 10 hidden_sizes Number of hidden size for each hidden layer [30, 30] -==== ==================== ========================================================================================= =================================== ====================================================================== - -MVLSTM -###### - -Model Documentation -******************* - -MVLSTM Model. - -Examples: - >>> model = MVLSTM() - >>> model.params['lstm_units'] = 32 - >>> model.params['top_k'] = 50 - >>> model.params['mlp_num_layers'] = 2 - >>> model.params['mlp_num_units'] = 20 - >>> model.params['mlp_num_fan_out'] = 10 - >>> model.params['mlp_activation_func'] = 'relu' - >>> model.params['dropout_rate'] = 0.5 - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== =========================== ========================================================================================= ======================================= ===================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ========================================================================================= ======================================= ===================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 9 mlp_num_units Number of units in first `mlp_num_layers` layers. 128 quantitative uniform distribution in [8, 256), with a step size of 8 - 10 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 6), with a step size of 1 - 11 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 12 mlp_activation_func Activation function used in the multiple layer perceptron. relu - 13 lstm_units Integer, the hidden size in the bi-directional LSTM layer. 32 - 14 dropout_rate Float, the dropout rate. 0.0 - 15 top_k Integer, the size of top-k pooling layer. 10 quantitative uniform distribution in [2, 100), with a step size of 1 -==== =========================== ========================================================================================= ======================================= ===================================================================== - -MatchLSTM -######### - -Model Documentation -******************* - -Match LSTM model. - -Examples: - >>> model = MatchLSTM() - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 100 - >>> model.params['embedding_trainable'] = True - >>> model.params['fc_num_units'] = 200 - >>> model.params['lstm_num_units'] = 256 - >>> model.params['dropout_rate'] = 0.5 - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ========================================================================================= ====================================================== ========================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ========================================================================================= ====================================================== ========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 lstm_num_units The hidden size in the LSTM layer. 256 quantitative uniform distribution in [128, 384), with a step size of 32 - 9 fc_num_units The hidden size in the full connection layer. 200 quantitative uniform distribution in [100, 300), with a step size of 20 - 10 dropout_rate The dropout rate. 0.0 quantitative uniform distribution in [0.0, 0.9), with a step size of 0.01 -==== ==================== ========================================================================================= ====================================================== ========================================================================== - -ConvKNRM -######## - -Model Documentation -******************* - -ConvKNRM model. - -Examples: - >>> model = ConvKNRM() - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['embedding_trainable'] = True - >>> model.params['filters'] = 128 - >>> model.params['conv_activation_func'] = 'tanh' - >>> model.params['max_ngram'] = 3 - >>> model.params['use_crossmatch'] = True - >>> model.params['kernel_num'] = 11 - >>> model.params['sigma'] = 0.1 - >>> model.params['exact_sigma'] = 0.001 - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ========================================================================================= ============================================ =========================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ========================================================================================= ============================================ =========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 kernel_num The number of RBF kernels. 11 quantitative uniform distribution in [5, 20), with a step size of 1 - 9 sigma The `sigma` defines the kernel width. 0.1 quantitative uniform distribution in [0.01, 0.2), with a step size of 0.01 - 10 exact_sigma The `exact_sigma` denotes the `sigma` for exact match. 0.001 - 11 filters The filter size in the convolution layer. 128 - 12 conv_activation_func The activation function in the convolution layer. relu - 13 max_ngram The maximum length of n-grams for the convolution layer. 3 - 14 use_crossmatch Whether to match left n-grams and right n-grams of different lengths True -==== ==================== ========================================================================================= ============================================ =========================================================================== - diff --git a/docs/source/modules.rst b/docs/source/modules.rst deleted file mode 100644 index 123f26e7..00000000 --- a/docs/source/modules.rst +++ /dev/null @@ -1,7 +0,0 @@ -matchzoo -======== - -.. toctree:: - :maxdepth: 4 - - matchzoo diff --git a/matchzoo/__init__.py b/matchzoo/__init__.py deleted file mode 100644 index 6205cb23..00000000 --- a/matchzoo/__init__.py +++ /dev/null @@ -1,56 +0,0 @@ -from pathlib import Path - -USER_DIR = Path.expanduser(Path('~')).joinpath('.matchzoo') -if not USER_DIR.exists(): - USER_DIR.mkdir() -USER_DATA_DIR = USER_DIR.joinpath('datasets') -if not USER_DATA_DIR.exists(): - USER_DATA_DIR.mkdir() -USER_TUNED_MODELS_DIR = USER_DIR.joinpath('tuned_models') - -from .version import __version__ - -from .data_pack import DataPack -from .data_pack import pack -from .data_pack import load_data_pack - -from . import metrics -from . import tasks - -from . import preprocessors -from . import data_generator -from .data_generator import DataGenerator -from .data_generator import DataGeneratorBuilder - -from .preprocessors.chain_transform import chain_transform - -from . import metrics -from . import losses -from . import engine -from . import models -from . import embedding -from . import datasets -from . import layers -from . import auto -from . import contrib - -from .engine import hyper_spaces -from .engine.base_model import load_model -from .engine.base_preprocessor import load_preprocessor -from .engine import callbacks -from .engine.param import Param -from .engine.param_table import ParamTable - -from .embedding.embedding import Embedding - -from .utils import one_hot, make_keras_optimizer_picklable -from .preprocessors.build_unit_from_data_pack import build_unit_from_data_pack -from .preprocessors.build_vocab_unit import build_vocab_unit - -# deprecated, should be removed in v2.2 -from .contrib.legacy_data_generator import DPoolDataGenerator -from .contrib.legacy_data_generator import DPoolPairDataGenerator -from .contrib.legacy_data_generator import HistogramDataGenerator -from .contrib.legacy_data_generator import HistogramPairDataGenerator -from .contrib.legacy_data_generator import DynamicDataGenerator -from .contrib.legacy_data_generator import PairDataGenerator diff --git a/matchzoo/auto/__init__.py b/matchzoo/auto/__init__.py deleted file mode 100644 index cc2f0547..00000000 --- a/matchzoo/auto/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from .preparer import prepare -from .preparer import Preparer - -from .tuner import Tuner -from .tuner import tune - -# mz.auto.tuner.callbacks -from . import tuner diff --git a/matchzoo/auto/preparer/__init__.py b/matchzoo/auto/preparer/__init__.py deleted file mode 100644 index d589c3d6..00000000 --- a/matchzoo/auto/preparer/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .preparer import Preparer -from .prepare import prepare diff --git a/matchzoo/auto/preparer/prepare.py b/matchzoo/auto/preparer/prepare.py deleted file mode 100644 index 2cc36805..00000000 --- a/matchzoo/auto/preparer/prepare.py +++ /dev/null @@ -1,45 +0,0 @@ -import typing - -import matchzoo as mz -from .preparer import Preparer -from matchzoo.engine.base_task import BaseTask -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.base_preprocessor import BasePreprocessor - - -def prepare( - task: BaseTask, - model_class: typing.Type[BaseModel], - data_pack: mz.DataPack, - preprocessor: typing.Optional[BasePreprocessor] = None, - embedding: typing.Optional['mz.Embedding'] = None, - config: typing.Optional[dict] = None, -): - """ - A simple shorthand for using :class:`matchzoo.Preparer`. - - `config` is used to control specific behaviors. The default `config` - will be updated accordingly if a `config` dictionary is passed. e.g. to - override the default `bin_size`, pass `config={'bin_size': 15}`. - - :param task: Task. - :param model_class: Model class. - :param data_pack: DataPack used to fit the preprocessor. - :param preprocessor: Preprocessor used to fit the `data_pack`. - (default: the default preprocessor of `model_class`) - :param embedding: Embedding to build a embedding matrix. If not set, - then a correctly shaped randomized matrix will be built. - :param config: Configuration of specific behaviors. (default: return - value of `mz.Preparer.get_default_config()`) - - :return: A tuple of `(model, preprocessor, data_generator_builder, - embedding_matrix)`. - - """ - preparer = Preparer(task=task, config=config) - return preparer.prepare( - model_class=model_class, - data_pack=data_pack, - preprocessor=preprocessor, - embedding=embedding - ) diff --git a/matchzoo/auto/preparer/preparer.py b/matchzoo/auto/preparer/preparer.py deleted file mode 100644 index eef74f91..00000000 --- a/matchzoo/auto/preparer/preparer.py +++ /dev/null @@ -1,245 +0,0 @@ -import typing - -import numpy as np - -import matchzoo as mz -from matchzoo.engine.base_task import BaseTask -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.base_preprocessor import BasePreprocessor -from matchzoo.data_generator import DataGeneratorBuilder - - -class Preparer(object): - """ - Unified setup processes of all MatchZoo models. - - `config` is used to control specific behaviors. The default `config` - will be updated accordingly if a `config` dictionary is passed. e.g. to - override the default `bin_size`, pass `config={'bin_size': 15}`. - - See `tutorials/automation.ipynb` for a detailed walkthrough on usage. - - Default `config`: - - { - # pair generator builder kwargs - 'num_dup': 1, - - # histogram unit of DRMM - 'bin_size': 30, - 'hist_mode': 'LCH', - - # dynamic Pooling of MatchPyramid - 'compress_ratio_left': 1.0, - 'compress_ratio_right': 1.0, - - # if no `matchzoo.Embedding` is passed to `tune` - 'embedding_output_dim': 50 - } - - :param task: Task. - :param config: Configuration of specific behaviors. - - Example: - >>> import matchzoo as mz - >>> task = mz.tasks.Ranking(loss=mz.losses.RankCrossEntropyLoss()) - >>> preparer = mz.auto.Preparer(task) - >>> model_class = mz.models.DenseBaseline - >>> train_raw = mz.datasets.toy.load_data('train', 'ranking') - >>> model, prpr, gen_builder, matrix = preparer.prepare(model_class, - ... train_raw) - >>> model.params.completed() - True - - """ - - def __init__( - self, - task: BaseTask, - config: typing.Optional[dict] = None - ): - """Init.""" - self._task = task - self._config = self.get_default_config() - if config: - self._config.update(config) - - self._infer_num_neg() - - def prepare( - self, - model_class: typing.Type[BaseModel], - data_pack: mz.DataPack, - preprocessor: typing.Optional[BasePreprocessor] = None, - embedding: typing.Optional['mz.Embedding'] = None, - ) -> typing.Tuple[ - BaseModel, - BasePreprocessor, - DataGeneratorBuilder, - np.ndarray - ]: - """ - Prepare. - - :param model_class: Model class. - :param data_pack: DataPack used to fit the preprocessor. - :param preprocessor: Preprocessor used to fit the `data_pack`. - (default: the default preprocessor of `model_class`) - :param embedding: Embedding to build a embedding matrix. If not set, - then a correctly shaped randomized matrix will be built. - - :return: A tuple of `(model, preprocessor, data_generator_builder, - embedding_matrix)`. - - """ - if not preprocessor: - preprocessor = model_class.get_default_preprocessor() - - if issubclass(model_class, (mz.models.DSSM, mz.models.CDSSM)): - preprocessor.with_word_hashing = False - - preprocessor.fit(data_pack, verbose=0) - - model, embedding_matrix = self._build_model( - model_class, - preprocessor, - embedding - ) - - data_gen_builder = self._build_data_gen_builder( - model, - embedding_matrix, - preprocessor - ) - - return ( - model, - preprocessor, - data_gen_builder, - embedding_matrix - ) - - def _build_model( - self, - model_class, - preprocessor, - embedding - ) -> typing.Tuple[BaseModel, np.ndarray]: - - model = model_class() - model.params['task'] = self._task - model.params.update(preprocessor.context) - - if 'with_embedding' in model.params: - embedding_matrix = self._build_matrix(preprocessor, embedding) - model.params['embedding_input_dim'] = embedding_matrix.shape[0] - model.params['embedding_output_dim'] = embedding_matrix.shape[1] - else: - embedding_matrix = None - - self._handle_match_pyramid_dpool_size(model) - self._handle_drmm_input_shapes(model) - - assert model.params.completed() - model.build() - model.compile() - - if 'with_embedding' in model.params: - model.load_embedding_matrix(embedding_matrix) - - return model, embedding_matrix - - def _handle_match_pyramid_dpool_size(self, model): - if isinstance(model, mz.models.MatchPyramid): - suggestion = mz.layers.DynamicPoolingLayer.get_size_suggestion( - msize1=model.params['input_shapes'][0][0], - msize2=model.params['input_shapes'][1][0], - psize1=model.params['dpool_size'][0], - psize2=model.params['dpool_size'][1], - ) - model.params['dpool_size'] = suggestion - - def _handle_drmm_input_shapes(self, model): - if isinstance(model, mz.models.DRMM): - left = model.params['input_shapes'][0] - right = left + (self._config['bin_size'],) - model.params['input_shapes'] = (left, right) - - def _build_matrix(self, preprocessor, embedding): - if embedding: - vocab_unit = preprocessor.context['vocab_unit'] - term_index = vocab_unit.state['term_index'] - return embedding.build_matrix(term_index) - else: - matrix_shape = ( - preprocessor.context['vocab_size'], - self._config['embedding_output_dim'] - ) - return np.random.uniform(-0.2, 0.2, matrix_shape) - - def _build_data_gen_builder(self, model, embedding_matrix, preprocessor): - builder_kwargs = dict(callbacks=[]) - - if isinstance(self._task.loss, (mz.losses.RankHingeLoss, - mz.losses.RankCrossEntropyLoss)): - builder_kwargs.update(dict( - mode='pair', - num_dup=self._config['num_dup'], - num_neg=self._config['num_neg'] - )) - - if isinstance(model, mz.models.DRMM): - histo_callback = mz.data_generator.callbacks.Histogram( - embedding_matrix=embedding_matrix, - bin_size=self._config['bin_size'], - hist_mode=self._config['hist_mode'] - ) - builder_kwargs['callbacks'].append(histo_callback) - - if isinstance(model, mz.models.MatchPyramid): - dpool_callback = mz.data_generator.callbacks.DynamicPooling( - fixed_length_left=model.params['input_shapes'][0][0], - fixed_length_right=model.params['input_shapes'][1][0], - compress_ratio_left=self._config['compress_ratio_left'], - compress_ratio_right=self._config['compress_ratio_right'] - ) - builder_kwargs['callbacks'].append(dpool_callback) - - if isinstance(model, (mz.models.DSSM, mz.models.CDSSM)): - term_index = preprocessor.context['vocab_unit'].state['term_index'] - hashing_unit = mz.preprocessors.units.WordHashing(term_index) - hashing_callback = mz.data_generator.callbacks.LambdaCallback( - on_batch_data_pack=lambda data_pack: - data_pack.apply_on_text( - func=hashing_unit.transform, - inplace=True, - verbose=0 - ) - ) - builder_kwargs['callbacks'].append(hashing_callback) - - return DataGeneratorBuilder(**builder_kwargs) - - def _infer_num_neg(self): - if isinstance(self._task.loss, (mz.losses.RankHingeLoss, - mz.losses.RankCrossEntropyLoss)): - self._config['num_neg'] = self._task.loss.num_neg - - @classmethod - def get_default_config(cls) -> dict: - """Default config getter.""" - return { - # pair generator builder kwargs - 'num_dup': 1, - - # histogram unit of DRMM - 'bin_size': 30, - 'hist_mode': 'LCH', - - # dynamic Pooling of MatchPyramid - 'compress_ratio_left': 1.0, - 'compress_ratio_right': 1.0, - - # if no `matchzoo.Embedding` is passed to `tune` - 'embedding_output_dim': 100 - } diff --git a/matchzoo/auto/tuner/__init__.py b/matchzoo/auto/tuner/__init__.py deleted file mode 100644 index f68e96d8..00000000 --- a/matchzoo/auto/tuner/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from . import callbacks -from .tuner import Tuner -from .tune import tune diff --git a/matchzoo/auto/tuner/callbacks/__init__.py b/matchzoo/auto/tuner/callbacks/__init__.py deleted file mode 100644 index 503ca288..00000000 --- a/matchzoo/auto/tuner/callbacks/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .callback import Callback -from .lambda_callback import LambdaCallback -from .load_embedding_matrix import LoadEmbeddingMatrix -from .save_model import SaveModel diff --git a/matchzoo/auto/tuner/callbacks/callback.py b/matchzoo/auto/tuner/callbacks/callback.py deleted file mode 100644 index f1ff6661..00000000 --- a/matchzoo/auto/tuner/callbacks/callback.py +++ /dev/null @@ -1,53 +0,0 @@ -import matchzoo -from matchzoo.engine.base_model import BaseModel - - -class Callback(object): - """ - Tuner callback base class. - - To build your own callbacks, inherit `mz.auto.tuner.callbacks.Callback` - and overrides corresponding methods. - - A run proceeds in the following way: - - - run start (callback) - - build model - - build end (callback) - - fit and evaluate model - - collect result - - run end (callback) - - This process is repeated for `num_runs` times in a tuner. - - """ - - def on_run_start(self, tuner: 'matchzoo.auto.Tuner', sample: dict): - """ - Callback on run start stage. - - :param tuner: Tuner. - :param sample: Sampled hyper space. Changes to this dictionary affects - the model building process of the tuner. - """ - - def on_build_end(self, tuner: 'matchzoo.auto.Tuner', model: BaseModel): - """ - Callback on build end stage. - - :param tuner: Tuner. - :param model: A built model ready for fitting and evluating. Changes - to this model affect the fitting and evaluating process. - """ - - def on_run_end(self, tuner: 'matchzoo.auto.Tuner', model: BaseModel, - result: dict): - """ - Callback on run end stage. - - :param tuner: Tuner. - :param model: A built model done fitting and evaluating. Changes to - the model will no longer affect the result. - :param result: Result of the run. Changes to this dictionary will be - visible in the return value of the `tune` method. - """ diff --git a/matchzoo/auto/tuner/callbacks/lambda_callback.py b/matchzoo/auto/tuner/callbacks/lambda_callback.py deleted file mode 100644 index c64090de..00000000 --- a/matchzoo/auto/tuner/callbacks/lambda_callback.py +++ /dev/null @@ -1,64 +0,0 @@ -from matchzoo.engine.base_model import BaseModel -from matchzoo.auto.tuner.callbacks.callback import Callback - - -class LambdaCallback(Callback): - """ - LambdaCallback. Just a shorthand for creating a callback class. - - See :class:`matchzoo.tuner.callbacks.Callback` for more details. - - Example: - - >>> import matchzoo as mz - >>> model = mz.models.Naive() - >>> model.guess_and_fill_missing_params(verbose=0) - >>> data = mz.datasets.toy.load_data() - >>> data = model.get_default_preprocessor().fit_transform( - ... data, verbose=0) - >>> def show_inputs(*args): - ... print(' '.join(map(str, map(type, args)))) - >>> callback = mz.auto.tuner.callbacks.LambdaCallback( - ... on_run_start=show_inputs, - ... on_build_end=show_inputs, - ... on_run_end=show_inputs - ... ) - >>> _ = mz.auto.tune( - ... params=model.params, - ... train_data=data, - ... test_data=data, - ... num_runs=1, - ... callbacks=[callback], - ... verbose=0, - ... ) # noqa: E501 - - - - - """ - - def __init__( - self, - on_run_start=None, - on_build_end=None, - on_run_end=None - ): - """Init.""" - self._on_run_start = on_run_start - self._on_build_end = on_build_end - self._on_run_end = on_run_end - - def on_run_start(self, tuner, sample: dict): - """`on_run_start`.""" - if self._on_run_start: - self._on_run_start(tuner, sample) - - def on_build_end(self, tuner, model: BaseModel): - """`on_build_end`.""" - if self._on_build_end: - self._on_build_end(tuner, model) - - def on_run_end(self, tuner, model: BaseModel, result: dict): - """`on_run_end`.""" - if self._on_run_end: - self._on_run_end(tuner, model, result) diff --git a/matchzoo/auto/tuner/callbacks/load_embedding_matrix.py b/matchzoo/auto/tuner/callbacks/load_embedding_matrix.py deleted file mode 100644 index 931d38b2..00000000 --- a/matchzoo/auto/tuner/callbacks/load_embedding_matrix.py +++ /dev/null @@ -1,45 +0,0 @@ -from matchzoo.engine.base_model import BaseModel -from matchzoo.auto.tuner.callbacks.callback import Callback - - -class LoadEmbeddingMatrix(Callback): - """ - Load a pre-trained embedding after the model is built. - - Used with tuner to load a pre-trained embedding matrix for each newly built - model instance. - - :param embedding_matrix: Embedding matrix to load. - - Example: - - >>> import matchzoo as mz - >>> model = mz.models.ArcI() - >>> prpr = model.get_default_preprocessor() - >>> data = mz.datasets.toy.load_data() - >>> data = prpr.fit_transform(data, verbose=0) - >>> embed = mz.datasets.toy.load_embedding() - >>> term_index = prpr.context['vocab_unit'].state['term_index'] - >>> matrix = embed.build_matrix(term_index) - >>> callback = mz.auto.tuner.callbacks.LoadEmbeddingMatrix(matrix) - >>> model.params.update(prpr.context) - >>> model.params['task'] = mz.tasks.Ranking() - >>> model.params['embedding_output_dim'] = embed.output_dim - >>> result = mz.auto.tune( - ... params=model.params, - ... train_data=data, - ... test_data=data, - ... num_runs=1, - ... callbacks=[callback], - ... verbose=0 - ... ) - - """ - - def __init__(self, embedding_matrix): - """Init.""" - self._embedding_matrix = embedding_matrix - - def on_build_end(self, tuner, model: BaseModel): - """`on_build_end`.""" - model.load_embedding_matrix(self._embedding_matrix) diff --git a/matchzoo/auto/tuner/callbacks/save_model.py b/matchzoo/auto/tuner/callbacks/save_model.py deleted file mode 100644 index e50d5aef..00000000 --- a/matchzoo/auto/tuner/callbacks/save_model.py +++ /dev/null @@ -1,35 +0,0 @@ -import typing -from pathlib import Path -import uuid - -import matchzoo as mz -from matchzoo.engine.base_model import BaseModel -from .callback import Callback - - -class SaveModel(Callback): - """ - Save trained model. - - For each trained model, a UUID will be generated as the `model_id`, the - model will be saved under the `dir_path/model_id`. A `model_id` key will - also be inserted into the result, which will visible in the return value of - the `tune` method. - - :param dir_path: Path to save the models to. (default: - `matchzoo.USER_TUNED_MODELS_DIR`) - - """ - - def __init__( - self, - dir_path: typing.Union[str, Path] = mz.USER_TUNED_MODELS_DIR - ): - """Init.""" - self._dir_path = dir_path - - def on_run_end(self, tuner, model: BaseModel, result: dict): - """Save model on run end.""" - model_id = str(uuid.uuid4()) - model.save(self._dir_path.joinpath(model_id)) - result['model_id'] = model_id diff --git a/matchzoo/auto/tuner/tune.py b/matchzoo/auto/tuner/tune.py deleted file mode 100644 index 08b44f34..00000000 --- a/matchzoo/auto/tuner/tune.py +++ /dev/null @@ -1,89 +0,0 @@ -import typing - -import matchzoo as mz -from matchzoo.engine.base_metric import BaseMetric -from .tuner import Tuner - - -def tune( - params: 'mz.ParamTable', - train_data: typing.Union[mz.DataPack, mz.DataGenerator], - test_data: typing.Union[mz.DataPack, mz.DataGenerator], - fit_kwargs: dict = None, - evaluate_kwargs: dict = None, - metric: typing.Union[str, BaseMetric] = None, - mode: str = 'maximize', - num_runs: int = 10, - callbacks: typing.List['mz.auto.tuner.callbacks.Callback'] = None, - verbose=1 -): - """ - Tune model hyper-parameters. - - A simple shorthand for using :class:`matchzoo.auto.Tuner`. - - `model.params.hyper_space` reprensents the model's hyper-parameters - search space, which is the cross-product of individual hyper parameter's - hyper space. When a `Tuner` builds a model, for each hyper parameter in - `model.params`, if the hyper-parameter has a hyper-space, then a sample - will be taken in the space. However, if the hyper-parameter does not - have a hyper-space, then the default value of the hyper-parameter will - be used. - - See `tutorials/model_tuning.ipynb` for a detailed walkthrough on usage. - - :param params: A completed parameter table to tune. Usually `model.params` - of the desired model to tune. `params.completed()` should be `True`. - :param train_data: Training data to use. Either a preprocessed `DataPack`, - or a `DataGenerator`. - :param test_data: Testing data to use. A preprocessed `DataPack`. - :param fit_kwargs: Extra keyword arguments to pass to `fit`. - (default: `dict(epochs=10, verbose=0)`) - :param evaluate_kwargs: Extra keyword arguments to pass to `evaluate`. - :param metric: Metric to tune upon. Must be one of the metrics in - `model.params['task'].metrics`. (default: the first metric in - `params.['task'].metrics`. - :param mode: Either `maximize` the metric or `minimize` the metric. - (default: 'maximize') - :param num_runs: Number of runs. Each run takes a sample in - `params.hyper_space` and build a model based on the sample. - (default: 10) - :param callbacks: A list of callbacks to handle. Handled sequentially - at every callback point. - :param verbose: Verbosity. (default: 1) - - Example: - >>> import matchzoo as mz - >>> train = mz.datasets.toy.load_data('train') - >>> dev = mz.datasets.toy.load_data('dev') - >>> prpr = mz.models.DenseBaseline.get_default_preprocessor() - >>> train = prpr.fit_transform(train, verbose=0) - >>> dev = prpr.transform(dev, verbose=0) - >>> model = mz.models.DenseBaseline() - >>> model.params['input_shapes'] = prpr.context['input_shapes'] - >>> model.params['task'] = mz.tasks.Ranking() - >>> results = mz.auto.tune( - ... params=model.params, - ... train_data=train, - ... test_data=dev, - ... num_runs=1, - ... verbose=0 - ... ) - >>> sorted(results['best'].keys()) - ['#', 'params', 'sample', 'score'] - - """ - - tuner = Tuner( - params=params, - train_data=train_data, - test_data=test_data, - fit_kwargs=fit_kwargs, - evaluate_kwargs=evaluate_kwargs, - metric=metric, - mode=mode, - num_runs=num_runs, - callbacks=callbacks, - verbose=verbose - ) - return tuner.tune() diff --git a/matchzoo/auto/tuner/tuner.py b/matchzoo/auto/tuner/tuner.py deleted file mode 100644 index 0dd6b153..00000000 --- a/matchzoo/auto/tuner/tuner.py +++ /dev/null @@ -1,402 +0,0 @@ -import copy -import typing -import logging - -import hyperopt - -import matchzoo as mz -from matchzoo.engine.base_metric import BaseMetric -from .callbacks.callback import Callback - - -class Tuner(object): - """ - Model hyper-parameters tuner. - - `model.params.hyper_space` reprensents the model's hyper-parameters - search space, which is the cross-product of individual hyper parameter's - hyper space. When a `Tuner` builds a model, for each hyper parameter in - `model.params`, if the hyper-parameter has a hyper-space, then a sample - will be taken in the space. However, if the hyper-parameter does not - have a hyper-space, then the default value of the hyper-parameter will - be used. - - See `tutorials/model_tuning.ipynb` for a detailed walkthrough on usage. - - :param params: A completed parameter table to tune. Usually `model.params` - of the desired model to tune. `params.completed()` should be `True`. - :param train_data: Training data to use. Either a preprocessed `DataPack`, - or a `DataGenerator`. - :param test_data: Testing data to use. A preprocessed `DataPack`. - :param fit_kwargs: Extra keyword arguments to pass to `fit`. - (default: `dict(epochs=10, verbose=0)`) - :param evaluate_kwargs: Extra keyword arguments to pass to `evaluate`. - :param metric: Metric to tune upon. Must be one of the metrics in - `model.params['task'].metrics`. (default: the first metric in - `params.['task'].metrics`. - :param mode: Either `maximize` the metric or `minimize` the metric. - (default: 'maximize') - :param num_runs: Number of runs. Each run takes a sample in - `params.hyper_space` and build a model based on the sample. - (default: 10) - :param callbacks: A list of callbacks to handle. Handled sequentially - at every callback point. - :param verbose: Verbosity. (default: 1) - - Example: - >>> import matchzoo as mz - >>> train = mz.datasets.toy.load_data('train') - >>> dev = mz.datasets.toy.load_data('dev') - >>> prpr = mz.models.DenseBaseline.get_default_preprocessor() - >>> train = prpr.fit_transform(train, verbose=0) - >>> dev = prpr.transform(dev, verbose=0) - >>> model = mz.models.DenseBaseline() - >>> model.params['input_shapes'] = prpr.context['input_shapes'] - >>> model.params['task'] = mz.tasks.Ranking() - >>> tuner = mz.auto.Tuner( - ... params=model.params, - ... train_data=train, - ... test_data=dev, - ... num_runs=1, - ... verbose=0 - ... ) - >>> results = tuner.tune() - >>> sorted(results['best'].keys()) - ['#', 'params', 'sample', 'score'] - - """ - - def __init__( - self, - params: 'mz.ParamTable', - train_data: typing.Union[mz.DataPack, mz.DataGenerator], - test_data: typing.Union[mz.DataPack, mz.DataGenerator], - fit_kwargs: dict = None, - evaluate_kwargs: dict = None, - metric: typing.Union[str, BaseMetric] = None, - mode: str = 'maximize', - num_runs: int = 10, - callbacks: typing.List[Callback] = None, - verbose=1 - ): - """Tuner.""" - if fit_kwargs is None: - fit_kwargs = dict(epochs=10, verbose=0) - if evaluate_kwargs is None: - evaluate_kwargs = {} - if callbacks is None: - callbacks = [] - - self._validate_params(params) - metric = metric or params['task'].metrics[0] - self._validate_data(train_data) - self._validate_data(test_data) - self._validate_kwargs(fit_kwargs) - self._validate_kwargs(evaluate_kwargs) - self._validate_mode(mode) - self._validate_metric(params, metric) - self._validate_callbacks(callbacks) - - self.__curr_run_num = 0 - - # these variables should not change within the same `tune` call - self._params = params - self._train_data = train_data - self._test_data = test_data - self._fit_kwargs = fit_kwargs - self._evaluate_kwargs = evaluate_kwargs - self._metric = metric - self._mode = mode - self._num_runs = num_runs - self._callbacks = callbacks - self._verbose = verbose - - def tune(self): - """ - Start tuning. - - Notice that `tune` does not affect the tuner's inner state, so each - new call to `tune` starts fresh. In other words, hyperspaces are - suggestive only within the same `tune` call. - """ - if self.__curr_run_num != 0: - print( - """WARNING: `tune` does not affect the tuner's inner state, so - each new call to `tune` starts fresh. In other words, - hyperspaces are suggestive only within the same `tune` call.""" - ) - self.__curr_run_num = 0 - logging.getLogger('hyperopt').setLevel(logging.CRITICAL) - - trials = hyperopt.Trials() - - self._fmin(trials) - - return { - 'best': trials.best_trial['result']['mz_result'], - 'trials': [trial['result']['mz_result'] for trial in trials.trials] - } - - def _fmin(self, trials): - # new version of hyperopt has keyword argument `show_progressbar` that - # breaks doctests, so here's a workaround - fmin_kwargs = dict( - fn=self._run, - space=self._params.hyper_space, - algo=hyperopt.tpe.suggest, - max_evals=self._num_runs, - trials=trials - ) - try: - hyperopt.fmin( - **fmin_kwargs, - show_progressbar=False - ) - except TypeError: - hyperopt.fmin(**fmin_kwargs) - - def _run(self, sample): - self.__curr_run_num += 1 - - # build start - self._handle_callbacks_run_start(sample) - - # build model - params = self._create_full_params(sample) - model = params['model_class'](params=params) - model.build() - model.compile() - self._handle_callbacks_build_end(model) - - # fit & evaluate - self._fit_model(model) - lookup = self._evaluate_model(model) - score = lookup[self._metric] - - # collect result - # this result is for users, visible outside - mz_result = { - '#': self.__curr_run_num, - 'params': params, - 'sample': sample, - 'score': score - } - - self._handle_callbacks_run_end(model, mz_result) - - if self._verbose: - self._log_result(mz_result) - - return { - # these two items are for hyperopt - 'loss': self._fix_loss_sign(score), - 'status': hyperopt.STATUS_OK, - - # this item is for storing matchzoo information - 'mz_result': mz_result - } - - def _create_full_params(self, sample): - params = copy.deepcopy(self._params) - params.update(sample) - return params - - def _handle_callbacks_run_start(self, sample): - for callback in self._callbacks: - callback.on_run_start(self, sample) - - def _handle_callbacks_build_end(self, model): - for callback in self._callbacks: - callback.on_build_end(self, model) - - def _handle_callbacks_run_end(self, model, result): - for callback in self._callbacks: - callback.on_run_end(self, model, result) - - def _fit_model(self, model): - if isinstance(self._train_data, mz.DataPack): - x, y = self._train_data.unpack() - model.fit(x, y, **self._fit_kwargs) - elif isinstance(self._train_data, mz.DataGenerator): - model.fit_generator(self._train_data, **self._fit_kwargs) - else: - raise ValueError(f"Invalid data type: `train_data`." - f"{type(self._train_data)} received." - f"Must be one of `DataPack` and `DataGenerator`.") - - def _evaluate_model(self, model): - if isinstance(self._test_data, mz.DataPack): - x, y = self._test_data.unpack() - return model.evaluate(x, y, **self._evaluate_kwargs) - elif isinstance(self._test_data, mz.DataGenerator): - return model.evaluate_generator(self._test_data, - **self._evaluate_kwargs) - else: - raise ValueError(f"Invalid data type: `test_data`." - f"{type(self._test_data)} received." - f"Must be one of `DataPack` and `DataGenerator`.") - - def _fix_loss_sign(self, loss): - if self._mode == 'maximize': - loss = -loss - return loss - - @classmethod - def _log_result(cls, result): - print(f"Run #{result['#']}") - print(f"Score: {result['score']}") - print(result['params']) - print() - - @property - def params(self): - """`params` getter.""" - return self._params - - @params.setter - def params(self, value): - """`params` setter.""" - self._validate_params(value) - self._validate_metric(value, self._metric) - self._params = value - - @property - def train_data(self): - """`train_data` getter.""" - return self._train_data - - @train_data.setter - def train_data(self, value): - """`train_data` setter.""" - self._validate_data(value) - self._train_data = value - - @property - def test_data(self): - """`test_data` getter.""" - return self._test_data - - @test_data.setter - def test_data(self, value): - """`test_data` setter.""" - self._validate_data(value) - self._test_data = value - - @property - def fit_kwargs(self): - """`fit_kwargs` getter.""" - return self._fit_kwargs - - @fit_kwargs.setter - def fit_kwargs(self, value): - """`fit_kwargs` setter.""" - self._validate_kwargs(value) - self._fit_kwargs = value - - @property - def evaluate_kwargs(self): - """`evaluate_kwargs` getter.""" - return self._evaluate_kwargs - - @evaluate_kwargs.setter - def evaluate_kwargs(self, value): - """`evaluate_kwargs` setter.""" - self._validate_kwargs(value) - self._evaluate_kwargs = value - - @property - def metric(self): - """`metric` getter.""" - return self._metric - - @metric.setter - def metric(self, value): - """`metric` setter.""" - self._validate_metric(self._params, value) - self._metric = value - - @property - def mode(self): - """`mode` getter.""" - return self._mode - - @mode.setter - def mode(self, value): - """`mode` setter.""" - self._validate_mode(value) - self._mode = value - - @property - def num_runs(self): - """`num_runs` getter.""" - return self._num_runs - - @num_runs.setter - def num_runs(self, value): - """`num_runs` setter.""" - self._validate_num_runs(value) - self._num_runs = value - - @property - def callbacks(self): - """`callbacks` getter.""" - return self._callbacks - - @callbacks.setter - def callbacks(self, value): - """`callbacks` setter.""" - self._validate_callbacks(value) - self._callbacks = value - - @property - def verbose(self): - """`verbose` getter.""" - return self._verbose - - @verbose.setter - def verbose(self, value): - """`verbose` setter.""" - self._verbose = value - - @classmethod - def _validate_params(cls, params): - if not isinstance(params, mz.ParamTable): - raise TypeError("Only accepts a `ParamTable` instance.") - if not params.hyper_space: - raise ValueError("Parameter hyper-space empty.") - if not params.completed(): - raise ValueError("Parameters not complete.") - - @classmethod - def _validate_data(cls, train_data): - if not isinstance(train_data, (mz.DataPack, mz.DataGenerator)): - raise TypeError( - "Only accepts a `DataPack` or `DataGenerator` instance.") - - @classmethod - def _validate_kwargs(cls, kwargs): - if not isinstance(kwargs, dict): - raise TypeError('Only accepts a `dict` instance.') - - @classmethod - def _validate_mode(cls, mode): - if mode not in ('maximize', 'minimize'): - raise ValueError('`mode` should be one of `maximize`, `minimize`.') - - @classmethod - def _validate_metric(cls, params, metric): - if metric not in params['task'].metrics: - raise ValueError('Target metric does not exist in the task.') - - @classmethod - def _validate_num_runs(cls, num_runs): - if not isinstance(num_runs, int): - raise TypeError('Only accepts an `int` value.') - - @classmethod - def _validate_callbacks(cls, callbacks): - for callback in callbacks: - if not isinstance(callback, Callback): - if issubclass(callback, Callback): - raise TypeError("Make sure to instantiate the callbacks.") - raise TypeError('Only accepts a `callbacks` instance.') diff --git a/matchzoo/contrib/README.md b/matchzoo/contrib/README.md deleted file mode 100644 index 9b000bd8..00000000 --- a/matchzoo/contrib/README.md +++ /dev/null @@ -1 +0,0 @@ -A module containing volatile, experimental and legacy code. diff --git a/matchzoo/contrib/__init__.py b/matchzoo/contrib/__init__.py deleted file mode 100644 index 0ef157ed..00000000 --- a/matchzoo/contrib/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -""" -A module containing volatile, experimental and legacy code. -""" - -from . import layers -from . import models diff --git a/matchzoo/contrib/layers/__init__.py b/matchzoo/contrib/layers/__init__.py deleted file mode 100644 index 09ef7e7c..00000000 --- a/matchzoo/contrib/layers/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from .attention_layer import AttentionLayer -from .multi_perspective_layer import MultiPerspectiveLayer -from .matching_tensor_layer import MatchingTensorLayer -from .spatial_gru import SpatialGRU -from .decaying_dropout_layer import DecayingDropoutLayer -from .semantic_composite_layer import EncodingLayer - -layer_dict = { - "MatchingTensorLayer": MatchingTensorLayer, - "SpatialGRU": SpatialGRU, - "DecayingDropoutLayer": DecayingDropoutLayer, - "EncodingLayer": EncodingLayer -} diff --git a/matchzoo/contrib/layers/attention_layer.py b/matchzoo/contrib/layers/attention_layer.py deleted file mode 100644 index 049d72dc..00000000 --- a/matchzoo/contrib/layers/attention_layer.py +++ /dev/null @@ -1,144 +0,0 @@ -"""An implementation of Attention Layer for Bimpm model.""" - -import tensorflow as tf -from keras import backend as K -from keras.engine import Layer - - -class AttentionLayer(Layer): - """ - Layer that compute attention for BiMPM model. - - For detailed information, see Bilateral Multi-Perspective Matching for - Natural Language Sentences, section 3.2. - - Reference: - https://github.com/zhiguowang/BiMPM/blob/master/src/layer_utils.py#L145-L196 - - Examples: - >>> import matchzoo as mz - >>> layer = mz.contrib.layers.AttentionLayer(att_dim=50) - >>> layer.compute_output_shape([(32, 10, 100), (32, 40, 100)]) - (32, 10, 40) - - """ - - def __init__(self, - att_dim: int, - att_type: str = 'default', - dropout_rate: float = 0.0): - """ - class: `AttentionLayer` constructor. - - :param att_dim: int - :param att_type: int - """ - super(AttentionLayer, self).__init__() - self._att_dim = att_dim - self._att_type = att_type - self._dropout_rate = dropout_rate - - @property - def att_dim(self): - """Get the attention dimension.""" - return self._att_dim - - @property - def att_type(self): - """Get the attention type.""" - return self._att_type - - def build(self, input_shapes): - """ - Build the layer. - - :param input_shapes: input_shape_lt, input_shape_rt - """ - if not isinstance(input_shapes, list): - raise ValueError('A attention layer should be called ' - 'on a list of inputs.') - - hidden_dim_lt = input_shapes[0][2] - hidden_dim_rt = input_shapes[1][2] - - self.attn_w1 = self.add_weight(name='attn_w1', - shape=(hidden_dim_lt, - self._att_dim), - initializer='uniform', - trainable=True) - if hidden_dim_lt == hidden_dim_rt: - self.attn_w2 = self.attn_w1 - else: - self.attn_w2 = self.add_weight(name='attn_w2', - shape=(hidden_dim_rt, - self._att_dim), - initializer='uniform', - trainable=True) - # diagonal_W: (1, 1, a) - self.diagonal_W = self.add_weight(name='diagonal_W', - shape=(1, - 1, - self._att_dim), - initializer='uniform', - trainable=True) - self.built = True - - def call(self, x: list, **kwargs): - """ - Calculate attention. - - :param x: [reps_lt, reps_rt] - :return attn_prob: [b, s_lt, s_rt] - """ - - if not isinstance(x, list): - raise ValueError('A attention layer should be called ' - 'on a list of inputs.') - - reps_lt, reps_rt = x - - attn_w1 = self.attn_w1 - attn_w1 = tf.expand_dims(tf.expand_dims(attn_w1, axis=0), axis=0) - # => [1, 1, d, a] - - reps_lt = tf.expand_dims(reps_lt, axis=-1) - attn_reps_lt = tf.reduce_sum(reps_lt * attn_w1, axis=2) - # => [b, s_lt, d, -1] - - attn_w2 = self.attn_w2 - attn_w2 = tf.expand_dims(tf.expand_dims(attn_w2, axis=0), axis=0) - # => [1, 1, d, a] - - reps_rt = tf.expand_dims(reps_rt, axis=-1) - attn_reps_rt = tf.reduce_sum(reps_rt * attn_w2, axis=2) # [b, s_rt, d, -1] - - attn_reps_lt = tf.tanh(attn_reps_lt) # [b, s_lt, a] - attn_reps_rt = tf.tanh(attn_reps_rt) # [b, s_rt, a] - - # diagonal_W - attn_reps_lt = attn_reps_lt * self.diagonal_W # [b, s_lt, a] - attn_reps_rt = tf.transpose(attn_reps_rt, (0, 2, 1)) - # => [b, a, s_rt] - - attn_value = K.batch_dot(attn_reps_lt, attn_reps_rt) # [b, s_lt, s_rt] - - # Softmax operation - attn_prob = tf.nn.softmax(attn_value) # [b, s_lt, s_rt] - - # TODO(tjf) remove diagonal or not for normalization - # if remove_diagonal: attn_value = attn_value * diagonal - - if len(x) == 4: - mask_lt, mask_rt = x[2], x[3] - attn_prob *= tf.expand_dims(mask_lt, axis=2) - attn_prob *= tf.expand_dims(mask_rt, axis=1) - - return attn_prob - - def compute_output_shape(self, input_shapes): - """Calculate the layer output shape.""" - if not isinstance(input_shapes, list): - raise ValueError('A attention layer should be called ' - 'on a list of inputs.') - input_shape_lt, input_shape_rt = input_shapes[0], input_shapes[1] - return input_shape_lt[0], input_shape_lt[1], input_shape_rt[1] diff --git a/matchzoo/contrib/layers/decaying_dropout_layer.py b/matchzoo/contrib/layers/decaying_dropout_layer.py deleted file mode 100644 index eb3f5949..00000000 --- a/matchzoo/contrib/layers/decaying_dropout_layer.py +++ /dev/null @@ -1,99 +0,0 @@ -"""An implementation of Decaying Dropout Layer.""" - -import tensorflow as tf -from keras import backend as K -from keras.engine import Layer - -class DecayingDropoutLayer(Layer): - """ - Layer that processes dropout with exponential decayed keep rate during - training. - - :param initial_keep_rate: the initial keep rate of decaying dropout. - :param decay_interval: the decay interval of decaying dropout. - :param decay_rate: the decay rate of decaying dropout. - :param noise_shape: a 1D integer tensor representing the shape of the - binary dropout mask that will be multiplied with the input. - :param seed: a python integer to use as random seed. - :param kwargs: standard layer keyword arguments. - - Examples: - >>> import matchzoo as mz - >>> layer = mz.contrib.layers.DecayingDropoutLayer( - ... initial_keep_rate=1.0, - ... decay_interval=10000, - ... decay_rate=0.977, - ... ) - >>> num_batch, num_dim =5, 10 - >>> layer.build([num_batch, num_dim]) - """ - - def __init__(self, - initial_keep_rate: float = 1.0, - decay_interval: int = 10000, - decay_rate: float = 0.977, - noise_shape=None, - seed=None, - **kwargs): - """:class: 'DecayingDropoutLayer' constructor.""" - super(DecayingDropoutLayer, self).__init__(**kwargs) - self._iterations = None - self._initial_keep_rate = initial_keep_rate - self._decay_interval = decay_interval - self._decay_rate = min(1.0, max(0.0, decay_rate)) - self._noise_shape = noise_shape - self._seed = seed - - def _get_noise_shape(self, inputs): - if self._noise_shape is None: - return self._noise_shape - - symbolic_shape = tf.shape(inputs) - noise_shape = [symbolic_shape[axis] if shape is None else shape - for axis, shape in enumerate(self._noise_shape)] - return tuple(noise_shape) - - def build(self, input_shape): - """ - Build the layer. - - :param input_shape: the shape of the input tensor, - for DecayingDropoutLayer we need one input tensor. - """ - - self._iterations = self.add_weight(name='iterations', - shape=(1,), - dtype=K.floatx(), - initializer='zeros', - trainable=False) - super(DecayingDropoutLayer, self).build(input_shape) - - def call(self, inputs, training=None): - """ - The computation logic of DecayingDropoutLayer. - - :param inputs: an input tensor. - """ - noise_shape = self._get_noise_shape(inputs) - t = tf.cast(self._iterations, K.floatx()) + 1 - p = t / float(self._decay_interval) - - keep_rate = self._initial_keep_rate * tf.pow(self._decay_rate, p) - - def dropped_inputs(): - update_op = self._iterations.assign_add([1]) - with tf.control_dependencies([update_op]): - return tf.nn.dropout(inputs, 1 - keep_rate[0], noise_shape, - seed=self._seed) - - return K.in_train_phase(dropped_inputs, inputs, training=training) - - def get_config(self): - """Get the config dict of DecayingDropoutLayer.""" - config = {'initial_keep_rate': self._initial_keep_rate, - 'decay_interval': self._decay_interval, - 'decay_rate': self._decay_rate, - 'noise_shape': self._noise_shape, - 'seed': self._seed} - base_config = super(DecayingDropoutLayer, self).get_config() - return dict(list(base_config.items()) + list(config.items())) diff --git a/matchzoo/contrib/layers/matching_tensor_layer.py b/matchzoo/contrib/layers/matching_tensor_layer.py deleted file mode 100644 index 0578ed1c..00000000 --- a/matchzoo/contrib/layers/matching_tensor_layer.py +++ /dev/null @@ -1,135 +0,0 @@ -"""An implementation of Matching Tensor Layer.""" -import typing - -import numpy as np -import tensorflow as tf -from keras import backend as K -from keras.engine import Layer -from keras.initializers import constant - - -class MatchingTensorLayer(Layer): - """ - Layer that captures the basic interactions between two tensors. - - :param channels: Number of word interaction tensor channels - :param normalize: Whether to L2-normalize samples along the - dot product axis before taking the dot product. - If set to True, then the output of the dot product - is the cosine proximity between the two samples. - :param init_diag: Whether to initialize the diagonal elements - of the matrix. - :param kwargs: Standard layer keyword arguments. - - Examples: - >>> import matchzoo as mz - >>> layer = mz.contrib.layers.MatchingTensorLayer(channels=4, - ... normalize=True, - ... init_diag=True) - >>> num_batch, left_len, right_len, num_dim = 5, 3, 2, 10 - >>> layer.build([[num_batch, left_len, num_dim], - ... [num_batch, right_len, num_dim]]) - - """ - - def __init__(self, channels: int = 4, normalize: bool = True, - init_diag: bool = True, **kwargs): - """:class:`MatchingTensorLayer` constructor.""" - super().__init__(**kwargs) - self._channels = channels - self._normalize = normalize - self._init_diag = init_diag - self._shape1 = None - self._shape2 = None - - def build(self, input_shape: list): - """ - Build the layer. - - :param input_shape: the shapes of the input tensors, - for MatchingTensorLayer we need two input tensors. - """ - # Used purely for shape validation. - if not isinstance(input_shape, list) or len(input_shape) != 2: - raise ValueError('A `MatchingTensorLayer` layer should be called ' - 'on a list of 2 inputs.') - self._shape1 = input_shape[0] - self._shape2 = input_shape[1] - for idx in (0, 2): - if self._shape1[idx] != self._shape2[idx]: - raise ValueError( - 'Incompatible dimensions: ' - f'{self._shape1[idx]} != {self._shape2[idx]}.' - f'Layer shapes: {self._shape1}, {self._shape2}.' - ) - - if self._init_diag: - interaction_matrix = np.float32( - np.random.uniform( - -0.05, 0.05, - [self._channels, self._shape1[2], self._shape2[2]] - ) - ) - for channel_index in range(self._channels): - np.fill_diagonal(interaction_matrix[channel_index], 0.1) - self.interaction_matrix = self.add_weight( - name='interaction_matrix', - shape=(self._channels, self._shape1[2], self._shape2[2]), - initializer=constant(interaction_matrix), - trainable=True - ) - else: - self.interaction_matrix = self.add_weight( - name='interaction_matrix', - shape=(self._channels, self._shape1[2], self._shape2[2]), - initializer='uniform', - trainable=True - ) - super(MatchingTensorLayer, self).build(input_shape) - - def call(self, inputs: list, **kwargs) -> typing.Any: - """ - The computation logic of MatchingTensorLayer. - - :param inputs: two input tensors. - """ - x1 = inputs[0] - x2 = inputs[1] - # Normalize x1 and x2 - if self._normalize: - x1 = K.l2_normalize(x1, axis=2) - x2 = K.l2_normalize(x2, axis=2) - - # b = batch size - # l = length of `x1` - # r = length of `x2` - # d, e = embedding size - # c = number of channels - # output = [b, c, l, r] - output = tf.einsum( - 'bld,cde,bre->bclr', - x1, self.interaction_matrix, x2 - ) - return output - - def compute_output_shape(self, input_shape: list) -> tuple: - """ - Calculate the layer output shape. - - :param input_shape: the shapes of the input tensors, - for MatchingTensorLayer we need two input tensors. - """ - if not isinstance(input_shape, list) or len(input_shape) != 2: - raise ValueError('A `MatchingTensorLayer` layer should be called ' - 'on a list of 2 inputs.') - shape1 = list(input_shape[0]) - shape2 = list(input_shape[1]) - if len(shape1) != 3 or len(shape2) != 3: - raise ValueError('A `MatchingTensorLayer` layer should be called ' - 'on 2 inputs with 3 dimensions.') - if shape1[0] != shape2[0] or shape1[2] != shape2[2]: - raise ValueError('A `MatchingTensorLayer` layer should be called ' - 'on 2 inputs with same 0,2 dimensions.') - - output_shape = [shape1[0], self._channels, shape1[1], shape2[1]] - return tuple(output_shape) diff --git a/matchzoo/contrib/layers/multi_perspective_layer.py b/matchzoo/contrib/layers/multi_perspective_layer.py deleted file mode 100644 index 64cfd338..00000000 --- a/matchzoo/contrib/layers/multi_perspective_layer.py +++ /dev/null @@ -1,468 +0,0 @@ -"""An implementation of MultiPerspectiveLayer for Bimpm model.""" - -import tensorflow as tf -from keras import backend as K -from keras.engine import Layer - -from matchzoo.contrib.layers.attention_layer import AttentionLayer - - -class MultiPerspectiveLayer(Layer): - """ - A keras implementation of multi-perspective layer of BiMPM. - - For detailed information, see Bilateral Multi-Perspective - Matching for Natural Language Sentences, section 3.2. - - Examples: - >>> import matchzoo as mz - >>> perspective={'full': True, 'max-pooling': True, - ... 'attentive': True, 'max-attentive': True} - >>> layer = mz.contrib.layers.MultiPerspectiveLayer( - ... att_dim=50, mp_dim=20, perspective=perspective) - >>> layer.compute_output_shape( - ... [(32, 10, 100), (32, 50), None, (32, 50), None, - ... [(32, 40, 100), (32, 50), None, (32, 50), None]]) - (32, 10, 83) - - """ - - def __init__(self, - att_dim: int, - mp_dim: int, - perspective: dict): - """Class initialization.""" - super(MultiPerspectiveLayer, self).__init__() - self._att_dim = att_dim - self._mp_dim = mp_dim - self._perspective = perspective - - @classmethod - def list_available_perspectives(cls) -> list: - """List available strategy for multi-perspective matching.""" - return ['full', 'max-pooling', 'attentive', 'max-attentive'] - - @property - def num_perspective(self): - """Get the number of perspectives that is True.""" - return sum(self._perspective.values()) - - def build(self, input_shape: list): - """Input shape.""" - # The shape of the weights is l * d. - if self._perspective.get('full'): - self.full_match = MpFullMatch(self._mp_dim) - - if self._perspective.get('max-pooling'): - self.max_pooling_match = MpMaxPoolingMatch(self._mp_dim) - - if self._perspective.get('attentive'): - self.attentive_match = MpAttentiveMatch(self._att_dim, - self._mp_dim) - - if self._perspective.get('max-attentive'): - self.max_attentive_match = MpMaxAttentiveMatch(self._att_dim) - self.built = True - - def call(self, x: list, **kwargs): - """Call.""" - seq_lt, seq_rt = x[:5], x[5:] - # unpack seq_left and seq_right - # all hidden states, last hidden state of forward pass, - # last cell state of forward pass, last hidden state of - # backward pass, last cell state of backward pass. - lstm_reps_lt, forward_h_lt, _, backward_h_lt, _ = seq_lt - lstm_reps_rt, forward_h_rt, _, backward_h_rt, _ = seq_rt - - match_tensor_list = [] - match_dim = 0 - if self._perspective.get('full'): - # Each forward & backward contextual embedding compare - # with the last step of the last time step of the other sentence. - h_lt = tf.concat([forward_h_lt, backward_h_lt], axis=-1) - full_match_tensor = self.full_match([h_lt, lstm_reps_rt]) - match_tensor_list.append(full_match_tensor) - match_dim += self._mp_dim + 1 - - if self._perspective.get('max-pooling'): - # Each contextual embedding compare with each contextual embedding. - # retain the maximum of each dimension. - max_match_tensor = self.max_pooling_match([lstm_reps_lt, - lstm_reps_rt]) - match_tensor_list.append(max_match_tensor) - match_dim += self._mp_dim - - if self._perspective.get('attentive'): - # Each contextual embedding compare with each contextual embedding. - # retain sum of weighted mean of each dimension. - attentive_tensor = self.attentive_match([lstm_reps_lt, - lstm_reps_rt]) - match_tensor_list.append(attentive_tensor) - match_dim += self._mp_dim + 1 - - if self._perspective.get('max-attentive'): - # Each contextual embedding compare with each contextual embedding. - # retain max of weighted mean of each dimension. - relevancy_matrix = _calc_relevancy_matrix(lstm_reps_lt, - lstm_reps_rt) - max_attentive_tensor = self.max_attentive_match([lstm_reps_lt, - lstm_reps_rt, - relevancy_matrix]) - match_tensor_list.append(max_attentive_tensor) - match_dim += self._mp_dim + 1 - - mp_tensor = tf.concat(match_tensor_list, axis=-1) - return mp_tensor - - def compute_output_shape(self, input_shape: list): - """Compute output shape.""" - shape_a = input_shape[0] - - match_dim = 0 - if self._perspective.get('full'): - match_dim += self._mp_dim + 1 - if self._perspective.get('max-pooling'): - match_dim += self._mp_dim - if self._perspective.get('attentive'): - match_dim += self._mp_dim + 1 - if self._perspective.get('max-attentive'): - match_dim += self._mp_dim + 1 - - return shape_a[0], shape_a[1], match_dim - - -class MpFullMatch(Layer): - """Mp Full Match Layer.""" - - def __init__(self, mp_dim): - """Init.""" - super(MpFullMatch, self).__init__() - self.mp_dim = mp_dim - - def build(self, input_shapes): - """Build.""" - # input_shape = input_shapes[0] - self.built = True - - def call(self, x, **kwargs): - """Call. - """ - rep_lt, reps_rt = x - att_lt = tf.expand_dims(rep_lt, 1) - - match_tensor, match_dim = _multi_perspective_match(self.mp_dim, - reps_rt, - att_lt) - # match_tensor => [b, len_rt, mp_dim+1] - return match_tensor - - def compute_output_shape(self, input_shape): - """Compute output shape.""" - return input_shape[1][0], input_shape[1][1], self.mp_dim + 1 - - -class MpMaxPoolingMatch(Layer): - """MpMaxPoolingMatch.""" - - def __init__(self, mp_dim): - """Init.""" - super(MpMaxPoolingMatch, self).__init__() - self.mp_dim = mp_dim - - def build(self, input_shapes): - """Build.""" - d = input_shapes[0][-1] - self.kernel = self.add_weight(name='kernel', - shape=(1, 1, 1, self.mp_dim, d), - initializer='uniform', - trainable=True) - self.built = True - - def call(self, x, **kwargs): - """Call.""" - reps_lt, reps_rt = x - - # kernel: [1, 1, 1, mp_dim, d] - # lstm_lt => [b, len_lt, 1, 1, d] - reps_lt = tf.expand_dims(reps_lt, axis=2) - reps_lt = tf.expand_dims(reps_lt, axis=2) - reps_lt = reps_lt * self.kernel - - # lstm_rt -> [b, 1, len_rt, 1, d] - reps_rt = tf.expand_dims(reps_rt, axis=2) - reps_rt = tf.expand_dims(reps_rt, axis=1) - - match_tensor = _cosine_distance(reps_lt, reps_rt, cosine_norm=False) - max_match_tensor = tf.reduce_max(match_tensor, axis=1) - # match_tensor => [b, len_rt, m] - return max_match_tensor - - def compute_output_shape(self, input_shape): - """Compute output shape.""" - return input_shape[1][0], input_shape[1][1], self.mp_dim - - -class MpAttentiveMatch(Layer): - """ - MpAttentiveMatch Layer. - - Reference: - https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L188-L193 - - Examples: - >>> import matchzoo as mz - >>> layer = mz.contrib.layers.multi_perspective_layer.MpAttentiveMatch( - ... att_dim=30, mp_dim=20) - >>> layer.compute_output_shape([(32, 10, 100), (32, 40, 100)]) - (32, 40, 20) - - """ - - def __init__(self, att_dim, mp_dim): - """Init.""" - super(MpAttentiveMatch, self).__init__() - self.att_dim = att_dim - self.mp_dim = mp_dim - - def build(self, input_shapes): - """Build.""" - # input_shape = input_shapes[0] - self.built = True - - def call(self, x, **kwargs): - """Call.""" - reps_lt, reps_rt = x[0], x[1] - # attention prob matrix - attention_layer = AttentionLayer(self.att_dim) - attn_prob = attention_layer([reps_rt, reps_lt]) - # attention reps - att_lt = K.batch_dot(attn_prob, reps_lt) - # mp match - attn_match_tensor, match_dim = _multi_perspective_match(self.mp_dim, - reps_rt, - att_lt) - return attn_match_tensor - - def compute_output_shape(self, input_shape): - """Compute output shape.""" - return input_shape[1][0], input_shape[1][1], self.mp_dim - - -class MpMaxAttentiveMatch(Layer): - """MpMaxAttentiveMatch.""" - - def __init__(self, mp_dim): - """Init.""" - super(MpMaxAttentiveMatch, self).__init__() - self.mp_dim = mp_dim - - def build(self, input_shapes): - """Build.""" - # input_shape = input_shapes[0] - self.built = True - - def call(self, x): - """Call.""" - reps_lt, reps_rt = x[0], x[1] - relevancy_matrix = x[2] - max_att_lt = cal_max_question_representation(reps_lt, relevancy_matrix) - max_attentive_tensor, match_dim = _multi_perspective_match(self.mp_dim, - reps_rt, - max_att_lt) - return max_attentive_tensor - - -def cal_max_question_representation(reps_lt, attn_scores): - """ - Calculate max_question_representation. - - :param reps_lt: [batch_size, passage_len, hidden_size] - :param attn_scores: [] - :return: [batch_size, passage_len, hidden_size]. - """ - attn_positions = tf.argmax(attn_scores, axis=2) - max_reps_lt = collect_representation(reps_lt, attn_positions) - return max_reps_lt - - -def collect_representation(representation, positions): - """ - Collect_representation. - - :param representation: [batch_size, node_num, feature_dim] - :param positions: [batch_size, neighbour_num] - :return: [batch_size, neighbour_num]? - """ - return collect_probs(representation, positions) - - -def collect_final_step_of_lstm(lstm_representation, lengths): - """ - Collect final step of lstm. - - :param lstm_representation: [batch_size, len_rt, dim] - :param lengths: [batch_size] - :return: [batch_size, dim] - """ - lengths = tf.maximum(lengths, K.zeros_like(lengths)) - - batch_size = tf.shape(lengths)[0] - # shape (batch_size) - batch_nums = tf.range(0, limit=batch_size) - # shape (batch_size, 2) - indices = tf.stack((batch_nums, lengths), axis=1) - result = tf.gather_nd(lstm_representation, indices, - name='last-forwar-lstm') - # [batch_size, dim] - return result - - -def collect_probs(probs, positions): - """ - Collect Probabilities. - - Reference: - https://github.com/zhiguowang/BiMPM/blob/master/src/layer_utils.py#L128-L140 - :param probs: [batch_size, chunks_size] - :param positions: [batch_size, pair_size] - :return: [batch_size, pair_size] - """ - batch_size = tf.shape(probs)[0] - pair_size = tf.shape(positions)[1] - # shape (batch_size) - batch_nums = K.arange(0, batch_size) - # [batch_size, 1] - batch_nums = tf.reshape(batch_nums, shape=[-1, 1]) - # [batch_size, pair_size] - batch_nums = K.tile(batch_nums, [1, pair_size]) - - # shape (batch_size, pair_size, 2) - # Alert: to solve error message - positions = tf.cast(positions, tf.int32) - indices = tf.stack([batch_nums, positions], axis=2) - - pair_probs = tf.gather_nd(probs, indices) - # pair_probs = tf.reshape(pair_probs, shape=[batch_size, pair_size]) - return pair_probs - - -def _multi_perspective_match(mp_dim, reps_rt, att_lt, - with_cosine=True, with_mp_cosine=True): - """ - The core function of zhiguowang's implementation. - - reference: - https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L207-L223 - :param mp_dim: about 20 - :param reps_rt: [batch, len_rt, dim] - :param att_lt: [batch, len_rt, dim] - :param with_cosine: True - :param with_mp_cosine: True - :return: [batch, len, 1 + mp_dim] - """ - shape_rt = tf.shape(reps_rt) - batch_size = shape_rt[0] - len_lt = shape_rt[1] - - match_dim = 0 - match_result_list = [] - if with_cosine: - cosine_tensor = _cosine_distance(reps_rt, att_lt, False) - cosine_tensor = tf.reshape(cosine_tensor, - [batch_size, len_lt, 1]) - match_result_list.append(cosine_tensor) - match_dim += 1 - - if with_mp_cosine: - mp_cosine_layer = MpCosineLayer(mp_dim) - mp_cosine_tensor = mp_cosine_layer([reps_rt, att_lt]) - mp_cosine_tensor = tf.reshape(mp_cosine_tensor, - [batch_size, len_lt, mp_dim]) - match_result_list.append(mp_cosine_tensor) - match_dim += mp_cosine_layer.mp_dim - - match_result = tf.concat(match_result_list, 2) - return match_result, match_dim - - -class MpCosineLayer(Layer): - """ - Implementation of Multi-Perspective Cosine Distance. - - Reference: - https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L121-L129 - - Examples: - >>> import matchzoo as mz - >>> layer = mz.contrib.layers.multi_perspective_layer.MpCosineLayer( - ... mp_dim=50) - >>> layer.compute_output_shape([(32, 10, 100), (32, 10, 100)]) - (32, 10, 50) - - """ - - def __init__(self, mp_dim, **kwargs): - """Init.""" - self.mp_dim = mp_dim - super(MpCosineLayer, self).__init__(**kwargs) - - def build(self, input_shape): - """Build.""" - self.kernel = self.add_weight(name='kernel', - shape=(1, 1, self.mp_dim, - input_shape[0][-1]), - initializer='uniform', - trainable=True) - super(MpCosineLayer, self).build(input_shape) - - def call(self, x, **kwargs): - """Call.""" - v1, v2 = x - v1 = tf.expand_dims(v1, 2) * self.kernel # [b, s_lt, m, d] - v2 = tf.expand_dims(v2, 2) # [b, s_lt, 1, d] - return _cosine_distance(v1, v2, False) - - def compute_output_shape(self, input_shape): - """Compute output shape.""" - return input_shape[0][0], input_shape[0][1], self.mp_dim - - -def _calc_relevancy_matrix(reps_lt, reps_rt): - reps_lt = tf.expand_dims(reps_lt, 1) # [b, 1, len_lt, d] - reps_rt = tf.expand_dims(reps_rt, 2) # [b, len_rt, 1, d] - relevancy_matrix = _cosine_distance(reps_lt, reps_rt) - # => [b, len_rt, len_lt, d] - return relevancy_matrix - - -def _mask_relevancy_matrix(relevancy_matrix, mask_lt, mask_rt): - """ - Mask relevancy matrix. - - :param relevancy_matrix: [b, len_rt, len_lt] - :param mask_lt: [b, len_lt] - :param mask_rt: [b, len_rt] - :return: masked_matrix: [b, len_rt, len_lt] - """ - if mask_lt is not None: - relevancy_matrix = relevancy_matrix * tf.expand_dims(mask_lt, 1) - relevancy_matrix = relevancy_matrix * tf.expand_dims(mask_rt, 2) - return relevancy_matrix - - -def _cosine_distance(v1, v2, cosine_norm=True, eps=1e-6): - """ - Only requires `tf.reduce_sum(v1 * v2, axis=-1)`. - - :param v1: [batch, time_steps(v1), 1, m, d] - :param v2: [batch, 1, time_steps(v2), m, d] - :param cosine_norm: True - :param eps: 1e-6 - :return: [batch, time_steps(v1), time_steps(v2), m] - """ - cosine_numerator = tf.reduce_sum(v1 * v2, axis=-1) - if not cosine_norm: - return K.tanh(cosine_numerator) - v1_norm = K.sqrt(tf.maximum(tf.reduce_sum(tf.square(v1), axis=-1), eps)) - v2_norm = K.sqrt(tf.maximum(tf.reduce_sum(tf.square(v2), axis=-1), eps)) - return cosine_numerator / v1_norm / v2_norm diff --git a/matchzoo/contrib/layers/semantic_composite_layer.py b/matchzoo/contrib/layers/semantic_composite_layer.py deleted file mode 100644 index 9f6cb5b4..00000000 --- a/matchzoo/contrib/layers/semantic_composite_layer.py +++ /dev/null @@ -1,121 +0,0 @@ -"""An implementation of EncodingModule for DIIN model.""" - -import tensorflow as tf -from keras import backend as K -from keras.engine import Layer - -from matchzoo.contrib.layers import DecayingDropoutLayer - - -class EncodingLayer(Layer): - """ - Apply a self-attention layer and a semantic composite fuse gate - to compute the encoding result of one tensor. - - :param initial_keep_rate: the initial_keep_rate parameter of - DecayingDropoutLayer. - :param decay_interval: the decay_interval parameter of - DecayingDropoutLayer. - :param decay_rate: the decay_rate parameter of DecayingDropoutLayer. - :param kwargs: standard layer keyword arguments. - - Example: - >>> import matchzoo as mz - >>> layer = mz.contrib.layers.EncodingLayer(1.0, 10000, 0.977) - >>> num_batch, left_len, num_dim = 5, 32, 10 - >>> layer.build([num_batch, left_len, num_dim]) - """ - - def __init__(self, - initial_keep_rate: float, - decay_interval: int, - decay_rate: float, - **kwargs): - """:class: 'EncodingLayer' constructor.""" - super(EncodingLayer, self).__init__(**kwargs) - self._initial_keep_rate = initial_keep_rate - self._decay_interval = decay_interval - self._decay_rate = decay_rate - self._w_itr_att = None - self._w1 = None - self._w2 = None - self._w3 = None - self._b1 = None - self._b2 = None - self._b3 = None - - def build(self, input_shape): - """ - Build the layer. - - :param input_shape: the shape of the input tensor, - for EncodingLayer we need one input tensor. - """ - d = input_shape[-1] - - self._w_itr_att = self.add_weight( - name='w_itr_att', shape=(3 * d,), initializer='glorot_uniform') - self._w1 = self.add_weight( - name='w1', shape=(2 * d, d,), initializer='glorot_uniform') - self._w2 = self.add_weight( - name='w2', shape=(2 * d, d,), initializer='glorot_uniform') - self._w3 = self.add_weight( - name='w3', shape=(2 * d, d,), initializer='glorot_uniform') - self._b1 = self.add_weight( - name='b1', shape=(d,), initializer='zeros') - self._b2 = self.add_weight( - name='b2', shape=(d,), initializer='zeros') - self._b3 = self.add_weight( - name='b3', shape=(d,), initializer='zeros') - - super(EncodingLayer, self).build(input_shape) - - def call(self, inputs, **kwargs): - """ - The computation logic of EncodingLayer. - - :param inputs: an input tensor. - """ - # Scalar dimensions referenced here: - # b = batch size - # p = inputs.shape()[1] - # d = inputs.shape()[2] - - # The input shape is [b, p, d] - # shape = [b, 1, p, d] - x = tf.expand_dims(inputs, 1) * 0 - # shape = [b, 1, d, p] - x = tf.transpose(x, (0, 1, 3, 2)) - # shape = [b, p, d, p] - mid = x + tf.expand_dims(inputs, -1) - # shape = [b, p, d, p] - up = tf.transpose(mid, (0, 3, 2, 1)) - # shape = [b, p, 3d, p] - inputs_concat = tf.concat([up, mid, up * mid], axis=2) - - # Self-attention layer. - # shape = [b, p, p] - A = K.dot(self._w_itr_att, inputs_concat) - # shape = [b, p, p] - SA = tf.nn.softmax(A, axis=2) - # shape = [b, p, d] - itr_attn = K.batch_dot(SA, inputs) - - # Semantic composite fuse gate. - # shape = [b, p, 2d] - inputs_attn_concat = tf.concat([inputs, itr_attn], axis=2) - concat_dropout = DecayingDropoutLayer( - initial_keep_rate=self._initial_keep_rate, - decay_interval=self._decay_interval, - decay_rate=self._decay_rate - )(inputs_attn_concat) - # shape = [b, p, d] - z = tf.tanh(K.dot(concat_dropout, self._w1) + self._b1) - # shape = [b, p, d] - r = tf.sigmoid(K.dot(concat_dropout, self._w2) + self._b2) - # shape = [b, p, d] - f = tf.sigmoid(K.dot(concat_dropout, self._w3) + self._b3) - # shape = [b, p, d] - encoding = r * inputs + f * z - - return encoding diff --git a/matchzoo/contrib/layers/spatial_gru.py b/matchzoo/contrib/layers/spatial_gru.py deleted file mode 100644 index c583c9d6..00000000 --- a/matchzoo/contrib/layers/spatial_gru.py +++ /dev/null @@ -1,290 +0,0 @@ -"""An implementation of Spatial GRU Layer.""" -import typing -import tensorflow as tf -from keras import backend as K -from keras.engine import Layer -from keras.layers import Permute -from keras.layers import Reshape -from keras import activations -from keras import initializers - - -class SpatialGRU(Layer): - """ - Spatial GRU layer. - - :param units: Number of SpatialGRU units. - :param activation: Activation function to use. Default: - hyperbolic tangent (`tanh`). If you pass `None`, no - activation is applied (ie. "linear" activation: `a(x) = x`). - :param recurrent_activation: Activation function to use for - the recurrent step. Default: sigmoid (`sigmoid`). - If you pass `None`, no activation is applied (ie. "linear" - activation: `a(x) = x`). - :param kernel_initializer: Initializer for the `kernel` weights - matrix, used for the linear transformation of the inputs. - :param recurrent_initializer: Initializer for the `recurrent_kernel` - weights matrix, used for the linear transformation of the - recurrent state. - :param direction: Scanning direction. `lt` (i.e., left top) - indicates the scanning from left top to right bottom, and - `rb` (i.e., right bottom) indicates the scanning from - right bottom to left top. - :param kwargs: Standard layer keyword arguments. - - Examples: - >>> import matchzoo as mz - >>> layer = mz.contrib.layers.SpatialGRU(units=10, - ... direction='lt') - >>> num_batch, channel, left_len, right_len = 5, 5, 3, 2 - >>> layer.build([num_batch, channel, left_len, right_len]) - - """ - - def __init__( - self, - units: int = 10, - activation: str = 'tanh', - recurrent_activation: str = 'sigmoid', - kernel_initializer: str = 'glorot_uniform', - recurrent_initializer: str = 'orthogonal', - direction: str = 'lt', - **kwargs - ): - """:class:`SpatialGRU` constructor.""" - super().__init__(**kwargs) - self._units = units - self._activation = activations.get(activation) - self._recurrent_activation = activations.get(recurrent_activation) - - self._kernel_initializer = initializers.get(kernel_initializer) - self._recurrent_initializer = initializers.get(recurrent_initializer) - self._direction = direction - - def build(self, input_shape: typing.Any): - """ - Build the layer. - - :param input_shape: the shapes of the input tensors. - """ - # Scalar dimensions referenced here: - # B = batch size (number of sequences) - # L = `input_left` sequence length - # R = `input_right` sequence length - # C = number of channels - # U = number of units - - # input_shape = [B, C, L, R] - self._batch_size = input_shape[0] - self._channel = input_shape[1] - self._input_dim = self._channel + 3 * self._units - - self._text1_maxlen = input_shape[2] - self._text2_maxlen = input_shape[3] - self._recurrent_step = self._text1_maxlen * self._text2_maxlen - # W = [3*U+C, 7*U] - self._W = self.add_weight( - name='W', - shape=(self._input_dim, self._units * 7), - initializer=self._kernel_initializer, - trainable=True - ) - # U = [3*U, U] - self._U = self.add_weight( - name='U', - shape=(self._units * 3, self._units), - initializer=self._recurrent_initializer, - trainable=True - ) - # bias = [8*U,] - self._bias = self.add_weight( - name='bias', - shape=(self._units * 8,), - initializer='zeros', - trainable=True - ) - - # w_rl, w_rt, w_rd = [B, 3*U] - self._wr = self._W[:, :self._units * 3] - # b_rl, b_rt, b_rd = [B, 3*U] - self._br = self._bias[:self._units * 3] - # w_zi, w_zl, w_zt, w_zd = [B, 4*U] - self._wz = self._W[:, self._units * 3: self._units * 7] - # b_zi, b_zl, b_zt, b_zd = [B, 4*U] - self._bz = self._bias[self._units * 3: self._units * 7] - # w_ij = [C, U] - self._w_ij = self.add_weight( - name='W_ij', - shape=(self._channel, self._units), - initializer=self._recurrent_initializer, - trainable=True - ) - # b_ij = [7*U] - self._b_ij = self._bias[self._units * 7:] - super(SpatialGRU, self).build(input_shape) - - def softmax_by_row(self, z: typing.Any) -> tuple: - """Conduct softmax on each dimension across the four gates.""" - - # z_transform: [B, U, 4] - z_transform = Permute((2, 1))(Reshape((4, self._units))(z)) - size = [-1, 1, -1] - # Perform softmax on each slice - for i in range(0, self._units): - begin = [0, i, 0] - # z_slice: [B, 1, 4] - z_slice = tf.slice(z_transform, begin, size) - if i == 0: - z_s = tf.nn.softmax(z_slice) - else: - z_s = tf.concat([z_s, tf.nn.softmax(z_slice)], 1) - # zi, zl, zt, zd: [B, U] - zi, zl, zt, zd = tf.unstack(z_s, axis=2) - return zi, zl, zt, zd - - def calculate_recurrent_unit( - self, - inputs: typing.Any, - states: typing.Any, - step: int, - h: typing.Any, - ) -> tuple: - """ - Calculate recurrent unit. - - :param inputs: A TensorArray which contains interaction - between left text and right text. - :param states: A TensorArray which stores the hidden state - of every step. - :param step: Recurrent step. - :param h: Hidden state from last operation. - """ - # Get index i, j - i = tf.math.floordiv(step, tf.constant(self._text2_maxlen)) - j = tf.math.mod(step, tf.constant(self._text2_maxlen)) - - # Get hidden state h_diag, h_top, h_left - # h_diag, h_top, h_left = [B, U] - h_diag = states.read(i * (self._text2_maxlen + 1) + j) - h_top = states.read(i * (self._text2_maxlen + 1) + j + 1) - h_left = states.read((i + 1) * (self._text2_maxlen + 1) + j) - - # Get interaction between word i, j: s_ij - # s_ij = [B, C] - s_ij = inputs.read(step) - - # Concatenate h_top, h_left, h_diag, s_ij - # q = [B, 3*U+C] - q = tf.concat([tf.concat([h_top, h_left], 1), - tf.concat([h_diag, s_ij], 1)], 1) - - # Calculate reset gate - # r = [B, 3*U] - r = self._recurrent_activation( - self._time_distributed_dense(self._wr, q, self._br)) - - # Calculate updating gate - # z: [B, 4*U] - z = self._time_distributed_dense(self._wz, q, self._bz) - - # Perform softmax - # zi, zl, zt, zd: [B, U] - zi, zl, zt, zd = self.softmax_by_row(z) - - # Get h_ij_ - # h_ij_ = [B, U] - h_ij_l = self._time_distributed_dense(self._w_ij, s_ij, self._b_ij) - h_ij_r = K.dot(r * (tf.concat([h_left, h_top, h_diag], 1)), self._U) - h_ij_ = self._activation(h_ij_l + h_ij_r) - - # Calculate h_ij - # h_ij = [B, U] - h_ij = zl * h_left + zt * h_top + zd * h_diag + zi * h_ij_ - - # Write h_ij to states - states = states.write(((i + 1) * (self._text2_maxlen + 1) + j + 1), - h_ij) - h_ij.set_shape(h_top.get_shape()) - - return inputs, states, step + 1, h_ij - - def call(self, inputs: list, **kwargs) -> typing.Any: - """ - The computation logic of SpatialGRU. - - :param inputs: input tensors. - """ - batch_size = tf.shape(inputs)[0] - # h0 = [B, U] - self._bounder_state_h0 = tf.zeros([batch_size, self._units]) - - # input_x = [L, R, B, C] - input_x = tf.transpose(inputs, [2, 3, 0, 1]) - if self._direction == 'rb': - # input_x: [R, L, B, C] - input_x = tf.reverse(input_x, [0, 1]) - elif self._direction != 'lt': - raise ValueError(f"Invalid direction. " - f"`{self._direction}` received. " - f"Must be in `lt`, `rb`.") - # input_x = [L*R*B, C] - input_x = tf.reshape(input_x, [-1, self._channel]) - # input_x = L*R * [B, C] - input_x = tf.split( - axis=0, - num_or_size_splits=self._text1_maxlen * self._text2_maxlen, - value=input_x - ) - - # inputs = L*R * [B, C] - inputs = tf.TensorArray( - dtype=tf.float32, - size=self._text1_maxlen * self._text2_maxlen, - name='inputs' - ) - inputs = inputs.unstack(input_x) - - # states = (L+1)*(R+1) * [B, U] - states = tf.TensorArray( - dtype=tf.float32, - size=(self._text1_maxlen + 1) * (self._text2_maxlen + 1), - name='states', - clear_after_read=False - ) - # Initialize states - for i in range(self._text2_maxlen + 1): - states = states.write(i, self._bounder_state_h0) - for i in range(1, self._text1_maxlen + 1): - states = states.write(i * (self._text2_maxlen + 1), - self._bounder_state_h0) - - # Calculate h_ij - # h_ij = [B, U] - _, _, _, h_ij = tf.while_loop( - cond=lambda _0, _1, i, _3: tf.less(i, self._recurrent_step), - body=self.calculate_recurrent_unit, - loop_vars=( - inputs, - states, - tf.constant(0, dtype=tf.int32), - self._bounder_state_h0 - ), - parallel_iterations=1, - swap_memory=True - ) - return h_ij - - def compute_output_shape(self, input_shape: typing.Any) -> tuple: - """ - Calculate the layer output shape. - - :param input_shape: the shapes of the input tensors. - """ - output_shape = [input_shape[0], self._units] - return tuple(output_shape) - - @classmethod - def _time_distributed_dense(cls, w, x, b): - x = K.dot(x, w) - x = K.bias_add(x, b) - return x diff --git a/matchzoo/contrib/legacy_data_generator.py b/matchzoo/contrib/legacy_data_generator.py deleted file mode 100644 index d0588443..00000000 --- a/matchzoo/contrib/legacy_data_generator.py +++ /dev/null @@ -1,153 +0,0 @@ -import numpy as np - -import matchzoo as mz - - -def print_deprecation_warning(instance): - name = instance.__class__.__name__ - print(f"WARNING: {name} will be deprecated in MatchZoo v2.2. " - "Use `DataGenerator` with callbacks instead.") - - -class HistogramDataGenerator(mz.DataGenerator): - def __init__( - self, - data_pack: mz.DataPack, - embedding_matrix: np.ndarray, - bin_size: int = 30, - hist_mode: str = 'CH', - batch_size: int = 32, - shuffle: bool = True - ): - super().__init__( - data_pack=data_pack, - batch_size=batch_size, - shuffle=shuffle, - callbacks=[ - mz.data_generator.callbacks.Histogram( - embedding_matrix=embedding_matrix, - bin_size=bin_size, - hist_mode=hist_mode - ) - ] - ) - print_deprecation_warning(self) - - -class HistogramPairDataGenerator(mz.DataGenerator): - def __init__( - self, - data_pack: mz.DataPack, - embedding_matrix: np.ndarray, - bin_size: int = 30, - hist_mode: str = 'CH', - num_dup: int = 1, - num_neg: int = 1, - batch_size: int = 32, - shuffle: bool = True - ): - super().__init__( - data_pack=data_pack, - mode='pair', - num_dup=num_dup, - num_neg=num_neg, - batch_size=batch_size, - shuffle=shuffle, - callbacks=[ - mz.data_generator.callbacks.Histogram( - embedding_matrix=embedding_matrix, - bin_size=bin_size, - hist_mode=hist_mode - ) - ] - ) - print_deprecation_warning(self) - - -class DPoolDataGenerator(mz.DataGenerator): - def __init__( - self, - data_pack: mz.DataPack, - fixed_length_left: int, - fixed_length_right: int, - compress_ratio_left: float = 1, - compress_ratio_right: float = 1, - batch_size: int = 32, - shuffle: bool = True - ): - super().__init__( - data_pack=data_pack, - shuffle=shuffle, - batch_size=batch_size, - callbacks=[ - mz.data_generator.callbacks.DynamicPooling( - fixed_length_left=fixed_length_left, - fixed_length_right=fixed_length_right, - compress_ratio_left=compress_ratio_left, - compress_ratio_right=compress_ratio_right - ) - ] - ) - print_deprecation_warning(self) - - -class DPoolPairDataGenerator(mz.DataGenerator): - def __init__( - self, - data_pack: mz.DataPack, - fixed_length_left: int, - fixed_length_right: int, - compress_ratio_left: float = 1, - compress_ratio_right: float = 1, - num_dup: int = 1, - num_neg: int = 1, - batch_size: int = 32, - shuffle: bool = True - ): - super().__init__( - data_pack=data_pack, - mode='pair', - num_dup=num_dup, - num_neg=num_neg, - batch_size=batch_size, - shuffle=shuffle, - callbacks=[ - mz.data_generator.callbacks.DynamicPooling( - fixed_length_left=fixed_length_left, - fixed_length_right=fixed_length_right, - compress_ratio_left=compress_ratio_left, - compress_ratio_right=compress_ratio_right - ) - ] - ) - print_deprecation_warning(self) - - -class PairDataGenerator(mz.DataGenerator): - def __init__( - self, - data_pack: mz.DataPack, - num_dup: int = 1, - num_neg: int = 1, - batch_size: int = 32, - shuffle: bool = True - ): - super().__init__( - data_pack=data_pack, - mode='pair', - num_dup=num_dup, - num_neg=num_neg, - batch_size=batch_size, - shuffle=shuffle, - - ) - print_deprecation_warning(self) - - -class DynamicDataGenerator(mz.DataGenerator): - def __init__(self, func, *args, **kwargs): - super().__init__(*args, **kwargs) - callback = mz.data_generator.callbacks.LambdaCallback( - on_batch_data_pack=func) - self.callbacks.append(callback) - print_deprecation_warning(self) diff --git a/matchzoo/contrib/models/__init__.py b/matchzoo/contrib/models/__init__.py deleted file mode 100644 index cefd02a0..00000000 --- a/matchzoo/contrib/models/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .match_lstm import MatchLSTM -from .match_srnn import MatchSRNN -from .hbmp import HBMP -from .esim import ESIM -from .bimpm import BiMPM -from .diin import DIIN diff --git a/matchzoo/contrib/models/bimpm.py b/matchzoo/contrib/models/bimpm.py deleted file mode 100644 index 112967ea..00000000 --- a/matchzoo/contrib/models/bimpm.py +++ /dev/null @@ -1,149 +0,0 @@ -"""BiMPM.""" - -from keras.models import Model -from keras.layers import Dense, Concatenate, Dropout -from keras.layers import Bidirectional, LSTM - -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable -from matchzoo.engine.base_model import BaseModel -from matchzoo.contrib.layers import MultiPerspectiveLayer - - -class BiMPM(BaseModel): - """ - BiMPM. - - Reference: - https://github.com/zhiguowang/BiMPM/blob/master/src/SentenceMatchModelGraph.py#L43-L186 - Examples: - >>> import matchzoo as mz - >>> model = mz.contrib.models.BiMPM() - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params(with_embedding=True) - params['optimizer'] = 'adam' - - # params.add(Param('dim_word_embedding', 50)) - # TODO(tjf): remove unused params in the final version - # params.add(Param('dim_char_embedding', 50)) - # params.add(Param('word_embedding_mat')) - # params.add(Param('char_embedding_mat')) - # params.add(Param('embedding_random_scale', 0.2)) - # params.add(Param('activation_embedding', 'softmax')) - - # BiMPM Setting - params.add(Param('perspective', {'full': True, - 'max-pooling': True, - 'attentive': True, - 'max-attentive': True})) - params.add(Param('mp_dim', 3)) - params.add(Param('att_dim', 3)) - params.add(Param('hidden_size', 4)) - params.add(Param('dropout_rate', 0.0)) - params.add(Param('w_initializer', 'glorot_uniform')) - params.add(Param('b_initializer', 'zeros')) - params.add(Param('activation_hidden', 'linear')) - - params.add(Param('with_match_highway', False)) - params.add(Param('with_aggregation_highway', False)) - - return params - - def build(self): - """Build model structure.""" - # ~ Input Layer - input_left, input_right = self._make_inputs() - - # Word Representation Layer - # TODO: concatenate word level embedding and character level embedding. - embedding = self._make_embedding_layer() - embed_left = embedding(input_left) - embed_right = embedding(input_right) - - # L119-L121 - # https://github.com/zhiguowang/BiMPM/blob/master/src/SentenceMatchModelGraph.py#L119-L121 - embed_left = Dropout(self._params['dropout_rate'])(embed_left) - embed_right = Dropout(self._params['dropout_rate'])(embed_right) - - # ~ Word Level Matching Layer - # Reference: - # https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L207-L223 - # TODO - pass - - # ~ Encoding Layer - # Note: When merge_mode = None, output will be [forward, backward], - # The default merge_mode is concat, and the output will be [lstm]. - # If with return_state, then the output would append [h,c,h,c]. - bi_lstm = Bidirectional( - LSTM(self._params['hidden_size'], - return_sequences=True, - return_state=True, - dropout=self._params['dropout_rate'], - kernel_initializer=self._params['w_initializer'], - bias_initializer=self._params['b_initializer']), - merge_mode='concat') - # x_left = [lstm_lt, forward_h_lt, _, backward_h_lt, _ ] - x_left = bi_lstm(embed_left) - x_right = bi_lstm(embed_right) - - # ~ Multi-Perspective Matching layer. - # Output is two sequence of vectors. - # Cons: Haven't support multiple context layer - multi_perspective = MultiPerspectiveLayer(self._params['att_dim'], - self._params['mp_dim'], - self._params['perspective']) - # Note: input to `keras layer` must be list of tensors. - mp_left = multi_perspective(x_left + x_right) - mp_right = multi_perspective(x_right + x_left) - - # ~ Dropout Layer - mp_left = Dropout(self._params['dropout_rate'])(mp_left) - mp_right = Dropout(self._params['dropout_rate'])(mp_right) - - # ~ Highway Layer - # reference: - # https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L289-L295 - if self._params['with_match_highway']: - # the input is left matching representations (question / passage) - pass - - # ~ Aggregation layer - # TODO: mask the above layer - aggregation = Bidirectional( - LSTM(self._params['hidden_size'], - return_sequences=False, - return_state=False, - dropout=self._params['dropout_rate'], - kernel_initializer=self._params['w_initializer'], - bias_initializer=self._params['b_initializer']), - merge_mode='concat') - rep_left = aggregation(mp_left) - rep_right = aggregation(mp_right) - - # Concatenate the concatenated vector of left and right. - x = Concatenate()([rep_left, rep_right]) - - # ~ Highway Network - # reference: - # https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L289-L295 - if self._params['with_aggregation_highway']: - pass - - # ~ Prediction layer. - # reference: - # https://github.com/zhiguowang/BiMPM/blob/master/src/SentenceMatchModelGraph.py#L140-L153 - x = Dense(self._params['hidden_size'], - activation=self._params['activation_hidden'])(x) - x = Dense(self._params['hidden_size'], - activation=self._params['activation_hidden'])(x) - x_out = self._make_output_layer()(x) - self._backend = Model(inputs=[input_left, input_right], - outputs=x_out) diff --git a/matchzoo/contrib/models/diin.py b/matchzoo/contrib/models/diin.py deleted file mode 100644 index a346c84c..00000000 --- a/matchzoo/contrib/models/diin.py +++ /dev/null @@ -1,313 +0,0 @@ -"""DIIN model.""" -import typing - -import keras -import keras.backend as K -import tensorflow as tf - -from matchzoo import preprocessors -from matchzoo.contrib.layers import DecayingDropoutLayer -from matchzoo.contrib.layers import EncodingLayer -from matchzoo.engine import hyper_spaces -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable - - -class DIIN(BaseModel): - """ - DIIN model. - - Examples: - >>> model = DIIN() - >>> model.guess_and_fill_missing_params() - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['embedding_trainable'] = True - >>> model.params['optimizer'] = 'adam' - >>> model.params['dropout_initial_keep_rate'] = 1.0 - >>> model.params['dropout_decay_interval'] = 10000 - >>> model.params['dropout_decay_rate'] = 0.977 - >>> model.params['char_embedding_input_dim'] = 100 - >>> model.params['char_embedding_output_dim'] = 8 - >>> model.params['char_conv_filters'] = 100 - >>> model.params['char_conv_kernel_size'] = 5 - >>> model.params['first_scale_down_ratio'] = 0.3 - >>> model.params['nb_dense_blocks'] = 3 - >>> model.params['layers_per_dense_block'] = 8 - >>> model.params['growth_rate'] = 20 - >>> model.params['transition_scale_down_ratio'] = 0.5 - >>> model.build() - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params(with_embedding=True) - params['optimizer'] = 'adam' - params.add(Param(name='dropout_decay_interval', value=10000, - desc="The decay interval of decaying_dropout.")) - params.add(Param(name='char_embedding_input_dim', value=100, - desc="The input dimension of character embedding " - "layer.")) - params.add(Param(name='char_embedding_output_dim', value=2, - desc="The output dimension of character embedding " - "layer.")) - params.add(Param(name='char_conv_filters', value=8, - desc="The filter size of character convolution " - "layer.")) - params.add(Param(name='char_conv_kernel_size', value=2, - desc="The kernel size of character convolution " - "layer.")) - params.add(Param(name='first_scale_down_ratio', value=0.3, - desc="The channel scale down ratio of the " - "convolution layer before densenet.")) - params.add(Param(name='nb_dense_blocks', value=1, - desc="The number of blocks in densenet.")) - params.add(Param(name='layers_per_dense_block', value=2, - desc="The number of convolution layers in dense " - "block.")) - params.add(Param(name='growth_rate', value=2, - desc="The filter size of each convolution layer in " - "dense block.")) - params.add(Param(name='transition_scale_down_ratio', value=0.5, - desc="The channel scale down ratio of the " - "convolution layer in transition block.")) - params.add(Param( - name='dropout_initial_keep_rate', value=1.0, - hyper_space=hyper_spaces.quniform( - low=0.8, high=1.0, q=0.02), - desc="The initial keep rate of decaying_dropout." - )) - params.add(Param( - name='dropout_decay_rate', value=0.97, - hyper_space=hyper_spaces.quniform( - low=0.90, high=0.99, q=0.01), - desc="The decay rate of decaying_dropout." - )) - return params - - @classmethod - def get_default_preprocessor(cls): - """:return: Default preprocessor.""" - return preprocessors.DIINPreprocessor() - - def guess_and_fill_missing_params(self, verbose: int = 1): - """ - Guess and fill missing parameters in :attr:'params'. - - Use this method to automatically fill-in hyper parameters. - This involves some guessing so the parameter it fills could be - wrong. For example, the default task is 'Ranking', and if we do not - set it to 'Classification' manually for data packs prepared for - classification, then the shape of the model output and the data will - mismatch. - - :param verbose: Verbosity. - """ - self._params.get('input_shapes').set_default([(32,), - (32,), - (32, 16), - (32, 16), - (32,), - (32,)], verbose) - super().guess_and_fill_missing_params(verbose) - - def _make_inputs(self) -> list: - text_left = keras.layers.Input( - name='text_left', - shape=self._params['input_shapes'][0] - ) - text_right = keras.layers.Input( - name='text_right', - shape=self._params['input_shapes'][1] - ) - char_left = keras.layers.Input( - name='char_left', - shape=self._params['input_shapes'][2] - ) - char_right = keras.layers.Input( - name='char_right', - shape=self._params['input_shapes'][3] - ) - match_left = keras.layers.Input( - name='match_left', - shape=self._params['input_shapes'][4] - ) - match_right = keras.layers.Input( - name='match_right', - shape=self._params['input_shapes'][5] - ) - return [text_left, text_right, - char_left, char_right, - match_left, match_right] - - def build(self): - """Build model structure.""" - - # Scalar dimensions referenced here: - # B = batch size (number of sequences) - # D = word embedding size - # L = 'input_left' sequence length - # R = 'input_right' sequence length - # C = fixed word length - - inputs = self._make_inputs() - # Left text and right text. - # shape = [B, L] - # shape = [B, R] - text_left, text_right = inputs[0:2] - # Left character and right character. - # shape = [B, L, C] - # shape = [B, R, C] - char_left, char_right = inputs[2:4] - # Left exact match and right exact match. - # shape = [B, L] - # shape = [B, R] - match_left, match_right = inputs[4:6] - - # Embedding module - left_embeddings = [] - right_embeddings = [] - - # Word embedding feature - word_embedding = self._make_embedding_layer() - # shape = [B, L, D] - left_word_embedding = word_embedding(text_left) - # shape = [B, R, D] - right_word_embedding = word_embedding(text_right) - left_word_embedding = DecayingDropoutLayer( - initial_keep_rate=self._params['dropout_initial_keep_rate'], - decay_interval=self._params['dropout_decay_interval'], - decay_rate=self._params['dropout_decay_rate'] - )(left_word_embedding) - right_word_embedding = DecayingDropoutLayer( - initial_keep_rate=self._params['dropout_initial_keep_rate'], - decay_interval=self._params['dropout_decay_interval'], - decay_rate=self._params['dropout_decay_rate'] - )(right_word_embedding) - left_embeddings.append(left_word_embedding) - right_embeddings.append(right_word_embedding) - - # Exact match feature - # shape = [B, L, 1] - left_exact_match = keras.layers.Reshape( - target_shape=(K.int_shape(match_left)[1], 1,) - )(match_left) - # shape = [B, R, 1] - right_exact_match = keras.layers.Reshape( - target_shape=(K.int_shape(match_left)[1], 1,) - )(match_right) - left_embeddings.append(left_exact_match) - right_embeddings.append(right_exact_match) - - # Char embedding feature - char_embedding = self._make_char_embedding_layer() - char_embedding.build( - input_shape=(None, None, K.int_shape(char_left)[-1])) - left_char_embedding = char_embedding(char_left) - right_char_embedding = char_embedding(char_right) - left_embeddings.append(left_char_embedding) - right_embeddings.append(right_char_embedding) - - # Concatenate - left_embedding = keras.layers.Concatenate()(left_embeddings) - right_embedding = keras.layers.Concatenate()(right_embeddings) - d = K.int_shape(left_embedding)[-1] - - # Encoding module - left_encoding = EncodingLayer( - initial_keep_rate=self._params['dropout_initial_keep_rate'], - decay_interval=self._params['dropout_decay_interval'], - decay_rate=self._params['dropout_decay_rate'] - )(left_embedding) - right_encoding = EncodingLayer( - initial_keep_rate=self._params['dropout_initial_keep_rate'], - decay_interval=self._params['dropout_decay_interval'], - decay_rate=self._params['dropout_decay_rate'] - )(right_embedding) - - # Interaction module - interaction = keras.layers.Lambda(self._make_interaction)( - [left_encoding, right_encoding]) - - # Feature extraction module - feature_extractor_input = keras.layers.Conv2D( - filters=int(d * self._params['first_scale_down_ratio']), - kernel_size=(1, 1), - activation=None)(interaction) - feature_extractor = self._create_densenet() - features = feature_extractor(feature_extractor_input) - - # Output module - features = DecayingDropoutLayer( - initial_keep_rate=self._params['dropout_initial_keep_rate'], - decay_interval=self._params['dropout_decay_interval'], - decay_rate=self._params['dropout_decay_rate'])(features) - out = self._make_output_layer()(features) - - self._backend = keras.Model(inputs=inputs, outputs=out) - - def _make_char_embedding_layer(self) -> keras.layers.Layer: - """ - Apply embedding, conv and maxpooling operation over time dimension - for each token to obtain a vector. - - :return: Wrapper Keras 'Layer' as character embedding feature - extractor. - """ - - return keras.layers.TimeDistributed(keras.Sequential([ - keras.layers.Embedding( - input_dim=self._params['char_embedding_input_dim'], - output_dim=self._params['char_embedding_output_dim'], - input_length=self._params['input_shapes'][2][-1]), - keras.layers.Conv1D( - filters=self._params['char_conv_filters'], - kernel_size=self._params['char_conv_kernel_size']), - keras.layers.GlobalMaxPooling1D()])) - - def _make_interaction(self, inputs_) -> typing.Any: - left_encoding = inputs_[0] - right_encoding = inputs_[1] - - left_encoding = tf.expand_dims(left_encoding, axis=2) - right_encoding = tf.expand_dims(right_encoding, axis=1) - - interaction = left_encoding * right_encoding - return interaction - - def _create_densenet(self) -> typing.Callable: - """ - DenseNet is consisted of 'nb_dense_blocks' sets of Dense block - and Transition block pair. - - :return: Wrapper Keras 'Layer' as DenseNet, tensor in tensor out. - """ - def _wrapper(x): - for _ in range(self._params['nb_dense_blocks']): - # Dense block - # Apply 'layers_per_dense_block' convolution layers. - for _ in range(self._params['layers_per_dense_block']): - out_conv = keras.layers.Conv2D( - filters=self._params['growth_rate'], - kernel_size=(3, 3), - padding='same', - activation='relu')(x) - x = keras.layers.Concatenate(axis=-1)([x, out_conv]) - - # Transition block - # Apply a convolution layer and a maxpooling layer. - scale_down_ratio = self._params['transition_scale_down_ratio'] - nb_filter = int(K.int_shape(x)[-1] * scale_down_ratio) - x = keras.layers.Conv2D( - filters=nb_filter, - kernel_size=(1, 1), - padding='same', - activation=None)(x) - x = keras.layers.MaxPool2D(strides=(2, 2))(x) - - out_densenet = keras.layers.Flatten()(x) - return out_densenet - - return _wrapper diff --git a/matchzoo/contrib/models/esim.py b/matchzoo/contrib/models/esim.py deleted file mode 100644 index 539903ba..00000000 --- a/matchzoo/contrib/models/esim.py +++ /dev/null @@ -1,212 +0,0 @@ -"""ESIM model.""" - -import keras -import keras.backend as K -import tensorflow as tf - -import matchzoo as mz -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable - - -class ESIM(BaseModel): - """ - ESIM model. - - Examples: - >>> model = ESIM() - >>> task = classification_task = mz.tasks.Classification(num_classes=2) - >>> model.params['task'] = task - >>> model.params['input_shapes'] = [(20, ), (40, )] - >>> model.params['lstm_dim'] = 300 - >>> model.params['mlp_num_units'] = 300 - >>> model.params['embedding_input_dim'] = 5000 - >>> model.params['embedding_output_dim'] = 10 - >>> model.params['embedding_trainable'] = False - >>> model.params['mlp_num_layers'] = 0 - >>> model.params['mlp_num_fan_out'] = 300 - >>> model.params['mlp_activation_func'] = 'tanh' - >>> model.params['mask_value'] = 0 - >>> model.params['dropout_rate'] = 0.5 - >>> model.params['optimizer'] = keras.optimizers.Adam(lr=4e-4) - >>> model.guess_and_fill_missing_params() - >>> model.build() - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """Get default parameters.""" - params = super().get_default_params(with_embedding=True, - with_multi_layer_perceptron=True) - - params.add(Param( - name='dropout_rate', - value=0.5, - desc="The dropout rate for all fully-connected layer" - )) - - params.add(Param( - name='lstm_dim', - value=8, - desc="The dimension of LSTM layer." - )) - - params.add(Param( - name='mask_value', - value=0, - desc="The value would be regarded as pad" - )) - - return params - - def _expand_dim(self, inp: tf.Tensor, axis: int) -> keras.layers.Layer: - """ - Wrap keras.backend.expand_dims into a Lambda layer. - - :param inp: input tensor to expand the dimension - :param axis: the axis of new dimension - """ - return keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=axis))(inp) - - def _make_atten_mask_layer(self) -> keras.layers.Layer: - """ - Make mask layer for attention weight matrix so that - each word won't pay attention to timestep. - """ - return keras.layers.Lambda( - lambda weight_mask: weight_mask[0] + (1.0 - weight_mask[1]) * -1e7, - name="atten_mask") - - def _make_bilstm_layer(self, lstm_dim: int) -> keras.layers.Layer: - """ - Bidirectional LSTM layer in ESIM. - - :param lstm_dim: int, dimension of LSTM layer - :return: `keras.layers.Layer`. - """ - return keras.layers.Bidirectional( - layer=keras.layers.LSTM(lstm_dim, return_sequences=True), - merge_mode='concat') - - def _max(self, texts: tf.Tensor, mask: tf.Tensor) -> tf.Tensor: - """ - Compute the max of each text according to their real length - - :param texts: np.array with shape [B, T, H] - :param lengths: np.array with shape [B, T, ], - where 1 means valid, 0 means pad - """ - mask = self._expand_dim(mask, axis=2) - new_texts = keras.layers.Multiply()([texts, mask]) - - text_max = keras.layers.Lambda( - lambda x: tf.reduce_max(x, axis=1), - )(new_texts) - - return text_max - - def _avg(self, texts: tf.Tensor, mask: tf.Tensor) -> tf.Tensor: - """ - Compute the mean of each text according to their real length - - :param texts: np.array with shape [B, T, H] - :param lengths: np.array with shape [B, T, ], - where 1 means valid, 0 means pad - """ - mask = self._expand_dim(mask, axis=2) - new_texts = keras.layers.Multiply()([texts, mask]) - - # timestep-wise division, exclude the PAD number when calc avg - text_avg = keras.layers.Lambda( - lambda text_mask: - tf.reduce_sum(text_mask[0], axis=1) / tf.reduce_sum(text_mask[1], axis=1), - )([new_texts, mask]) - - return text_avg - - def build(self): - """Build model.""" - # parameters - lstm_dim = self._params['lstm_dim'] - dropout_rate = self._params['dropout_rate'] - - # layers - create_mask = keras.layers.Lambda( - lambda x: - tf.cast(tf.not_equal(x, self._params['mask_value']), K.floatx()) - ) - embedding = self._make_embedding_layer() - lstm_compare = self._make_bilstm_layer(lstm_dim) - lstm_compose = self._make_bilstm_layer(lstm_dim) - dense_compare = keras.layers.Dense(units=lstm_dim, - activation='relu', - use_bias=True) - dropout = keras.layers.Dropout(dropout_rate) - - # model - a, b = self._make_inputs() # [B, T_a], [B, T_b] - a_mask = create_mask(a) # [B, T_a] - b_mask = create_mask(b) # [B, T_b] - - # encoding - a_emb = dropout(embedding(a)) # [B, T_a, E_dim] - b_emb = dropout(embedding(b)) # [B, T_b, E_dim] - - a_ = lstm_compare(a_emb) # [B, T_a, H*2] - b_ = lstm_compare(b_emb) # [B, T_b, H*2] - - # mask a_ and b_, since the position is no more zero - a_ = keras.layers.Multiply()([a_, self._expand_dim(a_mask, axis=2)]) - b_ = keras.layers.Multiply()([b_, self._expand_dim(b_mask, axis=2)]) - - # local inference - e = keras.layers.Dot(axes=-1)([a_, b_]) # [B, T_a, T_b] - _ab_mask = keras.layers.Multiply()( # _ab_mask: [B, T_a, T_b] - [self._expand_dim(a_mask, axis=2), # [B, T_a, 1] - self._expand_dim(b_mask, axis=1)]) # [B, 1, T_b] - - pm = keras.layers.Permute((2, 1)) - mask_layer = self._make_atten_mask_layer() - softmax_layer = keras.layers.Softmax(axis=-1) - - e_a = softmax_layer(mask_layer([e, _ab_mask])) # [B, T_a, T_b] - e_b = softmax_layer(mask_layer([pm(e), pm(_ab_mask)])) # [B, T_b, T_a] - - # alignment (a_t = a~, b_t = b~ ) - a_t = keras.layers.Dot(axes=(2, 1))([e_a, b_]) # [B, T_a, H*2] - b_t = keras.layers.Dot(axes=(2, 1))([e_b, a_]) # [B, T_b, H*2] - - # local inference info enhancement - m_a = keras.layers.Concatenate(axis=-1)([ - a_, - a_t, - keras.layers.Subtract()([a_, a_t]), - keras.layers.Multiply()([a_, a_t])]) # [B, T_a, H*2*4] - m_b = keras.layers.Concatenate(axis=-1)([ - b_, - b_t, - keras.layers.Subtract()([b_, b_t]), - keras.layers.Multiply()([b_, b_t])]) # [B, T_b, H*2*4] - - # project m_a and m_b from 4*H*2 dim to H dim - m_a = dropout(dense_compare(m_a)) # [B, T_a, H] - m_b = dropout(dense_compare(m_b)) # [B, T_a, H] - - # inference composition - v_a = lstm_compose(m_a) # [B, T_a, H*2] - v_b = lstm_compose(m_b) # [B, T_b, H*2] - - # pooling - v_a = keras.layers.Concatenate(axis=-1)( - [self._avg(v_a, a_mask), self._max(v_a, a_mask)]) # [B, H*4] - v_b = keras.layers.Concatenate(axis=-1)( - [self._avg(v_b, b_mask), self._max(v_b, b_mask)]) # [B, H*4] - v = keras.layers.Concatenate(axis=-1)([v_a, v_b]) # [B, H*8] - - # mlp (multilayer perceptron) classifier - output = self._make_multi_layer_perceptron_layer()(v) # [B, H] - output = dropout(output) - output = self._make_output_layer()(output) # [B, #classes] - - self._backend = keras.Model(inputs=[a, b], outputs=output) diff --git a/matchzoo/contrib/models/hbmp.py b/matchzoo/contrib/models/hbmp.py deleted file mode 100644 index bc16605d..00000000 --- a/matchzoo/contrib/models/hbmp.py +++ /dev/null @@ -1,154 +0,0 @@ -"""HBMP model.""" -import keras -import typing - -from matchzoo.engine import hyper_spaces -from matchzoo.engine.param_table import ParamTable -from matchzoo.engine.param import Param -from matchzoo.engine.base_model import BaseModel - - -class HBMP(BaseModel): - """ - HBMP model. - - Examples: - >>> model = HBMP() - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.params['embedding_input_dim'] = 200 - >>> model.params['embedding_output_dim'] = 100 - >>> model.params['embedding_trainable'] = True - >>> model.params['alpha'] = 0.1 - >>> model.params['mlp_num_layers'] = 3 - >>> model.params['mlp_num_units'] = [10, 10] - >>> model.params['lstm_num_units'] = 5 - >>> model.params['dropout_rate'] = 0.1 - >>> model.build() - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params(with_embedding=True) - params['optimizer'] = 'adam' - params.add(Param(name='alpha', value=0.1, - desc="Negative slope coefficient of LeakyReLU " - "function.")) - params.add(Param(name='mlp_num_layers', value=3, - desc="The number of layers of mlp.")) - params.add(Param(name='mlp_num_units', value=[10, 10], - desc="The hidden size of the FC layers, but not " - "include the final layer.")) - params.add(Param(name='lstm_num_units', value=5, - desc="The hidden size of the LSTM layer.")) - params.add(Param( - name='dropout_rate', value=0.1, - hyper_space=hyper_spaces.quniform( - low=0.0, high=0.8, q=0.01), - desc="The dropout rate." - )) - return params - - def build(self): - """Build model structure.""" - input_left, input_right = self._make_inputs() - - embedding = self._make_embedding_layer() - embed_left = embedding(input_left) - embed_right = embedding(input_right) - - # Get sentence embedding - embed_sen_left = self._sentence_encoder( - embed_left, - lstm_num_units=self._params['lstm_num_units'], - drop_rate=self._params['dropout_rate']) - embed_sen_right = self._sentence_encoder( - embed_right, - lstm_num_units=self._params['lstm_num_units'], - drop_rate=self._params['dropout_rate']) - - # Concatenate two sentence embedding: [embed_sen_left, embed_sen_right, - # |embed_sen_left-embed_sen_right|, embed_sen_left*embed_sen_right] - embed_minus = keras.layers.Subtract()( - [embed_sen_left, embed_sen_right]) - embed_minus_abs = keras.layers.Lambda(lambda x: abs(x))(embed_minus) - embed_multiply = keras.layers.Multiply()( - [embed_sen_left, embed_sen_right]) - concat = keras.layers.Concatenate(axis=1)( - [embed_sen_left, embed_sen_right, embed_minus_abs, embed_multiply]) - - # Multiply perception layers to classify - mlp_out = self._classifier( - concat, - mlp_num_layers=self._params['mlp_num_layers'], - mlp_num_units=self._params['mlp_num_units'], - drop_rate=self._params['dropout_rate'], - leaky_relu_alpah=self._params['alpha']) - out = self._make_output_layer()(mlp_out) - - self._backend = keras.Model( - inputs=[input_left, input_right], outputs=out) - - def _classifier( - self, - input_: typing.Any, - mlp_num_layers: int, - mlp_num_units: list, - drop_rate: float, - leaky_relu_alpah: float - ) -> typing.Any: - for i in range(mlp_num_layers - 1): - input_ = keras.layers.Dropout(rate=drop_rate)(input_) - input_ = keras.layers.Dense(mlp_num_units[i])(input_) - input_ = keras.layers.LeakyReLU(alpha=leaky_relu_alpah)(input_) - - return input_ - - def _sentence_encoder( - self, - input_: typing.Any, - lstm_num_units: int, - drop_rate: float - ) -> typing.Any: - """ - Stack three BiLSTM MaxPooling blocks as a hierarchical structure. - Concatenate the output of three blocs as the input sentence embedding. - Each BiLSTM layer reads the input sentence as the input. - Each BiLSTM layer except the first one is initialized(the initial - hidden state and the cell state) with the final state of the previous - layer. - """ - emb1 = keras.layers.Bidirectional( - keras.layers.LSTM( - units=lstm_num_units, - return_sequences=True, - return_state=True, - dropout=drop_rate, - recurrent_dropout=drop_rate), - merge_mode='concat')(input_) - emb1_maxpooling = keras.layers.GlobalMaxPooling1D()(emb1[0]) - - emb2 = keras.layers.Bidirectional( - keras.layers.LSTM( - units=lstm_num_units, - return_sequences=True, - return_state=True, - dropout=drop_rate, - recurrent_dropout=drop_rate), - merge_mode='concat')(input_, initial_state=emb1[1:5]) - emb2_maxpooling = keras.layers.GlobalMaxPooling1D()(emb2[0]) - - emb3 = keras.layers.Bidirectional( - keras.layers.LSTM( - units=lstm_num_units, - return_sequences=True, - return_state=True, - dropout=drop_rate, - recurrent_dropout=drop_rate), - merge_mode='concat')(input_, initial_state=emb2[1:5]) - emb3_maxpooling = keras.layers.GlobalMaxPooling1D()(emb3[0]) - - emb = keras.layers.Concatenate(axis=1)( - [emb1_maxpooling, emb2_maxpooling, emb3_maxpooling]) - - return emb diff --git a/matchzoo/contrib/models/match_lstm.py b/matchzoo/contrib/models/match_lstm.py deleted file mode 100644 index f8c073d3..00000000 --- a/matchzoo/contrib/models/match_lstm.py +++ /dev/null @@ -1,103 +0,0 @@ -"""Match LSTM model.""" -import keras -import keras.backend as K -import tensorflow as tf - -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine import hyper_spaces - - -class MatchLSTM(BaseModel): - """ - Match LSTM model. - - Examples: - >>> model = MatchLSTM() - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 100 - >>> model.params['embedding_trainable'] = True - >>> model.params['fc_num_units'] = 200 - >>> model.params['lstm_num_units'] = 256 - >>> model.params['dropout_rate'] = 0.5 - >>> model.build() - - """ - - @classmethod - def get_default_params(cls): - """Get default parameters.""" - params = super().get_default_params(with_embedding=True) - params.add(Param( - 'lstm_num_units', 256, - hyper_space=hyper_spaces.quniform(low=128, high=384, q=32), - desc="The hidden size in the LSTM layer." - )) - params.add(Param( - 'fc_num_units', 200, - hyper_space=hyper_spaces.quniform( - low=100, high=300, q=20), - desc="The hidden size in the full connection layer." - )) - params.add(Param( - 'dropout_rate', 0.0, - hyper_space=hyper_spaces.quniform( - low=0.0, high=0.9, q=0.01), - desc="The dropout rate." - )) - return params - - def build(self): - """Build model.""" - input_left, input_right = self._make_inputs() - len_left = input_left.shape[1] - len_right = input_right.shape[1] - embedding = self._make_embedding_layer() - embed_left = embedding(input_left) - embed_right = embedding(input_right) - - lstm_left = keras.layers.LSTM(self._params['lstm_num_units'], - return_sequences=True, - name='lstm_left') - lstm_right = keras.layers.LSTM(self._params['lstm_num_units'], - return_sequences=True, - name='lstm_right') - encoded_left = lstm_left(embed_left) - encoded_right = lstm_right(embed_right) - - def attention(tensors): - """Attention layer.""" - left, right = tensors - tensor_left = tf.expand_dims(left, axis=2) - tensor_right = tf.expand_dims(right, axis=1) - tensor_left = K.repeat_elements(tensor_left, len_right, 2) - tensor_right = K.repeat_elements(tensor_right, len_left, 1) - tensor_merged = tf.concat([tensor_left, tensor_right], axis=-1) - middle_output = keras.layers.Dense(self._params['fc_num_units'], - activation='tanh')( - tensor_merged) - attn_scores = keras.layers.Dense(1)(middle_output) - attn_scores = tf.squeeze(attn_scores, axis=3) - exp_attn_scores = tf.math.exp( - attn_scores - tf.reduce_max(attn_scores, axis=-1, keepdims=True)) - exp_sum = tf.reduce_sum(exp_attn_scores, axis=-1, keepdims=True) - attention_weights = exp_attn_scores / exp_sum - return K.batch_dot(attention_weights, right) - - attn_layer = keras.layers.Lambda(attention) - left_attn_vec = attn_layer([encoded_left, encoded_right]) - concat = keras.layers.Concatenate(axis=1)( - [left_attn_vec, encoded_right]) - lstm_merge = keras.layers.LSTM(self._params['lstm_num_units'] * 2, - return_sequences=False, - name='lstm_merge') - merged = lstm_merge(concat) - dropout = keras.layers.Dropout( - rate=self._params['dropout_rate'])(merged) - - phi = keras.layers.Dense(self._params['fc_num_units'], - activation='tanh')(dropout) - inputs = [input_left, input_right] - out = self._make_output_layer()(phi) - self._backend = keras.Model(inputs=inputs, outputs=[out]) diff --git a/matchzoo/contrib/models/match_srnn.py b/matchzoo/contrib/models/match_srnn.py deleted file mode 100644 index 66ae800a..00000000 --- a/matchzoo/contrib/models/match_srnn.py +++ /dev/null @@ -1,93 +0,0 @@ -"""An implementation of Match-SRNN Model.""" - -import keras - -from matchzoo.contrib.layers import MatchingTensorLayer -from matchzoo.contrib.layers import SpatialGRU -from matchzoo.engine import hyper_spaces -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable - - -class MatchSRNN(BaseModel): - """ - Match-SRNN Model. - - Examples: - >>> model = MatchSRNN() - >>> model.params['channels'] = 4 - >>> model.params['units'] = 10 - >>> model.params['dropout_rate'] = 0.0 - >>> model.params['direction'] = 'lt' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params(with_embedding=True) - params.add(Param(name='channels', value=4, - desc="Number of word interaction tensor channels")) - params.add(Param(name='units', value=10, - desc="Number of SpatialGRU units")) - params.add(Param(name='direction', value='lt', - desc="Direction of SpatialGRU scanning")) - params.add(Param( - name='dropout_rate', value=0.0, - hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, - q=0.01), - desc="The dropout rate." - )) - return params - - def build(self): - """ - Build model structure. - - Match-SRNN: Modeling the Recursive Matching Structure - with Spatial RNN - """ - - # Scalar dimensions referenced here: - # B = batch size (number of sequences) - # D = embedding size - # L = `input_left` sequence length - # R = `input_right` sequence length - # C = number of channels - - # Left input and right input. - # query = [B, L] - # doc = [B, R] - query, doc = self._make_inputs() - - # Process left and right input. - # embed_query = [B, L, D] - # embed_doc = [B, R, D] - embedding = self._make_embedding_layer() - embed_query = embedding(query) - embed_doc = embedding(doc) - - # Get matching tensor - # matching_tensor = [B, C, L, R] - matching_tensor_layer = MatchingTensorLayer( - channels=self._params['channels']) - matching_tensor = matching_tensor_layer([embed_query, embed_doc]) - - # Apply spatial GRU to the word level interaction tensor - # h_ij = [B, U] - spatial_gru = SpatialGRU( - units=self._params['units'], - direction=self._params['direction']) - h_ij = spatial_gru(matching_tensor) - - # Apply Dropout - x = keras.layers.Dropout( - rate=self._params['dropout_rate'])(h_ij) - - # Make output layer - x_out = self._make_output_layer()(x) - - self._backend = keras.Model(inputs=[query, doc], outputs=x_out) diff --git a/matchzoo/data_generator/__init__.py b/matchzoo/data_generator/__init__.py deleted file mode 100644 index 3feb6a0e..00000000 --- a/matchzoo/data_generator/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from . import callbacks -from .data_generator import DataGenerator -from .data_generator_builder import DataGeneratorBuilder diff --git a/matchzoo/data_generator/callbacks/__init__.py b/matchzoo/data_generator/callbacks/__init__.py deleted file mode 100644 index 93e60667..00000000 --- a/matchzoo/data_generator/callbacks/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .callback import Callback -from .lambda_callback import LambdaCallback -from .dynamic_pooling import DynamicPooling -from .histogram import Histogram diff --git a/matchzoo/data_generator/callbacks/callback.py b/matchzoo/data_generator/callbacks/callback.py deleted file mode 100644 index af02369d..00000000 --- a/matchzoo/data_generator/callbacks/callback.py +++ /dev/null @@ -1,36 +0,0 @@ -import numpy as np - -import matchzoo as mz - - -class Callback(object): - """ - DataGenerator callback base class. - - To build your own callbacks, inherit `mz.data_generator.callbacks.Callback` - and overrides corresponding methods. - - A batch is processed in the following way: - - - slice data pack based on batch index - - handle `on_batch_data_pack` callbacks - - unpack data pack into x, y - - handle `on_batch_x_y` callbacks - - return x, y - - """ - - def on_batch_data_pack(self, data_pack: mz.DataPack): - """ - `on_batch_data_pack`. - - :param data_pack: a sliced DataPack before unpacking. - """ - - def on_batch_unpacked(self, x: dict, y: np.ndarray): - """ - `on_batch_unpacked`. - - :param x: unpacked x. - :param y: unpacked y. - """ diff --git a/matchzoo/data_generator/callbacks/dynamic_pooling.py b/matchzoo/data_generator/callbacks/dynamic_pooling.py deleted file mode 100644 index 4a1a1f45..00000000 --- a/matchzoo/data_generator/callbacks/dynamic_pooling.py +++ /dev/null @@ -1,92 +0,0 @@ -import numpy as np - -from matchzoo.data_generator.callbacks import Callback - - -class DynamicPooling(Callback): - """:class:`DPoolPairDataGenerator` constructor. - - :param fixed_length_left: max length of left text. - :param fixed_length_right: max length of right text. - :param compress_ratio_left: the length change ratio, - especially after normal pooling layers. - :param compress_ratio_right: the length change ratio, - especially after normal pooling layers. - """ - - def __init__( - self, - fixed_length_left: int, - fixed_length_right: int, - compress_ratio_left: float = 1, - compress_ratio_right: float = 1, - ): - """Init.""" - self._fixed_length_left = fixed_length_left - self._fixed_length_right = fixed_length_right - self._compress_ratio_left = compress_ratio_left - self._compress_ratio_right = compress_ratio_right - - def on_batch_unpacked(self, x, y): - """ - Insert `dpool_index` into `x`. - - :param x: unpacked x. - :param y: unpacked y. - """ - x['dpool_index'] = _dynamic_pooling_index( - x['length_left'], - x['length_right'], - self._fixed_length_left, - self._fixed_length_right, - self._compress_ratio_left, - self._compress_ratio_right - ) - - -def _dynamic_pooling_index(length_left: np.array, - length_right: np.array, - fixed_length_left: int, - fixed_length_right: int, - compress_ratio_left: float, - compress_ratio_right: float) -> np.array: - def _dpool_index(one_length_left: int, - one_length_right: int, - fixed_length_left: int, - fixed_length_right: int): - if one_length_left == 0: - stride_left = fixed_length_left - else: - stride_left = 1.0 * fixed_length_left / one_length_left - - if one_length_right == 0: - stride_right = fixed_length_right - else: - stride_right = 1.0 * fixed_length_right / one_length_right - - one_idx_left = [int(i / stride_left) - for i in range(fixed_length_left)] - one_idx_right = [int(i / stride_right) - for i in range(fixed_length_right)] - mesh1, mesh2 = np.meshgrid(one_idx_left, one_idx_right) - index_one = np.transpose( - np.stack([mesh1, mesh2]), (2, 1, 0)) - return index_one - - index = [] - dpool_bias_left = dpool_bias_right = 0 - if fixed_length_left % compress_ratio_left != 0: - dpool_bias_left = 1 - if fixed_length_right % compress_ratio_right != 0: - dpool_bias_right = 1 - cur_fixed_length_left = int( - fixed_length_left // compress_ratio_left) + dpool_bias_left - cur_fixed_length_right = int( - fixed_length_right // compress_ratio_right) + dpool_bias_right - for i in range(len(length_left)): - index.append(_dpool_index( - length_left[i] // compress_ratio_left, - length_right[i] // compress_ratio_right, - cur_fixed_length_left, - cur_fixed_length_right)) - return np.array(index) diff --git a/matchzoo/data_generator/callbacks/histogram.py b/matchzoo/data_generator/callbacks/histogram.py deleted file mode 100644 index c5d56d39..00000000 --- a/matchzoo/data_generator/callbacks/histogram.py +++ /dev/null @@ -1,65 +0,0 @@ -import numpy as np - -import matchzoo as mz -from matchzoo.data_generator.callbacks import Callback - - -class Histogram(Callback): - """ - Generate data with matching histogram. - - :param embedding_matrix: The embedding matrix used to generator match - histogram. - :param bin_size: The number of bin size of the histogram. - :param hist_mode: The mode of the :class:`MatchingHistogramUnit`, one of - `CH`, `NH`, and `LCH`. - """ - - def __init__( - self, - embedding_matrix: np.ndarray, - bin_size: int = 30, - hist_mode: str = 'CH', - ): - """Init.""" - self._match_hist_unit = mz.preprocessors.units.MatchingHistogram( - bin_size=bin_size, - embedding_matrix=embedding_matrix, - normalize=True, - mode=hist_mode - ) - - def on_batch_unpacked(self, x, y): - """Insert `match_histogram` to `x`.""" - x['match_histogram'] = _build_match_histogram(x, self._match_hist_unit) - - -def _trunc_text(input_text: list, length: list) -> list: - """ - Truncating the input text according to the input length. - - :param input_text: The input text need to be truncated. - :param length: The length used to truncated the text. - :return: The truncated text. - """ - return [row[:length[idx]] for idx, row in enumerate(input_text)] - - -def _build_match_histogram( - x: dict, - match_hist_unit: mz.preprocessors.units.MatchingHistogram -) -> np.ndarray: - """ - Generate the matching hisogram for input. - - :param x: The input `dict`. - :param match_hist_unit: The histogram unit :class:`MatchingHistogramUnit`. - :return: The matching histogram. - """ - match_hist = [] - text_left = x['text_left'].tolist() - text_right = _trunc_text(x['text_right'].tolist(), - x['length_right'].tolist()) - for pair in zip(text_left, text_right): - match_hist.append(match_hist_unit.transform(list(pair))) - return np.asarray(match_hist) diff --git a/matchzoo/data_generator/callbacks/lambda_callback.py b/matchzoo/data_generator/callbacks/lambda_callback.py deleted file mode 100644 index 684171ba..00000000 --- a/matchzoo/data_generator/callbacks/lambda_callback.py +++ /dev/null @@ -1,40 +0,0 @@ -from matchzoo.data_generator.callbacks.callback import Callback - - -class LambdaCallback(Callback): - """ - LambdaCallback. Just a shorthand for creating a callback class. - - See :class:`matchzoo.data_generator.callbacks.Callback` for more details. - - Example: - - >>> import matchzoo as mz - >>> from matchzoo.data_generator.callbacks import LambdaCallback - >>> data = mz.datasets.toy.load_data() - >>> batch_func = lambda x: print(type(x)) - >>> unpack_func = lambda x, y: print(type(x), type(y)) - >>> callback = LambdaCallback(on_batch_data_pack=batch_func, - ... on_batch_unpacked=unpack_func) - >>> data_gen = mz.DataGenerator( - ... data, batch_size=len(data), callbacks=[callback]) - >>> _ = data_gen[0] - - - - """ - - def __init__(self, on_batch_data_pack=None, on_batch_unpacked=None): - """Init.""" - self._on_batch_unpacked = on_batch_unpacked - self._on_batch_data_pack = on_batch_data_pack - - def on_batch_data_pack(self, data_pack): - """`on_batch_data_pack`.""" - if self._on_batch_data_pack: - self._on_batch_data_pack(data_pack) - - def on_batch_unpacked(self, x, y): - """`on_batch_unpacked`.""" - if self._on_batch_unpacked: - self._on_batch_unpacked(x, y) diff --git a/matchzoo/data_generator/data_generator.py b/matchzoo/data_generator/data_generator.py deleted file mode 100644 index f088d164..00000000 --- a/matchzoo/data_generator/data_generator.py +++ /dev/null @@ -1,293 +0,0 @@ -"""Base generator.""" - -import math -import typing - -import keras -import numpy as np -import pandas as pd - -import matchzoo as mz -from matchzoo.data_generator.callbacks import Callback - - -class DataGenerator(keras.utils.Sequence): - """ - Data Generator. - - Used to divide a :class:`matchzoo.DataPack` into batches. This is helpful - for generating batch-wise features and delaying data preprocessing to the - `fit` time. - - See `tutorials/data_handling.ipynb` for a walkthrough. - - :param data_pack: DataPack to generator data from. - :param mode: One of "point", "pair", and "list". (default: "point") - :param num_dup: Number of duplications per instance, only effective when - `mode` is "pair". (default: 1) - :param num_neg: Number of negative samples per instance, only effective - when `mode` is "pair". (default: 1) - :param resample: Either to resample for each epoch, only effective when - `mode` is "pair". (default: `True`) - :param batch_size: Batch size. (default: 128) - :param shuffle: Either to shuffle the samples/instances. (default: `True`) - :param callbacks: Callbacks. See `matchzoo.data_generator.callbacks` for - more details. - - Examples:: - >>> import numpy as np - >>> import matchzoo as mz - >>> np.random.seed(0) - >>> data_pack = mz.datasets.toy.load_data() - >>> batch_size = 8 - - To generate data points: - >>> point_gen = mz.DataGenerator( - ... data_pack=data_pack, - ... batch_size=batch_size - ... ) - >>> len(point_gen) - 13 - >>> x, y = point_gen[0] - >>> for key, value in sorted(x.items()): - ... print(key, str(value)[:30]) - id_left ['Q6' 'Q17' 'Q1' 'Q13' 'Q16' ' - id_right ['D6-6' 'D17-1' 'D1-2' 'D13-3' - text_left ['how long is the term for fed - text_right ['See Article I and Article II - - To generate data pairs: - >>> pair_gen = mz.DataGenerator( - ... data_pack=data_pack, - ... mode='pair', - ... num_dup=4, - ... num_neg=4, - ... batch_size=batch_size, - ... shuffle=False - ... ) - >>> len(pair_gen) - 3 - >>> x, y = pair_gen[0] - >>> for key, value in sorted(x.items()): - ... print(key, str(value)[:30]) - id_left ['Q1' 'Q1' 'Q1' 'Q1' 'Q1' 'Q1' - id_right ['D1-3' 'D1-4' 'D1-0' 'D1-1' ' - text_left ['how are glacier caves formed - text_right ['A glacier cave is a cave for - - To generate data lists: - # TODO: - - """ - - def __init__( - self, - data_pack: mz.DataPack, - mode='point', - num_dup: int = 1, - num_neg: int = 1, - resample: bool = True, - batch_size: int = 128, - shuffle: bool = True, - callbacks: typing.List[Callback] = None - ): - """Init.""" - if callbacks is None: - callbacks = [] - - if mode not in ('point', 'pair', 'list'): - raise ValueError(f"{mode} is not a valid mode type." - f"Must be one of `point`, `pair` or `list`.") - - self._mode = mode - self._num_dup = num_dup - self._num_neg = num_neg - self._batch_size = batch_size - self._shuffle = shuffle - self._resample = resample - self._orig_relation = data_pack.relation - self._callbacks = callbacks - - if mode == 'pair': - data_pack.relation = self._reorganize_pair_wise( - data_pack.relation, - num_dup=num_dup, - num_neg=num_neg - ) - - self._data_pack = data_pack - self._batch_indices = None - - self.reset_index() - - def __getitem__(self, item: int) -> typing.Tuple[dict, np.ndarray]: - """Get a batch from index idx. - - :param item: the index of the batch. - """ - if isinstance(item, slice): - indices = sum(self._batch_indices[item], []) - else: - indices = self._batch_indices[item] - batch_data_pack = self._data_pack[indices] - self._handle_callbacks_on_batch_data_pack(batch_data_pack) - x, y = batch_data_pack.unpack() - self._handle_callbacks_on_batch_unpacked(x, y) - return x, y - - def __len__(self) -> int: - """Get the total number of batches.""" - return len(self._batch_indices) - - def on_epoch_end(self): - """Reorganize the index array while epoch is ended.""" - if self._mode == 'pair' and self._resample: - self._data_pack.relation = self._reorganize_pair_wise( - relation=self._orig_relation, - num_dup=self._num_dup, - num_neg=self._num_neg - ) - self.reset_index() - - def reset_index(self): - """ - Set the :attr:`index_array`. - - Here the :attr:`index_array` records the index of all the instances. - """ - # index pool: index -> instance index - if self._mode == 'point': - num_instances = len(self._data_pack) - index_pool = list(range(num_instances)) - elif self._mode == 'pair': - index_pool = [] - step_size = self._num_neg + 1 - num_instances = int(len(self._data_pack) / step_size) - for i in range(num_instances): - lower = i * step_size - upper = (i + 1) * step_size - indices = list(range(lower, upper)) - if indices: - index_pool.append(indices) - elif self._mode == 'list': - raise NotImplementedError( - f'{self._mode} data generator not implemented.') - else: - raise ValueError(f"{self._mode} is not a valid mode type" - f"Must be one of `point`, `pair` or `list`.") - - if self._shuffle: - np.random.shuffle(index_pool) - - # batch_indices: index -> batch of indices - self._batch_indices = [] - for i in range(math.ceil(num_instances / self._batch_size)): - lower = self._batch_size * i - upper = self._batch_size * (i + 1) - candidates = index_pool[lower:upper] - if self._mode == 'pair': - candidates = sum(candidates, []) - if candidates: - self._batch_indices.append(candidates) - - def _handle_callbacks_on_batch_data_pack(self, batch_data_pack): - for callback in self._callbacks: - callback.on_batch_data_pack(batch_data_pack) - - def _handle_callbacks_on_batch_unpacked(self, x, y): - for callback in self._callbacks: - callback.on_batch_unpacked(x, y) - - @property - def callbacks(self): - """`callbacks` getter.""" - return self._callbacks - - @callbacks.setter - def callbacks(self, value): - """`callbacks` setter.""" - self._callbacks = value - - @property - def num_neg(self): - """`num_neg` getter.""" - return self._num_neg - - @num_neg.setter - def num_neg(self, value): - """`num_neg` setter.""" - self._num_neg = value - self.reset_index() - - @property - def num_dup(self): - """`num_dup` getter.""" - return self._num_dup - - @num_dup.setter - def num_dup(self, value): - """`num_dup` setter.""" - self._num_dup = value - self.reset_index() - - @property - def mode(self): - """`mode` getter.""" - return self._mode - - @mode.setter - def mode(self, value): - """`mode` setter.""" - self._mode = value - self.reset_index() - - @property - def batch_size(self): - """`batch_size` getter.""" - return self._batch_size - - @batch_size.setter - def batch_size(self, value): - """`batch_size` setter.""" - self._batch_size = value - self.reset_index() - - @property - def shuffle(self): - """`shuffle` getter.""" - return self._shuffle - - @shuffle.setter - def shuffle(self, value): - """`shuffle` setter.""" - self._shuffle = value - self.reset_index() - - @property - def batch_indices(self): - """`batch_indices` getter.""" - return self._batch_indices - - @classmethod - def _reorganize_pair_wise( - cls, - relation: pd.DataFrame, - num_dup: int = 1, - num_neg: int = 1 - ): - """Re-organize the data pack as pair-wise format.""" - pairs = [] - groups = relation.sort_values( - 'label', ascending=False).groupby('id_left') - for idx, group in groups: - labels = group.label.unique() - for label in labels[:-1]: - pos_samples = group[group.label == label] - pos_samples = pd.concat([pos_samples] * num_dup) - neg_samples = group[group.label < label] - for _, pos_sample in pos_samples.iterrows(): - pos_sample = pd.DataFrame([pos_sample]) - neg_sample = neg_samples.sample(num_neg, replace=True) - pairs.extend((pos_sample, neg_sample)) - new_relation = pd.concat(pairs, ignore_index=True) - return new_relation diff --git a/matchzoo/data_generator/data_generator_builder.py b/matchzoo/data_generator/data_generator_builder.py deleted file mode 100644 index d13e3208..00000000 --- a/matchzoo/data_generator/data_generator_builder.py +++ /dev/null @@ -1,36 +0,0 @@ -import matchzoo as mz -from matchzoo.data_generator.data_generator import DataGenerator - - -class DataGeneratorBuilder(object): - """ - Data Generator Bulider. In essense a wrapped partial function. - - Example: - >>> import matchzoo as mz - >>> builder = mz.DataGeneratorBuilder(mode='pair', batch_size=32) - >>> data = mz.datasets.toy.load_data() - >>> gen = builder.build(data) - >>> type(gen) - - >>> gen.batch_size - 32 - >>> gen_64 = builder.build(data, batch_size=64) - >>> gen_64.batch_size - 64 - - """ - - def __init__(self, **kwargs): - """Init.""" - self._kwargs = kwargs - - def build(self, data_pack, **kwargs) -> DataGenerator: - """ - Build a DataGenerator. - - :param data_pack: DataPack to build upon. - :param kwargs: Additional keyword arguments to override the keyword - arguments passed in `__init__`. - """ - return mz.DataGenerator(data_pack, **{**self._kwargs, **kwargs}) diff --git a/matchzoo/data_pack/__init__.py b/matchzoo/data_pack/__init__.py deleted file mode 100644 index c685f73c..00000000 --- a/matchzoo/data_pack/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .data_pack import DataPack, load_data_pack -from .pack import pack diff --git a/matchzoo/data_pack/data_pack.py b/matchzoo/data_pack/data_pack.py deleted file mode 100644 index 5b3e862d..00000000 --- a/matchzoo/data_pack/data_pack.py +++ /dev/null @@ -1,517 +0,0 @@ -"""Matchzoo DataPack, pair-wise tuple (feature) and context as input.""" - -import typing -import inspect -from pathlib import Path -import functools - -import dill -from tqdm import tqdm -import numpy as np -import pandas as pd - -import matchzoo - -tqdm.pandas() - - -def _convert_to_list_index( - index: typing.Union[int, slice, np.array], - length: int -): - if isinstance(index, int): - index = [index] - elif isinstance(index, slice): - index = list(range(*index.indices(length))) - return index - - -class DataPack(object): - """ - Matchzoo :class:`DataPack` data structure, store dataframe and context. - - `DataPack` is a MatchZoo native data structure that most MatchZoo data - handling processes build upon. A `DataPack` consists of three parts: - `left`, `right` and `relation`, each one of is a `pandas.DataFrame`. - - :param relation: Store the relation between left document - and right document use ids. - :param left: Store the content or features for id_left. - :param right: Store the content or features for - id_right. - - Example: - >>> left = [ - ... ['qid1', 'query 1'], - ... ['qid2', 'query 2'] - ... ] - >>> right = [ - ... ['did1', 'document 1'], - ... ['did2', 'document 2'] - ... ] - >>> relation = [['qid1', 'did1', 1], ['qid2', 'did2', 1]] - >>> relation_df = pd.DataFrame(relation) - >>> left = pd.DataFrame(left) - >>> right = pd.DataFrame(right) - >>> dp = DataPack( - ... relation=relation_df, - ... left=left, - ... right=right, - ... ) - >>> len(dp) - 2 - """ - - DATA_FILENAME = 'data.dill' - - def __init__( - self, - relation: pd.DataFrame, - left: pd.DataFrame, - right: pd.DataFrame - ): - """:class:`DataPack` initializer.""" - self._relation = relation - self._left = left - self._right = right - - @property - def has_label(self) -> bool: - """:return: `True` if `label` column exists, `False` other wise.""" - return 'label' in self._relation.columns - - def __len__(self) -> int: - """Get numer of rows in the class:`DataPack` object.""" - return self._relation.shape[0] - - @property - def frame(self) -> 'DataPack.FrameView': - """ - View the data pack as a :class:`pandas.DataFrame`. - - Returned data frame is created by merging the left data frame, - the right dataframe and the relation data frame. Use `[]` to access - an item or a slice of items. - - :return: A :class:`matchzoo.DataPack.FrameView` instance. - - Example: - >>> import matchzoo as mz - >>> data_pack = mz.datasets.toy.load_data() - >>> type(data_pack.frame) - - >>> frame_slice = data_pack.frame[0:5] - >>> type(frame_slice) - - >>> list(frame_slice.columns) - ['id_left', 'text_left', 'id_right', 'text_right', 'label'] - >>> full_frame = data_pack.frame() - >>> len(full_frame) == len(data_pack) - True - - """ - return DataPack.FrameView(self) - - def unpack(self) -> typing.Tuple[typing.Dict[str, np.array], - typing.Optional[np.array]]: - """ - Unpack the data for training. - - The return value can be directly feed to `model.fit` or - `model.fit_generator`. - - :return: A tuple of (X, y). `y` is `None` if `self` has no label. - - Example: - >>> import matchzoo as mz - >>> data_pack = mz.datasets.toy.load_data() - >>> X, y = data_pack.unpack() - >>> type(X) - - >>> sorted(X.keys()) - ['id_left', 'id_right', 'text_left', 'text_right'] - >>> type(y) - - >>> X, y = data_pack.drop_label().unpack() - >>> type(y) - - - """ - frame = self.frame() - - columns = list(frame.columns) - if self.has_label: - columns.remove('label') - y = np.vstack(np.asarray(frame['label'])) - else: - y = None - - x = frame[columns].to_dict(orient='list') - for key, val in x.items(): - x[key] = np.array(val) - - return x, y - - def __getitem__(self, index: typing.Union[int, slice, np.array] - ) -> 'DataPack': - """ - Get specific item(s) as a new :class:`DataPack`. - - The returned :class:`DataPack` will be a copy of the subset of the - original :class:`DataPack`. - - :param index: Index of the item(s) to get. - :return: An instance of :class:`DataPack`. - """ - index = _convert_to_list_index(index, len(self)) - relation = self._relation.loc[index].reset_index(drop=True) - left = self._left.loc[relation['id_left'].unique()] - right = self._right.loc[relation['id_right'].unique()] - return DataPack(left=left.copy(), - right=right.copy(), - relation=relation.copy()) - - @property - def relation(self): - """`relation` getter.""" - return self._relation - - @relation.setter - def relation(self, value): - """`relation` setter.""" - self._relation = value - - @property - def left(self) -> pd.DataFrame: - """Get :meth:`left` of :class:`DataPack`.""" - return self._left - - @property - def right(self) -> pd.DataFrame: - """Get :meth:`right` of :class:`DataPack`.""" - return self._right - - def copy(self) -> 'DataPack': - """:return: A deep copy.""" - return DataPack(left=self._left.copy(), - right=self._right.copy(), - relation=self._relation.copy()) - - def save(self, dirpath: typing.Union[str, Path]): - """ - Save the :class:`DataPack` object. - - A saved :class:`DataPack` is represented as a directory with a - :class:`DataPack` object (transformed user input as features and - context), it will be saved by `pickle`. - - :param dirpath: directory path of the saved :class:`DataPack`. - """ - dirpath = Path(dirpath) - data_file_path = dirpath.joinpath(self.DATA_FILENAME) - - if data_file_path.exists(): - raise FileExistsError( - f'{data_file_path} already exist, fail to save') - elif not dirpath.exists(): - dirpath.mkdir() - - dill.dump(self, open(data_file_path, mode='wb')) - - def _optional_inplace(func): - """ - Decorator that adds `inplace` key word argument to a method. - - Decorate any method that modifies inplace to make that inplace change - optional. - """ - doc = ":param inplace: `True` to modify inplace, `False` to return " \ - "a modified copy. (default: `False`)" - - def _clean(s): - return s.replace(' ', '').replace('\n', '') - - if _clean(doc) not in _clean(inspect.getdoc(func)): - raise NotImplementedError( - f"`inplace` parameter of {func} not documented.\n" - f"Please add the following line to its documentation:\n{doc}") - - @functools.wraps(func) - def wrapper( - self, *args, inplace: bool = False, **kwargs - ) -> typing.Optional['DataPack']: - - if inplace: - target = self - else: - target = self.copy() - - func(target, *args, **kwargs) - - if not inplace: - return target - - return wrapper - - @_optional_inplace - def shuffle(self): - """ - Shuffle the data pack by shuffling the relation column. - - :param inplace: `True` to modify inplace, `False` to return a modified - copy. (default: `False`) - - Example: - >>> import matchzoo as mz - >>> import numpy.random - >>> numpy.random.seed(0) - >>> data_pack = mz.datasets.toy.load_data() - >>> orig_ids = data_pack.relation['id_left'] - >>> shuffled = data_pack.shuffle() - >>> (shuffled.relation['id_left'] != orig_ids).any() - True - - """ - self._relation = self._relation.sample(frac=1) - self._relation.reset_index(drop=True, inplace=True) - - @_optional_inplace - def drop_label(self): - """ - Remove `label` column from the data pack. - - :param inplace: `True` to modify inplace, `False` to return a modified - copy. (default: `False`) - - Example: - >>> import matchzoo as mz - >>> data_pack = mz.datasets.toy.load_data() - >>> data_pack.has_label - True - >>> data_pack.drop_label(inplace=True) - >>> data_pack.has_label - False - """ - self._relation = self._relation.drop(columns='label') - - @_optional_inplace - def drop_invalid(self): - """ - Remove rows from the data pack where the length is zero. - - :param inplace: `True` to modify inplace, `False` to return a modified - copy. (default: `False`) - - Example: - >>> import matchzoo as mz - >>> data_pack = mz.datasets.toy.load_data() - >>> data_pack.append_text_length(inplace=True, verbose=0) - >>> data_pack.drop_invalid(inplace=True) - """ - if not ('length_left' in self._left and 'length_right' in self._right): - raise ValueError(f"`lenght_left` or `length_right` is missing. " - f"Please call `append_text_length` in advance.") - valid_left = self._left.loc[self._left.length_left != 0] - valid_right = self._right.loc[self._right.length_right != 0] - self._left = self._left[self._left.index.isin(valid_left.index)] - self._right = self._right[self._right.index.isin(valid_right.index)] - self._relation = self._relation[self._relation.id_left.isin( - valid_left.index) & self._relation.id_right.isin( - valid_right.index)] - self._relation.reset_index(drop=True, inplace=True) - - @_optional_inplace - def append_text_length(self, verbose=1): - """ - Append `length_left` and `length_right` columns. - - :param inplace: `True` to modify inplace, `False` to return a modified - copy. (default: `False`) - :param verbose: Verbosity. - - Example: - >>> import matchzoo as mz - >>> data_pack = mz.datasets.toy.load_data() - >>> 'length_left' in data_pack.frame[0].columns - False - >>> new_data_pack = data_pack.append_text_length(verbose=0) - >>> 'length_left' in new_data_pack.frame[0].columns - True - >>> 'length_left' in data_pack.frame[0].columns - False - >>> data_pack.append_text_length(inplace=True, verbose=0) - >>> 'length_left' in data_pack.frame[0].columns - True - - """ - self.apply_on_text(len, rename=('length_left', 'length_right'), - inplace=True, verbose=verbose) - - @_optional_inplace - def apply_on_text( - self, func: typing.Callable, - mode: str = 'both', - rename: typing.Optional[str] = None, - verbose: int = 1 - ): - """ - Apply `func` to text columns based on `mode`. - - :param func: The function to apply. - :param mode: One of "both", "left" and "right". - :param rename: If set, use new names for results instead of replacing - the original columns. To set `rename` in "both" mode, use a tuple - of `str`, e.g. ("text_left_new_name", "text_right_new_name"). - :param inplace: `True` to modify inplace, `False` to return a modified - copy. (default: `False`) - :param verbose: Verbosity. - - Examples:: - >>> import matchzoo as mz - >>> data_pack = mz.datasets.toy.load_data() - >>> frame = data_pack.frame - - To apply `len` on the left text and add the result as 'length_left': - >>> data_pack.apply_on_text(len, mode='left', - ... rename='length_left', - ... inplace=True, - ... verbose=0) - >>> list(frame[0].columns) # noqa: E501 - ['id_left', 'text_left', 'length_left', 'id_right', 'text_right', 'label'] - - To do the same to the right text: - >>> data_pack.apply_on_text(len, mode='right', - ... rename='length_right', - ... inplace=True, - ... verbose=0) - >>> list(frame[0].columns) # noqa: E501 - ['id_left', 'text_left', 'length_left', 'id_right', 'text_right', 'length_right', 'label'] - - To do the same to the both texts at the same time: - >>> data_pack.apply_on_text(len, mode='both', - ... rename=('extra_left', 'extra_right'), - ... inplace=True, - ... verbose=0) - >>> list(frame[0].columns) # noqa: E501 - ['id_left', 'text_left', 'length_left', 'extra_left', 'id_right', 'text_right', 'length_right', 'extra_right', 'label'] - - To suppress outputs: - >>> data_pack.apply_on_text(len, mode='both', verbose=0, - ... inplace=True) - - """ - if mode == 'both': - self._apply_on_text_both(func, rename, verbose=verbose) - elif mode == 'left': - self._apply_on_text_left(func, rename, verbose=verbose) - elif mode == 'right': - self._apply_on_text_right(func, rename, verbose=verbose) - else: - raise ValueError(f"{mode} is not a valid mode type." - f"Must be one of `left` `right` `both`.") - - def _apply_on_text_right(self, func, rename, verbose=1): - name = rename or 'text_right' - if verbose: - tqdm.pandas(desc="Processing " + name + " with " + func.__name__) - self._right[name] = self._right['text_right'].progress_apply(func) - else: - self._right[name] = self._right['text_right'].apply(func) - - def _apply_on_text_left(self, func, rename, verbose=1): - name = rename or 'text_left' - if verbose: - tqdm.pandas(desc="Processing " + name + " with " + func.__name__) - self._left[name] = self._left['text_left'].progress_apply(func) - else: - self._left[name] = self._left['text_left'].apply(func) - - def _apply_on_text_both(self, func, rename, verbose=1): - left_name, right_name = rename or ('text_left', 'text_right') - self._apply_on_text_left(func, rename=left_name, verbose=verbose) - self._apply_on_text_right(func, rename=right_name, verbose=verbose) - - @_optional_inplace - def one_hot_encode_label(self, num_classes=2): - """ - One-hot encode `label` column of `relation`. - - :param num_classes: Number of classes. - :param inplace: `True` to modify inplace, `False` to return a modified - copy. (default: `False`) - :return: - """ - self._relation['label'] = self._relation['label'].apply( - lambda idx: matchzoo.one_hot(idx, num_classes)) - - class FrameView(object): - """FrameView.""" - - def __init__(self, data_pack: 'DataPack'): - """ - View a data pack as a frame. - - A slice of the view is genereated by merging three parts of the - data pack being viewed into a big table. - - :param data_pack: :class:`DataPack` to view. - - Examples:: - >>> import matchzoo as mz - >>> data_pack = mz.datasets.toy.load_data() - >>> frame = data_pack.frame - - Use `()` to get a full copy of the frame: - >>> list(frame().columns) - ['id_left', 'text_left', 'id_right', 'text_right', 'label'] - >>> len(frame()) == len(data_pack) - True - - Notice that a view is binded to the original data pack, so changing - contents of the data pack will affect a view previously created: - >>> data_pack.drop_label(inplace=True) - >>> list(frame().columns) - ['id_left', 'text_left', 'id_right', 'text_right'] - - To slice the view: - >>> frame_slice = frame[3:5] - >>> len(frame_slice) - 2 - - """ - self._data_pack = data_pack - - def __getitem__(self, index: typing.Union[int, slice, np.array] - ) -> pd.DataFrame: - """Slicer.""" - dp = self._data_pack - index = _convert_to_list_index(index, len(dp)) - left_df = dp.left.loc[dp.relation['id_left'][index]].reset_index() - right_df = dp.right.loc[ - dp.relation['id_right'][index]].reset_index() - joined_table = left_df.join(right_df) - for column in dp.relation.columns: - if column not in ['id_left', 'id_right']: - labels = dp.relation[column][index].to_frame() - labels = labels.reset_index(drop=True) - joined_table = joined_table.join(labels) - return joined_table - - def __call__(self): - """:return: A full copy. Equivalant to `frame[:]`.""" - return self[:] - - -def load_data_pack(dirpath: typing.Union[str, Path]) -> DataPack: - """ - Load a :class:`DataPack`. The reverse function of :meth:`save`. - - :param dirpath: directory path of the saved model. - :return: a :class:`DataPack` instance. - """ - dirpath = Path(dirpath) - - data_file_path = dirpath.joinpath(DataPack.DATA_FILENAME) - dp = dill.load(open(data_file_path, 'rb')) - - return dp diff --git a/matchzoo/data_pack/pack.py b/matchzoo/data_pack/pack.py deleted file mode 100644 index 15a627b6..00000000 --- a/matchzoo/data_pack/pack.py +++ /dev/null @@ -1,76 +0,0 @@ -"""Convert list of input into class:`DataPack` expected format.""" - -import typing - -import pandas as pd -import numpy as np - -import matchzoo - - -def pack(df: pd.DataFrame) -> 'matchzoo.DataPack': - """ - Pack a :class:`DataPack` using `df`. - - The `df` must have `text_left` and `text_right` columns. Optionally, - the `df` can have `id_left`, `id_right` to index `text_left` and - `text_right` respectively. `id_left`, `id_right` will be automatically - generated if not specified. - - :param df: Input :class:`pandas.DataFrame` to use. - - Examples:: - >>> import matchzoo as mz - >>> import pandas as pd - >>> df = pd.DataFrame(data={'text_left': list('AABC'), - ... 'text_right': list('abbc'), - ... 'label': [0, 1, 1, 0]}) - >>> mz.pack(df).frame() - id_left text_left id_right text_right label - 0 L-0 A R-0 a 0 - 1 L-0 A R-1 b 1 - 2 L-1 B R-1 b 1 - 3 L-2 C R-2 c 0 - - """ - if 'text_left' not in df or 'text_right' not in df: - raise ValueError( - 'Input data frame must have `text_left` and `text_right`.') - - # Gather IDs - if 'id_left' not in df: - id_left = _gen_ids(df, 'text_left', 'L-') - else: - id_left = df['id_left'] - if 'id_right' not in df: - id_right = _gen_ids(df, 'text_right', 'R-') - else: - id_right = df['id_right'] - - # Build Relation - relation = pd.DataFrame(data={'id_left': id_left, 'id_right': id_right}) - for col in df: - if col not in ['id_left', 'id_right', 'text_left', 'text_right']: - relation[col] = df[col] - - # Build Left and Right - left = _merge(df, id_left, 'text_left', 'id_left') - right = _merge(df, id_right, 'text_right', 'id_right') - return matchzoo.DataPack(relation, left, right) - - -def _merge(data: pd.DataFrame, ids: typing.Union[list, np.array], - text_label: str, id_label: str): - left = pd.DataFrame(data={ - text_label: data[text_label], id_label: ids - }) - left.drop_duplicates(id_label, inplace=True) - left.set_index(id_label, inplace=True) - return left - - -def _gen_ids(data: pd.DataFrame, col: str, prefix: str): - lookup = {} - for text in data[col].unique(): - lookup[text] = prefix + str(len(lookup)) - return data[col].map(lookup) diff --git a/matchzoo/datasets/__init__.py b/matchzoo/datasets/__init__.py deleted file mode 100644 index 44eb4669..00000000 --- a/matchzoo/datasets/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from . import toy -from . import wiki_qa -from . import embeddings -from . import snli -from . import quora_qp -from . import cqa_ql_16 -from pathlib import Path - - -def list_available(): - return [p.name for p in Path(__file__).parent.iterdir() - if p.is_dir() and not p.name.startswith('_')] diff --git a/matchzoo/datasets/bert_resources/uncased_vocab_100.txt b/matchzoo/datasets/bert_resources/uncased_vocab_100.txt deleted file mode 100644 index bbdb8526..00000000 --- a/matchzoo/datasets/bert_resources/uncased_vocab_100.txt +++ /dev/null @@ -1,101 +0,0 @@ -[PAD] -##ness -episode -bed -added -table -indian -private -charles -route -available -idea -throughout -centre -addition -appointed -style -1994 -books -eight -construction -press -mean -wall -friends -remained -schools -study -##ch -##um -institute -oh -chinese -sometimes -events -possible -1992 -australian -type -brown -forward -talk -process -food -debut -seat -performance -committee -features -character -arts -herself -else -lot -strong -russian -range -hours -peter -arm -##da -morning -dr -sold -##ry -quickly -directed -1993 -guitar -china -##w -31 -list -##ma -performed -media -uk -players -smile -##rs -myself -40 -placed -coach -province -##gawa -typed -##dry -favors -allegheny -glaciers -##rly -recalling -aziz -##log -parasite -requiem -auf -##berto -##llin -[UNK] \ No newline at end of file diff --git a/matchzoo/datasets/cqa_ql_16/__init__.py b/matchzoo/datasets/cqa_ql_16/__init__.py deleted file mode 100644 index 0394d77f..00000000 --- a/matchzoo/datasets/cqa_ql_16/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .load_data import load_data \ No newline at end of file diff --git a/matchzoo/datasets/cqa_ql_16/load_data.py b/matchzoo/datasets/cqa_ql_16/load_data.py deleted file mode 100644 index 5b6b0a2e..00000000 --- a/matchzoo/datasets/cqa_ql_16/load_data.py +++ /dev/null @@ -1,203 +0,0 @@ -"""CQA-QL-16 data loader.""" - -import xml -import typing -from pathlib import Path - -import keras -import pandas as pd - -import matchzoo - - -_train_dev_url = "http://alt.qcri.org/semeval2016/task3/data/uploads/" \ - "semeval2016-task3-cqa-ql-traindev-v3.2.zip" -_test_url = "http://alt.qcri.org/semeval2016/task3/data/uploads/" \ - "semeval2016_task3_test.zip" - - -def load_data( - stage: str = 'train', - task: str = 'classification', - target_label: str = 'PerfectMatch', - return_classes: bool = False, - match_type: str = 'question', - mode: str = 'both', -) -> typing.Union[matchzoo.DataPack, tuple]: - """ - Load CQA-QL-16 data. - - :param stage: One of `train`, `dev`, and `test`. - (default: `train`) - :param task: Could be one of `ranking`, `classification` or instance - of :class:`matchzoo.engine.BaseTask`. (default: `classification`) - :param target_label: If `ranking`, choose one of classification - label as the positive label. (default: `PerfectMatch`) - :param return_classes: `True` to return classes for classification - task, `False` otherwise. - :param match_type: Matching text types. One of `question`, - `answer`, and `external_answer`. (default: `question`) - :param mode: Train data use method. One of `part1`, `part2`, - and `both`. (default: `both`) - - :return: A DataPack unless `task` is `classification` and `return_classes` - is `True`: a tuple of `(DataPack, classes)` in that case. - """ - if stage not in ('train', 'dev', 'test'): - raise ValueError(f"{stage} is not a valid stage." - f"Must be one of `train`, `dev`, and `test`.") - - if match_type not in ('question', 'answer', 'external_answer'): - raise ValueError(f"{match_type} is not a valid method. Must be one of" - f" `question`, `answer`, `external_answer`.") - - if mode not in ('part1', 'part2', 'both'): - raise ValueError(f"{mode} is not a valid method." - f"Must be one of `part1`, `part2`, `both`.") - - data_root = _download_data(stage) - data_pack = _read_data(data_root, stage, match_type, mode) - - if task == 'ranking': - if match_type in ('anwer', 'external_answer') and target_label not in [ - 'Good', 'PotentiallyUseful', 'Bad']: - raise ValueError(f"{target_label} is not a valid target label." - f"Must be one of `Good`, `PotentiallyUseful`," - f" `Bad`.") - elif match_type == 'question' and target_label not in [ - 'PerfectMatch', 'Relevant', 'Irrelevant']: - raise ValueError(f"{target_label} is not a valid target label." - f" Must be one of `PerfectMatch`, `Relevant`," - f" `Irrelevant`.") - binary = (data_pack.relation['label'] == target_label).astype(float) - data_pack.relation['label'] = binary - return data_pack - elif task == 'classification': - if match_type in ('answer', 'external_answer'): - classes = ['Good', 'PotentiallyUseful', 'Bad'] - else: - classes = ['PerfectMatch', 'Relevant', 'Irrelevant'] - label = data_pack.relation['label'].apply(classes.index) - data_pack.relation['label'] = label - data_pack.one_hot_encode_label(num_classes=3, inplace=True) - if return_classes: - return data_pack, classes - else: - return data_pack - else: - raise ValueError(f"{task} is not a valid task." - f"Must be one of `Ranking` and `Classification`.") - - -def _download_data(stage): - if stage in ['train', 'dev']: - return _download_train_dev_data() - else: - return _download_test_data() - - -def _download_train_dev_data(): - ref_path = keras.utils.data_utils.get_file( - 'semeval_train', _train_dev_url, extract=True, - cache_dir=matchzoo.USER_DATA_DIR, - cache_subdir='semeval_train' - ) - return Path(ref_path).parent.joinpath('v3.2') - - -def _download_test_data(): - ref_path = keras.utils.data_utils.get_file( - 'semeval_test', _test_url, extract=True, - cache_dir=matchzoo.USER_DATA_DIR, - cache_subdir='semeval_test' - ) - return Path(ref_path).parent.joinpath('SemEval2016_task3_test/English') - - -def _read_data(path, stage, match_type, mode='both'): - if stage == 'train': - if mode == 'part1': - path = path.joinpath( - 'train/SemEval2016-Task3-CQA-QL-train-part1.xml') - data = _load_data_by_type(path, match_type) - elif mode == 'part2': - path = path.joinpath( - 'train/SemEval2016-Task3-CQA-QL-train-part2.xml') - data = _load_data_by_type(path, match_type) - else: - part1 = path.joinpath( - 'train/SemEval2016-Task3-CQA-QL-train-part1.xml') - p1 = _load_data_by_type(part1, match_type) - part2 = path.joinpath( - 'train/SemEval2016-Task3-CQA-QL-train-part1.xml') - p2 = _load_data_by_type(part2, match_type) - data = pd.concat([p1, p2], ignore_index=True) - return matchzoo.pack(data) - elif stage == 'dev': - path = path.joinpath('dev/SemEval2016-Task3-CQA-QL-dev.xml') - data = _load_data_by_type(path, match_type) - return matchzoo.pack(data) - else: - path = path.joinpath('SemEval2016-Task3-CQA-QL-test.xml') - data = _load_data_by_type(path, match_type) - return matchzoo.pack(data) - - -def _load_data_by_type(path, match_type): - if match_type == 'question': - return _load_question(path) - elif match_type == 'answer': - return _load_answer(path) - else: - return _load_external_answer(path) - - -def _load_question(path): - doc = xml.etree.ElementTree.parse(path) - dataset = [] - for question in doc.iterfind('OrgQuestion'): - qid = question.attrib['ORGQ_ID'] - query = question.findtext('OrgQBody') - rel_question = question.find('Thread').find('RelQuestion') - question = rel_question.findtext('RelQBody') - question_id = rel_question.attrib['RELQ_ID'] - dataset.append([qid, question_id, query, question, - rel_question.attrib['RELQ_RELEVANCE2ORGQ']]) - df = pd.DataFrame(dataset, columns=[ - 'id_left', 'id_right', 'text_left', 'text_right', 'label']) - return df - - -def _load_answer(path): - doc = xml.etree.ElementTree.parse(path) - dataset = [] - for org_q in doc.iterfind('OrgQuestion'): - for thread in org_q.iterfind('Thread'): - ques = thread.find('RelQuestion') - qid = ques.attrib['RELQ_ID'] - question = ques.findtext('RelQBody') - for comment in thread.iterfind('RelComment'): - aid = comment.attrib['RELC_ID'] - answer = comment.findtext('RelCText') - dataset.append([qid, aid, question, answer, - comment.attrib['RELC_RELEVANCE2RELQ']]) - df = pd.DataFrame(dataset, columns=[ - 'id_left', 'id_right', 'text_left', 'text_right', 'label']) - return df - - -def _load_external_answer(path): - doc = xml.etree.ElementTree.parse(path) - dataset = [] - for question in doc.iterfind('OrgQuestion'): - qid = question.attrib['ORGQ_ID'] - query = question.findtext('OrgQBody') - thread = question.find('Thread') - for comment in thread.iterfind('RelComment'): - answer = comment.findtext('RelCText') - aid = comment.attrib['RELC_ID'] - dataset.append([qid, aid, query, answer, - comment.attrib['RELC_RELEVANCE2ORGQ']]) - df = pd.DataFrame(dataset, columns=[ - 'id_left', 'id_right', 'text_left', 'text_right', 'label']) - return df diff --git a/matchzoo/datasets/embeddings/__init__.py b/matchzoo/datasets/embeddings/__init__.py deleted file mode 100644 index c62ff00c..00000000 --- a/matchzoo/datasets/embeddings/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from pathlib import Path -from .load_glove_embedding import load_glove_embedding - -DATA_ROOT = Path(__file__).parent -EMBED_RANK = DATA_ROOT.joinpath('embed_rank.txt') -EMBED_10 = DATA_ROOT.joinpath('embed_10_word2vec.txt') -EMBED_10_GLOVE = DATA_ROOT.joinpath('embed_10_glove.txt') diff --git a/matchzoo/datasets/embeddings/embed_10_glove.txt b/matchzoo/datasets/embeddings/embed_10_glove.txt deleted file mode 100644 index 9bb2be5a..00000000 --- a/matchzoo/datasets/embeddings/embed_10_glove.txt +++ /dev/null @@ -1,5 +0,0 @@ -A 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 -B 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 -C 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 -D 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 -E 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 diff --git a/matchzoo/datasets/embeddings/embed_10_word2vec.txt b/matchzoo/datasets/embeddings/embed_10_word2vec.txt deleted file mode 100644 index 0c514eb5..00000000 --- a/matchzoo/datasets/embeddings/embed_10_word2vec.txt +++ /dev/null @@ -1,6 +0,0 @@ -5 10 -A 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 -B 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 -C 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 -D 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 -E 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 diff --git a/matchzoo/datasets/embeddings/embed_err.txt.gb2312 b/matchzoo/datasets/embeddings/embed_err.txt.gb2312 deleted file mode 100644 index dbd1abe9..00000000 --- a/matchzoo/datasets/embeddings/embed_err.txt.gb2312 +++ /dev/null @@ -1,6 +0,0 @@ -5 10 -B 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 - 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 -D 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 -A 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 -E 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 diff --git a/matchzoo/datasets/embeddings/embed_rank.txt b/matchzoo/datasets/embeddings/embed_rank.txt deleted file mode 100644 index 1286e329..00000000 --- a/matchzoo/datasets/embeddings/embed_rank.txt +++ /dev/null @@ -1,501 +0,0 @@ -501 50 -the 0.481192 0.114944 0.146258 0.0401524 -1.10979 -0.147589 -0.0632034 0.240782 0.708124 0.177662 0.385847 -0.553977 -0.287812 -0.0337654 -0.380266 0.0722159 -0.813727 0.236374 -0.0853274 0.150912 0.538448 0.207157 0.656751 0.350483 0.03201 0.252512 0.358818 -0.713966 0.281348 -0.181327 -0.404944 -0.0396742 0.0236526 -0.471879 -0.410541 0.0128125 0.10784 0.521248 0.0801202 -0.417603 -0.308933 0.0870871 0.606664 0.138819 0.130256 0.114381 -0.997155 -0.229526 0.460283 -0.0746122 -of 0.422419 -0.31498 0.289009 0.251502 -0.664988 -0.00388272 -0.120225 1.00474 0.751213 0.204627 0.105957 -0.191011 -0.520845 0.379055 -0.463837 -0.286444 -1.1002 -0.0300834 -0.0602586 -0.152382 0.389427 0.0221524 0.276443 -0.213152 -0.566351 0.448351 0.314629 -0.674973 0.0402766 -0.78957 -0.728583 -0.0351845 0.221431 -0.376866 -0.283492 0.006311 -0.166525 -0.02155 0.15057 0.073431 -0.467883 0.106812 0.590167 -0.147108 -0.250329 0.189407 -1.03214 0.128791 0.132738 0.189068 -and 0.414205 -0.0585991 0.341188 0.307435 -0.938015 -0.163284 -0.00489662 0.717564 0.676849 0.198116 0.243216 -0.344182 -0.329224 0.267194 -0.428923 -0.266595 -0.897164 -0.0738648 -0.0378047 0.0276701 0.556949 0.14016 0.407714 0.163641 -0.459422 0.235771 0.26932 -0.369004 -0.249506 -0.56535 -0.398916 -0.160479 0.283353 -0.0887171 -0.20105 0.146284 -0.134439 0.253019 0.261753 -0.123803 -0.491314 0.0466079 0.434152 0.320111 -0.0344912 0.191271 -0.941325 -0.0918534 0.366077 0.1307 -in 0.218001 -0.18912 0.504318 0.280393 -0.775002 0.11091 -0.236861 0.80575 0.657629 0.302427 0.433355 -0.059612 -0.533702 0.420355 -0.392482 -0.365949 -0.975239 -0.156092 -0.184766 -0.147236 0.328548 0.142733 0.328588 0.219087 -0.559219 0.29521 0.399618 -0.436524 -0.0428307 -0.620783 -0.454802 0.19574 0.0295431 -0.3088 -0.415296 -0.158645 -0.136125 0.308207 0.217258 -0.0045154 -0.455841 -0.0690503 0.453701 0.1836 -0.120332 0.269079 -0.933471 0.113543 0.284837 0.403468 -to 0.097482 -0.0247346 0.455669 0.0713241 -0.432324 -0.428366 -0.0519544 0.765518 0.711727 0.110414 -0.138872 -0.356299 -0.253427 0.259594 -0.231505 0.116964 -0.766074 -0.039603 -0.281872 -0.0746024 0.518813 0.770758 0.270969 0.649161 -0.42849 0.0977476 -0.012218 -0.292233 -0.385778 -0.846938 -0.380041 0.0495779 0.284051 -0.29755 0.0136957 0.141674 -0.287226 0.286373 0.220797 -0.239113 -0.731136 -0.116539 0.69585 0.16209 -0.171808 0.274095 -1.17902 0.344541 0.226595 0.509018 -was 0.743503 -0.219687 0.630859 0.189543 -0.734124 0.281838 -0.256248 0.932596 0.425846 -0.307763 0.568447 0.155871 -0.575594 0.703042 -0.0490383 -0.0620161 -0.801524 -0.251176 -0.301118 -0.375997 0.337905 0.653957 0.855469 0.123425 -0.334748 0.236197 1.05479 -0.451971 0.0817836 -0.149519 -0.595272 0.149396 -0.124963 -0.284149 0.0751628 -0.10068 0.268971 0.390084 0.208225 0.214898 -0.0168618 0.220242 0.201806 0.171113 -0.0456709 -0.169592 -1.10004 -0.017665 0.211219 0.23398 -is 0.459386 -0.294196 0.576932 0.415239 -0.753269 0.148147 -0.691806 0.554182 0.606315 0.268711 0.611888 -0.235741 -0.140472 0.372451 -0.498448 -0.305131 -0.733118 -0.279021 0.0870202 -0.504272 0.852962 0.461728 0.603495 -0.530359 -0.49192 0.919233 0.72612 -0.495833 -0.0916867 -0.233348 -0.832931 -0.0718616 0.02688 -0.544653 -0.902341 0.0400422 -0.485333 -0.137943 0.481405 0.0690371 -0.927495 -0.0382945 0.150568 -0.372555 -0.257146 -0.057132 -0.437076 -0.63018 0.0677571 0.119527 -for 0.240618 0.166711 0.382984 0.361427 -0.840945 -0.152259 -0.0497632 0.69922 0.748511 0.337517 -0.0393315 -0.200059 -0.495781 0.352012 -0.38444 -0.0505624 -0.528893 -0.0211197 -0.340248 -0.499523 0.335665 0.335719 0.266335 0.18704 -0.614594 0.191106 0.611558 -0.451507 -0.0583181 -0.902317 -0.588862 0.088819 0.411641 -0.157597 -0.127485 0.0343373 -0.29291 0.14304 0.358493 0.130073 -0.409847 -0.202126 0.345995 0.41354 -0.333838 -0.0422347 -0.960556 0.123897 0.00153163 0.368889 -as 0.178929 0.0311233 0.383683 0.285478 -0.938057 -0.0407058 0.103355 0.664167 0.591581 0.0336315 0.301046 -0.134275 -0.447313 0.394273 -0.784662 -0.0829763 -0.394875 -0.199912 -0.0197685 -0.321068 0.54858 0.357356 0.150532 0.145926 -0.288452 0.244956 0.439825 -0.304926 -0.233311 -0.580267 -0.7138 0.00219208 0.0873197 -0.0466839 -0.26586 -0.218687 -0.204121 0.35253 0.140335 0.209495 -0.800932 -0.0468413 0.2368 0.105298 -0.108704 0.292249 -0.617625 -0.112399 0.261603 0.297329 -on 0.361393 0.194677 0.386321 0.101013 -0.6303 0.021094 -0.094212 0.673848 0.643189 0.144393 0.0196457 -0.190431 -0.653567 0.410097 -0.271139 -0.192813 -0.891066 -0.423621 -0.363702 0.0170993 0.165249 0.352704 0.594466 -0.148003 -0.270243 0.33983 0.0414032 -0.384019 -0.0758417 -0.530481 -0.16999 0.369073 0.232679 -0.408866 -0.0986386 0.0342333 0.0314466 0.334493 0.500842 -0.0390505 -0.532535 -0.248381 0.108413 0.372316 -0.215145 0.392357 -1.26173 0.105443 0.0306274 0.642751 -with 0.414189 -0.0201856 0.596477 0.431318 -0.699933 -0.233491 0.0829651 0.773067 0.448824 0.232143 0.181464 -0.119701 -0.304451 0.419556 -0.236413 -0.136404 -0.971148 -0.136031 -0.159345 -0.323072 0.482769 0.209675 0.431341 0.117836 -0.346871 0.253549 0.212466 -0.220648 -0.293341 -0.584324 -0.325732 -0.201978 0.761816 -0.0935512 0.0944946 0.0589293 -0.244274 0.142954 0.303255 -0.053 -0.566408 0.0399402 0.332823 0.315135 -0.491303 0.122719 -0.877587 -0.0616097 0.286452 0.174432 -by 0.164598 0.117743 0.447771 0.586842 -0.781262 -0.0285669 0.147976 0.806706 0.507283 0.00115935 -0.381605 -0.0896638 -0.603885 0.196833 -0.376773 -0.0546903 -0.964904 -0.316372 -0.300429 0.140248 0.13765 0.279148 0.130351 0.135054 -0.217016 0.362413 -0.0317792 -0.278805 -0.16142 -0.408079 -0.749136 -0.113397 -0.0902098 -0.345095 -0.111432 0.266522 -0.0734709 0.514639 0.278386 0.15478 -0.727428 0.0983311 0.590012 0.38901 0.0129612 0.202999 -1.05376 0.188324 0.25339 0.271067 -he -0.00215639 -0.348425 0.227953 0.540406 -1.29868 -0.468757 -0.182312 0.225502 0.198551 0.00280781 1.28092 -0.271463 -0.211642 -0.106504 -0.212531 0.246637 0.0315608 -0.116296 -0.271059 0.354953 0.336552 0.513029 0.827706 0.864021 -0.191875 0.0742888 0.83226 -0.172331 -0.13244 -0.474771 -0.354279 0.572046 -0.411989 0.0477242 0.424785 -0.509935 -0.18299 0.71168 -0.3204 -0.0663534 -0.356052 -0.463358 1.07169 0.688653 0.654073 0.237653 -0.919869 0.356708 0.274691 0.620945 -that -0.0636978 0.0305305 0.603934 0.266724 -0.568214 -0.46313 -0.0176214 0.0367927 0.309121 -0.145396 -0.343789 -0.535671 -0.195677 0.00810845 -0.600446 0.47427 -0.459608 -0.0801537 -0.30156 -0.00529369 0.47216 0.685532 0.345063 0.519714 0.12533 0.158459 0.271266 -0.10173 -0.0384262 -0.706795 -0.50702 0.0556162 -0.243177 -0.551525 -0.175341 0.255024 -0.3177 0.386972 -0.00500236 -0.232892 -0.626938 0.0240547 0.620032 -0.0516308 -0.229234 0.65433 -1.0744 0.319074 0.177489 0.330443 -from 0.545828 -0.182198 0.420776 0.277229 -0.621808 -0.106732 -0.00616282 0.659348 0.672152 0.188202 0.373023 -0.0877687 -0.284524 0.590145 -0.170934 -0.299052 -1.0906 -0.111158 -0.185043 0.0211288 0.504501 0.254885 0.426567 0.104803 -0.316893 0.267371 0.45389 -0.127518 -0.160256 -0.43257 -0.423472 0.0778388 0.0911174 -0.187355 0.00354515 -0.117082 -0.0185801 0.149666 0.119748 -0.222826 -0.440618 -0.119505 0.040647 -0.0631326 -0.10337 -0.184534 -0.970628 -0.0331648 -0.0862167 0.0904932 -at 0.329125 -0.445077 0.470047 0.232406 -0.462517 -0.124786 -0.385009 0.779465 0.528197 0.259924 0.284657 -0.3738 -0.467329 0.53591 -0.452265 -0.245269 -1.03961 -0.387408 -0.253999 -0.0263969 -0.0469276 0.435928 0.55072 0.0151536 -0.449506 0.0641795 0.352112 -0.463714 0.269793 -0.59694 -0.151035 0.350948 0.197643 -0.0631544 -0.184065 -0.301646 -0.211673 0.0963206 0.476683 -0.281493 -0.249371 -0.287159 0.533484 0.222331 -0.257611 -0.191521 -0.99521 0.250043 0.215126 0.805286 -his 0.399311 -0.11455 0.182618 0.391222 -1.38577 -0.836881 0.0683948 0.212949 0.14031 -0.305645 1.1147 -0.543351 -0.0765271 0.0887827 -0.0498956 0.278891 0.11061 -0.0402854 0.0901623 0.0473649 0.646213 0.0482608 1.18749 0.890662 -0.0203007 0.0510798 0.719535 -0.526675 -0.0613403 -0.519035 -0.299473 0.134028 0.0568395 0.228009 0.973674 -0.299101 0.0913128 0.358576 -0.0290992 -0.134082 0.00284892 -0.265241 1.21392 0.656945 0.411714 0.425909 -0.93552 -0.093997 0.204234 0.111327 -it -0.084556 0.326117 0.284061 0.36073 -0.436328 -0.205005 -0.366607 0.0301945 0.260852 0.286393 -0.0282026 -0.499147 -0.282631 -0.216273 -0.380151 0.368874 -0.726648 0.000957822 -0.269952 0.547038 0.552131 0.756692 0.62617 0.623924 -0.220279 0.139214 0.309936 -0.373758 0.261497 0.0705929 -0.691468 0.110236 -0.505099 -0.635808 -0.834653 0.207433 -0.285344 0.777932 0.0548777 -0.328381 -0.461198 0.0843667 0.310122 -0.392204 0.00201131 0.599579 -0.592596 -0.0292739 0.400259 0.304472 -an 0.483782 0.235291 0.0263575 0.385075 -1.05886 -0.308849 -0.0115418 0.369159 0.695994 0.459544 0.316152 -0.611351 -0.0887699 -0.19684 -0.695032 0.131454 -0.102384 -0.129733 -0.21768 0.11078 0.599457 0.15929 0.727238 0.361606 -0.0150142 0.263774 0.587862 -0.35202 0.390385 -0.0959102 -0.479527 0.170982 -0.202502 -0.221149 -0.334234 0.318643 -0.128748 0.522958 0.222153 -0.21743 -0.203237 -0.133904 0.451675 0.166252 0.22904 0.293301 -0.500455 -0.173757 0.487114 0.09427 -are 0.366673 0.103601 0.846736 -0.420805 -0.912456 -0.246012 -0.0552818 0.0593654 0.392212 -0.333661 0.105397 -0.702711 -0.138733 0.121925 -0.745552 -0.565972 -1.03052 0.271044 0.399679 -0.582794 0.885406 0.500963 0.363753 -0.362677 -0.460116 0.34899 0.459616 0.0736139 -0.444187 -0.615631 -0.690071 -0.58814 0.497567 -0.156204 -0.778628 -0.122807 -0.182225 -0.0933879 0.167186 -0.134256 -1.1949 0.276532 -0.157254 -0.225095 -0.731362 -0.33991 -0.939736 -0.623315 0.149308 -0.412065 -were 0.840957 0.105139 0.861012 -0.330634 -0.711901 -0.173337 0.451832 0.678777 0.129506 -0.951322 -0.171984 -0.123183 -0.485573 0.460478 -0.199897 -0.27218 -1.38525 0.320327 0.0250825 -0.433046 0.223563 0.646032 0.549658 0.296498 -0.289681 0.00227862 0.953287 0.15998 -0.103964 -0.395408 -0.529628 -0.437679 0.503697 -0.158398 0.0155813 -0.101234 0.526122 0.515628 0.140935 -0.0380044 0.0148411 0.658474 -0.0802261 0.299616 -0.457511 -0.564663 -1.52646 0.0884447 0.370672 -0.513596 -which -0.0308938 0.427428 0.236848 0.188871 -0.822977 -0.277875 0.0461739 0.220514 0.408007 0.259788 -0.136725 -0.564335 -0.358749 -0.241445 -0.396207 0.111499 -0.718946 0.0211391 -0.0887908 0.367177 0.339535 0.316353 0.397141 0.468351 -0.0857437 0.181777 0.000663481 -0.381664 0.248886 -0.266597 -0.427537 0.0528625 -0.166818 -0.446 -0.419508 0.175837 -0.182984 0.594403 -0.00418527 -0.362404 -0.373804 0.126156 0.494845 0.0332208 -0.045882 0.162113 -0.645121 0.211181 0.364624 0.0827299 -this 0.121805 0.583971 0.196161 0.036571 -0.832267 -0.3718 -0.0225544 -0.0540604 0.357722 0.175991 -0.081612 -0.713298 -0.234863 -0.00761619 -0.596301 0.359407 -0.433697 0.116133 -0.168708 0.00202869 0.482463 0.384224 0.609998 0.635127 0.324477 0.140734 0.0516987 -0.358923 0.387948 -0.372281 -0.515654 0.0410327 -0.231868 -0.50299 -0.370285 0.0425439 -0.092505 0.507837 0.158584 -0.281709 -0.304212 0.140062 0.639773 -0.250451 -0.18679 0.513726 -0.657437 -0.0214616 0.0675287 0.0994675 -be 0.160188 0.538497 1.05828 0.124406 -0.919728 -0.182331 -0.0148059 -0.257866 0.404362 -0.15517 -0.104514 -0.630551 -0.367752 0.157929 -0.585499 0.11645 -0.229083 0.612074 -0.237913 -0.173878 0.839758 1.1917 0.684893 0.142468 0.43403 0.0635464 0.859538 0.567257 -0.0035431 -0.0508324 -0.817688 -0.373177 -0.496663 -0.556389 -0.39784 -0.400874 -0.00534964 0.496525 -0.0228899 -0.207959 -0.636355 0.088959 -0.0973592 -0.46596 -0.139211 0.0366355 -1.07109 -0.331162 -0.155493 0.137701 -or 0.422606 0.127367 0.434548 -0.0651944 -0.80775 -0.534074 0.342007 0.445345 0.926771 0.115725 0.153505 -0.566136 -0.111956 -0.427414 -0.541772 -0.188438 -0.564553 -0.16036 0.167948 -0.375467 0.802877 0.443689 0.389698 0.226472 -0.339401 0.337808 0.371779 -0.134416 0.341626 -0.869185 -0.665759 -0.444235 0.167933 -0.34207 -0.237812 0.110028 -0.0875291 0.145752 0.424775 -0.277523 -0.910668 0.211459 0.079061 -0.310082 -0.326308 -0.15603 -0.581165 0.0286628 0.163977 -0.393129 -has 0.0907975 -0.370868 0.472671 0.372227 -0.55963 -0.254437 -0.740559 0.443456 -0.0349219 0.123583 0.0671734 -0.211036 -0.130216 0.494627 -0.904902 -0.285619 -0.667336 -0.571808 -0.227803 -0.709041 0.763616 0.410411 0.470513 0.0150921 -0.925126 0.395067 0.88121 -0.578509 -0.278119 -0.241923 -0.753102 0.184303 0.23288 -0.334024 -0.626839 0.160766 -0.301326 -0.273075 -0.057127 0.249279 -0.713739 0.143788 0.049026 0.15795 -0.622983 -0.0627328 -0.619326 -0.387947 0.411062 -0.0031616 -also -0.131348 0.23309 0.228439 0.235306 -0.916172 -0.219213 -0.10435 0.372016 0.403192 0.115023 0.128407 -0.220099 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1.56883 -1.49504 -0.906251 -0.767229 -0.846675 0.49656 -0.0492062 0.295307 1.54989 -0.00749338 1.14308 0.901384 -0.607899 0.436132 -1.83421 1.26945 0.124798 -0.0823941 0.729581 0.839542 -0.587786 -0.356903 -0.40957 0.234654 diff --git a/matchzoo/datasets/embeddings/embed_word.txt b/matchzoo/datasets/embeddings/embed_word.txt deleted file mode 100644 index d8ee0b63..00000000 --- a/matchzoo/datasets/embeddings/embed_word.txt +++ /dev/null @@ -1,7 +0,0 @@ -7 5 -asia 1 2 3 4 5 -beijing 1 1 1 1 1 -hot 2 2 2 2 2 -east 3 3 3 3 3 -capital 4 4 4 4 4 -china 5 5 5 5 5 diff --git a/matchzoo/datasets/embeddings/load_glove_embedding.py b/matchzoo/datasets/embeddings/load_glove_embedding.py deleted file mode 100644 index f53c63b3..00000000 --- a/matchzoo/datasets/embeddings/load_glove_embedding.py +++ /dev/null @@ -1,28 +0,0 @@ -"""Embedding data loader.""" - -from pathlib import Path - -import keras - -import matchzoo as mz - -_glove_embedding_url = "http://nlp.stanford.edu/data/glove.6B.zip" - - -def load_glove_embedding(dimension: int = 50) -> mz.embedding.Embedding: - """ - Return the pretrained glove embedding. - - :param dimension: the size of embedding dimension, the value can only be - 50, 100, or 300. - :return: The :class:`mz.embedding.Embedding` object. - """ - file_name = 'glove.6B.' + str(dimension) + 'd.txt' - file_path = (Path(mz.USER_DATA_DIR) / 'glove').joinpath(file_name) - if not file_path.exists(): - keras.utils.data_utils.get_file('glove_embedding', - _glove_embedding_url, - extract=True, - cache_dir=mz.USER_DATA_DIR, - cache_subdir='glove') - return mz.embedding.load_from_file(file_path=str(file_path), mode='glove') diff --git a/matchzoo/datasets/quora_qp/__init__.py b/matchzoo/datasets/quora_qp/__init__.py deleted file mode 100644 index 5b6c9450..00000000 --- a/matchzoo/datasets/quora_qp/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .load_data import load_data diff --git a/matchzoo/datasets/quora_qp/load_data.py b/matchzoo/datasets/quora_qp/load_data.py deleted file mode 100644 index f26132df..00000000 --- a/matchzoo/datasets/quora_qp/load_data.py +++ /dev/null @@ -1,84 +0,0 @@ -"""Quora Question Pairs data loader.""" - -import typing -from pathlib import Path - -import keras -import pandas as pd - -import matchzoo - -_url = "https://firebasestorage.googleapis.com/v0/b/mtl-sentence" \ - "-representations.appspot.com/o/data%2FQQP.zip?alt=media&" \ - "token=700c6acf-160d-4d89-81d1-de4191d02cb5" - - -def load_data( - stage: str = 'train', - task: str = 'classification', - return_classes: bool = False, -) -> typing.Union[matchzoo.DataPack, tuple]: - """ - Load QuoraQP data. - - :param path: `None` for download from quora, specific path for - downloaded data. - :param stage: One of `train`, `dev`, and `test`. - :param task: Could be one of `ranking`, `classification` or a - :class:`matchzoo.engine.BaseTask` instance. - :param return_classes: Whether return classes for classification task. - :return: A DataPack if `ranking`, a tuple of (DataPack, classes) if - `classification`. - """ - if stage not in ('train', 'dev', 'test'): - raise ValueError(f"{stage} is not a valid stage." - f"Must be one of `train`, `dev`, and `test`.") - - data_root = _download_data() - file_path = data_root.joinpath(f"{stage}.tsv") - data_pack = _read_data(file_path, stage) - - if task == 'ranking': - task = matchzoo.tasks.Ranking() - elif task == 'classification': - task = matchzoo.tasks.Classification() - - if isinstance(task, matchzoo.tasks.Ranking): - return data_pack - elif isinstance(task, matchzoo.tasks.Classification): - if stage != 'test': - data_pack.one_hot_encode_label(num_classes=2, inplace=True) - if return_classes: - return data_pack, [False, True] - else: - return data_pack - else: - raise ValueError(f"{task} is not a valid task.") - - -def _download_data(): - ref_path = keras.utils.data_utils.get_file( - 'quora_qp', _url, extract=True, - cache_dir=matchzoo.USER_DATA_DIR, - cache_subdir='quora_qp' - ) - return Path(ref_path).parent.joinpath('QQP') - - -def _read_data(path, stage): - data = pd.read_csv(path, sep='\t', error_bad_lines=False) - data = data.dropna(axis=0, how='any').reset_index(drop=True) - if stage in ['train', 'dev']: - df = pd.DataFrame({ - 'id_left': data['qid1'], - 'id_right': data['qid2'], - 'text_left': data['question1'], - 'text_right': data['question2'], - 'label': data['is_duplicate'].astype(int) - }) - else: - df = pd.DataFrame({ - 'text_left': data['question1'], - 'text_right': data['question2'] - }) - return matchzoo.pack(df) diff --git a/matchzoo/datasets/snli/__init__.py b/matchzoo/datasets/snli/__init__.py deleted file mode 100644 index 5b6c9450..00000000 --- a/matchzoo/datasets/snli/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .load_data import load_data diff --git a/matchzoo/datasets/snli/load_data.py b/matchzoo/datasets/snli/load_data.py deleted file mode 100644 index d27f376b..00000000 --- a/matchzoo/datasets/snli/load_data.py +++ /dev/null @@ -1,87 +0,0 @@ -"""SNLI data loader.""" - -import typing -from pathlib import Path - -import pandas as pd -import keras - -import matchzoo - -_url = "https://nlp.stanford.edu/projects/snli/snli_1.0.zip" - - -def load_data( - stage: str = 'train', - task: str = 'classification', - target_label: str = 'entailment', - return_classes: bool = False -) -> typing.Union[matchzoo.DataPack, tuple]: - """ - Load SNLI data. - - :param stage: One of `train`, `dev`, and `test`. (default: `train`) - :param task: Could be one of `ranking`, `classification` or a - :class:`matchzoo.engine.BaseTask` instance. (default: `ranking`) - :param target_label: If `ranking`, chose one of `entailment`, - `contradiction`, `neutral`, and `-` as the positive label. - (default: `entailment`) - :param return_classes: `True` to return classes for classification task, - `False` otherwise. - - :return: A DataPack unless `task` is `classificiation` and `return_classes` - is `True`: a tuple of `(DataPack, classes)` in that case. - """ - if stage not in ('train', 'dev', 'test'): - raise ValueError(f"{stage} is not a valid stage." - f"Must be one of `train`, `dev`, and `test`.") - - data_root = _download_data() - file_path = data_root.joinpath(f'snli_1.0_{stage}.txt') - data_pack = _read_data(file_path) - - if task == 'ranking': - task = matchzoo.tasks.Ranking() - if task == 'classification': - task = matchzoo.tasks.Classification() - - if isinstance(task, matchzoo.tasks.Ranking): - if target_label not in ['entailment', 'contradiction', 'neutral', '-']: - raise ValueError(f"{target_label} is not a valid target label." - f"Must be one of `entailment`, `contradiction`, " - f"`neutral` and `-`.") - binary = (data_pack.relation['label'] == target_label).astype(float) - data_pack.relation['label'] = binary - return data_pack - elif isinstance(task, matchzoo.tasks.Classification): - classes = ['entailment', 'contradiction', 'neutral', '-'] - label = data_pack.relation['label'].apply(classes.index) - data_pack.relation['label'] = label - data_pack.one_hot_encode_label(num_classes=4, inplace=True) - if return_classes: - return data_pack, classes - else: - return data_pack - else: - raise ValueError(f"{task} is not a valid task." - f"Must be one of `Ranking` and `Classification`.") - - -def _download_data(): - ref_path = keras.utils.data_utils.get_file( - 'snli', _url, extract=True, - cache_dir=matchzoo.USER_DATA_DIR, - cache_subdir='snli' - ) - return Path(ref_path).parent.joinpath('snli_1.0') - - -def _read_data(path): - table = pd.read_csv(path, sep='\t') - df = pd.DataFrame({ - 'text_left': table['sentence1'], - 'text_right': table['sentence2'], - 'label': table['gold_label'] - }) - df = df.dropna(axis=0, how='any').reset_index(drop=True) - return matchzoo.pack(df) diff --git a/matchzoo/datasets/toy/__init__.py b/matchzoo/datasets/toy/__init__.py deleted file mode 100644 index de49f8bc..00000000 --- a/matchzoo/datasets/toy/__init__.py +++ /dev/null @@ -1,64 +0,0 @@ -import typing -from pathlib import Path - -import pandas as pd - -import matchzoo - - -def load_data( - stage: str = 'train', - task: str = 'ranking', - return_classes: bool = False -) -> typing.Union[matchzoo.DataPack, typing.Tuple[matchzoo.DataPack, list]]: - """ - Load WikiQA data. - - :param stage: One of `train`, `dev`, and `test`. - :param task: Could be one of `ranking`, `classification` or a - :class:`matchzoo.engine.BaseTask` instance. - :param return_classes: `True` to return classes for classification task, - `False` otherwise. - - :return: A DataPack unless `task` is `classificiation` and `return_classes` - is `True`: a tuple of `(DataPack, classes)` in that case. - - Example: - >>> import matchzoo as mz - >>> stages = 'train', 'dev', 'test' - >>> tasks = 'ranking', 'classification' - >>> for stage in stages: - ... for task in tasks: - ... _ = mz.datasets.toy.load_data(stage, task) - """ - if stage not in ('train', 'dev', 'test'): - raise ValueError(f"{stage} is not a valid stage." - f"Must be one of `train`, `dev`, and `test`.") - - if task == 'ranking': - task = matchzoo.tasks.Ranking() - if task == 'classification': - task = matchzoo.tasks.Classification() - - path = Path(__file__).parent.joinpath(f'{stage}.csv') - data_pack = matchzoo.pack(pd.read_csv(path, index_col=0)) - - if isinstance(task, matchzoo.tasks.Ranking): - data_pack.relation['label'] = \ - data_pack.relation['label'].astype('float32') - return data_pack - elif isinstance(task, matchzoo.tasks.Classification): - data_pack.relation['label'] = data_pack.relation['label'].astype(int) - data_pack = data_pack.one_hot_encode_label(num_classes=2) - if return_classes: - return data_pack, [False, True] - else: - return data_pack - else: - raise ValueError(f"{task} is not a valid task." - f"Must be one of `Ranking` and `Classification`.") - - -def load_embedding(): - path = Path(__file__).parent.joinpath('embedding.2d.txt') - return matchzoo.embedding.load_from_file(path, mode='glove') diff --git a/matchzoo/datasets/toy/dev.csv b/matchzoo/datasets/toy/dev.csv deleted file mode 100644 index 4e7674d5..00000000 --- a/matchzoo/datasets/toy/dev.csv +++ /dev/null @@ -1,21 +0,0 @@ -,id_left,text_left,id_right,text_right,label -0,Q18,how a rocket engine works,D18-0,RS-68 being tested at NASA's Stennis Space Center.,0.0 -1,Q18,how a rocket engine works,D18-1,"The nearly transparent exhaust is due to this engine's exhaust being mostly superheated steam (water vapor from its propellants, hydrogen and oxygen)",0.0 -2,Q18,how a rocket engine works,D18-2,Viking 5C rocket engine,0.0 -3,Q18,how a rocket engine works,D18-3,"A rocket engine, or simply ""rocket"", is a jet engine that uses only stored propellant mass for forming its high speed propulsive jet .",1.0 -4,Q18,how a rocket engine works,D18-4,Rocket engines are reaction engines and obtain thrust in accordance with Newton's third law .,0.0 -5,Q18,how a rocket engine works,D18-5,"Since they need no external material to form their jet, rocket engines can be used for spacecraft propulsion as well as terrestrial uses, such as missiles .",0.0 -6,Q18,how a rocket engine works,D18-6,"Most rocket engines are internal combustion engines , although non-combusting forms also exist.",0.0 -7,Q18,how a rocket engine works,D18-7,"Rocket engines as a group have the highest exhaust velocities, are by far the lightest, but are the least propellant efficient of all types of jet engines.",0.0 -8,Q19,how old was britney spears when she recorded hit me baby one more time,D19-0,"""...Baby One More Time"" is a song by American recording artist Britney Spears .",0.0 -9,Q19,how old was britney spears when she recorded hit me baby one more time,D19-1,"It served as Spears's debut single and title track from her debut studio album, ...Baby One More Time (1999).",0.0 -10,Q19,how old was britney spears when she recorded hit me baby one more time,D19-2,"Written by Max Martin and produced by Martin and Rami , ""...Baby One More Time"" was released on September 30, 1998, by Jive Records .",0.0 -11,Q19,how old was britney spears when she recorded hit me baby one more time,D19-3,"After recording and sending a demo tape with an unused song from Toni Braxton , Spears signed a multi-album deal with Jive.",0.0 -12,Q19,how old was britney spears when she recorded hit me baby one more time,D19-4,"""...Baby One More Time"" is a teen pop and dance-pop song that refers to a girl who regrets breaking up with her boyfriend.",0.0 -13,Q19,how old was britney spears when she recorded hit me baby one more time,D19-5,"The song received generally favorable reviews from critics, who praised its composition.",0.0 -14,Q19,how old was britney spears when she recorded hit me baby one more time,D19-6,"""...Baby One More Time"" attained global success, reaching number one in every country it charted.",0.0 -15,Q19,how old was britney spears when she recorded hit me baby one more time,D19-7,"It also received numerous certifications around the world, and is one of the best-selling singles of all time , with over 10 million copies sold.",0.0 -16,Q19,how old was britney spears when she recorded hit me baby one more time,D19-8,"An accompanying music video, directed by Nigel Dick , portrays Spears as a student from a Catholic high school, who starts to daydream that she is singing and dancing around the school, while watching her love interest from afar.",0.0 -17,Q19,how old was britney spears when she recorded hit me baby one more time,D19-9,"The music video was later referenced in the music video of "" If U Seek Amy "" (2008), where Spears's fictional daughter is dressed with a similar schoolgirl outfit while wearing pink ribbons in her hair.",0.0 -18,Q19,how old was britney spears when she recorded hit me baby one more time,D19-10,"In 2010, the music video for ""...Baby One More Time"" was voted the third most influential video in the history of pop music, in a poll held by Jam! .",0.0 -19,Q19,how old was britney spears when she recorded hit me baby one more time,D19-11,"Spears has performed ""...Baby One More Time"" in a number of live appearances and in all of her concert tours.",0.0 diff --git a/matchzoo/datasets/toy/embedding.2d.txt b/matchzoo/datasets/toy/embedding.2d.txt deleted file mode 100644 index f3ae540a..00000000 --- a/matchzoo/datasets/toy/embedding.2d.txt +++ /dev/null @@ -1,1000 +0,0 @@ -the 0.4 0.2 -, 0.0 0.2 -. 0.2 0.3 -of 0.7 0.6 -to 0.7 -0.0 -and 0.3 0.1 -in 0.3 0.2 -a 0.2 0.5 -"""""""""" 0.3 0.5 -'s 0.2 0.4 -for 0.2 0.4 -- -0.2 1.2 -that 0.9 -0.1 -on 0.3 0.2 -is 0.6 0.6 -was 0.1 -0.2 -said 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-dead 0.9 0.2 -opened 0.2 0.1 -'ll -0.0 0.2 -event 0.0 1.4 -previous -0.1 0.2 -cost 0.9 0.0 -instead 0.2 -0.1 -canada -0.7 0.4 -band -0.8 0.2 -teams -0.5 -0.0 -daily -0.0 0.7 -2001 -0.2 0.2 -available 0.6 0.4 -drug 1.3 -1.1 -coming 0.2 0.0 -2003 -0.2 0.2 -investment 0.8 0.5 -’s 0.1 1.0 -michael -0.4 0.8 -civil -0.3 -0.4 -woman -0.2 0.6 -training 0.3 0.2 -appeared 0.2 -0.2 -9 -0.4 0.7 -involved 1.0 -0.4 -indian -0.5 -0.9 -similar 0.8 0.2 -situation 0.7 -0.4 -24 -0.2 0.4 -los -0.0 1.0 -running -0.4 -0.1 -fighting 0.7 -1.0 -mark -1.1 1.0 -40 -0.1 0.5 -trial 0.8 -0.1 -hold 0.2 0.4 -australian -1.4 -0.1 -thought 0.4 -0.1 -! -0.6 0.4 -study 0.7 0.7 -fall -0.0 -0.1 -mother 0.4 1.1 -met 0.3 1.0 -relations -0.0 1.0 -anti -0.0 -0.6 -2002 -0.2 0.4 -song -0.9 0.8 -popular -0.1 0.6 -base 0.3 0.6 -tv -0.1 0.5 -ground 0.6 -0.3 -markets 0.2 -0.8 -ii 1.1 1.0 -newspaper -0.5 0.2 -staff 0.2 0.2 -saw -0.0 -0.2 -hand 0.1 -0.4 -hope 0.3 0.8 -operations 1.2 -1.2 -pressure 0.1 -0.2 -americans -0.0 -0.0 -eastern 0.4 0.2 -st. 0.5 2.2 -legal 0.1 -0.2 -asia 0.6 0.4 -budget 0.2 -0.2 -returned 0.0 -0.1 -considered 0.3 0.1 -love -0.1 1.1 -wrote -0.1 0.4 -stop 0.3 -0.5 -fight 0.2 -0.9 -currently 0.1 0.3 -charges 0.4 -0.4 -try 0.4 -0.5 -aid 1.1 -0.2 -ended -0.5 -0.4 -management 0.5 -0.1 -brought 0.3 -0.1 -cases 1.1 -0.6 -decided 0.2 -0.5 -failed 0.4 -0.7 -network 0.9 0.3 -works 0.6 0.6 -gas 0.5 -0.0 -turned -0.0 -0.3 -fact 0.4 0.1 -vice -0.5 0.7 -ca 0.0 -0.1 -mexico 0.4 -0.1 -trading -0.4 -0.5 -especially 0.3 0.1 -reporters 0.4 0.0 -afghanistan 1.2 -0.6 -common 0.5 0.7 -looking 0.5 0.0 -space 1.6 1.0 -rates 0.0 0.2 -manager -0.1 0.3 -loss 0.1 0.1 -2011 -0.6 0.6 -justice -0.4 -0.6 -thousands 1.2 -0.4 -james -0.6 0.8 -rather 0.4 -0.2 -fund 1.0 0.6 -thing -0.0 0.2 -republic -0.3 0.2 -opening -0.0 0.4 -accused 0.1 -0.7 -winning -1.4 0.8 -scored -1.0 0.2 -championship -1.6 0.7 -example 0.5 0.6 -getting 0.0 -0.5 -biggest 0.2 0.1 -performance -0.5 0.3 -sports -1.2 1.5 -1998 -0.4 0.3 -let 0.1 -0.0 -allowed 0.1 -0.2 -schools -0.6 0.4 -means 0.2 0.2 -turn 0.4 -0.2 -leave 0.8 0.1 -no. -1.1 0.1 -robert -0.2 0.4 -personal 0.1 1.0 -stocks -0.3 -1.5 -showed -0.1 -0.1 -light 0.0 0.5 -arrested 0.2 -0.6 -person 0.6 0.4 -either 0.4 -0.1 -offer 1.1 0.7 -majority 0.1 -0.8 -battle 1.1 -0.3 -19 -0.4 0.5 -class -0.4 1.0 -evidence 1.1 -0.2 -makes 0.3 0.3 -society -0.3 0.6 -products -0.0 -0.5 -regional 0.1 0.4 -needed 0.5 -0.2 -stage 0.9 0.6 -am 0.4 0.4 -doing 0.0 -0.4 -families 1.1 0.3 -construction 0.8 0.3 -various 0.6 0.4 -1996 -0.4 0.3 -sold 0.4 -0.2 -independent -0.4 -0.6 -kind 0.2 0.2 -airport 1.6 0.6 -paul -0.1 1.1 -judge -0.4 -0.8 -internet 0.6 -0.2 -movement -0.3 -0.3 -room 0.5 0.8 -followed 0.1 0.1 -original 0.4 0.5 -angeles -0.2 0.8 -italy 1.8 -0.8 -` -0.1 0.6 -data 0.5 -0.6 -comes 0.3 0.5 -parties 0.3 -0.2 -nothing 0.4 0.0 -sea 1.4 1.0 -bring 0.4 0.2 -2012 -0.7 0.8 -annual -0.4 1.1 -officer 0.0 -0.6 -beijing -0.3 0.9 -present 0.7 0.6 -remain 0.7 -0.0 -nato 1.4 -0.4 -1999 -0.3 0.2 -22 -0.4 0.5 -remains 0.8 0.3 -allow 0.6 -0.3 -florida -0.5 0.2 -computer 0.1 -0.8 -21 -0.4 0.4 -contract -0.1 0.2 -coast 0.5 -0.1 -created 0.4 0.1 -demand -0.2 -0.4 -operation 1.8 -0.7 -events 0.2 1.3 -islamic -0.0 -0.5 -beat -1.5 -0.3 -analysts 0.6 -0.7 -interview 0.3 0.5 -helped -0.1 -0.2 -child 0.3 0.4 -probably 0.6 -0.2 -spent 0.3 0.3 -asian -0.7 0.2 -effort 0.7 -0.6 -cooperation 0.7 0.9 -shows 0.1 0.8 -calls 0.3 0.1 -investigation 1.2 -0.6 -lives 0.8 0.7 -video -0.1 -0.0 -yen -0.5 0.1 -runs -0.1 0.6 -tried 0.4 -0.8 -bad -0.2 -0.4 -described 0.6 0.3 -1994 -0.3 0.1 -toward 0.0 0.0 -written -0.1 0.4 -throughout 0.1 0.1 -established 0.1 0.2 -mission 1.9 0.4 -associated 0.2 1.1 -buy 0.8 -0.1 -growing 0.2 0.4 -green -0.6 0.9 -forward -0.2 -0.1 -competition -0.9 0.2 -poor -0.5 -0.4 -latest 0.5 0.0 -banks 0.8 -0.5 -question 0.2 0.2 -1997 -0.5 0.3 -prison 0.1 0.4 -feel -0.0 -0.3 -attention -0.1 0.5 diff --git a/matchzoo/datasets/toy/test.csv b/matchzoo/datasets/toy/test.csv deleted file mode 100644 index 8f98dafc..00000000 --- a/matchzoo/datasets/toy/test.csv +++ /dev/null @@ -1,21 +0,0 @@ -,id_left,text_left,id_right,text_right,label -0,Q19,how old was britney spears when she recorded hit me baby one more time,D19-12,It was the encore of the ...Baby One More Time Tour (1999) and Dream Within a Dream Tour (2001); Spears also performed remixed versions of the song during the Oops!...,0.0 -1,Q19,how old was britney spears when she recorded hit me baby one more time,D19-13,"I Did It Again World Tour (2000), The Onyx Hotel Tour (2004), The M+M's Tour (2007), The Circus Starring Britney Spears (2009), and the Femme Fatale Tour (2011).",0.0 -2,Q19,how old was britney spears when she recorded hit me baby one more time,D19-14,"""...Baby One More Time"" was nominated for a Grammy Award for Best Female Pop Vocal Performance , and has been included in lists by Blender , Rolling Stone and VH1 .",0.0 -3,Q19,how old was britney spears when she recorded hit me baby one more time,D19-15,It has been noted for redefining the sound of late 1990s music.,0.0 -4,Q19,how old was britney spears when she recorded hit me baby one more time,D19-16,"Spears has named ""...Baby One More Time"" as one of her favorite songs from her career.",0.0 -5,Q19,how old was britney spears when she recorded hit me baby one more time,D19-17,It was also the final song to be played on the BBC 's music programme Top of the Pops .,0.0 -6,Q21,how are cholera and typhus transmitted and prevented,D21-0,Cholera is an infection in the small intestine caused by the bacterium Vibrio cholerae .,0.0 -7,Q21,how are cholera and typhus transmitted and prevented,D21-1,The main symptoms are watery diarrhea and vomiting .,0.0 -8,Q21,how are cholera and typhus transmitted and prevented,D21-2,"Transmission occurs primarily by drinking water or eating food that has been contaminated by the feces (waste product) of an infected person, including one with no apparent symptoms.",1.0 -9,Q21,how are cholera and typhus transmitted and prevented,D21-3,"The severity of the diarrhea and vomiting can lead to rapid dehydration and electrolyte imbalance, and death in some cases.",0.0 -10,Q21,how are cholera and typhus transmitted and prevented,D21-4,"The primary treatment is oral rehydration therapy , typically with oral rehydration solution , to replace water and electrolytes.",0.0 -11,Q21,how are cholera and typhus transmitted and prevented,D21-5,"If this is not tolerated or does not provide improvement fast enough, intravenous fluids can also be used.",0.0 -12,Q21,how are cholera and typhus transmitted and prevented,D21-6,Antibacterial drugs are beneficial in those with severe disease to shorten its duration and severity.,0.0 -13,Q21,how are cholera and typhus transmitted and prevented,D21-7,"Worldwide, it affects 3–5 million people and causes 100,000–130,000 deaths a year .",0.0 -14,Q21,how are cholera and typhus transmitted and prevented,D21-8,Cholera was one of the earliest infections to be studied by epidemiological methods.,0.0 -15,Q22,how old is sybil vane in the picture of dorian gray,D22-0,"The Picture of Dorian Gray is the only published novel by Oscar Wilde , appearing as the lead story in Lippincott's Monthly Magazine on 20 June 1890, printed as the July 1890 issue of this magazine.",0.0 -16,Q22,how old is sybil vane in the picture of dorian gray,D22-1,"The magazine's editors feared the story was indecent as submitted, so they censored roughly 500 words, without Wilde's knowledge, before publication.",0.0 -17,Q22,how old is sybil vane in the picture of dorian gray,D22-2,"But even with that, the story was still greeted with outrage by British reviewers, some of whom suggested that Wilde should be prosecuted on moral grounds, leading Wilde to defend the novel aggressively in letters to the British press.",0.0 -18,Q22,how old is sybil vane in the picture of dorian gray,D22-3,"Wilde later revised the story for book publication, making substantial alterations, deleting controversial passages, adding new chapters and including an aphoristic Preface which has since become famous in its own right.",0.0 -19,Q22,how old is sybil vane in the picture of dorian gray,D22-4,"The amended version was published by Ward, Lock and Company in April 1891.",0.0 diff --git a/matchzoo/datasets/toy/train.csv b/matchzoo/datasets/toy/train.csv deleted file mode 100644 index 5e930648..00000000 --- a/matchzoo/datasets/toy/train.csv +++ /dev/null @@ -1,101 +0,0 @@ -,id_left,text_left,id_right,text_right,label -0,Q1,how are glacier caves formed?,D1-0,A partly submerged glacier cave on Perito Moreno Glacier .,0.0 -1,Q1,how are glacier caves formed?,D1-1,The ice facade is approximately 60 m high,0.0 -2,Q1,how are glacier caves formed?,D1-2,Ice formations in the Titlis glacier cave,0.0 -3,Q1,how are glacier caves formed?,D1-3,A glacier cave is a cave formed within the ice of a glacier .,1.0 -4,Q1,how are glacier caves formed?,D1-4,"Glacier caves are often called ice caves , but this term is properly used to describe bedrock caves that contain year-round ice.",0.0 -5,Q2,How are the directions of the velocity and force vectors related in a circular motion,D2-0,"In physics , circular motion is a movement of an object along the circumference of a circle or rotation along a circular path.",0.0 -6,Q2,How are the directions of the velocity and force vectors related in a circular motion,D2-1,"It can be uniform, with constant angular rate of rotation (and constant speed), or non-uniform with a changing rate of rotation.",0.0 -7,Q2,How are the directions of the velocity and force vectors related in a circular motion,D2-2,The rotation around a fixed axis of a three-dimensional body involves circular motion of its parts.,0.0 -8,Q2,How are the directions of the velocity and force vectors related in a circular motion,D2-3,The equations of motion describe the movement of the center of mass of a body.,0.0 -9,Q2,How are the directions of the velocity and force vectors related in a circular motion,D2-4,"Examples of circular motion include: an artificial satellite orbiting the Earth at constant height, a stone which is tied to a rope and is being swung in circles, a car turning through a curve in a race track , an electron moving perpendicular to a uniform magnetic field , and a gear turning inside a mechanism.",0.0 -10,Q2,How are the directions of the velocity and force vectors related in a circular motion,D2-5,"Since the object's velocity vector is constantly changing direction, the moving object is undergoing acceleration by a centripetal force in the direction of the center of rotation.",0.0 -11,Q2,How are the directions of the velocity and force vectors related in a circular motion,D2-6,"Without this acceleration, the object would move in a straight line, according to Newton's laws of motion .",0.0 -12,Q5,how did apollo creed die,D5-0,"Apollo Creed is a fictional character from the Rocky films , initially portrayed as the Undisputed Heavyweight Champion of the World.",0.0 -13,Q5,how did apollo creed die,D5-1,He was played by Carl Weathers .,0.0 -14,Q5,how did apollo creed die,D5-2,"Creed had multiple nicknames, including The Master of Disaster, The King of Sting, The Dancing Destroyer, The Prince of Punch, The One and Only and The Count of Monte Fisto.",0.0 -15,Q5,how did apollo creed die,D5-3,"Urban legend states that Apollo Creed's name is a wordplay on the Apostles' Creed , a statement of belief used in Christian churches.",0.0 -16,Q5,how did apollo creed die,D5-4,All of Apollo's championship fights were scheduled for the 15 round distance.,0.0 -17,Q5,how did apollo creed die,D5-5,Championship fights did not convert from 15 rounds to 12 rounds until 1987.,0.0 -18,Q5,how did apollo creed die,D5-6,"Rocky Balboa is often wrongly credited with popularizing the red, white, and blue trunks; Creed was the first man to wear them (latterly worn by Rocky Balboa in the 3rd and 4th installments and finally by Tommy ""The Machine"" Gunn ( Tommy Morrison ) in the 5th installment) although normally he wore red and white, as seen in Rocky II.",0.0 -19,Q5,how did apollo creed die,D5-7,Balboa's signature colors were black and gold—colors he used in the latest movie .,0.0 -20,Q6,how long is the term for federal judges,D6-0,"In the United States, the title of federal judge usually means a judge appointed by the President of the United States and confirmed by the United States Senate pursuant to the Appointments Clause in Article II of the United States Constitution .",0.0 -21,Q6,how long is the term for federal judges,D6-1,"In addition to the Supreme Court of the United States , whose existence and some aspects of whose jurisdiction are beyond the constitutional power of Congress to alter, acts of Congress have established 13 courts of appeals (also called ""circuit courts"") with appellate jurisdiction over different regions of the United States, and 94 United States district courts .",0.0 -22,Q6,how long is the term for federal judges,D6-2,"Every judge appointed to such a court may be categorized as a federal judge; such positions include the Chief Justice and Associate Justices of the Supreme Court, Circuit Judges of the courts of appeals, and district judges of the United States district courts .",0.0 -23,Q6,how long is the term for federal judges,D6-3,"All of these judges described thus far are referred to sometimes as ""Article III judges"" because they exercise the judicial power vested in the judicial branch of the federal government by Article III of the U.S. Constitution.",0.0 -24,Q6,how long is the term for federal judges,D6-4,"In addition, judges of the Court of International Trade exercise judicial power pursuant to Article III.",0.0 -25,Q6,how long is the term for federal judges,D6-5,"Other judges serving in the federal courts, including magistrate judges and bankruptcy judges , are also sometimes referred to as ""federal judges""; however, they are neither appointed by the President nor confirmed by the Senate, and their power derives from Article I instead.",0.0 -26,Q6,how long is the term for federal judges,D6-6,See Article I and Article III tribunals .,0.0 -27,Q7,how a beretta model 21 pistols magazines works,D7-0,The Beretta 21A Bobcat is a small pocket-sized semi-automatic pistol designed by Beretta in Italy.,0.0 -28,Q7,how a beretta model 21 pistols magazines works,D7-1,"Production began in late 1984, solely in the Beretta U.S.A. facility in Accokeek, Maryland.",0.0 -29,Q7,how a beretta model 21 pistols magazines works,D7-2,"It is a further development of the Beretta Model 20, whose production ended in 1985.",0.0 -30,Q9,how a vul works,D9-0,Variable Universal Life Insurance (often shortened to VUL) is a type of life insurance that builds a cash value.,0.0 -31,Q9,how a vul works,D9-1,"In a VUL, the cash value can be invested in a wide variety of separate accounts , similar to mutual funds , and the choice of which of the available separate accounts to use is entirely up to the contract owner.",0.0 -32,Q9,how a vul works,D9-2,The 'variable' component in the name refers to this ability to invest in separate accounts whose values vary—they vary because they are invested in stock and/or bond markets.,0.0 -33,Q9,how a vul works,D9-3,The 'universal' component in the name refers to the flexibility the owner has in making premium payments.,0.0 -34,Q9,how a vul works,D9-4,The premiums can vary from nothing in a given month up to maximums defined by the Internal Revenue Code for life insurance.,0.0 -35,Q9,how a vul works,D9-5,"This flexibility is in contrast to whole life insurance that has fixed premium payments that typically cannot be missed without lapsing the policy (although one may exercise an Automatic Premium Loan feature, or surrender dividends to pay a Whole Life premium).",0.0 -36,Q9,how a vul works,D9-6,"Variable universal life is a type of permanent life insurance , because the death benefit will be paid if the insured dies any time as long as there is sufficient cash value to pay the costs of insurance in the policy.",0.0 -37,Q9,how a vul works,D9-7,"With most if not all VULs, unlike whole life, there is no endowment age (the age at which the cash value equals the death benefit amount, which for whole life is typically 100).",0.0 -38,Q9,how a vul works,D9-8,This is yet another key advantage of VUL over Whole Life.,0.0 -39,Q9,how a vul works,D9-9,"With a typical whole life policy, the death benefit is limited to the face amount specified in the policy, and at endowment age, the face amount is all that is paid out.",0.0 -40,Q9,how a vul works,D9-10,"Thus with either death or endowment, the insurance company keeps any cash value built up over the years.",0.0 -41,Q9,how a vul works,D9-11,"However, some participating whole life policies offer riders which specify that any dividends paid on the policy be used to purchase ""paid up additions"" to the policy which increase both the cash value and the death benefit over time.",0.0 -42,Q9,how a vul works,D9-12,"If investments made in the separate accounts out-perform the general account of the insurance company, a higher rate-of-return can occur than the fixed rates-of-return typical for whole life.",0.0 -43,Q9,how a vul works,D9-13,"The combination over the years of no endowment age, continually increasing death benefit, and if a high rate-of-return is earned in the separate accounts of a VUL policy, this could result in higher value to the owner or beneficiary than that of a whole life policy with the same amounts of money paid in as premiums.",0.0 -44,Q10,how an outdoor wood boiler works,D10-0,The outdoor wood boiler is a variant of the classic wood stove adapted for set-up outdoors while still transferring the heat to interior buildings.,0.0 -45,Q12,how big did girl scout cookie boxes used to be,D12-0,A mound of Girl Scout cookies.,0.0 -46,Q12,how big did girl scout cookie boxes used to be,D12-1,This mound contains 74 boxes of cookies,0.0 -47,Q12,how big did girl scout cookie boxes used to be,D12-2,Girl Scout Cookies are cookies sold by Girl Scouts of the USA (GSUSA) as one of its major fundraisers for local Scout units.,0.0 -48,Q12,how big did girl scout cookie boxes used to be,D12-3,Members of the GSUSA have been selling cookies since 1917 to raise funds.,0.0 -49,Q12,how big did girl scout cookie boxes used to be,D12-4,Girls who participate can earn prizes for their efforts.,0.0 -50,Q12,how big did girl scout cookie boxes used to be,D12-5,There are also unit incentives if the unit as a whole does well.,0.0 -51,Q12,how big did girl scout cookie boxes used to be,D12-6,"As of 2007, sales were estimated at about 200 million boxes per year.",0.0 -52,Q13,how big is the purdue greek system,D13-0,University Hall,0.0 -53,Q13,how big is the purdue greek system,D13-1,"Purdue University, located in West Lafayette, Indiana , is the flagship university of the six-campus Purdue University system .",0.0 -54,Q13,how big is the purdue greek system,D13-2,"Purdue was founded on May 6, 1869, as a land-grant university when the Indiana General Assembly , taking advantage of the Morrill Act , accepted a donation of land and money from Lafayette businessman John Purdue to establish a college of science, technology, and agriculture in his name.",0.0 -55,Q13,how big is the purdue greek system,D13-3,"The first classes were held on September 16, 1874, with six instructors and 39 students.",0.0 -56,Q13,how big is the purdue greek system,D13-4,"Today, Purdue is a member of the Big Ten Conference , and is a well known world-class research institution.",0.0 -57,Q13,how big is the purdue greek system,D13-5,Purdue enrolls the second largest student body of any university in Indiana as well as the fourth largest international student population of any university in the United States.,0.0 -58,Q13,how big is the purdue greek system,D13-6,"Purdue offers both undergraduate and graduate programs in over 211 major areas of study, and is well known for its competitive engineering curricula.",0.0 -59,Q13,how big is the purdue greek system,D13-7,"The university has also been highly influential in America's history of aviation , having established the first college credit offered in flight training , the first four-year bachelor's degree in aviation, and the first university airport ( Purdue University Airport ).",0.0 -60,Q13,how big is the purdue greek system,D13-8,Purdue's aviation technology program remains one of the most competitive aviation-specific programs in the world.,0.0 -61,Q13,how big is the purdue greek system,D13-9,"In the mid-20th century, Purdue's aviation program expanded to encompass advanced spaceflight technology giving rise to Purdue's nickname, Cradle of Astronauts.",0.0 -62,Q13,how big is the purdue greek system,D13-10,"Twenty-three Purdue graduates have gone on to become astronauts, including Gus Grissom (one of the original Mercury Seven astronauts), Neil Armstrong (the first person to walk on the moon), and Eugene Cernan (the most recent person to walk on the moon).",0.0 -63,Q14,how big do sebaceous cysts get,D14-0,A sebaceous cyst () is a term that loosely refers to either epidermoid cysts (also known as epidermal cysts; L72.0 ) or pilar cysts (also known as trichilemmal cysts; L72.1 ).,0.0 -64,Q14,how big do sebaceous cysts get,D14-1,"Because an epidermoid cyst originates in the epidermis and a pilar cyst originates from hair follicles , by definition, neither type of cyst is strictly a sebaceous cyst.",0.0 -65,Q14,how big do sebaceous cysts get,D14-2,"The name is regarded as a misnomer as the fatty, white, semi-solid material in both of these cyst entities is not sebum , but keratin .",0.0 -66,Q14,how big do sebaceous cysts get,D14-3,"Furthermore, under the microscope neither entity contains sebaceous glands .",0.0 -67,Q14,how big do sebaceous cysts get,D14-4,"In practice, however, the terms are often used interchangeably.",0.0 -68,Q14,how big do sebaceous cysts get,D14-5,"""True"" sebaceous cysts are relatively rare and are known as steatocystomas or, if multiple, as steatocystoma multiplex .",0.0 -69,Q15,how are pointe shoes made,D15-0,Modern pointe shoes.,0.0 -70,Q15,how are pointe shoes made,D15-1,"The edge of the toe pad, which is inserted between the foot and toe box for cushioning, can be seen on the right foot.",0.0 -71,Q15,how are pointe shoes made,D15-2,A pointe shoe is a type of shoe worn by ballet dancers when performing pointe work .,0.0 -72,Q15,how are pointe shoes made,D15-3,Pointe shoes developed from the desire for dancers to appear weightless and sylph -like and have evolved to enable dancers to dance en pointe (on the tips of their toes) for extended periods of time.,0.0 -73,Q15,how are pointe shoes made,D15-4,"They are normally worn by female dancers, though male dancers may wear them for unorthodox roles such as the ugly stepsisters in Cinderella , Bottom in A Midsummer Night's Dream , or men performing as women in dance companies such as Les Ballets Trockadero and Grandiva.",0.0 -74,Q15,how are pointe shoes made,D15-5,"They are manufactured in a variety of colors, most commonly in shades of light pink.",0.0 -75,Q16,how much is 1 tablespoon of water,D16-0,This tablespoon has a capacity of about 15 mL.,1.0 -76,Q16,how much is 1 tablespoon of water,D16-1,Measuring Spoons,0.0 -77,Q16,how much is 1 tablespoon of water,D16-2,"In the US and parts of Canada, a tablespoon is the largest type of spoon used for eating from a bowl.",0.0 -78,Q16,how much is 1 tablespoon of water,D16-3,"In the UK, Europe and most Commonwealth countries, a tablespoon is a type of large spoon usually used for serving.",0.0 -79,Q16,how much is 1 tablespoon of water,D16-4,"In countries where a tablespoon is a serving spoon, the nearest equivalent to the US tablespoon is either the dessert spoon or the soup spoon .",0.0 -80,Q16,how much is 1 tablespoon of water,D16-5,"A tablespoonful, nominally the capacity of one tablespoon, is commonly used as a measure of volume in cooking .",0.0 -81,Q16,how much is 1 tablespoon of water,D16-6,"It is abbreviated as T, tb, tbs, tbsp, tblsp, or tblspn.",0.0 -82,Q16,how much is 1 tablespoon of water,D16-7,The capacity of ordinary tablespoons is not regulated by law and is subject to considerable variation.,0.0 -83,Q16,how much is 1 tablespoon of water,D16-8,In the USA one tablespoon (measurement unit) is approximately 15 mL; the capacity of an actual tablespoon (dining utensil) ranges from 7 mL to 14 mL.,1.0 -84,Q16,how much is 1 tablespoon of water,D16-9,In Australia one tablespoon (measurement unit) is 20 mL.,1.0 -85,Q17,how much are the harry potter movies worth,D17-0,Harry Potter is a series of seven fantasy novels written by the British author J. K. Rowling .,0.0 -86,Q17,how much are the harry potter movies worth,D17-1,"The books chronicle the adventures of a wizard , Harry Potter , and his friends Ronald Weasley and Hermione Granger , all of whom are students at Hogwarts School of Witchcraft and Wizardry .",0.0 -87,Q17,how much are the harry potter movies worth,D17-2,"The main story arc concerns Harry's quest to overcome the Dark wizard Lord Voldemort , whose aims are to become immortal, conquer the wizarding world , subjugate non-magical people, and destroy all those who stand in his way, especially Harry Potter.",0.0 -88,Q17,how much are the harry potter movies worth,D17-3,"Since the release of the first novel Harry Potter and the Philosopher's Stone on 30 June 1997, the books have gained immense popularity, critical acclaim and commercial success worldwide.",0.0 -89,Q17,how much are the harry potter movies worth,D17-4,"The series has also had some share of criticism, including concern for the increasingly dark tone.",0.0 -90,Q17,how much are the harry potter movies worth,D17-5,", the book series has sold about 450 million copies, making it the best-selling book series in history , and has been translated into 67 languages .",0.0 -91,Q17,how much are the harry potter movies worth,D17-6,The last four books consecutively set records as the fastest-selling books in history.,0.0 -92,Q17,how much are the harry potter movies worth,D17-7,"A series of many genres , including fantasy and coming of age (with elements of mystery , thriller , adventure , and romance ), it has many cultural meanings and references.",0.0 -93,Q17,how much are the harry potter movies worth,D17-8,"According to Rowling, the main theme is death.",0.0 -94,Q17,how much are the harry potter movies worth,D17-9,"There are also many other themes in the series, such as prejudice and corruption.",0.0 -95,Q17,how much are the harry potter movies worth,D17-10,The initial major publishers of the books were Bloomsbury in the United Kingdom and Scholastic Press in the United States.,0.0 -96,Q17,how much are the harry potter movies worth,D17-11,The books have since been published by many publishers worldwide.,0.0 -97,Q17,how much are the harry potter movies worth,D17-12,"The books, with the seventh book split into two parts, have been made into an eight-part film series by Warner Bros. Pictures , the highest-grossing film series of all time.",0.0 -98,Q17,how much are the harry potter movies worth,D17-13,"The series also originated much tie-in merchandise, making the Harry Potter brand worth in excess of $15 billion.",1.0 -99,Q17,how much are the harry potter movies worth,D17-14,"Also due to the success of the books and films, Harry Potter has been used for a theme park, The Wizarding World of Harry Potter in Universal Parks & Resorts 's Islands of Adventure .",0.0 diff --git a/matchzoo/datasets/wiki_qa/__init__.py b/matchzoo/datasets/wiki_qa/__init__.py deleted file mode 100644 index 5b6c9450..00000000 --- a/matchzoo/datasets/wiki_qa/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .load_data import load_data diff --git a/matchzoo/datasets/wiki_qa/load_data.py b/matchzoo/datasets/wiki_qa/load_data.py deleted file mode 100644 index dd3f2071..00000000 --- a/matchzoo/datasets/wiki_qa/load_data.py +++ /dev/null @@ -1,90 +0,0 @@ -"""WikiQA data loader.""" - -import typing -import csv -from pathlib import Path - -import keras -import pandas as pd - -import matchzoo - -_url = "https://download.microsoft.com/download/E/5/F/" \ - "E5FCFCEE-7005-4814-853D-DAA7C66507E0/WikiQACorpus.zip" - - -def load_data( - stage: str = 'train', - task: str = 'ranking', - filtered: bool = False, - return_classes: bool = False -) -> typing.Union[matchzoo.DataPack, tuple]: - """ - Load WikiQA data. - - :param stage: One of `train`, `dev`, and `test`. - :param task: Could be one of `ranking`, `classification` or a - :class:`matchzoo.engine.BaseTask` instance. - :param filtered: Whether remove the questions without correct answers. - :param return_classes: `True` to return classes for classification task, - `False` otherwise. - - :return: A DataPack unless `task` is `classificiation` and `return_classes` - is `True`: a tuple of `(DataPack, classes)` in that case. - """ - if stage not in ('train', 'dev', 'test'): - raise ValueError(f"{stage} is not a valid stage." - f"Must be one of `train`, `dev`, and `test`.") - - data_root = _download_data() - file_path = data_root.joinpath(f'WikiQA-{stage}.tsv') - data_pack = _read_data(file_path) - if filtered and stage in ('dev', 'test'): - ref_path = data_root.joinpath(f'WikiQA-{stage}.ref') - filter_ref_path = data_root.joinpath(f'WikiQA-{stage}-filtered.ref') - with open(filter_ref_path, mode='r') as f: - filtered_ids = set([line.split()[0] for line in f]) - filtered_lines = [] - with open(ref_path, mode='r') as f: - for idx, line in enumerate(f.readlines()): - if line.split()[0] in filtered_ids: - filtered_lines.append(idx) - data_pack = data_pack[filtered_lines] - - if task == 'ranking': - task = matchzoo.tasks.Ranking() - if task == 'classification': - task = matchzoo.tasks.Classification() - - if isinstance(task, matchzoo.tasks.Ranking): - return data_pack - elif isinstance(task, matchzoo.tasks.Classification): - data_pack.one_hot_encode_label(task.num_classes, inplace=True) - if return_classes: - return data_pack, [False, True] - else: - return data_pack - else: - raise ValueError(f"{task} is not a valid task." - f"Must be one of `Ranking` and `Classification`.") - - -def _download_data(): - ref_path = keras.utils.data_utils.get_file( - 'wikiqa', _url, extract=True, - cache_dir=matchzoo.USER_DATA_DIR, - cache_subdir='wiki_qa' - ) - return Path(ref_path).parent.joinpath('WikiQACorpus') - - -def _read_data(path): - table = pd.read_csv(path, sep='\t', header=0, quoting=csv.QUOTE_NONE) - df = pd.DataFrame({ - 'text_left': table['Question'], - 'text_right': table['Sentence'], - 'id_left': table['QuestionID'], - 'id_right': table['SentenceID'], - 'label': table['Label'] - }) - return matchzoo.pack(df) diff --git a/matchzoo/embedding/__init__.py b/matchzoo/embedding/__init__.py deleted file mode 100644 index 9bb7dd75..00000000 --- a/matchzoo/embedding/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .embedding import Embedding -from .embedding import load_from_file diff --git a/matchzoo/embedding/embedding.py b/matchzoo/embedding/embedding.py deleted file mode 100644 index fb21af36..00000000 --- a/matchzoo/embedding/embedding.py +++ /dev/null @@ -1,111 +0,0 @@ -"""Matchzoo toolkit for token embedding.""" - -import csv -import typing - -import numpy as np -import pandas as pd - -import matchzoo as mz - - -class Embedding(object): - """ - Embedding class. - - Examples:: - >>> import matchzoo as mz - >>> train_raw = mz.datasets.toy.load_data() - >>> pp = mz.preprocessors.NaivePreprocessor() - >>> train = pp.fit_transform(train_raw, verbose=0) - >>> vocab_unit = mz.build_vocab_unit(train, verbose=0) - >>> term_index = vocab_unit.state['term_index'] - >>> embed_path = mz.datasets.embeddings.EMBED_RANK - - To load from a file: - >>> embedding = mz.embedding.load_from_file(embed_path) - >>> matrix = embedding.build_matrix(term_index) - >>> matrix.shape[0] == len(term_index) - True - - To build your own: - >>> data = pd.DataFrame(data=[[0, 1], [2, 3]], index=['A', 'B']) - >>> embedding = mz.Embedding(data) - >>> matrix = embedding.build_matrix({'A': 2, 'B': 1, '_PAD': 0}) - >>> matrix.shape == (3, 2) - True - - """ - - def __init__(self, data: pd.DataFrame): - """ - Embedding. - - :param data: DataFrame to use as term to vector mapping. - """ - self._data = data - - @property - def input_dim(self) -> int: - """:return Embedding input dimension.""" - return self._data.shape[0] - - @property - def output_dim(self) -> int: - """:return Embedding output dimension.""" - return self._data.shape[1] - - def build_matrix( - self, - term_index: typing.Union[ - dict, mz.preprocessors.units.Vocabulary.TermIndex], - initializer=lambda: np.random.uniform(-0.2, 0.2) - ) -> np.ndarray: - """ - Build a matrix using `term_index`. - - :param term_index: A `dict` or `TermIndex` to build with. - :param initializer: A callable that returns a default value for missing - terms in data. (default: a random uniform distribution in range) - `(-0.2, 0.2)`). - :return: A matrix. - """ - input_dim = len(term_index) - - matrix = np.empty((input_dim, self.output_dim)) - for index in np.ndindex(*matrix.shape): - matrix[index] = initializer() - - valid_keys = set(self._data.index) - for term, index in term_index.items(): - if term in valid_keys: - matrix[index] = self._data.loc[term] - - return matrix - - -def load_from_file(file_path: str, mode: str = 'word2vec') -> Embedding: - """ - Load embedding from `file_path`. - - :param file_path: Path to file. - :param mode: Embedding file format mode, one of 'word2vec' or 'glove'. - (default: 'word2vec') - :return: An :class:`matchzoo.embedding.Embedding` instance. - """ - if mode == 'word2vec': - data = pd.read_csv(file_path, - sep=" ", - index_col=0, - header=None, - skiprows=1) - elif mode == 'glove': - data = pd.read_csv(file_path, - sep=" ", - index_col=0, - header=None, - quoting=csv.QUOTE_NONE) - else: - raise TypeError(f"{mode} is not a supported embedding type." - f"`word2vec` or `glove` expected.") - return Embedding(data) diff --git a/matchzoo/engine/__init__.py b/matchzoo/engine/__init__.py deleted file mode 100644 index c58af077..00000000 --- a/matchzoo/engine/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# `engine` dependencies span across the entire project, so it's better to -# leave this __init__.py empty, and use `from matchzoo.engine.package import -# x` or `from matchzoo.engine import package` instead of `from matchzoo -# import engine`. diff --git a/matchzoo/engine/base_metric.py b/matchzoo/engine/base_metric.py deleted file mode 100644 index 2a87f2ae..00000000 --- a/matchzoo/engine/base_metric.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Metric base class and some related utilities.""" - -import abc - -import numpy as np - - -class BaseMetric(abc.ABC): - """Metric base class.""" - - ALIAS = 'base_metric' - - @abc.abstractmethod - def __call__(self, y_true: np.array, y_pred: np.array) -> float: - """ - Call to compute the metric. - - :param y_true: An array of groud truth labels. - :param y_pred: An array of predicted values. - :return: Evaluation of the metric. - """ - - @abc.abstractmethod - def __repr__(self): - """:return: Formated string representation of the metric.""" - - def __eq__(self, other): - """:return: `True` if two metrics are equal, `False` otherwise.""" - return (type(self) is type(other)) and (vars(self) == vars(other)) - - def __hash__(self): - """:return: Hashing value using the metric as `str`.""" - return str(self).__hash__() - - -def sort_and_couple(labels: np.array, scores: np.array) -> np.array: - """Zip the `labels` with `scores` into a single list.""" - couple = list(zip(labels, scores)) - return np.array(sorted(couple, key=lambda x: x[1], reverse=True)) diff --git a/matchzoo/engine/base_model.py b/matchzoo/engine/base_model.py deleted file mode 100644 index c134bbb2..00000000 --- a/matchzoo/engine/base_model.py +++ /dev/null @@ -1,581 +0,0 @@ -"""Base Model.""" - -import abc -import typing -from pathlib import Path - -import dill -import numpy as np -import keras -import keras.backend as K -import pandas as pd - -import matchzoo -from matchzoo import DataGenerator -from matchzoo.engine import hyper_spaces -from matchzoo.engine.base_preprocessor import BasePreprocessor -from matchzoo.engine.base_metric import BaseMetric -from matchzoo.engine.param_table import ParamTable -from matchzoo.engine.param import Param -from matchzoo import tasks - - -class BaseModel(abc.ABC): - """ - Abstract base class of all MatchZoo models. - - MatchZoo models are wrapped over keras models, and the actual keras model - built can be accessed by `model.backend`. `params` is a set of model - hyper-parameters that deterministically builds a model. In other words, - `params['model_class'](params=params)` of the same `params` always create - models with the same structure. - - :param params: Model hyper-parameters. (default: return value from - :meth:`get_default_params`) - :param backend: A keras model as the model backend. Usually not passed as - an argument. - - Example: - >>> BaseModel() # doctest: +ELLIPSIS - Traceback (most recent call last): - ... - TypeError: Can't instantiate abstract class BaseModel ... - >>> class MyModel(BaseModel): - ... def build(self): - ... pass - >>> isinstance(MyModel(), BaseModel) - True - - """ - - BACKEND_WEIGHTS_FILENAME = 'backend_weights.h5' - PARAMS_FILENAME = 'params.dill' - - def __init__( - self, - params: typing.Optional[ParamTable] = None, - backend: typing.Optional[keras.models.Model] = None - ): - """Init.""" - self._params = params or self.get_default_params() - self._backend = backend - - @classmethod - def get_default_params( - cls, - with_embedding=False, - with_multi_layer_perceptron=False - ) -> ParamTable: - """ - Model default parameters. - - The common usage is to instantiate :class:`matchzoo.engine.ModelParams` - first, then set the model specific parametrs. - - Examples: - >>> class MyModel(BaseModel): - ... def build(self): - ... print(self._params['num_eggs'], 'eggs') - ... print('and', self._params['ham_type']) - ... - ... @classmethod - ... def get_default_params(cls): - ... params = ParamTable() - ... params.add(Param('num_eggs', 512)) - ... params.add(Param('ham_type', 'Parma Ham')) - ... return params - >>> my_model = MyModel() - >>> my_model.build() - 512 eggs - and Parma Ham - - Notice that all parameters must be serialisable for the entire model - to be serialisable. Therefore, it's strongly recommended to use python - native data types to store parameters. - - :return: model parameters - - """ - params = ParamTable() - params.add(Param( - name='model_class', value=cls, - desc="Model class. Used internally for save/load. " - "Changing this may cause unexpected behaviors." - )) - params.add(Param( - name='input_shapes', - desc="Dependent on the model and data. Should be set manually." - )) - params.add(Param( - name='task', - desc="Decides model output shape, loss, and metrics." - )) - params.add(Param( - name='optimizer', value='adam', - )) - if with_embedding: - params.add(Param( - name='with_embedding', value=True, - desc="A flag used help `auto` module. Shouldn't be changed." - )) - params.add(Param( - name='embedding_input_dim', - desc='Usually equals vocab size + 1. Should be set manually.' - )) - params.add(Param( - name='embedding_output_dim', - desc='Should be set manually.' - )) - params.add(Param( - name='embedding_trainable', value=True, - desc='`True` to enable embedding layer training, ' - '`False` to freeze embedding parameters.' - )) - if with_multi_layer_perceptron: - params.add(Param( - name='with_multi_layer_perceptron', value=True, - desc="A flag of whether a multiple layer perceptron is used. " - "Shouldn't be changed." - )) - params.add(Param( - name='mlp_num_units', value=128, - desc="Number of units in first `mlp_num_layers` layers.", - hyper_space=hyper_spaces.quniform(8, 256, 8) - )) - params.add(Param( - name='mlp_num_layers', value=3, - desc="Number of layers of the multiple layer percetron.", - hyper_space=hyper_spaces.quniform(1, 6) - )) - params.add(Param( - name='mlp_num_fan_out', value=64, - desc="Number of units of the layer that connects the multiple " - "layer percetron and the output.", - hyper_space=hyper_spaces.quniform(4, 128, 4) - )) - params.add(Param( - name='mlp_activation_func', value='relu', - desc='Activation function used in the multiple ' - 'layer perceptron.' - )) - return params - - @classmethod - def get_default_preprocessor(cls) -> BasePreprocessor: - """ - Model default preprocessor. - - The preprocessor's transform should produce a correctly shaped data - pack that can be used for training. Some extra configuration (e.g. - setting `input_shapes` in :class:`matchzoo.models.DSSMModel` may be - required on the user's end. - - :return: Default preprocessor. - """ - return matchzoo.preprocessors.BasicPreprocessor() - - @property - def params(self) -> ParamTable: - """:return: model parameters.""" - return self._params - - @params.setter - def params(self, val): - self._params = val - - @property - def backend(self) -> keras.models.Model: - """:return model backend, a keras model instance.""" - if not self._backend: - raise ValueError("Backend not found." - "Please build the model first.") - else: - return self._backend - - @abc.abstractmethod - def build(self): - """Build model, each subclass need to impelemnt this method.""" - - def compile(self): - """ - Compile model for training. - - Only `keras` native metrics are compiled together with backend. - MatchZoo metrics are evaluated only through :meth:`evaluate`. - Notice that `keras` count `loss` as one of the metrics while MatchZoo - :class:`matchzoo.engine.BaseTask` does not. - - Examples: - >>> from matchzoo import models - >>> model = models.Naive() - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.params['task'].metrics = ['mse', 'map'] - >>> model.params['task'].metrics - ['mse', mean_average_precision(0.0)] - >>> model.build() - >>> model.compile() - - """ - self._backend.compile(optimizer=self._params['optimizer'], - loss=self._params['task'].loss) - - def fit( - self, - x: typing.Union[np.ndarray, typing.List[np.ndarray], dict], - y: np.ndarray, - batch_size: int = 128, - epochs: int = 1, - verbose: int = 1, - **kwargs - ) -> keras.callbacks.History: - """ - Fit the model. - - See :meth:`keras.models.Model.fit` for more details. - - :param x: input data. - :param y: labels. - :param batch_size: number of samples per gradient update. - :param epochs: number of epochs to train the model. - :param verbose: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = verbose, - 2 = one log line per epoch. - - Key word arguments not listed above will be propagated to keras's fit. - - :return: A `keras.callbacks.History` instance. Its history attribute - contains all information collected during training. - """ - return self._backend.fit(x=x, y=y, - batch_size=batch_size, epochs=epochs, - verbose=verbose, **kwargs) - - def fit_generator( - self, - generator: matchzoo.DataGenerator, - epochs: int = 1, - verbose: int = 1, - **kwargs - ) -> keras.callbacks.History: - """ - Fit the model with matchzoo `generator`. - - See :meth:`keras.models.Model.fit_generator` for more details. - - :param generator: A generator, an instance of - :class:`engine.DataGenerator`. - :param epochs: Number of epochs to train the model. - :param verbose: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = verbose, - 2 = one log line per epoch. - - :return: A `keras.callbacks.History` instance. Its history attribute - contains all information collected during training. - """ - return self._backend.fit_generator( - generator=generator, - epochs=epochs, - verbose=verbose, **kwargs - ) - - def evaluate( - self, - x: typing.Dict[str, np.ndarray], - y: np.ndarray, - batch_size: int = 128 - ) -> typing.Dict[BaseMetric, float]: - """ - Evaluate the model. - - :param x: Input data. - :param y: Labels. - :param batch_size: Number of samples when `predict` for evaluation. - (default: 128) - - Examples:: - >>> import matchzoo as mz - >>> data_pack = mz.datasets.toy.load_data() - >>> preprocessor = mz.preprocessors.NaivePreprocessor() - >>> data_pack = preprocessor.fit_transform(data_pack, verbose=0) - >>> m = mz.models.DenseBaseline() - >>> m.params['task'] = mz.tasks.Ranking() - >>> m.params['task'].metrics = [ - ... 'acc', 'mse', 'mae', 'ce', - ... 'average_precision', 'precision', 'dcg', 'ndcg', - ... 'mean_reciprocal_rank', 'mean_average_precision', 'mrr', - ... 'map', 'MAP', - ... mz.metrics.AveragePrecision(threshold=1), - ... mz.metrics.Precision(k=2, threshold=2), - ... mz.metrics.DiscountedCumulativeGain(k=2), - ... mz.metrics.NormalizedDiscountedCumulativeGain( - ... k=3, threshold=-1), - ... mz.metrics.MeanReciprocalRank(threshold=2), - ... mz.metrics.MeanAveragePrecision(threshold=3) - ... ] - >>> m.guess_and_fill_missing_params(verbose=0) - >>> m.build() - >>> m.compile() - >>> x, y = data_pack.unpack() - >>> evals = m.evaluate(x, y) - >>> type(evals) - - - """ - result = dict() - matchzoo_metrics, keras_metrics = self._separate_metrics() - y_pred = self.predict(x, batch_size) - - for metric in keras_metrics: - metric_func = keras.metrics.get(metric) - result[metric] = K.eval(K.mean( - metric_func(K.variable(y), K.variable(y_pred)))) - - if matchzoo_metrics: - if not isinstance(self.params['task'], tasks.Ranking): - raise ValueError("Matchzoo metrics only works on ranking.") - for metric in matchzoo_metrics: - result[metric] = self._eval_metric_on_data_frame( - metric, x['id_left'], y, y_pred) - - return result - - def evaluate_generator( - self, - generator: DataGenerator, - batch_size: int = 128 - ) -> typing.Dict['BaseMetric', float]: - """ - Evaluate the model. - - :param generator: DataGenerator to evluate. - :param batch_size: Batch size. (default: 128) - """ - x, y = generator[:] - return self.evaluate(x, y, batch_size=batch_size) - - def _separate_metrics(self): - matchzoo_metrics = [] - keras_metrics = [] - for metric in self._params['task'].metrics: - if isinstance(metric, BaseMetric): - matchzoo_metrics.append(metric) - else: - keras_metrics.append(metric) - return matchzoo_metrics, keras_metrics - - @classmethod - def _eval_metric_on_data_frame( - cls, - metric: BaseMetric, - id_left: typing.Union[list, np.array], - y: typing.Union[list, np.array], - y_pred: typing.Union[list, np.array] - ): - eval_df = pd.DataFrame(data={ - 'id': id_left, - 'true': y.squeeze(), - 'pred': y_pred.squeeze() - }) - assert isinstance(metric, BaseMetric) - val = eval_df.groupby(by='id').apply( - lambda df: metric(df['true'].values, df['pred'].values) - ).mean() - return val - - def predict( - self, - x: typing.Dict[str, np.ndarray], - batch_size=128 - ) -> np.ndarray: - """ - Generate output predictions for the input samples. - - See :meth:`keras.models.Model.predict` for more details. - - :param x: input data - :param batch_size: number of samples per gradient update - :return: numpy array(s) of predictions - """ - return self._backend.predict(x=x, batch_size=batch_size) - - def save(self, dirpath: typing.Union[str, Path]): - """ - Save the model. - - A saved model is represented as a directory with two files. One is a - model parameters file saved by `pickle`, and the other one is a model - h5 file saved by `keras`. - - :param dirpath: directory path of the saved model - - Example: - - >>> import matchzoo as mz - >>> model = mz.models.Naive() - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - >>> model.save('temp-model') - >>> import shutil - >>> shutil.rmtree('temp-model') - - """ - dirpath = Path(dirpath) - params_path = dirpath.joinpath(self.PARAMS_FILENAME) - weights_path = dirpath.joinpath(self.BACKEND_WEIGHTS_FILENAME) - - if not dirpath.exists(): - dirpath.mkdir(parents=True) - else: - raise FileExistsError(f'{dirpath} already exist, fail to save.') - - self._backend.save_weights(weights_path) - with open(params_path, mode='wb') as params_file: - dill.dump(self._params, params_file) - - def get_embedding_layer( - self, name: str = 'embedding' - ) -> keras.layers.Layer: - """ - Get the embedding layer. - - All MatchZoo models with a single embedding layer set the embedding - layer name to `embedding`, and this method should return that layer. - - :param name: Name of the embedding layer. (default: `embedding`) - """ - for layer in self._backend.layers: - if layer.name == name: - return layer - raise ValueError(f"Layer {name} not found. Initialize your embedding " - f"layer with `name='{name}'`.") - - def load_embedding_matrix( - self, - embedding_matrix: np.ndarray, - name: str = 'embedding' - ): - """ - Load an embedding matrix. - - Load an embedding matrix into the model's embedding layer. The name - of the embedding layer is specified by `name`. For models with only - one embedding layer, set `name='embedding'` when creating the keras - layer, and use the default `name` when load the matrix. For models - with more than one embedding layers, initialize keras layer with - different layer names, and set `name` accordingly to load a matrix - to a chosen layer. - - :param embedding_matrix: Embedding matrix to be loaded. - :param name: Name of the layer. (default: 'embedding') - """ - self.get_embedding_layer(name).set_weights([embedding_matrix]) - - def guess_and_fill_missing_params(self, verbose=1): - """ - Guess and fill missing parameters in :attr:`params`. - - Use this method to automatically fill-in other hyper parameters. - This involves some guessing so the parameter it fills could be - wrong. For example, the default task is `Ranking`, and if we do not - set it to `Classification` manaully for data packs prepared for - classification, then the shape of the model output and the data will - mismatch. - - :param verbose: Verbosity. - """ - self._params.get('task').set_default(tasks.Ranking(), verbose) - self._params.get('input_shapes').set_default([(30,), (30,)], verbose) - if 'with_embedding' in self._params: - self._params.get('embedding_input_dim').set_default(300, verbose) - self._params.get('embedding_output_dim').set_default(300, verbose) - - def _set_param_default(self, name: str, - default_val: str, verbose: int = 0): - if self._params[name] is None: - self._params[name] = default_val - if verbose: - print(f"Parameter \"{name}\" set to {default_val}.") - - def _make_inputs(self) -> list: - input_left = keras.layers.Input( - name='text_left', - shape=self._params['input_shapes'][0] - ) - input_right = keras.layers.Input( - name='text_right', - shape=self._params['input_shapes'][1] - ) - return [input_left, input_right] - - def _make_output_layer(self) -> keras.layers.Layer: - """:return: a correctly shaped keras dense layer for model output.""" - task = self._params['task'] - if isinstance(task, tasks.Classification): - return keras.layers.Dense(task.num_classes, activation='softmax') - elif isinstance(task, tasks.Ranking): - return keras.layers.Dense(1, activation='linear') - else: - raise ValueError(f"{task} is not a valid task type." - f"Must be in `Ranking` and `Classification`.") - - def _make_embedding_layer( - self, - name: str = 'embedding', - **kwargs - ) -> keras.layers.Layer: - return keras.layers.Embedding( - self._params['embedding_input_dim'], - self._params['embedding_output_dim'], - trainable=self._params['embedding_trainable'], - name=name, - **kwargs - ) - - def _make_multi_layer_perceptron_layer(self) -> keras.layers.Layer: - # TODO: do not create new layers for a second call - if not self._params['with_multi_layer_perceptron']: - raise AttributeError( - 'Parameter `with_multi_layer_perception` not set.') - - def _wrapper(x): - activation = self._params['mlp_activation_func'] - for _ in range(self._params['mlp_num_layers']): - x = keras.layers.Dense(self._params['mlp_num_units'], - activation=activation)(x) - return keras.layers.Dense(self._params['mlp_num_fan_out'], - activation=activation)(x) - - return _wrapper - - -def load_model(dirpath: typing.Union[str, Path]) -> BaseModel: - """ - Load a model. The reverse function of :meth:`BaseModel.save`. - - :param dirpath: directory path of the saved model - :return: a :class:`BaseModel` instance - - Example: - - >>> import matchzoo as mz - >>> model = mz.models.Naive() - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - >>> model.save('my-model') - >>> model.params.keys() == mz.load_model('my-model').params.keys() - True - >>> import shutil - >>> shutil.rmtree('my-model') - - """ - dirpath = Path(dirpath) - - params_path = dirpath.joinpath(BaseModel.PARAMS_FILENAME) - weights_path = dirpath.joinpath(BaseModel.BACKEND_WEIGHTS_FILENAME) - - with open(params_path, mode='rb') as params_file: - params = dill.load(params_file) - - model_instance = params['model_class'](params=params) - model_instance.build() - model_instance.compile() - model_instance.backend.load_weights(weights_path) - return model_instance diff --git a/matchzoo/engine/base_preprocessor.py b/matchzoo/engine/base_preprocessor.py deleted file mode 100644 index 8e1abf0b..00000000 --- a/matchzoo/engine/base_preprocessor.py +++ /dev/null @@ -1,141 +0,0 @@ -""":class:`BasePreprocessor` define input and ouutput for processors.""" - -import abc -import functools -import typing -from pathlib import Path - -import dill - -import matchzoo as mz - - -def validate_context(func): - """Validate context in the preprocessor.""" - - @functools.wraps(func) - def transform_wrapper(self, *args, **kwargs): - if not self.context: - raise ValueError('Please call `fit` before calling `transform`.') - return func(self, *args, **kwargs) - - return transform_wrapper - - -class BasePreprocessor(metaclass=abc.ABCMeta): - """ - :class:`BasePreprocessor` to input handle data. - - A preprocessor should be used in two steps. First, `fit`, then, - `transform`. `fit` collects information into `context`, which includes - everything the preprocessor needs to `transform` together with other - useful information for later use. `fit` will only change the - preprocessor's inner state but not the input data. In contrast, - `transform` returns a modified copy of the input data without changing - the preprocessor's inner state. - - """ - - DATA_FILENAME = 'preprocessor.dill' - - def __init__(self): - """Initialization.""" - self._context = {} - - @property - def context(self): - """Return context.""" - return self._context - - @abc.abstractmethod - def fit( - self, - data_pack: 'mz.DataPack', - verbose: int = 1 - ) -> 'BasePreprocessor': - """ - Fit parameters on input data. - - This method is an abstract base method, need to be - implemented in the child class. - - This method is expected to return itself as a callable - object. - - :param data_pack: :class:`Datapack` object to be fitted. - :param verbose: Verbosity. - """ - - @abc.abstractmethod - def transform( - self, - data_pack: 'mz.DataPack', - verbose: int = 1 - ) -> 'mz.DataPack': - """ - Transform input data to expected manner. - - This method is an abstract base method, need to be - implemented in the child class. - - :param data_pack: :class:`DataPack` object to be transformed. - :param verbose: Verbosity. - or list of text-left, text-right tuples. - """ - - def fit_transform( - self, - data_pack: 'mz.DataPack', - verbose: int = 1 - ) -> 'mz.DataPack': - """ - Call fit-transform. - - :param data_pack: :class:`DataPack` object to be processed. - :param verbose: Verbosity. - """ - return self.fit(data_pack, verbose=verbose) \ - .transform(data_pack, verbose=verbose) - - def save(self, dirpath: typing.Union[str, Path]): - """ - Save the :class:`DSSMPreprocessor` object. - - A saved :class:`DSSMPreprocessor` is represented as a directory with - the `context` object (fitted parameters on training data), it will - be saved by `pickle`. - - :param dirpath: directory path of the saved :class:`DSSMPreprocessor`. - """ - dirpath = Path(dirpath) - data_file_path = dirpath.joinpath(self.DATA_FILENAME) - - if data_file_path.exists(): - raise FileExistsError( - f'{data_file_path} instance exist, fail to save.') - elif not dirpath.exists(): - dirpath.mkdir() - - dill.dump(self, open(data_file_path, mode='wb')) - - @classmethod - def _default_units(cls) -> list: - """Prepare needed process units.""" - return [ - mz.preprocessors.units.tokenize.Tokenize(), - mz.preprocessors.units.lowercase.Lowercase(), - mz.preprocessors.units.punc_removal.PuncRemoval(), - ] - - -def load_preprocessor(dirpath: typing.Union[str, Path]) -> 'mz.DataPack': - """ - Load the fitted `context`. The reverse function of :meth:`save`. - - :param dirpath: directory path of the saved model. - :return: a :class:`DSSMPreprocessor` instance. - """ - dirpath = Path(dirpath) - - data_file_path = dirpath.joinpath(BasePreprocessor.DATA_FILENAME) - return dill.load(open(data_file_path, 'rb')) diff --git a/matchzoo/engine/base_task.py b/matchzoo/engine/base_task.py deleted file mode 100644 index 9b18b66a..00000000 --- a/matchzoo/engine/base_task.py +++ /dev/null @@ -1,83 +0,0 @@ -"""Base task.""" - -import typing -import abc - -from matchzoo.engine import base_metric -from matchzoo.engine import parse_metric - - -class BaseTask(abc.ABC): - """Base Task, shouldn't be used directly.""" - - def __init__(self, loss=None, metrics=None): - """ - Base task constructor. - - :param loss: By default the first loss in available losses. - :param metrics: - """ - self._loss = loss - self._metrics = self._convert_metrics(metrics) - self._assure_loss() - self._assure_metrics() - - def _convert_metrics(self, metrics): - if not metrics: - metrics = [] - elif not isinstance(metrics, list): - metrics = [metrics] - return [ - parse_metric.parse_metric(metric, self) for metric in metrics - ] - - def _assure_loss(self): - if not self._loss: - self._loss = self.list_available_losses()[0] - - def _assure_metrics(self): - if not self._metrics: - first_available = self.list_available_metrics()[0] - self._metrics = self._convert_metrics(first_available) - - @property - def loss(self): - """:return: Loss used in the task.""" - return self._loss - - @property - def metrics(self): - """:return: Metrics used in the task.""" - return self._metrics - - @metrics.setter - def metrics( - self, - new_metrics: typing.Union[ - typing.List[str], - typing.List[base_metric.BaseMetric], - str, - base_metric.BaseMetric - ] - ): - self._metrics = self._convert_metrics(new_metrics) - - @classmethod - @abc.abstractmethod - def list_available_losses(cls) -> list: - """:return: a list of available losses.""" - - @classmethod - @abc.abstractmethod - def list_available_metrics(cls) -> list: - """:return: a list of available metrics.""" - - @property - @abc.abstractmethod - def output_shape(self) -> tuple: - """:return: output shape of a single sample of the task.""" - - @property - @abc.abstractmethod - def output_dtype(self): - """:return: output data type for specific task.""" diff --git a/matchzoo/engine/callbacks.py b/matchzoo/engine/callbacks.py deleted file mode 100644 index fe1c01c5..00000000 --- a/matchzoo/engine/callbacks.py +++ /dev/null @@ -1,73 +0,0 @@ -"""Callbacks.""" -import typing -from pathlib import Path - -import numpy as np -import keras - -import matchzoo -from matchzoo.engine.base_model import BaseModel - - -class EvaluateAllMetrics(keras.callbacks.Callback): - """ - Callback to evaluate all metrics. - - MatchZoo metrics can not be evaluated batch-wise since they require - dataset-level information. As a result, MatchZoo metrics are not - evaluated automatically when a Model `fit`. When this callback is used, - all metrics, including MatchZoo metrics and Keras metrics, are evluated - once every `once_every` epochs. - - :param model: Model to evaluate. - :param x: X. - :param y: y. - :param once_every: Evaluation only triggers when `epoch % once_every == 0`. - (default: 1, i.e. evaluate on every epoch's end) - :param batch_size: Number of samples per evaluation. This only affects the - evaluation of Keras metrics, since MatchZoo metrics are always - evaluated using the full data. - :param model_save_path: Directory path to save the model after each - evaluate callback, (default: None, i.e., no saving.) - :param verbose: Verbosity. - """ - - def __init__( - self, - model: 'BaseModel', - x: typing.Union[np.ndarray, typing.List[np.ndarray]], - y: np.ndarray, - once_every: int = 1, - batch_size: int = 128, - model_save_path: str = None, - verbose=1 - ): - """Initializer.""" - super().__init__() - self._model = model - self._dev_x = x - self._dev_y = y - self._valid_steps = once_every - self._batch_size = batch_size - self._model_save_path = model_save_path - self._verbose = verbose - - def on_epoch_end(self, epoch: int, logs: dict = None): - """ - Called at the end of en epoch. - - :param epoch: integer, index of epoch. - :param logs: dictionary of logs. - :return: dictionary of logs. - """ - if (epoch + 1) % self._valid_steps == 0: - val_logs = self._model.evaluate(self._dev_x, self._dev_y, - self._batch_size) - if self._verbose: - print('Validation: ' + ' - '.join( - f'{k}: {v}' for k, v in val_logs.items())) - for k, v in val_logs.items(): - logs[k] = v - if self._model_save_path: - curr_path = self._model_save_path + str('%d/' % (epoch + 1)) - self._model.save(curr_path) diff --git a/matchzoo/engine/hyper_spaces.py b/matchzoo/engine/hyper_spaces.py deleted file mode 100644 index 54232f9d..00000000 --- a/matchzoo/engine/hyper_spaces.py +++ /dev/null @@ -1,216 +0,0 @@ -"""Hyper parameter search spaces wrapping `hyperopt`.""" -import typing -import numbers - -import hyperopt -import hyperopt.pyll.base - - -class HyperoptProxy(object): - """ - Hyperopt proxy class. - - See `hyperopt`'s documentation for more details: - https://github.com/hyperopt/hyperopt/wiki/FMin - - Reason of these wrappers: - - A hyper space in `hyperopt` requires a `label` to instantiate. This - `label` is used later as a reference to original hyper space that is - sampled. In `matchzoo`, hyper spaces are used in - :class:`matchzoo.engine.Param`. Only if a hyper space's label - matches its parent :class:`matchzoo.engine.Param`'s name, `matchzoo` - can correctly back-refrenced the parameter got sampled. This can be - done by asking the user always use the same name for a parameter and - its hyper space, but typos can occur. As a result, these wrappers - are created to hide hyper spaces' `label`, and always correctly - bind them with its parameter's name. - - Examples:: - >>> import matchzoo as mz - >>> from hyperopt.pyll.stochastic import sample - - Basic Usage: - >>> model = mz.models.DenseBaseline() - >>> sample(model.params.hyper_space) # doctest: +SKIP - {'mlp_num_layers': 1.0, 'mlp_num_units': 274.0} - - Arithmetic Operations: - >>> new_space = 2 ** mz.hyper_spaces.quniform(2, 6) - >>> model.params.get('mlp_num_layers').hyper_space = new_space - >>> sample(model.params.hyper_space) # doctest: +SKIP - {'mlp_num_layers': 8.0, 'mlp_num_units': 292.0} - - """ - - def __init__( - self, - hyperopt_func: typing.Callable[..., hyperopt.pyll.Apply], - **kwargs - ): - """ - :class:`HyperoptProxy` constructor. - - :param hyperopt_func: Target `hyperopt.hp` function to proxy. - :param kwargs: Keyword arguments of the proxy function, must pass all - parameters in `hyperopt_func`. - """ - self._func = hyperopt_func - self._kwargs = kwargs - - def convert(self, name: str) -> hyperopt.pyll.Apply: - """ - Attach `name` as `hyperopt.hp`'s `label`. - - :param name: - :return: a `hyperopt` ready search space - """ - return self._func(name, **self._kwargs) - - def __add__(self, other): - """__add__.""" - return _wrap_as_composite_func(self, other, lambda x, y: x + y) - - def __radd__(self, other): - """__radd__.""" - return _wrap_as_composite_func(self, other, lambda x, y: x + y) - - def __sub__(self, other): - """__sub__.""" - return _wrap_as_composite_func(self, other, lambda x, y: x - y) - - def __rsub__(self, other): - """__rsub__.""" - return _wrap_as_composite_func(self, other, lambda x, y: y - x) - - def __mul__(self, other): - """__mul__.""" - return _wrap_as_composite_func(self, other, lambda x, y: x * y) - - def __rmul__(self, other): - """__rmul__.""" - return _wrap_as_composite_func(self, other, lambda x, y: x * y) - - def __truediv__(self, other): - """__truediv__.""" - return _wrap_as_composite_func(self, other, lambda x, y: x / y) - - def __rtruediv__(self, other): - """__rtruediv__.""" - return _wrap_as_composite_func(self, other, lambda x, y: y / x) - - def __floordiv__(self, other): - """__floordiv__.""" - return _wrap_as_composite_func(self, other, lambda x, y: x // y) - - def __rfloordiv__(self, other): - """__rfloordiv__.""" - return _wrap_as_composite_func(self, other, lambda x, y: y // x) - - def __pow__(self, other): - """__pow__.""" - return _wrap_as_composite_func(self, other, lambda x, y: x ** y) - - def __rpow__(self, other): - """__rpow__.""" - return _wrap_as_composite_func(self, other, lambda x, y: y ** x) - - def __neg__(self): - """__neg__.""" - return _wrap_as_composite_func(self, None, lambda x, _: -x) - - -def _wrap_as_composite_func(self, other, func): - def _wrapper(name, **kwargs): - return func(self._func(name, **kwargs), other) - - return HyperoptProxy(_wrapper, **self._kwargs) - - -class choice(HyperoptProxy): - """:func:`hyperopt.hp.choice` proxy.""" - - def __init__(self, options: list): - """ - :func:`hyperopt.hp.choice` proxy. - - :param options: options to search from - """ - super().__init__(hyperopt_func=hyperopt.hp.choice, options=options) - self._options = options - - def __str__(self): - """:return: `str` representation of the hyper space.""" - return f'choice in {self._options}' - - -class quniform(HyperoptProxy): - """:func:`hyperopt.hp.quniform` proxy.""" - - def __init__( - self, - low: numbers.Number, - high: numbers.Number, - q: numbers.Number = 1 - ): - """ - :func:`hyperopt.hp.quniform` proxy. - - If using with integer values, then `high` is exclusive. - - :param low: lower bound of the space - :param high: upper bound of the space - :param q: similar to the `step` in the python built-in `range` - """ - super().__init__(hyperopt_func=hyperopt.hp.quniform, - low=low, - high=high, q=q) - self._low = low - self._high = high - self._q = q - - def __str__(self): - """:return: `str` representation of the hyper space.""" - return f'quantitative uniform distribution in ' \ - f'[{self._low}, {self._high}), with a step size of {self._q}' - - -class uniform(HyperoptProxy): - """:func:`hyperopt.hp.uniform` proxy.""" - - def __init__( - self, - low: numbers.Number, - high: numbers.Number - ): - """ - :func:`hyperopt.hp.uniform` proxy. - - :param low: lower bound of the space - :param high: upper bound of the space - """ - super().__init__(hyperopt_func=hyperopt.hp.uniform, low=low, high=high) - self._low = low - self._high = high - - def __str__(self): - """:return: `str` representation of the hyper space.""" - return f'uniform distribution in [{self._low}, {self._high})' - - -def sample(space): - """ - Take a sample in the hyper space. - - This method is stateless, so the distribution of the samples is different - from that of `tune` call. This function just gives a general idea of what - a sample from the `space` looks like. - - Example: - >>> import matchzoo as mz - >>> space = mz.models.Naive.get_default_params().hyper_space - >>> mz.hyper_spaces.sample(space) # doctest: +ELLIPSIS - {'optimizer': ...} - - """ - return hyperopt.pyll.stochastic.sample(space) diff --git a/matchzoo/engine/param.py b/matchzoo/engine/param.py deleted file mode 100644 index 3f7d1189..00000000 --- a/matchzoo/engine/param.py +++ /dev/null @@ -1,243 +0,0 @@ -"""Parameter class.""" - -import inspect -import numbers -import typing - -import hyperopt.pyll - -from matchzoo.engine import hyper_spaces - -# Both hyperopt native spaces and matchzoo proxies are valid spaces. -SpaceType = typing.Union[hyperopt.pyll.Apply, hyper_spaces.HyperoptProxy] - - -class Param(object): - """ - Parameter class. - - Basic usages with a name and value: - - >>> param = Param('my_param', 10) - >>> param.name - 'my_param' - >>> param.value - 10 - - Use with a validator to make sure the parameter always keeps a valid - value. - - >>> param = Param( - ... name='my_param', - ... value=5, - ... validator=lambda x: 0 < x < 20 - ... ) - >>> param.validator # doctest: +ELLIPSIS - at 0x...> - >>> param.value - 5 - >>> param.value = 10 - >>> param.value - 10 - >>> param.value = -1 - Traceback (most recent call last): - ... - ValueError: Validator not satifised. - The validator's definition is as follows: - validator=lambda x: 0 < x < 20 - - Use with a hyper space. Setting up a hyper space for a parameter makes the - parameter tunable in a :class:`matchzoo.engine.Tuner`. - - >>> from matchzoo.engine.hyper_spaces import quniform - >>> param = Param( - ... name='positive_num', - ... value=1, - ... hyper_space=quniform(low=1, high=5) - ... ) - >>> param.hyper_space # doctest: +ELLIPSIS - - >>> from hyperopt.pyll.stochastic import sample - >>> hyperopt_space = param.hyper_space.convert(param.name) - >>> samples = [sample(hyperopt_space) for _ in range(64)] - >>> set(samples) == {1, 2, 3, 4, 5} - True - - The boolean value of a :class:`Param` instance is only `True` - when the value is not `None`. This is because some default falsy values - like zero or an empty list are valid parameter values. In other words, - the boolean value means to be "if the parameter value is filled". - - >>> param = Param('dropout') - >>> if param: - ... print('OK') - >>> param = Param('dropout', 0) - >>> if param: - ... print('OK') - OK - - A `_pre_assignment_hook` is initialized as a data type convertor if the - value is set as a number to keep data type consistency of the parameter. - This conversion supports python built-in numbers, `numpy` numbers, and - any number that inherits :class:`numbers.Number`. - - >>> param = Param('float_param', 0.5) - >>> param.value = 10 - >>> param.value - 10.0 - >>> type(param.value) - - - """ - - def __init__( - self, - name: str, - value: typing.Any = None, - hyper_space: typing.Optional[SpaceType] = None, - validator: typing.Optional[ - typing.Callable[[typing.Any], bool]] = None, - desc: typing.Optional[str] = None, - ): - """ - Parameter constructor. - - :param name: Name of the parameter. - :param value: Value of the parameter, `None` by default, which means - "this parameter is not filled yet." - :param hyper_space: Hyper space of the parameter, `None` by default. - If set, then a :class:`matchzoo.engine.ParamTable` that has this - parameter will include this `hyper_space` as a part of the - parameter table's search space. - :param validator: Validator of the parameter, `None` by default. If - validation is needed, pass a callable that, given a value, returns - a `bool`. The definition of the validator is retrieved when the - validation fails, so either use a function or a `lambda` that - occupies its own line for better readability. - """ - self._name = name - self._desc = desc - - self._value = None - self._hyper_space = None - self._validator = None - self._pre_assignment_hook = None - - self.validator = validator - self.hyper_space = hyper_space - - if value is not None: # bypass checking if no default - self.value = value - - @property - def name(self) -> str: - """:return: Name of the parameter.""" - return self._name - - @property - def value(self) -> typing.Any: - """:return: Value of the parameter.""" - return self._value - - @value.setter - def value(self, new_value: typing.Any): - """ - Set the value of parameter to `new_value`. - - Notice that this setter validates `new_value` before assignment. As - a result, if the validaiton fails, the value of the parameter is not - changed. - - :param new_value: New value of the parameter to set. - """ - if self._pre_assignment_hook: - new_value = self._pre_assignment_hook(new_value) - self._validate(new_value) - self._value = new_value - if not self._pre_assignment_hook: - self._infer_pre_assignment_hook() - - @property - def hyper_space(self) -> SpaceType: - """:return: Hyper space of the parameter.""" - return self._hyper_space - - @hyper_space.setter - def hyper_space(self, new_space: SpaceType): - """:param new_space: New space of the parameter to set.""" - self._hyper_space = new_space - - @property - def validator(self) -> typing.Callable[[typing.Any], bool]: - """:return: Validator of the parameter.""" - return self._validator - - @validator.setter - def validator(self, new_validator: typing.Callable[[typing.Any], bool]): - """:param new_validator: New space of the parameter to set.""" - if new_validator and not callable(new_validator): - raise TypeError("Validator must be a callable or None.") - self._validator = new_validator - - @property - def desc(self) -> str: - """:return: Parameter description.""" - return self._desc - - @desc.setter - def desc(self, value: str): - """:param value: New description of the parameter.""" - self._desc = value - - def _infer_pre_assignment_hook(self): - if isinstance(self._value, numbers.Number): - self._pre_assignment_hook = lambda x: type(self._value)(x) - - def _validate(self, value): - if self._validator: - valid = self._validator(value) - if not valid: - error_msg = "Validator not satifised.\n" - error_msg += "The validator's definition is as follows:\n" - error_msg += inspect.getsource(self._validator).strip() - raise ValueError(error_msg) - - def __bool__(self): - """:return: `False` when the value is `None`, `True` otherwise.""" - return self._value is not None - - def set_default(self, val, verbose=1): - """ - Set default value, has no effect if already has a value. - - :param val: Default value to set. - :param verbose: Verbosity. - """ - if self._value is None: - self.value = val - if verbose: - print(f"Parameter \"{self._name}\" set to {val}.") - - def reset(self): - """ - Set the parameter's value to `None`, which means "not set". - - This method bypasses validator. - - Example: - >>> import matchzoo as mz - >>> param = mz.Param( - ... name='str', validator=lambda x: isinstance(x, str)) - >>> param.value = 'hello' - >>> param.value = None - Traceback (most recent call last): - ... - ValueError: Validator not satifised. - The validator's definition is as follows: - name='str', validator=lambda x: isinstance(x, str)) - >>> param.reset() - >>> param.value is None - True - - """ - self._value = None diff --git a/matchzoo/engine/param_table.py b/matchzoo/engine/param_table.py deleted file mode 100644 index c82fc83e..00000000 --- a/matchzoo/engine/param_table.py +++ /dev/null @@ -1,169 +0,0 @@ -"""Parameters table class.""" - -import typing -import pandas as pd -import collections.abc - -from matchzoo.engine.param import Param -from matchzoo.engine import hyper_spaces - - -class ParamTable(object): - """ - Parameter table class. - - Example: - - >>> params = ParamTable() - >>> params.add(Param('ham', 'Parma Ham')) - >>> params.add(Param('egg', 'Over Easy')) - >>> params['ham'] - 'Parma Ham' - >>> params['egg'] - 'Over Easy' - >>> print(params) - ham Parma Ham - egg Over Easy - >>> params.add(Param('egg', 'Sunny side Up')) - Traceback (most recent call last): - ... - ValueError: Parameter named egg already exists. - To re-assign parameter egg value, use `params["egg"] = value` instead. - """ - - def __init__(self): - """Parameter table constrctor.""" - self._params = {} - - def add(self, param: Param): - """:param param: parameter to add.""" - if not isinstance(param, Param): - raise TypeError("Only accepts a Param instance.") - if param.name in self._params: - msg = f"Parameter named {param.name} already exists.\n" \ - f"To re-assign parameter {param.name} value, " \ - f"use `params[\"{param.name}\"] = value` instead." - raise ValueError(msg) - self._params[param.name] = param - - def get(self, key) -> Param: - """:return: The parameter in the table named `key`.""" - return self._params[key] - - def set(self, key, param: Param): - """Set `key` to parameter `param`.""" - if not isinstance(param, Param): - raise ValueError("Only accepts a Param instance.") - self._params[key] = param - - @property - def hyper_space(self) -> dict: - """:return: Hyper space of the table, a valid `hyperopt` graph.""" - full_space = {} - for param in self: - if param.hyper_space is not None: - param_space = param.hyper_space - if isinstance(param_space, hyper_spaces.HyperoptProxy): - param_space = param_space.convert(param.name) - full_space[param.name] = param_space - return full_space - - def to_frame(self) -> pd.DataFrame: - """ - Convert the parameter table into a pandas data frame. - - :return: A `pandas.DataFrame`. - - Example: - >>> import matchzoo as mz - >>> table = mz.ParamTable() - >>> table.add(mz.Param(name='x', value=10, desc='my x')) - >>> table.add(mz.Param(name='y', value=20, desc='my y')) - >>> table.to_frame() - Name Description Value Hyper-Space - 0 x my x 10 None - 1 y my y 20 None - - """ - df = pd.DataFrame(data={ - 'Name': [p.name for p in self], - 'Description': [p.desc for p in self], - 'Value': [p.value for p in self], - 'Hyper-Space': [p.hyper_space for p in self] - }, columns=['Name', 'Description', 'Value', 'Hyper-Space']) - return df - - def __getitem__(self, key: str) -> typing.Any: - """:return: The value of the parameter in the table named `key`.""" - return self._params[key].value - - def __setitem__(self, key: str, value: typing.Any): - """ - Set the value of the parameter named `key`. - - :param key: Name of the parameter. - :param value: New value of the parameter to set. - """ - self._params[key].value = value - - def __str__(self): - """:return: Pretty formatted parameter table.""" - return '\n'.join(param.name.ljust(30) + str(param.value) - for param in self._params.values()) - - def __iter__(self) -> typing.Iterator: - """:return: A iterator that iterates over all parameter instances.""" - yield from self._params.values() - - def completed(self) -> bool: - """ - :return: `True` if all params are filled, `False` otherwise. - - Example: - - >>> import matchzoo - >>> model = matchzoo.models.Naive() - >>> model.params.completed() - False - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.params.completed() - True - - """ - return all(param for param in self) - - def keys(self) -> collections.abc.KeysView: - """:return: Parameter table keys.""" - return self._params.keys() - - def __contains__(self, item): - """:return: `True` if parameter in parameters.""" - return item in self._params - - def update(self, other: dict): - """ - Update `self`. - - Update `self` with the key/value pairs from other, overwriting - existing keys. Notice that this does not add new keys to `self`. - - This method is usually used by models to obtain useful information - from a preprocessor's context. - - :param other: The dictionary used update. - - Example: - >>> import matchzoo as mz - >>> model = mz.models.DenseBaseline() - >>> model.params['input_shapes'] is None - True - >>> prpr = model.get_default_preprocessor() - >>> _ = prpr.fit(mz.datasets.toy.load_data(), verbose=0) - >>> model.params.update(prpr.context) - >>> model.params['input_shapes'] - [(30,), (30,)] - - """ - for key in other: - if key in self: - self[key] = other[key] diff --git a/matchzoo/engine/parse_metric.py b/matchzoo/engine/parse_metric.py deleted file mode 100644 index 6d921cc1..00000000 --- a/matchzoo/engine/parse_metric.py +++ /dev/null @@ -1,78 +0,0 @@ -import typing - -import matchzoo -from matchzoo.engine.base_metric import BaseMetric -from matchzoo.engine import base_task - - -def parse_metric( - metric: typing.Union[str, typing.Type[BaseMetric], BaseMetric], - task: 'base_task.BaseTask' = None -) -> typing.Union['BaseMetric', str]: - """ - Parse input metric in any form into a :class:`BaseMetric` instance. - - :param metric: Input metric in any form. - :param task: Task type for determining specific metric. - :return: A :class:`BaseMetric` instance - - Examples:: - >>> from matchzoo import metrics - >>> from matchzoo.engine.parse_metric import parse_metric - - Use `str` as keras native metrics: - >>> parse_metric('mse') - 'mse' - - Use `str` as MatchZoo metrics: - >>> mz_metric = parse_metric('map') - >>> type(mz_metric) - - - Use :class:`matchzoo.engine.BaseMetric` subclasses as MatchZoo metrics: - >>> type(parse_metric(metrics.AveragePrecision)) - - - Use :class:`matchzoo.engine.BaseMetric` instances as MatchZoo metrics: - >>> type(parse_metric(metrics.AveragePrecision())) - - - """ - if task is None: - task = matchzoo.tasks.Ranking() - - if isinstance(metric, str): - metric = metric.lower() # ignore case - - # matchzoo metrics in str form - for subclass in BaseMetric.__subclasses__(): - if metric == subclass.ALIAS or metric in subclass.ALIAS: - return subclass() - - # keras native metrics - return _remap_keras_metric(metric, task) - elif isinstance(metric, BaseMetric): - return metric - elif issubclass(metric, BaseMetric): - return metric() - else: - raise ValueError(metric) - - -def _remap_keras_metric(metric: str, task) -> str: - # we do not support sparse label in classification. - lookup = { - matchzoo.tasks.Ranking: { - 'acc': 'binary_accuracy', - 'accuracy': 'binary_accuracy', - 'crossentropy': 'binary_crossentropy', - 'ce': 'binary_crossentropy', - }, - matchzoo.tasks.Classification: { - 'acc': 'categorical_accuracy', - 'accuracy': 'categorical_accuracy', - 'crossentropy': 'categorical_crossentropy', - 'ce': 'categorical_crossentropy', - } - } - return lookup[type(task)].get(metric, metric) diff --git a/matchzoo/layers/__init__.py b/matchzoo/layers/__init__.py deleted file mode 100644 index bd270df7..00000000 --- a/matchzoo/layers/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -from .matching_layer import MatchingLayer -from .dynamic_pooling_layer import DynamicPoolingLayer - -layer_dict = { - "MatchingLayer": MatchingLayer, - "DynamicPoolingLayer": DynamicPoolingLayer -} diff --git a/matchzoo/layers/dynamic_pooling_layer.py b/matchzoo/layers/dynamic_pooling_layer.py deleted file mode 100644 index 049bbf82..00000000 --- a/matchzoo/layers/dynamic_pooling_layer.py +++ /dev/null @@ -1,128 +0,0 @@ -"""An implementation of Dynamic Pooling Layer.""" -import typing - -import tensorflow as tf -from keras.engine import Layer - - -class DynamicPoolingLayer(Layer): - """ - Layer that computes dynamic pooling of one tensor. - - :param psize1: pooling size of dimension 1 - :param psize2: pooling size of dimension 2 - :param kwargs: Standard layer keyword arguments. - - Examples: - >>> import matchzoo as mz - >>> layer = mz.layers.DynamicPoolingLayer(3, 2) - >>> num_batch, left_len, right_len, num_dim = 5, 3, 2, 10 - >>> layer.build([[num_batch, left_len, right_len, num_dim], - ... [num_batch, left_len, right_len, 3]]) - - """ - - def __init__(self, - psize1: int, - psize2: int, - **kwargs): - """:class:`DynamicPoolingLayer` constructor.""" - super().__init__(**kwargs) - self._psize1 = psize1 - self._psize2 = psize2 - - def build(self, input_shape: typing.List[int]): - """ - Build the layer. - - :param input_shape: the shapes of the input tensors, - for DynamicPoolingLayer we need tow input tensors. - """ - super().build(input_shape) - input_shape_one = input_shape[0] - self._msize1 = input_shape_one[1] - self._msize2 = input_shape_one[2] - - def call(self, inputs: list, **kwargs) -> typing.Any: - """ - The computation logic of DynamicPoolingLayer. - - :param inputs: two input tensors. - """ - self._validate_dpool_size() - x, dpool_index = inputs - dpool_shape = tf.shape(dpool_index) - batch_index_one = tf.expand_dims( - tf.expand_dims( - tf.range(dpool_shape[0]), axis=-1), - axis=-1) - batch_index = tf.expand_dims( - tf.tile(batch_index_one, [1, self._msize1, self._msize2]), - axis=-1) - dpool_index_ex = tf.concat([batch_index, dpool_index], axis=3) - x_expand = tf.gather_nd(x, dpool_index_ex) - stride1 = self._msize1 // self._psize1 - stride2 = self._msize2 // self._psize2 - - x_pool = tf.nn.max_pool(x_expand, - [1, stride1, stride2, 1], - [1, stride1, stride2, 1], - "VALID") - return x_pool - - def compute_output_shape(self, input_shape: list) -> tuple: - """ - Calculate the layer output shape. - - :param input_shape: the shapes of the input tensors, - for DynamicPoolingLayer we need tow input tensors. - """ - input_shape_one = input_shape[0] - return (None, self._psize1, self._psize2, input_shape_one[3]) - - def get_config(self) -> dict: - """Get the config dict of DynamicPoolingLayer.""" - config = { - 'psize1': self._psize1, - 'psize2': self._psize2 - } - base_config = super(DynamicPoolingLayer, self).get_config() - return dict(list(base_config.items()) + list(config.items())) - - def _validate_dpool_size(self): - suggestion = self.get_size_suggestion( - self._msize1, self._msize2, self._psize1, self._psize2 - ) - if suggestion != (self._psize1, self._psize2): - raise ValueError( - "DynamicPooling Layer can not " - f"generate ({self._psize1} x {self._psize2}) output " - f"feature map, please use ({suggestion[0]} x {suggestion[1]})" - f" instead. `model.params['dpool_size'] = {suggestion}` " - ) - - @classmethod - def get_size_suggestion( - cls, - msize1: int, - msize2: int, - psize1: int, - psize2: int - ) -> typing.Tuple[int, int]: - """ - Get `dpool_size` suggestion for a given shape. - - Returns the nearest legal `dpool_size` for the given combination of - `(psize1, psize2)`. - - :param msize1: size of the left text. - :param msize2: size of the right text. - :param psize1: base size of the pool. - :param psize2: base size of the pool. - :return: - """ - stride1 = msize1 // psize1 - stride2 = msize2 // psize2 - suggestion1 = msize1 // stride1 - suggestion2 = msize2 // stride2 - return (suggestion1, suggestion2) diff --git a/matchzoo/layers/matching_layer.py b/matchzoo/layers/matching_layer.py deleted file mode 100644 index 54dead2a..00000000 --- a/matchzoo/layers/matching_layer.py +++ /dev/null @@ -1,140 +0,0 @@ -"""An implementation of Matching Layer.""" -import typing - -import tensorflow as tf -from keras.engine import Layer - - -class MatchingLayer(Layer): - """ - Layer that computes a matching matrix between samples in two tensors. - - :param normalize: Whether to L2-normalize samples along the - dot product axis before taking the dot product. - If set to True, then the output of the dot product - is the cosine proximity between the two samples. - :param matching_type: the similarity function for matching - :param kwargs: Standard layer keyword arguments. - - Examples: - >>> import matchzoo as mz - >>> layer = mz.layers.MatchingLayer(matching_type='dot', - ... normalize=True) - >>> num_batch, left_len, right_len, num_dim = 5, 3, 2, 10 - >>> layer.build([[num_batch, left_len, num_dim], - ... [num_batch, right_len, num_dim]]) - - """ - - def __init__(self, normalize: bool = False, - matching_type: str = 'dot', **kwargs): - """:class:`MatchingLayer` constructor.""" - super().__init__(**kwargs) - self._normalize = normalize - self._validate_matching_type(matching_type) - self._matching_type = matching_type - self._shape1 = None - self._shape2 = None - - @classmethod - def _validate_matching_type(cls, matching_type: str = 'dot'): - valid_matching_type = ['dot', 'mul', 'plus', 'minus', 'concat'] - if matching_type not in valid_matching_type: - raise ValueError(f"{matching_type} is not a valid matching type, " - f"{valid_matching_type} expected.") - - def build(self, input_shape: list): - """ - Build the layer. - - :param input_shape: the shapes of the input tensors, - for MatchingLayer we need tow input tensors. - """ - # Used purely for shape validation. - if not isinstance(input_shape, list) or len(input_shape) != 2: - raise ValueError('A `MatchingLayer` layer should be called ' - 'on a list of 2 inputs.') - self._shape1 = input_shape[0] - self._shape2 = input_shape[1] - for idx in 0, 2: - if self._shape1[idx] != self._shape2[idx]: - raise ValueError( - 'Incompatible dimensions: ' - f'{self._shape1[idx]} != {self._shape2[idx]}.' - f'Layer shapes: {self._shape1}, {self._shape2}.' - ) - - def call(self, inputs: list, **kwargs) -> typing.Any: - """ - The computation logic of MatchingLayer. - - :param inputs: two input tensors. - """ - x1 = inputs[0] - x2 = inputs[1] - if self._matching_type == 'dot': - if self._normalize: - x1 = tf.math.l2_normalize(x1, axis=2) - x2 = tf.math.l2_normalize(x2, axis=2) - return tf.expand_dims(tf.einsum('abd,acd->abc', x1, x2), 3) - else: - if self._matching_type == 'mul': - def func(x, y): - return x * y - elif self._matching_type == 'plus': - def func(x, y): - return x + y - elif self._matching_type == 'minus': - def func(x, y): - return x - y - elif self._matching_type == 'concat': - def func(x, y): - return tf.concat([x, y], axis=3) - else: - raise ValueError(f"Invalid matching type." - f"{self._matching_type} received." - f"Mut be in `dot`, `mul`, `plus`, " - f"`minus` and `concat`.") - x1_exp = tf.stack([x1] * self._shape2[1], 2) - x2_exp = tf.stack([x2] * self._shape1[1], 1) - return func(x1_exp, x2_exp) - - def compute_output_shape(self, input_shape: list) -> tuple: - """ - Calculate the layer output shape. - - :param input_shape: the shapes of the input tensors, - for MatchingLayer we need tow input tensors. - """ - if not isinstance(input_shape, list) or len(input_shape) != 2: - raise ValueError('A `MatchingLayer` layer should be called ' - 'on a list of 2 inputs.') - shape1 = list(input_shape[0]) - shape2 = list(input_shape[1]) - if len(shape1) != 3 or len(shape2) != 3: - raise ValueError('A `MatchingLayer` layer should be called ' - 'on 2 inputs with 3 dimensions.') - if shape1[0] != shape2[0] or shape1[2] != shape2[2]: - raise ValueError('A `MatchingLayer` layer should be called ' - 'on 2 inputs with same 0,2 dimensions.') - - if self._matching_type in ['mul', 'plus', 'minus']: - return shape1[0], shape1[1], shape2[1], shape1[2] - elif self._matching_type == 'dot': - return shape1[0], shape1[1], shape2[1], 1 - elif self._matching_type == 'concat': - return shape1[0], shape1[1], shape2[1], shape1[2] + shape2[2] - else: - raise ValueError(f"Invalid `matching_type`." - f"{self._matching_type} received." - f"Must be in `mul`, `plus`, `minus` " - f"`dot` and `concat`.") - - def get_config(self) -> dict: - """Get the config dict of MatchingLayer.""" - config = { - 'normalize': self._normalize, - 'matching_type': self._matching_type, - } - base_config = super(MatchingLayer, self).get_config() - return dict(list(base_config.items()) + list(config.items())) diff --git a/matchzoo/losses/__init__.py b/matchzoo/losses/__init__.py deleted file mode 100644 index da9428fd..00000000 --- a/matchzoo/losses/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .rank_cross_entropy_loss import RankCrossEntropyLoss -from .rank_hinge_loss import RankHingeLoss diff --git a/matchzoo/losses/rank_cross_entropy_loss.py b/matchzoo/losses/rank_cross_entropy_loss.py deleted file mode 100644 index 97e13fe0..00000000 --- a/matchzoo/losses/rank_cross_entropy_loss.py +++ /dev/null @@ -1,65 +0,0 @@ -"""The rank cross entropy loss.""" - -import numpy as np -import tensorflow as tf -from keras import layers, backend as K -from keras.losses import Loss -from keras.utils import losses_utils - - -class RankCrossEntropyLoss(Loss): - """ - Rank cross entropy loss. - - Examples: - >>> from keras import backend as K - >>> softmax = lambda x: np.exp(x)/np.sum(np.exp(x), axis=0) - >>> x_pred = K.variable(np.array([[1.0], [1.2], [0.8]])) - >>> x_true = K.variable(np.array([[1], [0], [0]])) - >>> expect = -np.log(softmax(np.array([[1.0], [1.2], [0.8]]))) - >>> loss = K.eval(RankCrossEntropyLoss(num_neg=2)(x_true, x_pred)) - >>> np.isclose(loss, expect[0]).all() - True - - """ - - def __init__(self, num_neg: int = 1): - """ - :class:`RankCrossEntropyLoss` constructor. - - :param num_neg: number of negative instances in cross entropy loss. - """ - super().__init__(reduction=losses_utils.Reduction.SUM_OVER_BATCH_SIZE, - name="rank_crossentropy") - self._num_neg = num_neg - - def call(self, y_true: np.array, y_pred: np.array, - sample_weight=None) -> np.array: - """ - Calculate rank cross entropy loss. - - :param y_true: Label. - :param y_pred: Predicted result. - :return: Crossentropy loss computed by user-defined negative number. - """ - logits = layers.Lambda(lambda a: a[::(self._num_neg + 1), :])(y_pred) - labels = layers.Lambda(lambda a: a[::(self._num_neg + 1), :])(y_true) - logits, labels = [logits], [labels] - for neg_idx in range(self._num_neg): - neg_logits = layers.Lambda( - lambda a: a[neg_idx + 1::(self._num_neg + 1), :])(y_pred) - neg_labels = layers.Lambda( - lambda a: a[neg_idx + 1::(self._num_neg + 1), :])(y_true) - logits.append(neg_logits) - labels.append(neg_labels) - logits = tf.concat(logits, axis=-1) - labels = tf.concat(labels, axis=-1) - smoothed_prob = tf.nn.softmax(logits) + np.finfo(float).eps - loss = -(tf.reduce_sum(labels * tf.math.log(smoothed_prob), axis=-1)) - return losses_utils.compute_weighted_loss( - loss, sample_weight, reduction=self.reduction) - - @property - def num_neg(self): - """`num_neg` getter.""" - return self._num_neg diff --git a/matchzoo/losses/rank_hinge_loss.py b/matchzoo/losses/rank_hinge_loss.py deleted file mode 100644 index 157f8a85..00000000 --- a/matchzoo/losses/rank_hinge_loss.py +++ /dev/null @@ -1,71 +0,0 @@ -"""The rank hinge loss.""" - -import numpy as np -import tensorflow as tf -from keras import layers, backend as K -from keras.losses import Loss -from keras.utils import losses_utils - - -class RankHingeLoss(Loss): - """ - Rank hinge loss. - - Examples: - >>> from keras import backend as K - >>> x_pred = K.variable(np.array([[1.0], [1.2], [0.8], [1.4]])) - >>> x_true = K.variable(np.array([[1], [0], [1], [0]])) - >>> expect = ((1.0 + 1.2 - 1.0) + (1.0 + 1.4 - 0.8)) / 2 - >>> expect - 1.4 - >>> loss = K.eval(RankHingeLoss(num_neg=1, margin=1.0)(x_true, x_pred)) - >>> np.isclose(loss, expect) - True - - """ - - def __init__(self, num_neg: int = 1, margin: float = 1.0): - """ - :class:`RankHingeLoss` constructor. - - :param num_neg: number of negative instances in hinge loss. - :param margin: the margin between positive and negative scores. - """ - super().__init__(reduction=losses_utils.Reduction.SUM_OVER_BATCH_SIZE, - name="rank_hinge") - - self._num_neg = num_neg - self._margin = margin - - def call(self, y_true: np.array, y_pred: np.array, - sample_weight=None) -> np.array: - """ - Calculate rank hinge loss. - - :param y_true: Label. - :param y_pred: Predicted result. - :return: Hinge loss computed by user-defined margin. - """ - y_pos = layers.Lambda(lambda a: a[::(self._num_neg + 1), :], - output_shape=(1,))(y_pred) - y_neg = [] - for neg_idx in range(self._num_neg): - y_neg.append( - layers.Lambda( - lambda a: a[(neg_idx + 1)::(self._num_neg + 1), :], - output_shape=(1,))(y_pred)) - y_neg = tf.concat(y_neg, axis=-1) - y_neg = tf.reduce_mean(y_neg, axis=-1, keepdims=True) - loss = tf.maximum(0., self._margin + y_neg - y_pos) - return losses_utils.compute_weighted_loss( - loss, sample_weight, reduction=self.reduction) - - @property - def num_neg(self): - """`num_neg` getter.""" - return self._num_neg - - @property - def margin(self): - """`margin` getter.""" - return self._margin diff --git a/matchzoo/metrics/__init__.py b/matchzoo/metrics/__init__.py deleted file mode 100644 index e98062b1..00000000 --- a/matchzoo/metrics/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from .precision import Precision -from .average_precision import AveragePrecision -from .discounted_cumulative_gain import DiscountedCumulativeGain -from .mean_reciprocal_rank import MeanReciprocalRank -from .mean_average_precision import MeanAveragePrecision -from .normalized_discounted_cumulative_gain import \ - NormalizedDiscountedCumulativeGain - - -def list_available() -> list: - from matchzoo.engine.base_metric import BaseMetric - from matchzoo.utils import list_recursive_concrete_subclasses - return list_recursive_concrete_subclasses(BaseMetric) diff --git a/matchzoo/metrics/average_precision.py b/matchzoo/metrics/average_precision.py deleted file mode 100644 index 01b74a28..00000000 --- a/matchzoo/metrics/average_precision.py +++ /dev/null @@ -1,45 +0,0 @@ -"""Average precision metric for ranking.""" -import numpy as np - -from matchzoo.engine import base_metric -from . import Precision - - -class AveragePrecision(base_metric.BaseMetric): - """Average precision metric.""" - - ALIAS = ['average_precision', 'ap'] - - def __init__(self, threshold: float = 0.): - """ - :class:`AveragePrecision` constructor. - - :param threshold: The label threshold of relevance degree. - """ - self._threshold = threshold - - def __repr__(self) -> str: - """:return: Formated string representation of the metric.""" - return f"{self.ALIAS[0]}({self._threshold})" - - def __call__(self, y_true: np.array, y_pred: np.array) -> float: - """ - Calculate average precision (area under PR curve). - - Example: - >>> y_true = [0, 1] - >>> y_pred = [0.1, 0.6] - >>> round(AveragePrecision()(y_true, y_pred), 2) - 0.75 - >>> round(AveragePrecision()([], []), 2) - 0.0 - - :param y_true: The ground true label of each document. - :param y_pred: The predicted scores of each document. - :return: Average precision. - """ - precision_metrics = [Precision(k + 1) for k in range(len(y_pred))] - out = [metric(y_true, y_pred) for metric in precision_metrics] - if not out: - return 0. - return np.asscalar(np.mean(out)) diff --git a/matchzoo/metrics/discounted_cumulative_gain.py b/matchzoo/metrics/discounted_cumulative_gain.py deleted file mode 100644 index b20bccab..00000000 --- a/matchzoo/metrics/discounted_cumulative_gain.py +++ /dev/null @@ -1,62 +0,0 @@ -"""Discounted cumulative gain metric for ranking.""" -import math - -import numpy as np - -from matchzoo.engine.base_metric import BaseMetric, sort_and_couple - - -class DiscountedCumulativeGain(BaseMetric): - """Disconunted cumulative gain metric.""" - - ALIAS = ['discounted_cumulative_gain', 'dcg'] - - def __init__(self, k: int = 1, threshold: float = 0.): - """ - :class:`DiscountedCumulativeGain` constructor. - - :param k: Number of results to consider. - :param threshold: the label threshold of relevance degree. - """ - self._k = k - self._threshold = threshold - - def __repr__(self) -> str: - """:return: Formated string representation of the metric.""" - return f"{self.ALIAS[0]}@{self._k}({self._threshold})" - - def __call__(self, y_true: np.array, y_pred: np.array) -> float: - """ - Calculate discounted cumulative gain (dcg). - - Relevance is positive real values or binary values. - - Example: - >>> y_true = [0, 1, 2, 0] - >>> y_pred = [0.4, 0.2, 0.5, 0.7] - >>> DiscountedCumulativeGain(1)(y_true, y_pred) - 0.0 - >>> round(DiscountedCumulativeGain(k=-1)(y_true, y_pred), 2) - 0.0 - >>> round(DiscountedCumulativeGain(k=2)(y_true, y_pred), 2) - 2.73 - >>> round(DiscountedCumulativeGain(k=3)(y_true, y_pred), 2) - 2.73 - >>> type(DiscountedCumulativeGain(k=1)(y_true, y_pred)) - - - :param y_true: The ground true label of each document. - :param y_pred: The predicted scores of each document. - - :return: Discounted cumulative gain. - """ - if self._k <= 0: - return 0. - coupled_pair = sort_and_couple(y_true, y_pred) - result = 0. - for i, (label, score) in enumerate(coupled_pair): - if i >= self._k: - break - if label > self._threshold: - result += (math.pow(2., label) - 1.) / math.log(2. + i) - return result diff --git a/matchzoo/metrics/mean_average_precision.py b/matchzoo/metrics/mean_average_precision.py deleted file mode 100644 index 221c42a0..00000000 --- a/matchzoo/metrics/mean_average_precision.py +++ /dev/null @@ -1,48 +0,0 @@ -"""Mean average precision metric for ranking.""" -import numpy as np - -from matchzoo.engine.base_metric import BaseMetric, sort_and_couple - - -class MeanAveragePrecision(BaseMetric): - """Mean average precision metric.""" - - ALIAS = ['mean_average_precision', 'map'] - - def __init__(self, threshold: float = 0.): - """ - :class:`MeanAveragePrecision` constructor. - - :param threshold: The threshold of relevance degree. - """ - self._threshold = threshold - - def __repr__(self): - """:return: Formated string representation of the metric.""" - return f"{self.ALIAS[0]}({self._threshold})" - - def __call__(self, y_true: np.array, y_pred: np.array) -> float: - """ - Calculate mean average precision. - - Example: - >>> y_true = [0, 1, 0, 0] - >>> y_pred = [0.1, 0.6, 0.2, 0.3] - >>> MeanAveragePrecision()(y_true, y_pred) - 1.0 - - :param y_true: The ground true label of each document. - :param y_pred: The predicted scores of each document. - :return: Mean average precision. - """ - result = 0. - pos = 0 - coupled_pair = sort_and_couple(y_true, y_pred) - for idx, (label, score) in enumerate(coupled_pair): - if label > self._threshold: - pos += 1. - result += pos / (idx + 1.) - if pos == 0: - return 0. - else: - return result / pos diff --git a/matchzoo/metrics/mean_reciprocal_rank.py b/matchzoo/metrics/mean_reciprocal_rank.py deleted file mode 100644 index 8e416267..00000000 --- a/matchzoo/metrics/mean_reciprocal_rank.py +++ /dev/null @@ -1,43 +0,0 @@ -"""Mean reciprocal ranking metric.""" -import numpy as np - -from matchzoo.engine.base_metric import BaseMetric, sort_and_couple - - -class MeanReciprocalRank(BaseMetric): - """Mean reciprocal rank metric.""" - - ALIAS = ['mean_reciprocal_rank', 'mrr'] - - def __init__(self, threshold: float = 0.): - """ - :class:`MeanReciprocalRankMetric`. - - :param threshold: The label threshold of relevance degree. - """ - self._threshold = threshold - - def __repr__(self) -> str: - """:return: Formated string representation of the metric.""" - return f'{self.ALIAS[0]}({self._threshold})' - - def __call__(self, y_true: np.array, y_pred: np.array) -> float: - """ - Calculate reciprocal of the rank of the first relevant item. - - Example: - >>> import numpy as np - >>> y_pred = np.asarray([0.2, 0.3, 0.7, 1.0]) - >>> y_true = np.asarray([1, 0, 0, 0]) - >>> MeanReciprocalRank()(y_true, y_pred) - 0.25 - - :param y_true: The ground true label of each document. - :param y_pred: The predicted scores of each document. - :return: Mean reciprocal rank. - """ - coupled_pair = sort_and_couple(y_true, y_pred) - for idx, (label, pred) in enumerate(coupled_pair): - if label > self._threshold: - return 1. / (idx + 1) - return 0. diff --git a/matchzoo/metrics/normalized_discounted_cumulative_gain.py b/matchzoo/metrics/normalized_discounted_cumulative_gain.py deleted file mode 100644 index 24360486..00000000 --- a/matchzoo/metrics/normalized_discounted_cumulative_gain.py +++ /dev/null @@ -1,55 +0,0 @@ -"""Normalized discounted cumulative gain metric for ranking.""" -import numpy as np - -from matchzoo.engine.base_metric import BaseMetric, sort_and_couple -from .discounted_cumulative_gain import DiscountedCumulativeGain - - -class NormalizedDiscountedCumulativeGain(BaseMetric): - """Normalized discounted cumulative gain metric.""" - - ALIAS = ['normalized_discounted_cumulative_gain', 'ndcg'] - - def __init__(self, k: int = 1, threshold: float = 0.): - """ - :class:`NormalizedDiscountedCumulativeGain` constructor. - - :param k: Number of results to consider - :param threshold: the label threshold of relevance degree. - """ - self._k = k - self._threshold = threshold - - def __repr__(self) -> str: - """:return: Formated string representation of the metric.""" - return f"{self.ALIAS[0]}@{self._k}({self._threshold})" - - def __call__(self, y_true: np.array, y_pred: np.array) -> float: - """ - Calculate normalized discounted cumulative gain (ndcg). - - Relevance is positive real values or binary values. - - Example: - >>> y_true = [0, 1, 2, 0] - >>> y_pred = [0.4, 0.2, 0.5, 0.7] - >>> ndcg = NormalizedDiscountedCumulativeGain - >>> ndcg(k=1)(y_true, y_pred) - 0.0 - >>> round(ndcg(k=2)(y_true, y_pred), 2) - 0.52 - >>> round(ndcg(k=3)(y_true, y_pred), 2) - 0.52 - >>> type(ndcg()(y_true, y_pred)) - - - :param y_true: The ground true label of each document. - :param y_pred: The predicted scores of each document. - - :return: Normalized discounted cumulative gain. - """ - dcg_metric = DiscountedCumulativeGain(k=self._k, - threshold=self._threshold) - idcg_val = dcg_metric(y_true, y_true) - dcg_val = dcg_metric(y_true, y_pred) - return dcg_val / idcg_val if idcg_val != 0 else 0 diff --git a/matchzoo/metrics/precision.py b/matchzoo/metrics/precision.py deleted file mode 100644 index f5c58e75..00000000 --- a/matchzoo/metrics/precision.py +++ /dev/null @@ -1,57 +0,0 @@ -"""Precision for ranking.""" -import numpy as np - -from matchzoo.engine.base_metric import BaseMetric, sort_and_couple - - -class Precision(BaseMetric): - """Precision metric.""" - - ALIAS = 'precision' - - def __init__(self, k: int = 1, threshold: float = 0.): - """ - :class:`PrecisionMetric` constructor. - - :param k: Number of results to consider. - :param threshold: the label threshold of relevance degree. - """ - self._k = k - self._threshold = threshold - - def __repr__(self) -> str: - """:return: Formated string representation of the metric.""" - return f"{self.ALIAS}@{self._k}({self._threshold})" - - def __call__(self, y_true: np.array, y_pred: np.array) -> float: - """ - Calculate precision@k. - - Example: - >>> y_true = [0, 0, 0, 1] - >>> y_pred = [0.2, 0.4, 0.3, 0.1] - >>> Precision(k=1)(y_true, y_pred) - 0.0 - >>> Precision(k=2)(y_true, y_pred) - 0.0 - >>> Precision(k=4)(y_true, y_pred) - 0.25 - >>> Precision(k=5)(y_true, y_pred) - 0.2 - - :param y_true: The ground true label of each document. - :param y_pred: The predicted scores of each document. - :return: Precision @ k - :raises: ValueError: len(r) must be >= k. - """ - if self._k <= 0: - raise ValueError(f"k must be greater than 0." - f"{self._k} received.") - coupled_pair = sort_and_couple(y_true, y_pred) - precision = 0.0 - for idx, (label, score) in enumerate(coupled_pair): - if idx >= self._k: - break - if label > self._threshold: - precision += 1. - return precision / self._k diff --git a/matchzoo/models/README.rst b/matchzoo/models/README.rst deleted file mode 100644 index ee069670..00000000 --- a/matchzoo/models/README.rst +++ /dev/null @@ -1,615 +0,0 @@ -************************ -MatchZoo Model Reference -************************ - -Naive -##### - -Model Documentation -******************* - -Naive model with a simplest structure for testing purposes. - -Bare minimum functioning model. The best choice to get things rolling. -The worst choice to fit and evaluate performance. - -Model Hyper Parameters -********************** - -==== ============ ========================================================================================= ===================================== ======================================== - .. Name Description Default Value Default Hyper-Space -==== ============ ========================================================================================= ===================================== ======================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam choice in ['adam', 'adagrad', 'rmsprop'] -==== ============ ========================================================================================= ===================================== ======================================== - -DSSM -#### - -Model Documentation -******************* - -Deep structured semantic model. - -Examples: - >>> model = DSSM() - >>> model.params['mlp_num_layers'] = 3 - >>> model.params['mlp_num_units'] = 300 - >>> model.params['mlp_num_fan_out'] = 128 - >>> model.params['mlp_activation_func'] = 'relu' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== =========================== ========================================================================================= =================================== ===================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ========================================================================================= =================================== ===================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 5 mlp_num_units Number of units in first `mlp_num_layers` layers. 128 quantitative uniform distribution in [8, 256), with a step size of 8 - 6 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 6), with a step size of 1 - 7 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 8 mlp_activation_func Activation function used in the multiple layer perceptron. relu -==== =========================== ========================================================================================= =================================== ===================================================================== - -CDSSM -##### - -Model Documentation -******************* - -CDSSM Model implementation. - -Learning Semantic Representations Using Convolutional Neural Networks -for Web Search. (2014a) -A Latent Semantic Model with Convolutional-Pooling Structure for -Information Retrieval. (2014b) - -Examples: - >>> model = CDSSM() - >>> model.params['optimizer'] = 'adam' - >>> model.params['filters'] = 32 - >>> model.params['kernel_size'] = 3 - >>> model.params['conv_activation_func'] = 'relu' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== =========================== ============================================================================================= ===================================== ===================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ============================================================================================= ===================================== ===================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 5 mlp_num_units Number of units in first `mlp_num_layers` layers. 128 quantitative uniform distribution in [8, 256), with a step size of 8 - 6 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 6), with a step size of 1 - 7 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 8 mlp_activation_func Activation function used in the multiple layer perceptron. relu - 9 filters Number of filters in the 1D convolution layer. 32 - 10 kernel_size Number of kernel size in the 1D convolution layer. 3 - 11 strides Strides in the 1D convolution layer. 1 - 12 padding The padding mode in the convolution layer. It should be one of `same`, `valid`, and `causal`. same - 13 conv_activation_func Activation function in the convolution layer. relu - 14 w_initializer glorot_normal - 15 b_initializer zeros - 16 dropout_rate The dropout rate. 0.3 -==== =========================== ============================================================================================= ===================================== ===================================================================== - -DenseBaseline -############# - -Model Documentation -******************* - -A simple densely connected baseline model. - -Examples: - >>> model = DenseBaseline() - >>> model.params['mlp_num_layers'] = 2 - >>> model.params['mlp_num_units'] = 300 - >>> model.params['mlp_num_fan_out'] = 128 - >>> model.params['mlp_activation_func'] = 'relu' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - >>> model.compile() - -Model Hyper Parameters -********************** - -==== =========================== ========================================================================================= ====================================================== ====================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ========================================================================================= ====================================================== ====================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 5 mlp_num_units Number of units in first `mlp_num_layers` layers. 256 quantitative uniform distribution in [16, 512), with a step size of 1 - 6 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 5), with a step size of 1 - 7 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 8 mlp_activation_func Activation function used in the multiple layer perceptron. relu -==== =========================== ========================================================================================= ====================================================== ====================================================================== - -ArcI -#### - -Model Documentation -******************* - -ArcI Model. - -Examples: - >>> model = ArcI() - >>> model.params['num_blocks'] = 1 - >>> model.params['left_filters'] = [32] - >>> model.params['right_filters'] = [32] - >>> model.params['left_kernel_sizes'] = [3] - >>> model.params['right_kernel_sizes'] = [3] - >>> model.params['left_pool_sizes'] = [2] - >>> model.params['right_pool_sizes'] = [4] - >>> model.params['conv_activation_func'] = 'relu' - >>> model.params['mlp_num_layers'] = 1 - >>> model.params['mlp_num_units'] = 64 - >>> model.params['mlp_num_fan_out'] = 32 - >>> model.params['mlp_activation_func'] = 'relu' - >>> model.params['dropout_rate'] = 0.5 - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== =========================== ============================================================================================ =================================== ========================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ============================================================================================ =================================== ========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 9 mlp_num_units Number of units in first `mlp_num_layers` layers. 128 quantitative uniform distribution in [8, 256), with a step size of 8 - 10 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 6), with a step size of 1 - 11 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 12 mlp_activation_func Activation function used in the multiple layer perceptron. relu - 13 num_blocks Number of convolution blocks. 1 - 14 left_filters The filter size of each convolution blocks for the left input. [32] - 15 left_kernel_sizes The kernel size of each convolution blocks for the left input. [3] - 16 right_filters The filter size of each convolution blocks for the right input. [32] - 17 right_kernel_sizes The kernel size of each convolution blocks for the right input. [3] - 18 conv_activation_func The activation function in the convolution layer. relu - 19 left_pool_sizes The pooling size of each convolution blocks for the left input. [2] - 20 right_pool_sizes The pooling size of each convolution blocks for the right input. [2] - 21 padding The padding mode in the convolution layer. It should be oneof `same`, `valid`, and `causal`. same choice in ['same', 'valid', 'causal'] - 22 dropout_rate The dropout rate. 0.0 quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 -==== =========================== ============================================================================================ =================================== ========================================================================== - -ArcII -##### - -Model Documentation -******************* - -ArcII Model. - -Examples: - >>> model = ArcII() - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['num_blocks'] = 2 - >>> model.params['kernel_1d_count'] = 32 - >>> model.params['kernel_1d_size'] = 3 - >>> model.params['kernel_2d_count'] = [16, 32] - >>> model.params['kernel_2d_size'] = [[3, 3], [3, 3]] - >>> model.params['pool_2d_size'] = [[2, 2], [2, 2]] - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ========================================================================================= ===================================== ========================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ========================================================================================= ===================================== ========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam choice in ['adam', 'rmsprop', 'adagrad'] - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 num_blocks Number of 2D convolution blocks. 1 - 9 kernel_1d_count Kernel count of 1D convolution layer. 32 - 10 kernel_1d_size Kernel size of 1D convolution layer. 3 - 11 kernel_2d_count Kernel count of 2D convolution layer ineach block [32] - 12 kernel_2d_size Kernel size of 2D convolution layer in each block. [[3, 3]] - 13 activation Activation function. relu - 14 pool_2d_size Size of pooling layer in each block. [[2, 2]] - 15 padding The padding mode in the convolution layer. It should be oneof `same`, `valid`. same choice in ['same', 'valid'] - 16 dropout_rate The dropout rate. 0.0 quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 -==== ==================== ========================================================================================= ===================================== ========================================================================== - -MatchPyramid -############ - -Model Documentation -******************* - -MatchPyramid Model. - -Examples: - >>> model = MatchPyramid() - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['num_blocks'] = 2 - >>> model.params['kernel_count'] = [16, 32] - >>> model.params['kernel_size'] = [[3, 3], [3, 3]] - >>> model.params['dpool_size'] = [3, 10] - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ========================================================================================= ==================================================== ========================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ========================================================================================= ==================================================== ========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 num_blocks Number of convolution blocks. 1 - 9 kernel_count The kernel count of the 2D convolution of each block. [32] - 10 kernel_size The kernel size of the 2D convolution of each block. [[3, 3]] - 11 activation The activation function. relu - 12 dpool_size The max-pooling size of each block. [3, 10] - 13 padding The padding mode in the convolution layer. same - 14 dropout_rate The dropout rate. 0.0 quantitative uniform distribution in [0.0, 0.8), with a step size of 0.01 -==== ==================== ========================================================================================= ==================================================== ========================================================================== - -KNRM -#### - -Model Documentation -******************* - -KNRM model. - -Examples: - >>> model = KNRM() - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 10 - >>> model.params['embedding_trainable'] = True - >>> model.params['kernel_num'] = 11 - >>> model.params['sigma'] = 0.1 - >>> model.params['exact_sigma'] = 0.001 - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ========================================================================================= =================================== =========================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ========================================================================================= =================================== =========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 kernel_num The number of RBF kernels. 11 quantitative uniform distribution in [5, 20), with a step size of 1 - 9 sigma The `sigma` defines the kernel width. 0.1 quantitative uniform distribution in [0.01, 0.2), with a step size of 0.01 - 10 exact_sigma The `exact_sigma` denotes the `sigma` for exact match. 0.001 -==== ==================== ========================================================================================= =================================== =========================================================================== - -DUET -#### - -Model Documentation -******************* - -DUET Model. - -Examples: - >>> model = DUET() - >>> model.params['embedding_input_dim'] = 1000 - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['lm_filters'] = 32 - >>> model.params['lm_hidden_sizes'] = [64, 32] - >>> model.params['dropout_rate'] = 0.5 - >>> model.params['dm_filters'] = 32 - >>> model.params['dm_kernel_size'] = 3 - >>> model.params['dm_d_mpool'] = 4 - >>> model.params['dm_hidden_sizes'] = [64, 32] - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ============================================================================================= =================================== ========================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ============================================================================================= =================================== ========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 lm_filters Filter size of 1D convolution layer in the local model. 32 - 9 lm_hidden_sizes A list of hidden size of the MLP layer in the local model. [32] - 10 dm_filters Filter size of 1D convolution layer in the distributed model. 32 - 11 dm_kernel_size Kernel size of 1D convolution layer in the distributed model. 3 - 12 dm_q_hidden_size Hidden size of the MLP layer for the left text in the distributed model. 32 - 13 dm_d_mpool Max pooling size for the right text in the distributed model. 3 - 14 dm_hidden_sizes A list of hidden size of the MLP layer in the distributed model. [32] - 15 padding The padding mode in the convolution layer. It should be one of `same`, `valid`, and `causal`. same - 16 activation_func Activation function in the convolution layer. relu - 17 dropout_rate The dropout rate. 0.5 quantitative uniform distribution in [0.0, 0.8), with a step size of 0.02 -==== ==================== ============================================================================================= =================================== ========================================================================== - -DRMMTKS -####### - -Model Documentation -******************* - -DRMMTKS Model. - -Examples: - >>> model = DRMMTKS() - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 100 - >>> model.params['top_k'] = 20 - >>> model.params['mlp_num_layers'] = 1 - >>> model.params['mlp_num_units'] = 5 - >>> model.params['mlp_num_fan_out'] = 1 - >>> model.params['mlp_activation_func'] = 'tanh' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== =========================== ========================================================================================= ========================================= ===================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ========================================================================================= ========================================= ===================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. [(5,), (300,)] - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 9 mlp_num_units Number of units in first `mlp_num_layers` layers. 128 quantitative uniform distribution in [8, 256), with a step size of 8 - 10 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 6), with a step size of 1 - 11 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 12 mlp_activation_func Activation function used in the multiple layer perceptron. relu - 13 mask_value The value to be masked from inputs. -1 - 14 top_k Size of top-k pooling layer. 10 quantitative uniform distribution in [2, 100), with a step size of 1 -==== =========================== ========================================================================================= ========================================= ===================================================================== - -DRMM -#### - -Model Documentation -******************* - -DRMM Model. - -Examples: - >>> model = DRMM() - >>> model.params['mlp_num_layers'] = 1 - >>> model.params['mlp_num_units'] = 5 - >>> model.params['mlp_num_fan_out'] = 1 - >>> model.params['mlp_activation_func'] = 'tanh' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - >>> model.compile() - -Model Hyper Parameters -********************** - -==== =========================== ========================================================================================= =================================== ===================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ========================================================================================= =================================== ===================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. [(5,), (5, 30)] - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 9 mlp_num_units Number of units in first `mlp_num_layers` layers. 128 quantitative uniform distribution in [8, 256), with a step size of 8 - 10 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 6), with a step size of 1 - 11 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 12 mlp_activation_func Activation function used in the multiple layer perceptron. relu - 13 mask_value The value to be masked from inputs. -1 -==== =========================== ========================================================================================= =================================== ===================================================================== - -ANMM -#### - -Model Documentation -******************* - -ANMM Model. - -Examples: - >>> model = ANMM() - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ========================================================================================= =================================== ====================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ========================================================================================= =================================== ====================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 dropout_rate The dropout rate. 0.1 quantitative uniform distribution in [0, 1), with a step size of 0.05 - 9 num_layers Number of hidden layers in the MLP layer. 2 - 10 hidden_sizes Number of hidden size for each hidden layer [30, 30] -==== ==================== ========================================================================================= =================================== ====================================================================== - -MVLSTM -###### - -Model Documentation -******************* - -MVLSTM Model. - -Examples: - >>> model = MVLSTM() - >>> model.params['lstm_units'] = 32 - >>> model.params['top_k'] = 50 - >>> model.params['mlp_num_layers'] = 2 - >>> model.params['mlp_num_units'] = 20 - >>> model.params['mlp_num_fan_out'] = 10 - >>> model.params['mlp_activation_func'] = 'relu' - >>> model.params['dropout_rate'] = 0.5 - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== =========================== ========================================================================================= ======================================= ===================================================================== - .. Name Description Default Value Default Hyper-Space -==== =========================== ========================================================================================= ======================================= ===================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 with_multi_layer_perceptron A flag of whether a multiple layer perceptron is used. Shouldn't be changed. True - 9 mlp_num_units Number of units in first `mlp_num_layers` layers. 128 quantitative uniform distribution in [8, 256), with a step size of 8 - 10 mlp_num_layers Number of layers of the multiple layer percetron. 3 quantitative uniform distribution in [1, 6), with a step size of 1 - 11 mlp_num_fan_out Number of units of the layer that connects the multiple layer percetron and the output. 64 quantitative uniform distribution in [4, 128), with a step size of 4 - 12 mlp_activation_func Activation function used in the multiple layer perceptron. relu - 13 lstm_units Integer, the hidden size in the bi-directional LSTM layer. 32 - 14 dropout_rate Float, the dropout rate. 0.0 - 15 top_k Integer, the size of top-k pooling layer. 10 quantitative uniform distribution in [2, 100), with a step size of 1 -==== =========================== ========================================================================================= ======================================= ===================================================================== - -MatchLSTM -######### - -Model Documentation -******************* - -Match LSTM model. - -Examples: - >>> model = MatchLSTM() - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 100 - >>> model.params['embedding_trainable'] = True - >>> model.params['fc_num_units'] = 200 - >>> model.params['lstm_num_units'] = 256 - >>> model.params['dropout_rate'] = 0.5 - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ========================================================================================= ====================================================== ========================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ========================================================================================= ====================================================== ========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 lstm_num_units The hidden size in the LSTM layer. 256 quantitative uniform distribution in [128, 384), with a step size of 32 - 9 fc_num_units The hidden size in the full connection layer. 200 quantitative uniform distribution in [100, 300), with a step size of 20 - 10 dropout_rate The dropout rate. 0.0 quantitative uniform distribution in [0.0, 0.9), with a step size of 0.01 -==== ==================== ========================================================================================= ====================================================== ========================================================================== - -ConvKNRM -######## - -Model Documentation -******************* - -ConvKNRM model. - -Examples: - >>> model = ConvKNRM() - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['embedding_trainable'] = True - >>> model.params['filters'] = 128 - >>> model.params['conv_activation_func'] = 'tanh' - >>> model.params['max_ngram'] = 3 - >>> model.params['use_crossmatch'] = True - >>> model.params['kernel_num'] = 11 - >>> model.params['sigma'] = 0.1 - >>> model.params['exact_sigma'] = 0.001 - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - -Model Hyper Parameters -********************** - -==== ==================== ========================================================================================= ============================================ =========================================================================== - .. Name Description Default Value Default Hyper-Space -==== ==================== ========================================================================================= ============================================ =========================================================================== - 0 model_class Model class. Used internally for save/load. Changing this may cause unexpected behaviors. - 1 input_shapes Dependent on the model and data. Should be set manually. - 2 task Decides model output shape, loss, and metrics. - 3 optimizer adam - 4 with_embedding A flag used help `auto` module. Shouldn't be changed. True - 5 embedding_input_dim Usually equals vocab size + 1. Should be set manually. - 6 embedding_output_dim Should be set manually. - 7 embedding_trainable `True` to enable embedding layer training, `False` to freeze embedding parameters. True - 8 kernel_num The number of RBF kernels. 11 quantitative uniform distribution in [5, 20), with a step size of 1 - 9 sigma The `sigma` defines the kernel width. 0.1 quantitative uniform distribution in [0.01, 0.2), with a step size of 0.01 - 10 exact_sigma The `exact_sigma` denotes the `sigma` for exact match. 0.001 - 11 filters The filter size in the convolution layer. 128 - 12 conv_activation_func The activation function in the convolution layer. relu - 13 max_ngram The maximum length of n-grams for the convolution layer. 3 - 14 use_crossmatch Whether to match left n-grams and right n-grams of different lengths True -==== ==================== ========================================================================================= ============================================ =========================================================================== - diff --git a/matchzoo/models/__init__.py b/matchzoo/models/__init__.py deleted file mode 100644 index 29516ab3..00000000 --- a/matchzoo/models/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -from .naive import Naive -from .dssm import DSSM -from .cdssm import CDSSM -from .dense_baseline import DenseBaseline -from .arci import ArcI -from .arcii import ArcII -from .match_pyramid import MatchPyramid -from .knrm import KNRM -from .conv_knrm import ConvKNRM -from .duet import DUET -from .drmmtks import DRMMTKS -from .drmm import DRMM -from .anmm import ANMM -from .mvlstm import MVLSTM - - -def list_available() -> list: - from matchzoo.engine.base_model import BaseModel - from matchzoo.utils import list_recursive_concrete_subclasses - return list_recursive_concrete_subclasses(BaseModel) diff --git a/matchzoo/models/anmm.py b/matchzoo/models/anmm.py deleted file mode 100644 index ae30055b..00000000 --- a/matchzoo/models/anmm.py +++ /dev/null @@ -1,79 +0,0 @@ -"""An implementation of aNMM Model.""" - -import keras -from keras.activations import softmax -from keras.initializers import RandomUniform - -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable -from matchzoo.engine import hyper_spaces - - -class ANMM(BaseModel): - """ - ANMM Model. - - Examples: - >>> model = ANMM() - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params(with_embedding=True) - params.add(Param( - name='dropout_rate', value=0.1, - desc="The dropout rate.", - hyper_space=hyper_spaces.quniform(0, 1, 0.05) - )) - params.add(Param( - name='num_layers', value=2, - desc="Number of hidden layers in the MLP " - "layer." - )) - params.add(Param( - name='hidden_sizes', value=[30, 30], - desc="Number of hidden size for each hidden" - " layer" - )) - return params - - def build(self): - """ - Build model structure. - - aNMM model based on bin weighting and query term attentions - """ - # query is [batch_size, left_text_len] - # doc is [batch_size, right_text_len, bin_num] - query, doc = self._make_inputs() - embedding = self._make_embedding_layer() - - q_embed = embedding(query) - q_attention = keras.layers.Dense( - 1, kernel_initializer=RandomUniform(), use_bias=False)(q_embed) - q_text_len = self._params['input_shapes'][0][0] - - q_attention = keras.layers.Lambda( - lambda x: softmax(x, axis=1), - output_shape=(q_text_len,) - )(q_attention) - d_bin = keras.layers.Dropout( - rate=self._params['dropout_rate'])(doc) - for layer_id in range(self._params['num_layers'] - 1): - d_bin = keras.layers.Dense( - self._params['hidden_sizes'][layer_id], - kernel_initializer=RandomUniform())(d_bin) - d_bin = keras.layers.Activation('tanh')(d_bin) - d_bin = keras.layers.Dense( - self._params['hidden_sizes'][self._params['num_layers'] - 1])( - d_bin) - d_bin = keras.layers.Reshape((q_text_len,))(d_bin) - q_attention = keras.layers.Reshape((q_text_len,))(q_attention) - score = keras.layers.Dot(axes=[1, 1])([d_bin, q_attention]) - x_out = self._make_output_layer()(score) - self._backend = keras.Model(inputs=[query, doc], outputs=x_out) diff --git a/matchzoo/models/arci.py b/matchzoo/models/arci.py deleted file mode 100644 index fe577502..00000000 --- a/matchzoo/models/arci.py +++ /dev/null @@ -1,140 +0,0 @@ -"""An implementation of ArcI Model.""" -import typing - -import keras - -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable -from matchzoo.engine import hyper_spaces - - -class ArcI(BaseModel): - """ - ArcI Model. - - Examples: - >>> model = ArcI() - >>> model.params['num_blocks'] = 1 - >>> model.params['left_filters'] = [32] - >>> model.params['right_filters'] = [32] - >>> model.params['left_kernel_sizes'] = [3] - >>> model.params['right_kernel_sizes'] = [3] - >>> model.params['left_pool_sizes'] = [2] - >>> model.params['right_pool_sizes'] = [4] - >>> model.params['conv_activation_func'] = 'relu' - >>> model.params['mlp_num_layers'] = 1 - >>> model.params['mlp_num_units'] = 64 - >>> model.params['mlp_num_fan_out'] = 32 - >>> model.params['mlp_activation_func'] = 'relu' - >>> model.params['dropout_rate'] = 0.5 - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params( - with_embedding=True, - with_multi_layer_perceptron=True - ) - params['optimizer'] = 'adam' - params.add(Param(name='num_blocks', value=1, - desc="Number of convolution blocks.")) - params.add(Param(name='left_filters', value=[32], - desc="The filter size of each convolution " - "blocks for the left input.")) - params.add(Param(name='left_kernel_sizes', value=[3], - desc="The kernel size of each convolution " - "blocks for the left input.")) - params.add(Param(name='right_filters', value=[32], - desc="The filter size of each convolution " - "blocks for the right input.")) - params.add(Param(name='right_kernel_sizes', value=[3], - desc="The kernel size of each convolution " - "blocks for the right input.")) - params.add(Param(name='conv_activation_func', value='relu', - desc="The activation function in the " - "convolution layer.")) - params.add(Param(name='left_pool_sizes', value=[2], - desc="The pooling size of each convolution " - "blocks for the left input.")) - params.add(Param(name='right_pool_sizes', value=[2], - desc="The pooling size of each convolution " - "blocks for the right input.")) - params.add(Param( - name='padding', - value='same', - hyper_space=hyper_spaces.choice( - ['same', 'valid', 'causal']), - desc="The padding mode in the convolution layer. It should be one" - "of `same`, `valid`, and `causal`." - )) - params.add(Param( - 'dropout_rate', 0.0, - hyper_space=hyper_spaces.quniform( - low=0.0, high=0.8, q=0.01), - desc="The dropout rate." - )) - return params - - def build(self): - """ - Build model structure. - - ArcI use Siamese arthitecture. - """ - input_left, input_right = self._make_inputs() - - embedding = self._make_embedding_layer() - embed_left = embedding(input_left) - embed_right = embedding(input_right) - - for i in range(self._params['num_blocks']): - embed_left = self._conv_pool_block( - embed_left, - self._params['left_filters'][i], - self._params['left_kernel_sizes'][i], - self._params['padding'], - self._params['conv_activation_func'], - self._params['left_pool_sizes'][i] - ) - embed_right = self._conv_pool_block( - embed_right, - self._params['right_filters'][i], - self._params['right_kernel_sizes'][i], - self._params['padding'], - self._params['conv_activation_func'], - self._params['right_pool_sizes'][i] - ) - - rep_left = keras.layers.Flatten()(embed_left) - rep_right = keras.layers.Flatten()(embed_right) - concat = keras.layers.Concatenate(axis=1)([rep_left, rep_right]) - dropout = keras.layers.Dropout( - rate=self._params['dropout_rate'])(concat) - mlp = self._make_multi_layer_perceptron_layer()(dropout) - - inputs = [input_left, input_right] - x_out = self._make_output_layer()(mlp) - self._backend = keras.Model(inputs=inputs, outputs=x_out) - - def _conv_pool_block( - self, - input_: typing.Any, - filters: int, - kernel_size: int, - padding: str, - conv_activation_func: str, - pool_size: int - ) -> typing.Any: - output = keras.layers.Conv1D( - filters, - kernel_size, - padding=padding, - activation=conv_activation_func - )(input_) - output = keras.layers.MaxPooling1D(pool_size=pool_size)(output) - return output diff --git a/matchzoo/models/arcii.py b/matchzoo/models/arcii.py deleted file mode 100644 index 530198f9..00000000 --- a/matchzoo/models/arcii.py +++ /dev/null @@ -1,129 +0,0 @@ -"""An implementation of ArcII Model.""" -import typing - -import keras - -import matchzoo -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable -from matchzoo.engine import hyper_spaces - - -class ArcII(BaseModel): - """ - ArcII Model. - - Examples: - >>> model = ArcII() - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['num_blocks'] = 2 - >>> model.params['kernel_1d_count'] = 32 - >>> model.params['kernel_1d_size'] = 3 - >>> model.params['kernel_2d_count'] = [16, 32] - >>> model.params['kernel_2d_size'] = [[3, 3], [3, 3]] - >>> model.params['pool_2d_size'] = [[2, 2], [2, 2]] - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params(with_embedding=True) - params['optimizer'] = 'adam' - opt_space = hyper_spaces.choice(['adam', 'rmsprop', 'adagrad']) - params.get('optimizer').hyper_space = opt_space - params.add(Param(name='num_blocks', value=1, - desc="Number of 2D convolution blocks.")) - params.add(Param(name='kernel_1d_count', value=32, - desc="Kernel count of 1D convolution layer.")) - params.add(Param(name='kernel_1d_size', value=3, - desc="Kernel size of 1D convolution layer.")) - params.add(Param(name='kernel_2d_count', value=[32], - desc="Kernel count of 2D convolution layer in" - "each block")) - params.add(Param(name='kernel_2d_size', value=[[3, 3]], - desc="Kernel size of 2D convolution layer in" - " each block.")) - params.add(Param(name='activation', value='relu', - desc="Activation function.")) - params.add(Param(name='pool_2d_size', value=[[2, 2]], - desc="Size of pooling layer in each block.")) - params.add(Param( - name='padding', value='same', - hyper_space=hyper_spaces.choice( - ['same', 'valid']), - desc="The padding mode in the convolution layer. It should be one" - "of `same`, `valid`." - )) - params.add(Param( - name='dropout_rate', value=0.0, - hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, - q=0.01), - desc="The dropout rate." - )) - return params - - def build(self): - """ - Build model structure. - - ArcII has the desirable property of letting two sentences meet before - their own high-level representations mature. - """ - input_left, input_right = self._make_inputs() - - embedding = self._make_embedding_layer() - embed_left = embedding(input_left) - embed_right = embedding(input_right) - - # Phrase level representations - conv_1d_left = keras.layers.Conv1D( - self._params['kernel_1d_count'], - self._params['kernel_1d_size'], - padding=self._params['padding'] - )(embed_left) - conv_1d_right = keras.layers.Conv1D( - self._params['kernel_1d_count'], - self._params['kernel_1d_size'], - padding=self._params['padding'] - )(embed_right) - - # Interaction - matching_layer = matchzoo.layers.MatchingLayer(matching_type='plus') - embed_cross = matching_layer([conv_1d_left, conv_1d_right]) - - for i in range(self._params['num_blocks']): - embed_cross = self._conv_pool_block( - embed_cross, - self._params['kernel_2d_count'][i], - self._params['kernel_2d_size'][i], - self._params['padding'], - self._params['activation'], - self._params['pool_2d_size'][i] - ) - - embed_flat = keras.layers.Flatten()(embed_cross) - x = keras.layers.Dropout(rate=self._params['dropout_rate'])(embed_flat) - - inputs = [input_left, input_right] - x_out = self._make_output_layer()(x) - self._backend = keras.Model(inputs=inputs, outputs=x_out) - - @classmethod - def _conv_pool_block( - cls, x, - kernel_count: int, - kernel_size: int, - padding: str, - activation: str, - pool_size: int - ) -> typing.Any: - output = keras.layers.Conv2D(kernel_count, - kernel_size, - padding=padding, - activation=activation)(x) - output = keras.layers.MaxPooling2D(pool_size=pool_size)(output) - return output diff --git a/matchzoo/models/cdssm.py b/matchzoo/models/cdssm.py deleted file mode 100644 index 5531bd5e..00000000 --- a/matchzoo/models/cdssm.py +++ /dev/null @@ -1,132 +0,0 @@ -"""An implementation of CDSSM (CLSM) model.""" -import typing - -import keras -from keras.models import Model - -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable -from matchzoo import preprocessors -from matchzoo.utils import TensorType - - -class CDSSM(BaseModel): - """ - CDSSM Model implementation. - - Learning Semantic Representations Using Convolutional Neural Networks - for Web Search. (2014a) - A Latent Semantic Model with Convolutional-Pooling Structure for - Information Retrieval. (2014b) - - Examples: - >>> model = CDSSM() - >>> model.params['optimizer'] = 'adam' - >>> model.params['filters'] = 32 - >>> model.params['kernel_size'] = 3 - >>> model.params['conv_activation_func'] = 'relu' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - # set :attr:`with_multi_layer_perceptron` to False to support - # user-defined variable dense layer units - params = super().get_default_params(with_multi_layer_perceptron=True) - params.add(Param(name='filters', value=32, - desc="Number of filters in the 1D convolution " - "layer.")) - params.add(Param(name='kernel_size', value=3, - desc="Number of kernel size in the 1D " - "convolution layer.")) - params.add(Param(name='strides', value=1, - desc="Strides in the 1D convolution layer.")) - params.add(Param(name='padding', value='same', - desc="The padding mode in the convolution " - "layer. It should be one of `same`, " - "`valid`, ""and `causal`.")) - params.add(Param(name='conv_activation_func', value='relu', - desc="Activation function in the convolution" - " layer.")) - params.add(Param(name='w_initializer', value='glorot_normal')) - params.add(Param(name='b_initializer', value='zeros')) - params.add(Param(name='dropout_rate', value=0.3, - desc="The dropout rate.")) - return params - - def _create_base_network(self) -> typing.Callable: - """ - Apply conv and maxpooling operation towards to each letter-ngram. - - The input shape is `fixed_text_length`*`number of letter-ngram`, - as described in the paper, `n` is 3, `number of letter-trigram` - is about 30,000 according to their observation. - - :return: Wrapped Keras `Layer` as CDSSM network, tensor in tensor out. - """ - - def _wrapper(x: TensorType): - # Apply 1d convolutional on each word_ngram (lt). - # Input shape: (batch_size, num_tri_letters, 90000) - # Sequence of num_tri_letters vectors of 90000d vectors. - x = keras.layers.Conv1D( - filters=self._params['filters'], - kernel_size=self._params['kernel_size'], - strides=self._params['strides'], - padding=self._params['padding'], - activation=self._params['conv_activation_func'], - kernel_initializer=self._params['w_initializer'], - bias_initializer=self._params['b_initializer'])(x) - # Apply max pooling by take max at each dimension across - # all word_trigram features. - x = keras.layers.Dropout(self._params['dropout_rate'])(x) - x = keras.layers.GlobalMaxPool1D()(x) - # Apply a none-linear transformation use a tanh layer. - x = self._make_multi_layer_perceptron_layer()(x) - return x - - return _wrapper - - def build(self): - """ - Build model structure. - - CDSSM use Siamese architecture. - """ - base_network = self._create_base_network() - # Left input and right input. - input_left, input_right = self._make_inputs() - # Process left & right input. - x = [base_network(input_left), - base_network(input_right)] - # Dot product with cosine similarity. - x = keras.layers.Dot(axes=[1, 1], normalize=True)(x) - x_out = self._make_output_layer()(x) - self._backend = Model(inputs=[input_left, input_right], - outputs=x_out) - - @classmethod - def get_default_preprocessor(cls): - """:return: Default preprocessor.""" - return preprocessors.CDSSMPreprocessor() - - def guess_and_fill_missing_params(self, verbose: int = 1): - """ - Guess and fill missing parameters in :attr:`params`. - - Use this method to automatically fill-in hyper parameters. - This involves some guessing so the parameter it fills could be - wrong. For example, the default task is `Ranking`, and if we do not - set it to `Classification` manually for data packs prepared for - classification, then the shape of the model output and the data will - mismatch. - - :param verbose: Verbosity. - """ - self._params.get('input_shapes').set_default([(10, 30), - (10, 30)], verbose) - super().guess_and_fill_missing_params(verbose) diff --git a/matchzoo/models/conv_knrm.py b/matchzoo/models/conv_knrm.py deleted file mode 100644 index c074371e..00000000 --- a/matchzoo/models/conv_knrm.py +++ /dev/null @@ -1,97 +0,0 @@ -"""ConvKNRM model.""" - -import keras -import tensorflow as tf - -from .knrm import KNRM -from matchzoo.engine.param import Param - - -class ConvKNRM(KNRM): - """ - ConvKNRM model. - - Examples: - >>> model = ConvKNRM() - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['embedding_trainable'] = True - >>> model.params['filters'] = 128 - >>> model.params['conv_activation_func'] = 'tanh' - >>> model.params['max_ngram'] = 3 - >>> model.params['use_crossmatch'] = True - >>> model.params['kernel_num'] = 11 - >>> model.params['sigma'] = 0.1 - >>> model.params['exact_sigma'] = 0.001 - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - def get_default_params(cls): - """Get default parameters.""" - params = super().get_default_params() - params.add(Param(name='filters', value=128, - desc="The filter size in the convolution" - " layer.")) - params.add(Param(name='conv_activation_func', value='relu', - desc="The activation function in the " - "convolution layer.")) - params.add(Param(name='max_ngram', value=3, - desc="The maximum length of n-grams for the " - "convolution layer.")) - params.add(Param(name='use_crossmatch', value=True, - desc="Whether to match left n-grams and right " - "n-grams of different lengths")) - return params - - def build(self): - """Build model.""" - query, doc = self._make_inputs() - - embedding = self._make_embedding_layer() - - q_embed = embedding(query) - d_embed = embedding(doc) - - q_convs = [] - d_convs = [] - for i in range(self._params['max_ngram']): - c = keras.layers.Conv1D( - self._params['filters'], i + 1, - activation=self._params['conv_activation_func'], - padding='same' - ) - q_convs.append(c(q_embed)) - d_convs.append(c(d_embed)) - - KM = [] - for qi in range(self._params['max_ngram']): - for di in range(self._params['max_ngram']): - # do not match n-gram with different length if use crossmatch - if not self._params['use_crossmatch'] and qi != di: - continue - q_ngram = q_convs[qi] - d_ngram = d_convs[di] - mm = keras.layers.Dot(axes=[2, 2], - normalize=True)([q_ngram, d_ngram]) - - for i in range(self._params['kernel_num']): - mu = 1. / (self._params['kernel_num'] - 1) + (2. * i) / ( - self._params['kernel_num'] - 1) - 1.0 - sigma = self._params['sigma'] - if mu > 1.0: - sigma = self._params['exact_sigma'] - mu = 1.0 - mm_exp = self._kernel_layer(mu, sigma)(mm) - mm_doc_sum = keras.layers.Lambda( - lambda x: tf.reduce_sum(x, 2))( - mm_exp) - mm_log = keras.layers.Activation(tf.math.log1p)(mm_doc_sum) - mm_sum = keras.layers.Lambda( - lambda x: tf.reduce_sum(x, 1))(mm_log) - KM.append(mm_sum) - - phi = keras.layers.Lambda(lambda x: tf.stack(x, 1))(KM) - out = self._make_output_layer()(phi) - self._backend = keras.Model(inputs=[query, doc], outputs=[out]) diff --git a/matchzoo/models/dense_baseline.py b/matchzoo/models/dense_baseline.py deleted file mode 100644 index e25310a7..00000000 --- a/matchzoo/models/dense_baseline.py +++ /dev/null @@ -1,43 +0,0 @@ -"""A simple densely connected baseline model.""" - -import keras.layers - -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param_table import ParamTable -from matchzoo.engine import hyper_spaces - - -class DenseBaseline(BaseModel): - """ - A simple densely connected baseline model. - - Examples: - >>> model = DenseBaseline() - >>> model.params['mlp_num_layers'] = 2 - >>> model.params['mlp_num_units'] = 300 - >>> model.params['mlp_num_fan_out'] = 128 - >>> model.params['mlp_activation_func'] = 'relu' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - >>> model.compile() - - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params(with_multi_layer_perceptron=True) - params['mlp_num_units'] = 256 - params.get('mlp_num_units').hyper_space = \ - hyper_spaces.quniform(16, 512) - params.get('mlp_num_layers').hyper_space = \ - hyper_spaces.quniform(1, 5) - return params - - def build(self): - """Model structure.""" - x_in = self._make_inputs() - x = keras.layers.concatenate(x_in) - x = self._make_multi_layer_perceptron_layer()(x) - x_out = self._make_output_layer()(x) - self._backend = keras.models.Model(inputs=x_in, outputs=x_out) diff --git a/matchzoo/models/drmm.py b/matchzoo/models/drmm.py deleted file mode 100644 index f1b19b48..00000000 --- a/matchzoo/models/drmm.py +++ /dev/null @@ -1,121 +0,0 @@ -"""An implementation of DRMM Model.""" -import typing - -import keras -import keras.backend as K -import tensorflow as tf - -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable - - -class DRMM(BaseModel): - """ - DRMM Model. - - Examples: - >>> model = DRMM() - >>> model.params['mlp_num_layers'] = 1 - >>> model.params['mlp_num_units'] = 5 - >>> model.params['mlp_num_fan_out'] = 1 - >>> model.params['mlp_activation_func'] = 'tanh' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - >>> model.compile() - - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params(with_embedding=True, - with_multi_layer_perceptron=True) - params.add(Param(name='mask_value', value=-1, - desc="The value to be masked from inputs.")) - params['optimizer'] = 'adam' - params['input_shapes'] = [(5,), (5, 30,)] - return params - - def build(self): - """Build model structure.""" - - # Scalar dimensions referenced here: - # B = batch size (number of sequences) - # D = embedding size - # L = `input_left` sequence length - # R = `input_right` sequence length - # H = histogram size - # K = size of top-k - - # Left input and right input. - # query: shape = [B, L] - # doc: shape = [B, L, H] - # Note here, the doc is the matching histogram between original query - # and original document. - query = keras.layers.Input( - name='text_left', - shape=self._params['input_shapes'][0] - ) - match_hist = keras.layers.Input( - name='match_histogram', - shape=self._params['input_shapes'][1] - ) - - embedding = self._make_embedding_layer() - # Process left input. - # shape = [B, L, D] - embed_query = embedding(query) - # shape = [B, L] - atten_mask = tf.not_equal(query, self._params['mask_value']) - # shape = [B, L] - atten_mask = tf.cast(atten_mask, K.floatx()) - # shape = [B, L, D] - atten_mask = tf.expand_dims(atten_mask, axis=2) - # shape = [B, L, D] - attention_probs = self.attention_layer(embed_query, atten_mask) - - # Process right input. - # shape = [B, L, 1] - dense_output = self._make_multi_layer_perceptron_layer()(match_hist) - - # shape = [B, 1, 1] - dot_score = keras.layers.Dot(axes=[1, 1])( - [attention_probs, dense_output]) - - flatten_score = keras.layers.Flatten()(dot_score) - - x_out = self._make_output_layer()(flatten_score) - self._backend = keras.Model(inputs=[query, match_hist], outputs=x_out) - - @classmethod - def attention_layer(cls, attention_input: typing.Any, - attention_mask: typing.Any = None - ) -> keras.layers.Layer: - """ - Performs attention on the input. - - :param attention_input: The input tensor for attention layer. - :param attention_mask: A tensor to mask the invalid values. - :return: The masked output tensor. - """ - # shape = [B, L, 1] - dense_input = keras.layers.Dense(1, use_bias=False)(attention_input) - if attention_mask is not None: - # Since attention_mask is 1.0 for positions we want to attend and - # 0.0 for masked positions, this operation will create a tensor - # which is 0.0 for positions we want to attend and -10000.0 for - # masked positions. - - # shape = [B, L, 1] - dense_input = keras.layers.Lambda( - lambda x: x + (1.0 - attention_mask) * -10000.0, - name="attention_mask" - )(dense_input) - # shape = [B, L, 1] - attention_probs = keras.layers.Lambda( - lambda x: tf.nn.softmax(x, axis=1), - output_shape=lambda s: (s[0], s[1], s[2]), - name="attention_probs" - )(dense_input) - return attention_probs diff --git a/matchzoo/models/drmmtks.py b/matchzoo/models/drmmtks.py deleted file mode 100644 index 4ce5edee..00000000 --- a/matchzoo/models/drmmtks.py +++ /dev/null @@ -1,134 +0,0 @@ -"""An implementation of DRMMTKS Model.""" -import typing - -import keras -import tensorflow as tf - -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable -from matchzoo.engine import hyper_spaces - - -class DRMMTKS(BaseModel): - """ - DRMMTKS Model. - - Examples: - >>> model = DRMMTKS() - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 100 - >>> model.params['top_k'] = 20 - >>> model.params['mlp_num_layers'] = 1 - >>> model.params['mlp_num_units'] = 5 - >>> model.params['mlp_num_fan_out'] = 1 - >>> model.params['mlp_activation_func'] = 'tanh' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params( - with_embedding=True, - with_multi_layer_perceptron=True - ) - params.add(Param(name='mask_value', value=-1, - desc="The value to be masked from inputs.")) - params['input_shapes'] = [(5,), (300,)] - params.add(Param( - 'top_k', value=10, - hyper_space=hyper_spaces.quniform(low=2, high=100), - desc="Size of top-k pooling layer." - )) - return params - - def build(self): - """Build model structure.""" - - # Scalar dimensions referenced here: - # B = batch size (number of sequences) - # D = embedding size - # L = `input_left` sequence length - # R = `input_right` sequence length - # K = size of top-k - - # Left input and right input. - # shape = [B, L] - # shape = [B, R] - query, doc = self._make_inputs() - - embedding = self._make_embedding_layer() - # Process left input. - # shape = [B, L, D] - embed_query = embedding(query) - # shape = [B, R, D] - embed_doc = embedding(doc) - # shape = [B, L] - atten_mask = tf.not_equal(query, self._params['mask_value']) - # shape = [B, L] - atten_mask = tf.cast(atten_mask, keras.backend.floatx()) - # shape = [B, L, 1] - atten_mask = tf.expand_dims(atten_mask, axis=2) - # shape = [B, L, 1] - attention_probs = self.attention_layer(embed_query, atten_mask) - - # Matching histogram of top-k - # shape = [B, L, R] - matching_matrix = keras.layers.Dot(axes=[2, 2], normalize=True)( - [embed_query, - embed_doc]) - # shape = [B, L, K] - effective_top_k = min(self._params['top_k'], - self.params['input_shapes'][0][0], - self.params['input_shapes'][1][0]) - matching_topk = keras.layers.Lambda( - lambda x: tf.nn.top_k(x, k=effective_top_k, sorted=True)[0] - )(matching_matrix) - - # Process right input. - # shape = [B, L, 1] - dense_output = self._make_multi_layer_perceptron_layer()(matching_topk) - - # shape = [B, 1, 1] - dot_score = keras.layers.Dot(axes=[1, 1])( - [attention_probs, dense_output]) - - flatten_score = keras.layers.Flatten()(dot_score) - - x_out = self._make_output_layer()(flatten_score) - self._backend = keras.Model(inputs=[query, doc], outputs=x_out) - - @classmethod - def attention_layer(cls, attention_input: typing.Any, - attention_mask: typing.Any = None - ) -> keras.layers.Layer: - """ - Performs attention on the input. - - :param attention_input: The input tensor for attention layer. - :param attention_mask: A tensor to mask the invalid values. - :return: The masked output tensor. - """ - # shape = [B, L, 1] - dense_input = keras.layers.Dense(1, use_bias=False)(attention_input) - if attention_mask is not None: - # Since attention_mask is 1.0 for positions we want to attend and - # 0.0 for masked positions, this operation will create a tensor - # which is 0.0 for positions we want to attend and -10000.0 for - # masked positions. - - # shape = [B, L, 1] - dense_input = keras.layers.Lambda( - lambda x: x + (1.0 - attention_mask) * -10000.0, - name="attention_mask" - )(dense_input) - # shape = [B, L, 1] - attention_probs = keras.layers.Lambda( - lambda x: tf.nn.softmax(x, axis=1), - output_shape=lambda s: (s[0], s[1], s[2]), - name="attention_probs" - )(dense_input) - return attention_probs diff --git a/matchzoo/models/dssm.py b/matchzoo/models/dssm.py deleted file mode 100644 index 31b729be..00000000 --- a/matchzoo/models/dssm.py +++ /dev/null @@ -1,56 +0,0 @@ -"""An implementation of DSSM, Deep Structured Semantic Model.""" -from keras.models import Model -from keras.layers import Input, Dot - -from matchzoo.engine.param_table import ParamTable -from matchzoo.engine.base_model import BaseModel -from matchzoo import preprocessors - - -class DSSM(BaseModel): - """ - Deep structured semantic model. - - Examples: - >>> model = DSSM() - >>> model.params['mlp_num_layers'] = 3 - >>> model.params['mlp_num_units'] = 300 - >>> model.params['mlp_num_fan_out'] = 128 - >>> model.params['mlp_activation_func'] = 'relu' - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params(with_multi_layer_perceptron=True) - return params - - def build(self): - """ - Build model structure. - - DSSM use Siamese arthitecture. - """ - dim_triletter = self._params['input_shapes'][0][0] - input_shape = (dim_triletter,) - base_network = self._make_multi_layer_perceptron_layer() - # Left input and right input. - input_left = Input(name='text_left', shape=input_shape) - input_right = Input(name='text_right', shape=input_shape) - # Process left & right input. - x = [base_network(input_left), - base_network(input_right)] - # Dot product with cosine similarity. - x = Dot(axes=[1, 1], normalize=True)(x) - x_out = self._make_output_layer()(x) - self._backend = Model( - inputs=[input_left, input_right], - outputs=x_out) - - @classmethod - def get_default_preprocessor(cls): - """:return: Default preprocessor.""" - return preprocessors.DSSMPreprocessor() diff --git a/matchzoo/models/duet.py b/matchzoo/models/duet.py deleted file mode 100644 index 22783fac..00000000 --- a/matchzoo/models/duet.py +++ /dev/null @@ -1,161 +0,0 @@ -"""DUET Model.""" - -import keras -import tensorflow as tf - -from matchzoo.engine import hyper_spaces -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param - - -class DUET(BaseModel): - """ - DUET Model. - - Examples: - >>> model = DUET() - >>> model.params['embedding_input_dim'] = 1000 - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['lm_filters'] = 32 - >>> model.params['lm_hidden_sizes'] = [64, 32] - >>> model.params['dropout_rate'] = 0.5 - >>> model.params['dm_filters'] = 32 - >>> model.params['dm_kernel_size'] = 3 - >>> model.params['dm_d_mpool'] = 4 - >>> model.params['dm_hidden_sizes'] = [64, 32] - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - @classmethod - def get_default_params(cls): - """Get default parameters.""" - params = super().get_default_params(with_embedding=True) - params.add(Param(name='lm_filters', value=32, - desc="Filter size of 1D convolution layer in " - "the local model.")) - params.add(Param(name='lm_hidden_sizes', value=[32], - desc="A list of hidden size of the MLP layer " - "in the local model.")) - params.add(Param(name='dm_filters', value=32, - desc="Filter size of 1D convolution layer in " - "the distributed model.")) - params.add(Param(name='dm_kernel_size', value=3, - desc="Kernel size of 1D convolution layer in " - "the distributed model.")) - params.add(Param(name='dm_q_hidden_size', value=32, - desc="Hidden size of the MLP layer for the " - "left text in the distributed model.")) - params.add(Param(name='dm_d_mpool', value=3, - desc="Max pooling size for the right text in " - "the distributed model.")) - params.add(Param(name='dm_hidden_sizes', value=[32], - desc="A list of hidden size of the MLP layer " - "in the distributed model.")) - params.add(Param(name='padding', value='same', - desc="The padding mode in the convolution " - "layer. It should be one of `same`, " - "`valid`, ""and `causal`.")) - params.add(Param(name='activation_func', value='relu', - desc="Activation function in the convolution" - " layer.")) - params.add(Param( - name='dropout_rate', value=0.5, - hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, - q=0.02), - desc="The dropout rate.")) - return params - - def build(self): - """Build model.""" - query, doc = self._make_inputs() - - embedding = self._make_embedding_layer() - q_embed = embedding(query) - d_embed = embedding(doc) - - lm_xor = keras.layers.Lambda(self._xor_match)([query, doc]) - lm_conv = keras.layers.Conv1D( - self._params['lm_filters'], - self._params['input_shapes'][1][0], - padding=self._params['padding'], - activation=self._params['activation_func'] - )(lm_xor) - - lm_conv = keras.layers.Dropout(self._params['dropout_rate'])( - lm_conv) - lm_feat = keras.layers.Reshape((-1,))(lm_conv) - for hidden_size in self._params['lm_hidden_sizes']: - lm_feat = keras.layers.Dense( - hidden_size, - activation=self._params['activation_func'] - )(lm_feat) - lm_drop = keras.layers.Dropout(self._params['dropout_rate'])( - lm_feat) - lm_score = keras.layers.Dense(1)(lm_drop) - - dm_q_conv = keras.layers.Conv1D( - self._params['dm_filters'], - self._params['dm_kernel_size'], - padding=self._params['padding'], - activation=self._params['activation_func'] - )(q_embed) - dm_q_conv = keras.layers.Dropout(self._params['dropout_rate'])( - dm_q_conv) - dm_q_mp = keras.layers.MaxPooling1D( - pool_size=self._params['input_shapes'][0][0])(dm_q_conv) - dm_q_rep = keras.layers.Reshape((-1,))(dm_q_mp) - dm_q_rep = keras.layers.Dense(self._params['dm_q_hidden_size'])( - dm_q_rep) - dm_q_rep = keras.layers.Lambda(lambda x: tf.expand_dims(x, 1))( - dm_q_rep) - - dm_d_conv1 = keras.layers.Conv1D( - self._params['dm_filters'], - self._params['dm_kernel_size'], - padding=self._params['padding'], - activation=self._params['activation_func'] - )(d_embed) - dm_d_conv1 = keras.layers.Dropout(self._params['dropout_rate'])( - dm_d_conv1) - dm_d_mp = keras.layers.MaxPooling1D( - pool_size=self._params['dm_d_mpool'])(dm_d_conv1) - dm_d_conv2 = keras.layers.Conv1D( - self._params['dm_filters'], 1, - padding=self._params['padding'], - activation=self._params['activation_func'] - )(dm_d_mp) - dm_d_conv2 = keras.layers.Dropout(self._params['dropout_rate'])( - dm_d_conv2) - - h_dot = keras.layers.Lambda(self._hadamard_dot)([dm_q_rep, dm_d_conv2]) - dm_feat = keras.layers.Reshape((-1,))(h_dot) - for hidden_size in self._params['dm_hidden_sizes']: - dm_feat = keras.layers.Dense(hidden_size)(dm_feat) - dm_feat_drop = keras.layers.Dropout(self._params['dropout_rate'])( - dm_feat) - dm_score = keras.layers.Dense(1)(dm_feat_drop) - - add = keras.layers.Add()([lm_score, dm_score]) - out = self._make_output_layer()(add) - self._backend = keras.Model(inputs=[query, doc], outputs=out) - - @classmethod - def _xor_match(cls, x): - t1 = x[0] - t2 = x[1] - t1_shape = t1.get_shape() - t2_shape = t2.get_shape() - t1_expand = tf.stack([t1] * t2_shape[1], 2) - t2_expand = tf.stack([t2] * t1_shape[1], 1) - out_bool = tf.equal(t1_expand, t2_expand) - out = tf.cast(out_bool, tf.float32) - return out - - @classmethod - def _hadamard_dot(cls, x): - x1 = x[0] - x2 = x[1] - out = x1 * x2 - return out diff --git a/matchzoo/models/knrm.py b/matchzoo/models/knrm.py deleted file mode 100644 index 7d1ff915..00000000 --- a/matchzoo/models/knrm.py +++ /dev/null @@ -1,94 +0,0 @@ -"""KNRM model.""" -import keras -import tensorflow as tf - -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine import hyper_spaces - - -class KNRM(BaseModel): - """ - KNRM model. - - Examples: - >>> model = KNRM() - >>> model.params['embedding_input_dim'] = 10000 - >>> model.params['embedding_output_dim'] = 10 - >>> model.params['embedding_trainable'] = True - >>> model.params['kernel_num'] = 11 - >>> model.params['sigma'] = 0.1 - >>> model.params['exact_sigma'] = 0.001 - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - @classmethod - def get_default_params(cls): - """Get default parameters.""" - params = super().get_default_params(with_embedding=True) - params.add(Param( - name='kernel_num', - value=11, - hyper_space=hyper_spaces.quniform(low=5, high=20), - desc="The number of RBF kernels." - )) - params.add(Param( - name='sigma', - value=0.1, - hyper_space=hyper_spaces.quniform( - low=0.01, high=0.2, q=0.01), - desc="The `sigma` defines the kernel width." - )) - params.add(Param( - name='exact_sigma', value=0.001, - desc="The `exact_sigma` denotes the `sigma` " - "for exact match." - )) - return params - - def build(self): - """Build model.""" - query, doc = self._make_inputs() - - embedding = self._make_embedding_layer() - q_embed = embedding(query) - d_embed = embedding(doc) - - mm = keras.layers.Dot(axes=[2, 2], normalize=True)([q_embed, d_embed]) - - KM = [] - for i in range(self._params['kernel_num']): - mu = 1. / (self._params['kernel_num'] - 1) + (2. * i) / ( - self._params['kernel_num'] - 1) - 1.0 - sigma = self._params['sigma'] - if mu > 1.0: - sigma = self._params['exact_sigma'] - mu = 1.0 - mm_exp = self._kernel_layer(mu, sigma)(mm) - mm_doc_sum = keras.layers.Lambda( - lambda x: tf.reduce_sum(x, 2))(mm_exp) - mm_log = keras.layers.Activation(tf.math.log1p)(mm_doc_sum) - mm_sum = keras.layers.Lambda( - lambda x: tf.reduce_sum(x, 1))(mm_log) - KM.append(mm_sum) - - phi = keras.layers.Lambda(lambda x: tf.stack(x, 1))(KM) - out = self._make_output_layer()(phi) - self._backend = keras.Model(inputs=[query, doc], outputs=[out]) - - @classmethod - def _kernel_layer(cls, mu: float, sigma: float) -> keras.layers.Layer: - """ - Gaussian kernel layer in KNRM. - - :param mu: Float, mean of the kernel. - :param sigma: Float, sigma of the kernel. - :return: `keras.layers.Layer`. - """ - - def kernel(x): - return tf.math.exp(-0.5 * (x - mu) * (x - mu) / sigma / sigma) - - return keras.layers.Activation(kernel) diff --git a/matchzoo/models/match_pyramid.py b/matchzoo/models/match_pyramid.py deleted file mode 100644 index 2a28352e..00000000 --- a/matchzoo/models/match_pyramid.py +++ /dev/null @@ -1,112 +0,0 @@ -"""An implementation of MatchPyramid Model.""" -import typing - -import keras - -import matchzoo -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable -from matchzoo.engine import hyper_spaces - - -class MatchPyramid(BaseModel): - """ - MatchPyramid Model. - - Examples: - >>> model = MatchPyramid() - >>> model.params['embedding_output_dim'] = 300 - >>> model.params['num_blocks'] = 2 - >>> model.params['kernel_count'] = [16, 32] - >>> model.params['kernel_size'] = [[3, 3], [3, 3]] - >>> model.params['dpool_size'] = [3, 10] - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params(with_embedding=True) - params.add(Param(name='num_blocks', value=1, - desc="Number of convolution blocks.")) - params.add(Param(name='kernel_count', value=[32], - desc="The kernel count of the 2D convolution " - "of each block.")) - params.add(Param(name='kernel_size', value=[[3, 3]], - desc="The kernel size of the 2D convolution " - "of each block.")) - params.add(Param(name='activation', value='relu', - desc="The activation function.")) - params.add(Param(name='dpool_size', value=[3, 10], - desc="The max-pooling size of each block.")) - params.add(Param( - name='padding', value='same', - desc="The padding mode in the convolution layer." - )) - params.add(Param( - name='dropout_rate', value=0.0, - hyper_space=hyper_spaces.quniform(low=0.0, high=0.8, - q=0.01), - desc="The dropout rate." - )) - return params - - def build(self): - """ - Build model structure. - - MatchPyramid text matching as image recognition. - """ - input_left, input_right = self._make_inputs() - input_dpool_index = keras.layers.Input( - name='dpool_index', - shape=[self._params['input_shapes'][0][0], - self._params['input_shapes'][1][0], - 2], - dtype='int32') - - embedding = self._make_embedding_layer() - embed_left = embedding(input_left) - embed_right = embedding(input_right) - - # Interaction - matching_layer = matchzoo.layers.MatchingLayer(matching_type='dot') - embed_cross = matching_layer([embed_left, embed_right]) - - for i in range(self._params['num_blocks']): - embed_cross = self._conv_block( - embed_cross, - self._params['kernel_count'][i], - self._params['kernel_size'][i], - self._params['padding'], - self._params['activation'] - ) - - # Dynamic Pooling - dpool_layer = matchzoo.layers.DynamicPoolingLayer( - *self._params['dpool_size']) - embed_pool = dpool_layer([embed_cross, input_dpool_index]) - - embed_flat = keras.layers.Flatten()(embed_pool) - x = keras.layers.Dropout(rate=self._params['dropout_rate'])(embed_flat) - - inputs = [input_left, input_right, input_dpool_index] - x_out = self._make_output_layer()(x) - self._backend = keras.Model(inputs=inputs, outputs=x_out) - - @classmethod - def _conv_block( - cls, x, - kernel_count: int, - kernel_size: int, - padding: str, - activation: str - ) -> typing.Any: - output = keras.layers.Conv2D(kernel_count, - kernel_size, - padding=padding, - activation=activation)(x) - return output diff --git a/matchzoo/models/mvlstm.py b/matchzoo/models/mvlstm.py deleted file mode 100644 index 425a2b97..00000000 --- a/matchzoo/models/mvlstm.py +++ /dev/null @@ -1,83 +0,0 @@ -"""An implementation of MVLSTM Model.""" - -import keras -import tensorflow as tf - -from matchzoo.engine import hyper_spaces -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable - - -class MVLSTM(BaseModel): - """ - MVLSTM Model. - - Examples: - >>> model = MVLSTM() - >>> model.params['lstm_units'] = 32 - >>> model.params['top_k'] = 50 - >>> model.params['mlp_num_layers'] = 2 - >>> model.params['mlp_num_units'] = 20 - >>> model.params['mlp_num_fan_out'] = 10 - >>> model.params['mlp_activation_func'] = 'relu' - >>> model.params['dropout_rate'] = 0.5 - >>> model.guess_and_fill_missing_params(verbose=0) - >>> model.build() - - """ - - @classmethod - def get_default_params(cls) -> ParamTable: - """:return: model default parameters.""" - params = super().get_default_params( - with_embedding=True, with_multi_layer_perceptron=True) - params.add(Param(name='lstm_units', value=32, - desc="Integer, the hidden size in the " - "bi-directional LSTM layer.")) - params.add(Param(name='dropout_rate', value=0.0, - desc="Float, the dropout rate.")) - params.add(Param( - 'top_k', value=10, - hyper_space=hyper_spaces.quniform(low=2, high=100), - desc="Integer, the size of top-k pooling layer." - )) - params['optimizer'] = 'adam' - return params - - def build(self): - """Build model structure.""" - query, doc = self._make_inputs() - - # Embedding layer - embedding = self._make_embedding_layer(mask_zero=True) - embed_query = embedding(query) - embed_doc = embedding(doc) - - # Bi-directional LSTM layer - rep_query = keras.layers.Bidirectional(keras.layers.LSTM( - self._params['lstm_units'], - return_sequences=True, - dropout=self._params['dropout_rate'] - ))(embed_query) - rep_doc = keras.layers.Bidirectional(keras.layers.LSTM( - self._params['lstm_units'], - return_sequences=True, - dropout=self._params['dropout_rate'] - ))(embed_doc) - - # Top-k matching layer - matching_matrix = keras.layers.Dot( - axes=[2, 2], normalize=False)([rep_query, rep_doc]) - matching_signals = keras.layers.Reshape((-1,))(matching_matrix) - matching_topk = keras.layers.Lambda( - lambda x: tf.nn.top_k(x, k=self._params['top_k'], sorted=True)[0] - )(matching_signals) - - # Multilayer perceptron layer. - mlp = self._make_multi_layer_perceptron_layer()(matching_topk) - mlp = keras.layers.Dropout( - rate=self._params['dropout_rate'])(mlp) - - x_out = self._make_output_layer()(mlp) - self._backend = keras.Model(inputs=[query, doc], outputs=x_out) diff --git a/matchzoo/models/naive.py b/matchzoo/models/naive.py deleted file mode 100644 index 9852d36b..00000000 --- a/matchzoo/models/naive.py +++ /dev/null @@ -1,30 +0,0 @@ -"""Naive model with a simplest structure for testing purposes.""" - -import keras - -from matchzoo.engine.base_model import BaseModel -from matchzoo.engine import hyper_spaces - - -class Naive(BaseModel): - """ - Naive model with a simplest structure for testing purposes. - - Bare minimum functioning model. The best choice to get things rolling. - The worst choice to fit and evaluate performance. - """ - - @classmethod - def get_default_params(cls): - """Default parameters.""" - params = super().get_default_params() - params.get('optimizer').hyper_space = \ - hyper_spaces.choice(['adam', 'adagrad', 'rmsprop']) - return params - - def build(self): - """Build.""" - x_in = self._make_inputs() - x = keras.layers.concatenate(x_in) - x_out = self._make_output_layer()(x) - self._backend = keras.models.Model(inputs=x_in, outputs=x_out) diff --git a/matchzoo/models/parameter_readme_generator.py b/matchzoo/models/parameter_readme_generator.py deleted file mode 100644 index bc792a3a..00000000 --- a/matchzoo/models/parameter_readme_generator.py +++ /dev/null @@ -1,72 +0,0 @@ -"""matchzoo/models/README.md generater.""" - -from pathlib import Path - -import tabulate -import inspect -import pandas as pd - -import matchzoo - - -def _generate(): - full = _make_title() - for model_class in matchzoo.models.list_available(): - full += _make_model_class_subtitle(model_class) - full += _make_doc_section_subsubtitle() - full += _make_model_doc(model_class) - model = model_class() - full += _make_params_section_subsubtitle() - full += _make_model_params_table(model) - _write_to_files(full) - - -def _make_title(): - title = 'MatchZoo Model Reference' - line = '*' * len(title) - return line + '\n' + title + '\n' + line + '\n\n' - - -def _make_model_class_subtitle(model_class): - subtitle = model_class.__name__ - line = '#' * len(subtitle) - return subtitle + '\n' + line + '\n\n' - - -def _make_doc_section_subsubtitle(): - subsubtitle = 'Model Documentation' - line = '*' * len(subsubtitle) - return subsubtitle + '\n' + line + '\n\n' - - -def _make_params_section_subsubtitle(): - subsubtitle = 'Model Hyper Parameters' - line = '*' * len(subsubtitle) - return subsubtitle + '\n' + line + '\n\n' - - -def _make_model_doc(model_class): - return inspect.getdoc(model_class) + '\n\n' - - -def _make_model_params_table(model): - params = model.get_default_params() - df = params.to_frame() - df = df.rename({ - 'Value': 'Default Value', - 'Hyper-Space': 'Default Hyper-Space' - }, axis='columns') - return tabulate.tabulate(df, tablefmt='rst', headers='keys') + '\n\n' - - -def _write_to_files(full): - readme_file_path = Path(__file__).parent.joinpath('README.rst') - doc_file_path = Path(__file__).parent.parent.parent. \ - joinpath('docs').joinpath('source').joinpath('model_reference.rst') - for file_path in readme_file_path, doc_file_path: - with open(file_path, 'w', encoding='utf-8') as out_file: - out_file.write(full) - - -if __name__ == '__main__': - _generate() diff --git a/matchzoo/preprocessors/__init__.py b/matchzoo/preprocessors/__init__.py deleted file mode 100644 index f119f4f7..00000000 --- a/matchzoo/preprocessors/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from . import units -from .dssm_preprocessor import DSSMPreprocessor -from .naive_preprocessor import NaivePreprocessor -from .basic_preprocessor import BasicPreprocessor -from .cdssm_preprocessor import CDSSMPreprocessor -from .diin_preprocessor import DIINPreprocessor -from .bert_preprocessor import BertPreprocessor - - -def list_available() -> list: - from matchzoo.engine.base_preprocessor import BasePreprocessor - from matchzoo.utils import list_recursive_concrete_subclasses - return list_recursive_concrete_subclasses(BasePreprocessor) diff --git a/matchzoo/preprocessors/basic_preprocessor.py b/matchzoo/preprocessors/basic_preprocessor.py deleted file mode 100644 index 0fd82d37..00000000 --- a/matchzoo/preprocessors/basic_preprocessor.py +++ /dev/null @@ -1,149 +0,0 @@ -"""Basic Preprocessor.""" - -from tqdm import tqdm - -from . import units -from matchzoo import DataPack -from matchzoo.engine.base_preprocessor import BasePreprocessor -from .build_vocab_unit import build_vocab_unit -from .build_unit_from_data_pack import build_unit_from_data_pack -from .chain_transform import chain_transform - -tqdm.pandas() - - -class BasicPreprocessor(BasePreprocessor): - """ - Baisc preprocessor helper. - - :param fixed_length_left: Integer, maximize length of :attr:`left` in the - data_pack. - :param fixed_length_right: Integer, maximize length of :attr:`right` in the - data_pack. - :param filter_mode: String, mode used by :class:`FrequenceFilterUnit`, Can - be 'df', 'cf', and 'idf'. - :param filter_low_freq: Float, lower bound value used by - :class:`FrequenceFilterUnit`. - :param filter_high_freq: Float, upper bound value used by - :class:`FrequenceFilterUnit`. - :param remove_stop_words: Bool, use :class:`StopRemovalUnit` unit or not. - - Example: - >>> import matchzoo as mz - >>> train_data = mz.datasets.toy.load_data('train') - >>> test_data = mz.datasets.toy.load_data('test') - >>> preprocessor = mz.preprocessors.BasicPreprocessor( - ... fixed_length_left=10, - ... fixed_length_right=20, - ... filter_mode='df', - ... filter_low_freq=2, - ... filter_high_freq=1000, - ... remove_stop_words=True - ... ) - >>> preprocessor = preprocessor.fit(train_data, verbose=0) - >>> preprocessor.context['input_shapes'] - [(10,), (20,)] - >>> preprocessor.context['vocab_size'] - 228 - >>> processed_train_data = preprocessor.transform(train_data, - ... verbose=0) - >>> type(processed_train_data) - - >>> test_data_transformed = preprocessor.transform(test_data, - ... verbose=0) - >>> type(test_data_transformed) - - - """ - - def __init__(self, fixed_length_left: int = 30, - fixed_length_right: int = 30, - filter_mode: str = 'df', - filter_low_freq: float = 2, - filter_high_freq: float = float('inf'), - remove_stop_words: bool = False): - """Initialization.""" - super().__init__() - self._fixed_length_left = fixed_length_left - self._fixed_length_right = fixed_length_right - self._left_fixedlength_unit = units.FixedLength( - self._fixed_length_left, - pad_mode='post' - ) - self._right_fixedlength_unit = units.FixedLength( - self._fixed_length_right, - pad_mode='post' - ) - self._filter_unit = units.FrequencyFilter( - low=filter_low_freq, - high=filter_high_freq, - mode=filter_mode - ) - self._units = self._default_units() - if remove_stop_words: - self._units.append(units.stop_removal.StopRemoval()) - - def fit(self, data_pack: DataPack, verbose: int = 1): - """ - Fit pre-processing context for transformation. - - :param data_pack: data_pack to be preprocessed. - :param verbose: Verbosity. - :return: class:`BasicPreprocessor` instance. - """ - data_pack = data_pack.apply_on_text(chain_transform(self._units), - verbose=verbose) - fitted_filter_unit = build_unit_from_data_pack(self._filter_unit, - data_pack, - flatten=False, - mode='right', - verbose=verbose) - data_pack = data_pack.apply_on_text(fitted_filter_unit.transform, - mode='right', verbose=verbose) - self._context['filter_unit'] = fitted_filter_unit - - vocab_unit = build_vocab_unit(data_pack, verbose=verbose) - self._context['vocab_unit'] = vocab_unit - - vocab_size = len(vocab_unit.state['term_index']) - self._context['vocab_size'] = vocab_size - self._context['embedding_input_dim'] = vocab_size - self._context['input_shapes'] = [(self._fixed_length_left,), - (self._fixed_length_right,)] - - return self - - def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack: - """ - Apply transformation on data, create fixed length representation. - - :param data_pack: Inputs to be preprocessed. - :param verbose: Verbosity. - - :return: Transformed data as :class:`DataPack` object. - """ - data_pack = data_pack.copy() - data_pack.apply_on_text(chain_transform(self._units), inplace=True, - verbose=verbose) - - data_pack.apply_on_text(self._context['filter_unit'].transform, - mode='right', inplace=True, verbose=verbose) - data_pack.apply_on_text(self._context['vocab_unit'].transform, - mode='both', inplace=True, verbose=verbose) - data_pack.append_text_length(inplace=True, verbose=verbose) - data_pack.apply_on_text(self._left_fixedlength_unit.transform, - mode='left', inplace=True, verbose=verbose) - data_pack.apply_on_text(self._right_fixedlength_unit.transform, - mode='right', inplace=True, verbose=verbose) - - max_len_left = self._fixed_length_left - max_len_right = self._fixed_length_right - - data_pack.left['length_left'] = \ - data_pack.left['length_left'].apply( - lambda val: min(val, max_len_left)) - - data_pack.right['length_right'] = \ - data_pack.right['length_right'].apply( - lambda val: min(val, max_len_right)) - return data_pack diff --git a/matchzoo/preprocessors/bert_preprocessor.py b/matchzoo/preprocessors/bert_preprocessor.py deleted file mode 100644 index 2c6b64ce..00000000 --- a/matchzoo/preprocessors/bert_preprocessor.py +++ /dev/null @@ -1,139 +0,0 @@ -"""Bert Preprocessor.""" - -from tqdm import tqdm - -from . import units -from .chain_transform import chain_transform -from matchzoo import DataPack -from matchzoo.engine.base_preprocessor import BasePreprocessor -from .build_vocab_unit import built_bert_vocab_unit -from .build_unit_from_data_pack import build_unit_from_data_pack - -tqdm.pandas() - - -class BertPreprocessor(BasePreprocessor): - """Bert-base Model preprocessor.""" - - def __init__(self, bert_vocab_path: str, - fixed_length_left: int = 30, - fixed_length_right: int = 30, - filter_mode: str = 'df', - filter_low_freq: float = 2, - filter_high_freq: float = float('inf'), - remove_stop_words: bool = False, - lower_case: bool = True, - chinese_version: bool = False, - ): - """ - Bert-base Model preprocessor. - - Example: - >>> import matchzoo as mz - >>> train_data = mz.datasets.toy.load_data() - >>> test_data = mz.datasets.toy.load_data(stage='test') - >>> # The argument 'bert_vocab_path' must feed the bert vocab path - >>> bert_preprocessor = mz.preprocessors.BertPreprocessor( - ... bert_vocab_path= - ... 'matchzoo/datasets/bert_resources/uncased_vocab_100.txt') - >>> train_data_processed = bert_preprocessor.fit_transform( - ... train_data) - >>> test_data_processed = bert_preprocessor.transform(test_data) - - """ - super().__init__() - self._fixed_length_left = fixed_length_left - self._fixed_length_right = fixed_length_right - self._bert_vocab_path = bert_vocab_path - self._left_fixedlength_unit = units.FixedLength( - self._fixed_length_left, - pad_mode='post' - ) - self._right_fixedlength_unit = units.FixedLength( - self._fixed_length_right, - pad_mode='post' - ) - self._filter_unit = units.FrequencyFilter( - low=filter_low_freq, - high=filter_high_freq, - mode=filter_mode - ) - self._units = self._default_units() - self._vocab_unit = built_bert_vocab_unit(self._bert_vocab_path) - - if chinese_version: - self._units.insert(1, units.ChineseTokenize()) - if lower_case: - self._units.append(units.Lowercase()) - self._units.append(units.StripAccent()) - self._units.append(units.WordPieceTokenize( - self._vocab_unit.state['term_index'])) - if remove_stop_words: - self._units.append(units.StopRemoval()) - - def fit(self, data_pack: DataPack, verbose: int = 1): - """ - Fit pre-processing context for transformation. - - :param verbose: Verbosity. - :param data_pack: Data_pack to be preprocessed. - :return: class:`BertPreprocessor` instance. - """ - data_pack = data_pack.apply_on_text(chain_transform(self._units), - verbose=verbose) - fitted_filter_unit = build_unit_from_data_pack(self._filter_unit, - data_pack, - flatten=False, - mode='right', - verbose=verbose) - self._context['filter_unit'] = fitted_filter_unit - self._context['vocab_unit'] = self._vocab_unit - vocab_size = len(self._vocab_unit.state['term_index']) - self._context['vocab_size'] = vocab_size - self._context['embedding_input_dim'] = vocab_size - self._context['input_shapes'] = [(self._fixed_length_left,), - (self._fixed_length_right,)] - return self - - def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack: - """ - Apply transformation on data, create fixed length representation. - - :param data_pack: Inputs to be preprocessed. - :param verbose: Verbosity. - - :return: Transformed data as :class:`DataPack` object. - """ - data_pack = data_pack.copy() - data_pack.apply_on_text(chain_transform(self._units), inplace=True, - verbose=verbose) - - data_pack.apply_on_text(self._context['filter_unit'].transform, - mode='right', inplace=True, verbose=verbose) - data_pack.apply_on_text(self._context['vocab_unit'].transform, - mode='both', inplace=True, verbose=verbose) - data_pack.append_text_length(inplace=True, verbose=verbose) - data_pack.apply_on_text(self._left_fixedlength_unit.transform, - mode='left', inplace=True, verbose=verbose) - data_pack.apply_on_text(self._right_fixedlength_unit.transform, - mode='right', inplace=True, verbose=verbose) - - max_len_left = self._fixed_length_left - max_len_right = self._fixed_length_right - - data_pack.left['length_left'] = \ - data_pack.left['length_left'].apply( - lambda val: min(val, max_len_left)) - - data_pack.right['length_right'] = \ - data_pack.right['length_right'].apply( - lambda val: min(val, max_len_right)) - return data_pack - - @classmethod - def _default_units(cls) -> list: - """Prepare needed process units.""" - return [ - units.BertClean(), - units.BasicTokenize() - ] diff --git a/matchzoo/preprocessors/build_unit_from_data_pack.py b/matchzoo/preprocessors/build_unit_from_data_pack.py deleted file mode 100644 index 906a6558..00000000 --- a/matchzoo/preprocessors/build_unit_from_data_pack.py +++ /dev/null @@ -1,38 +0,0 @@ -"""Build unit from data pack.""" - -from tqdm import tqdm - -import matchzoo as mz -from .units import StatefulUnit - - -def build_unit_from_data_pack( - unit: StatefulUnit, - data_pack: mz.DataPack, mode: str = 'both', - flatten: bool = True, verbose: int = 1 -) -> StatefulUnit: - """ - Build a :class:`StatefulUnit` from a :class:`DataPack` object. - - :param unit: :class:`StatefulUnit` object to be built. - :param data_pack: The input :class:`DataPack` object. - :param mode: One of 'left', 'right', and 'both', to determine the source - data for building the :class:`VocabularyUnit`. - :param flatten: Flatten the datapack or not. `True` to organize the - :class:`DataPack` text as a list, and `False` to organize - :class:`DataPack` text as a list of list. - :param verbose: Verbosity. - :return: A built :class:`StatefulUnit` object. - - """ - corpus = [] - if flatten: - data_pack.apply_on_text(corpus.extend, mode=mode, verbose=verbose) - else: - data_pack.apply_on_text(corpus.append, mode=mode, verbose=verbose) - if verbose: - description = 'Building ' + unit.__class__.__name__ + \ - ' from a datapack.' - corpus = tqdm(corpus, desc=description) - unit.fit(corpus) - return unit diff --git a/matchzoo/preprocessors/build_vocab_unit.py b/matchzoo/preprocessors/build_vocab_unit.py deleted file mode 100644 index 77dc54a8..00000000 --- a/matchzoo/preprocessors/build_vocab_unit.py +++ /dev/null @@ -1,44 +0,0 @@ -from matchzoo.data_pack import DataPack -from .units import Vocabulary -from .build_unit_from_data_pack import build_unit_from_data_pack -from .units import BertVocabulary - - -def build_vocab_unit( - data_pack: DataPack, - mode: str = 'both', - verbose: int = 1 -) -> Vocabulary: - """ - Build a :class:`preprocessor.units.Vocabulary` given `data_pack`. - - The `data_pack` should be preprocessed forehand, and each item in - `text_left` and `text_right` columns of the `data_pack` should be a list - of tokens. - - :param data_pack: The :class:`DataPack` to build vocabulary upon. - :param mode: One of 'left', 'right', and 'both', to determine the source - data for building the :class:`VocabularyUnit`. - :param verbose: Verbosity. - :return: A built vocabulary unit. - - """ - return build_unit_from_data_pack( - unit=Vocabulary(), - data_pack=data_pack, - mode=mode, - flatten=True, verbose=verbose - ) - - -def built_bert_vocab_unit(vocab_path: str) -> BertVocabulary: - """ - Build a :class:`preprocessor.units.BertVocabulary` given `vocab_path`. - - :param vocab_path: bert vocabulary path. - :return: A built vocabulary unit. - - """ - vocab_unit = BertVocabulary(pad_value='[PAD]', oov_value='[UNK]') - vocab_unit.fit(vocab_path) - return vocab_unit diff --git a/matchzoo/preprocessors/cdssm_preprocessor.py b/matchzoo/preprocessors/cdssm_preprocessor.py deleted file mode 100644 index d7f16754..00000000 --- a/matchzoo/preprocessors/cdssm_preprocessor.py +++ /dev/null @@ -1,125 +0,0 @@ -"""CDSSM Preprocessor.""" - -from tqdm import tqdm - -from . import units -from .chain_transform import chain_transform -from matchzoo import DataPack -from matchzoo.engine.base_preprocessor import BasePreprocessor -from .build_vocab_unit import build_vocab_unit - -tqdm.pandas() - - -class CDSSMPreprocessor(BasePreprocessor): - """CDSSM Model preprocessor.""" - - def __init__(self, - fixed_length_left: int = 10, - fixed_length_right: int = 40, - with_word_hashing: bool = True): - """ - CDSSM Model preprocessor. - - The word hashing step could eats up a lot of memory. To workaround - this problem, set `with_word_hashing` to `False` and use a - :class:`matchzoo.DynamicDataGenerator` with a - :class:`matchzoo.preprocessor.units.WordHashing`. - - TODO: doc here. - - :param with_word_hashing: Include a word hashing step if `True`. - - Example: - >>> import matchzoo as mz - >>> train_data = mz.datasets.toy.load_data() - >>> test_data = mz.datasets.toy.load_data(stage='test') - >>> cdssm_preprocessor = mz.preprocessors.CDSSMPreprocessor() - >>> train_data_processed = cdssm_preprocessor.fit_transform( - ... train_data, verbose=0 - ... ) - >>> type(train_data_processed) - - >>> test_data_transformed = cdssm_preprocessor.transform(test_data, - ... verbose=0) - >>> type(test_data_transformed) - - - """ - super().__init__() - self._fixed_length_left = fixed_length_left - self._fixed_length_right = fixed_length_right - self._left_fixedlength_unit = units.FixedLength( - self._fixed_length_left, - pad_value='0', pad_mode='post' - ) - self._right_fixedlength_unit = units.FixedLength( - self._fixed_length_right, - pad_value='0', pad_mode='post' - ) - self._with_word_hashing = with_word_hashing - - def fit(self, data_pack: DataPack, verbose: int = 1): - """ - Fit pre-processing context for transformation. - - :param verbose: Verbosity. - :param data_pack: Data_pack to be preprocessed. - :return: class:`CDSSMPreprocessor` instance. - """ - fit_units = self._default_units() + [units.NgramLetter()] - func = chain_transform(fit_units) - data_pack = data_pack.apply_on_text(func, verbose=verbose) - vocab_unit = build_vocab_unit(data_pack, verbose=verbose) - - self._context['vocab_unit'] = vocab_unit - vocab_size = len(vocab_unit.state['term_index']) - self._context['input_shapes'] = [ - (self._fixed_length_left, vocab_size), - (self._fixed_length_right, vocab_size) - ] - return self - - def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack: - """ - Apply transformation on data, create `letter-ngram` representation. - - :param data_pack: Inputs to be preprocessed. - :param verbose: Verbosity. - - :return: Transformed data as :class:`DataPack` object. - """ - data_pack = data_pack.copy() - func = chain_transform(self._default_units()) - data_pack.apply_on_text(func, inplace=True, verbose=verbose) - data_pack.apply_on_text(self._left_fixedlength_unit.transform, - mode='left', inplace=True, verbose=verbose) - data_pack.apply_on_text(self._right_fixedlength_unit.transform, - mode='right', inplace=True, verbose=verbose) - post_units = [units.NgramLetter(reduce_dim=False)] - if self._with_word_hashing: - term_index = self._context['vocab_unit'].state['term_index'] - post_units.append(units.WordHashing(term_index)) - data_pack.apply_on_text(chain_transform(post_units), - inplace=True, verbose=verbose) - return data_pack - - @classmethod - def _default_units(cls) -> list: - """Prepare needed process units.""" - return [ - units.Tokenize(), - units.Lowercase(), - units.PuncRemoval(), - units.StopRemoval(), - ] - - @property - def with_word_hashing(self): - """`with_word_hashing` getter.""" - return self._with_word_hashing - - @with_word_hashing.setter - def with_word_hashing(self, value): - """`with_word_hashing` setter.""" - self._with_word_hashing = value diff --git a/matchzoo/preprocessors/chain_transform.py b/matchzoo/preprocessors/chain_transform.py deleted file mode 100644 index ceb1e877..00000000 --- a/matchzoo/preprocessors/chain_transform.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Wrapper function organizes a number of transform functions.""" -import typing -import functools - -from .units.unit import Unit - - -def chain_transform(units: typing.List[Unit]) -> typing.Callable: - """ - Compose unit transformations into a single function. - - :param units: List of :class:`matchzoo.StatelessUnit`. - """ - - @functools.wraps(chain_transform) - def wrapper(arg): - """Wrapper function of transformations composition.""" - for unit in units: - arg = unit.transform(arg) - return arg - - unit_names = ' => '.join(unit.__class__.__name__ for unit in units) - wrapper.__name__ += ' of ' + unit_names - return wrapper diff --git a/matchzoo/preprocessors/diin_preprocessor.py b/matchzoo/preprocessors/diin_preprocessor.py deleted file mode 100644 index 7d64ed16..00000000 --- a/matchzoo/preprocessors/diin_preprocessor.py +++ /dev/null @@ -1,159 +0,0 @@ -"""DIIN Preprocessor.""" - -from tqdm import tqdm -import pandas as pd - -from matchzoo.engine.base_preprocessor import BasePreprocessor -from matchzoo import DataPack -from .build_vocab_unit import build_vocab_unit -from .chain_transform import chain_transform -from . import units - -tqdm.pandas() - - -class DIINPreprocessor(BasePreprocessor): - """DIIN Model preprocessor.""" - - def __init__(self, - fixed_length_left: int = 10, - fixed_length_right: int = 10, - fixed_length_word: int = 5): - """ - DIIN Model preprocessor. - - :param fixed_length_left: Integer, maximize length of :attr:'left' in - the data_pack. - :param fixed_length_right: Integer, maximize length of :attr:'right' in - the data_pack. - :param fixed_length_word: Integer, maximize length of each word. - - Example: - >>> import matchzoo as mz - >>> train_data = mz.datasets.toy.load_data() - >>> test_data = mz.datasets.toy.load_data(stage='test') - >>> diin_preprocessor = mz.preprocessors.DIINPreprocessor( - ... fixed_length_left=5, - ... fixed_length_right=5, - ... fixed_length_word=3, - ... ) - >>> diin_preprocessor = diin_preprocessor.fit( - ... train_data, verbose=0) - >>> diin_preprocessor.context['input_shapes'] - [(5,), (5,), (5, 3), (5, 3), (5,), (5,)] - >>> diin_preprocessor.context['vocab_size'] - 893 - >>> train_data_processed = diin_preprocessor.transform( - ... train_data, verbose=0) - >>> type(train_data_processed) - - >>> test_data_processed = diin_preprocessor.transform( - ... test_data, verbose=0) - >>> type(test_data_processed) - - - """ - super().__init__() - self._fixed_length_left = fixed_length_left - self._fixed_length_right = fixed_length_right - self._fixed_length_word = fixed_length_word - self._left_fixedlength_unit = units.FixedLength( - self._fixed_length_left, - pad_value='0', - pad_mode='post' - ) - self._right_fixedlength_unit = units.FixedLength( - self._fixed_length_right, - pad_value='0', - pad_mode='post' - ) - self._units = self._default_units() - - def fit(self, data_pack: DataPack, verbose: int = 1): - """ - Fit pre-processing context for transformation. - - :param data_pack: data_pack to be preprocessed. - :param verbose: Verbosity. - :return: class:'DIINPreprocessor' instance. - """ - func = chain_transform(self._units) - data_pack = data_pack.apply_on_text(func, mode='both', verbose=verbose) - - vocab_unit = build_vocab_unit(data_pack, verbose=verbose) - vocab_size = len(vocab_unit.state['term_index']) - self._context['vocab_unit'] = vocab_unit - self._context['vocab_size'] = vocab_size - self._context['embedding_input_dim'] = vocab_size - - data_pack = data_pack.apply_on_text( - units.NgramLetter(ngram=1, reduce_dim=True).transform, - mode='both', verbose=verbose) - char_unit = build_vocab_unit(data_pack, verbose=verbose) - self._context['char_unit'] = char_unit - - self._context['input_shapes'] = [ - (self._fixed_length_left,), - (self._fixed_length_right,), - (self._fixed_length_left, self._fixed_length_word,), - (self._fixed_length_right, self._fixed_length_word,), - (self._fixed_length_left,), - (self._fixed_length_right,) - ] - return self - - def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack: - """ - Apply transformation on data. - - :param data_pack: Inputs to be preprocessed. - :param verbose: Verbosity. - - :return: Transformed data as :class:'DataPack' object. - """ - data_pack = data_pack.copy() - data_pack.apply_on_text( - chain_transform(self._units), - mode='both', inplace=True, verbose=verbose) - - # Process character representation - data_pack.apply_on_text( - units.NgramLetter(ngram=1, reduce_dim=False).transform, - rename=('char_left', 'char_right'), - mode='both', inplace=True, verbose=verbose) - char_index_dict = self._context['char_unit'].state['term_index'] - left_charindex_unit = units.CharacterIndex( - char_index_dict, self._fixed_length_left, self._fixed_length_word) - right_charindex_unit = units.CharacterIndex( - char_index_dict, self._fixed_length_right, self._fixed_length_word) - data_pack.left['char_left'] = data_pack.left['char_left'].apply( - left_charindex_unit.transform) - data_pack.right['char_right'] = data_pack.right['char_right'].apply( - right_charindex_unit.transform) - - # Process word representation - data_pack.apply_on_text( - self._context['vocab_unit'].transform, - mode='both', inplace=True, verbose=verbose) - - # Process exact match representation - frame = data_pack.relation.join( - data_pack.left, on='id_left', how='left' - ).join(data_pack.right, on='id_right', how='left') - left_exactmatch_unit = units.WordExactMatch( - self._fixed_length_left, match='text_left', to_match='text_right') - right_exactmatch_unit = units.WordExactMatch( - self._fixed_length_right, match='text_right', to_match='text_left') - data_pack.relation['match_left'] = frame.apply( - left_exactmatch_unit.transform, axis=1) - data_pack.relation['match_right'] = frame.apply( - right_exactmatch_unit.transform, axis=1) - - data_pack.apply_on_text( - self._left_fixedlength_unit.transform, - mode='left', inplace=True, verbose=verbose) - data_pack.apply_on_text( - self._right_fixedlength_unit.transform, - mode='right', inplace=True, verbose=verbose) - - return data_pack diff --git a/matchzoo/preprocessors/dssm_preprocessor.py b/matchzoo/preprocessors/dssm_preprocessor.py deleted file mode 100644 index 2c0212a4..00000000 --- a/matchzoo/preprocessors/dssm_preprocessor.py +++ /dev/null @@ -1,104 +0,0 @@ -"""DSSM Preprocessor.""" - -from tqdm import tqdm - -from matchzoo.data_pack import DataPack -from matchzoo.engine.base_preprocessor import BasePreprocessor -from .chain_transform import chain_transform -from .build_vocab_unit import build_vocab_unit -from . import units - -tqdm.pandas() - - -class DSSMPreprocessor(BasePreprocessor): - """DSSM Model preprocessor.""" - - def __init__(self, with_word_hashing: bool = True): - """ - DSSM Model preprocessor. - - The word hashing step could eats up a lot of memory. To workaround - this problem, set `with_word_hashing` to `False` and use a - :class:`matchzoo.DynamicDataGenerator` with a - :class:`matchzoo.preprocessor.units.WordHashing`. - - :param with_word_hashing: Include a word hashing step if `True`. - - Example: - >>> import matchzoo as mz - >>> train_data = mz.datasets.toy.load_data() - >>> test_data = mz.datasets.toy.load_data(stage='test') - >>> dssm_preprocessor = mz.preprocessors.DSSMPreprocessor() - >>> train_data_processed = dssm_preprocessor.fit_transform( - ... train_data, verbose=0 - ... ) - >>> type(train_data_processed) - - >>> test_data_transformed = dssm_preprocessor.transform(test_data, - ... verbose=0) - >>> type(test_data_transformed) - - - """ - super().__init__() - self._with_word_hashing = with_word_hashing - - def fit(self, data_pack: DataPack, verbose: int = 1): - """ - Fit pre-processing context for transformation. - - :param verbose: Verbosity. - :param data_pack: data_pack to be preprocessed. - :return: class:`DSSMPreprocessor` instance. - """ - - func = chain_transform(self._default_units()) - data_pack = data_pack.apply_on_text(func, verbose=verbose) - vocab_unit = build_vocab_unit(data_pack, verbose=verbose) - - self._context['vocab_unit'] = vocab_unit - vocab_size = len(vocab_unit.state['term_index']) - self._context['vocab_size'] = vocab_size - self._context['embedding_input_dim'] = vocab_size - self._context['input_shapes'] = [(vocab_size,), (vocab_size,)] - return self - - def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack: - """ - Apply transformation on data, create `tri-letter` representation. - - :param data_pack: Inputs to be preprocessed. - :param verbose: Verbosity. - - :return: Transformed data as :class:`DataPack` object. - """ - data_pack = data_pack.copy() - units_ = self._default_units() - if self._with_word_hashing: - term_index = self._context['vocab_unit'].state['term_index'] - units_.append(units.WordHashing(term_index)) - func = chain_transform(units_) - data_pack.apply_on_text(func, inplace=True, verbose=verbose) - return data_pack - - @classmethod - def _default_units(cls) -> list: - """Prepare needed process units.""" - return [ - units.Tokenize(), - units.Lowercase(), - units.PuncRemoval(), - units.StopRemoval(), - units.NgramLetter(), - ] - - @property - def with_word_hashing(self): - """`with_word_hashing` getter.""" - return self._with_word_hashing - - @with_word_hashing.setter - def with_word_hashing(self, value): - """`with_word_hashing` setter.""" - self._with_word_hashing = value diff --git a/matchzoo/preprocessors/naive_preprocessor.py b/matchzoo/preprocessors/naive_preprocessor.py deleted file mode 100644 index 139da4ec..00000000 --- a/matchzoo/preprocessors/naive_preprocessor.py +++ /dev/null @@ -1,61 +0,0 @@ -"""Naive Preprocessor.""" - -from tqdm import tqdm - -from matchzoo.engine.base_preprocessor import BasePreprocessor -from matchzoo import DataPack -from .chain_transform import chain_transform -from .build_vocab_unit import build_vocab_unit -from . import units - -tqdm.pandas() - - -class NaivePreprocessor(BasePreprocessor): - """ - Naive preprocessor. - - Example: - >>> import matchzoo as mz - >>> train_data = mz.datasets.toy.load_data() - >>> test_data = mz.datasets.toy.load_data(stage='test') - >>> preprocessor = mz.preprocessors.NaivePreprocessor() - >>> train_data_processed = preprocessor.fit_transform(train_data, - ... verbose=0) - >>> type(train_data_processed) - - >>> test_data_transformed = preprocessor.transform(test_data, - ... verbose=0) - >>> type(test_data_transformed) - - - """ - - def fit(self, data_pack: DataPack, verbose: int = 1): - """ - Fit pre-processing context for transformation. - - :param data_pack: data_pack to be preprocessed. - :param verbose: Verbosity. - :return: class:`NaivePreprocessor` instance. - """ - func = chain_transform(self._default_units()) - data_pack = data_pack.apply_on_text(func, verbose=verbose) - vocab_unit = build_vocab_unit(data_pack, verbose=verbose) - self._context['vocab_unit'] = vocab_unit - return self - - def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack: - """ - Apply transformation on data, create `tri-letter` representation. - - :param data_pack: Inputs to be preprocessed. - :param verbose: Verbosity. - - :return: Transformed data as :class:`DataPack` object. - """ - units_ = self._default_units() - units_.append(self._context['vocab_unit']) - units_.append(units.FixedLength(text_length=30, pad_mode='post')) - func = chain_transform(units_) - return data_pack.apply_on_text(func, verbose=verbose) diff --git a/matchzoo/preprocessors/units/__init__.py b/matchzoo/preprocessors/units/__init__.py deleted file mode 100644 index 7faa1c06..00000000 --- a/matchzoo/preprocessors/units/__init__.py +++ /dev/null @@ -1,28 +0,0 @@ -from .unit import Unit -from .digit_removal import DigitRemoval -from .fixed_length import FixedLength -from .frequency_filter import FrequencyFilter -from .lemmatization import Lemmatization -from .lowercase import Lowercase -from .matching_histogram import MatchingHistogram -from .ngram_letter import NgramLetter -from .punc_removal import PuncRemoval -from .stateful_unit import StatefulUnit -from .stemming import Stemming -from .stop_removal import StopRemoval -from .tokenize import Tokenize -from .vocabulary import Vocabulary -from .word_hashing import WordHashing -from .character_index import CharacterIndex -from .word_exact_match import WordExactMatch -from .bert_clean import BertClean -from .bert_clean import StripAccent -from .tokenize import ChineseTokenize -from .tokenize import BasicTokenize -from .tokenize import WordPieceTokenize -from .vocabulary import BertVocabulary - - -def list_available() -> list: - from matchzoo.utils import list_recursive_concrete_subclasses - return list_recursive_concrete_subclasses(Unit) diff --git a/matchzoo/preprocessors/units/bert_clean.py b/matchzoo/preprocessors/units/bert_clean.py deleted file mode 100644 index e6747a78..00000000 --- a/matchzoo/preprocessors/units/bert_clean.py +++ /dev/null @@ -1,42 +0,0 @@ -from .unit import Unit -from matchzoo.utils.bert_utils import \ - is_whitespace, is_control, run_strip_accents - - -class BertClean(Unit): - """Clean unit for raw text.""" - - def transform(self, input_: str) -> str: - """ - Process input data from raw terms to cleaned text. - - :param input_: raw textual input. - - :return cleaned_text: cleaned text. - """ - output = [] - for char in input_: - cp = ord(char) - if cp == 0 or cp == 0xfffd or is_control(char): - continue - if is_whitespace(char): - output.append(" ") - else: - output.append(char) - cleaned_text = "".join(output) - return cleaned_text - - -class StripAccent(Unit): - """Process unit for text lower case.""" - - def transform(self, input_: list) -> list: - """ - Strips accents from each token. - - :param input_: list of tokens. - - :return tokens: Accent-stripped list of tokens. - """ - - return [run_strip_accents(token) for token in input_] diff --git a/matchzoo/preprocessors/units/character_index.py b/matchzoo/preprocessors/units/character_index.py deleted file mode 100644 index 17126765..00000000 --- a/matchzoo/preprocessors/units/character_index.py +++ /dev/null @@ -1,62 +0,0 @@ -import numpy as np - -from .unit import Unit - - -class CharacterIndex(Unit): - """ - CharacterIndexUnit for DIIN model. - - The input of :class:'CharacterIndexUnit' should be a list of word - character list extracted from a text. The output is the character - index representation of this text. - - :class:`NgramLetterUnit` and :class:`VocabularyUnit` are two - essential prerequisite of :class:`CharacterIndexUnit`. - - Examples: - >>> input_ = [['#', 'a', '#'],['#', 'o', 'n', 'e', '#']] - >>> character_index = CharacterIndex( - ... char_index={ - ... '': 0, '': 1, 'a': 2, 'n': 3, 'e':4, '#':5}, - ... fixed_length_text=2, - ... fixed_length_word=5) - >>> index = character_index.transform(input_) - >>> index - [[5.0, 2.0, 5.0, 0.0, 0.0], [5.0, 1.0, 3.0, 4.0, 5.0]] - - """ - - def __init__( - self, - char_index: dict, - fixed_length_text: int, - fixed_length_word: int - ): - """ - Class initialization. - - :param char_index: character-index mapping generated by - :class:'VocabularyUnit'. - :param fixed_length_text: maximize length of a text. - :param fixed_length_word: maximize length of a word. - """ - self._char_index = char_index - self._fixed_length_text = fixed_length_text - self._fixed_length_word = fixed_length_word - - def transform(self, input_: list) -> list: - """ - Transform list of characters to corresponding indices. - - :param input_: list of characters generated by - :class:'NgramLetterUnit'. - - :return: character index representation of a text. - """ - idx = np.zeros((self._fixed_length_text, self._fixed_length_word)) - for i in range(min(len(input_), self._fixed_length_text)): - for j in range(min(len(input_[i]), self._fixed_length_word)): - idx[i, j] = self._char_index.get(input_[i][j], 1) - - return idx.tolist() diff --git a/matchzoo/preprocessors/units/digit_removal.py b/matchzoo/preprocessors/units/digit_removal.py deleted file mode 100644 index bff5f086..00000000 --- a/matchzoo/preprocessors/units/digit_removal.py +++ /dev/null @@ -1,15 +0,0 @@ -from .unit import Unit - - -class DigitRemoval(Unit): - """Process unit to remove digits.""" - - def transform(self, input_: list) -> list: - """ - Remove digits from list of tokens. - - :param input_: list of tokens to be filtered. - - :return tokens: tokens of tokens without digits. - """ - return [token for token in input_ if not token.isdigit()] diff --git a/matchzoo/preprocessors/units/fixed_length.py b/matchzoo/preprocessors/units/fixed_length.py deleted file mode 100644 index d1425f03..00000000 --- a/matchzoo/preprocessors/units/fixed_length.py +++ /dev/null @@ -1,79 +0,0 @@ -import typing - -import numpy as np - -from .unit import Unit - - -class FixedLength(Unit): - """ - FixedLengthUnit Class. - - Process unit to get the fixed length text. - - Examples: - >>> from matchzoo.preprocessors.units import FixedLength - >>> fixedlen = FixedLength(3) - >>> fixedlen.transform(list(range(1, 6))) == [3, 4, 5] - True - >>> fixedlen.transform(list(range(1, 3))) == [0, 1, 2] - True - - """ - - def __init__( - self, - text_length: int, - pad_value: typing.Union[int, str] = 0, - pad_mode: str = 'pre', - truncate_mode: str = 'pre' - ): - """ - Class initialization. - - :param text_length: fixed length of the text. - :param pad_value: if text length is smaller than :attr:`text_length`, - filling text with :attr:`pad_value`. - :param pad_mode: String, `pre` or `post`: - pad either before or after each sequence. - :param truncate_mode: String, `pre` or `post`: - remove values from sequences larger than :attr:`text_length`, - either at the beginning or at the end of the sequences. - """ - self._text_length = text_length - self._pad_value = pad_value - self._pad_mode = pad_mode - self._truncate_mode = truncate_mode - - def transform(self, input_: list) -> list: - """ - Transform list of tokenized tokens into the fixed length text. - - :param input_: list of tokenized tokens. - - :return tokens: list of tokenized tokens in fixed length. - """ - # padding process can not handle empty list as input - if len(input_) == 0: - input_ = [self._pad_value] - np_tokens = np.array(input_) - fixed_tokens = np.full([self._text_length], self._pad_value, - dtype=np_tokens.dtype) - - if self._truncate_mode == 'pre': - trunc_tokens = input_[-self._text_length:] - elif self._truncate_mode == 'post': - trunc_tokens = input_[:self._text_length] - else: - raise ValueError('{} is not a vaild ' - 'truncate mode.'.format(self._truncate_mode)) - - if self._pad_mode == 'post': - fixed_tokens[:len(trunc_tokens)] = trunc_tokens - elif self._pad_mode == 'pre': - fixed_tokens[-len(trunc_tokens):] = trunc_tokens - else: - raise ValueError('{} is not a vaild ' - 'pad mode.'.format(self._pad_mode)) - - return fixed_tokens.tolist() diff --git a/matchzoo/preprocessors/units/frequency_filter.py b/matchzoo/preprocessors/units/frequency_filter.py deleted file mode 100644 index 89a7523c..00000000 --- a/matchzoo/preprocessors/units/frequency_filter.py +++ /dev/null @@ -1,96 +0,0 @@ -import collections -import typing - -import numpy as np - -from .stateful_unit import StatefulUnit - - -class FrequencyFilter(StatefulUnit): - """ - Frequency filter unit. - - :param low: Lower bound, inclusive. - :param high: Upper bound, exclusive. - :param mode: One of `tf` (term frequency), `df` (document frequency), - and `idf` (inverse document frequency). - - Examples:: - >>> import matchzoo as mz - - To filter based on term frequency (tf): - >>> tf_filter = mz.preprocessors.units.FrequencyFilter( - ... low=2, mode='tf') - >>> tf_filter.fit([['A', 'B', 'B'], ['C', 'C', 'C']]) - >>> tf_filter.transform(['A', 'B', 'C']) - ['B', 'C'] - - To filter based on document frequency (df): - >>> tf_filter = mz.preprocessors.units.FrequencyFilter( - ... low=2, mode='df') - >>> tf_filter.fit([['A', 'B'], ['B', 'C']]) - >>> tf_filter.transform(['A', 'B', 'C']) - ['B'] - - To filter based on inverse document frequency (idf): - >>> idf_filter = mz.preprocessors.units.FrequencyFilter( - ... low=1.2, mode='idf') - >>> idf_filter.fit([['A', 'B'], ['B', 'C', 'D']]) - >>> idf_filter.transform(['A', 'B', 'C']) - ['A', 'C'] - - """ - - def __init__(self, low: float = 0, high: float = float('inf'), - mode: str = 'df'): - """Frequency filter unit.""" - super().__init__() - self._low = low - self._high = high - self._mode = mode - - def fit(self, list_of_tokens: typing.List[typing.List[str]]): - """Fit `list_of_tokens` by calculating `mode` states.""" - valid_terms = set() - if self._mode == 'tf': - stats = self._tf(list_of_tokens) - elif self._mode == 'df': - stats = self._df(list_of_tokens) - elif self._mode == 'idf': - stats = self._idf(list_of_tokens) - else: - raise ValueError(f"{self._mode} is not a valid filtering mode." - f"Mode must be one of `tf`, `df`, and `idf`.") - - for k, v in stats.items(): - if self._low <= v < self._high: - valid_terms.add(k) - - self._context[self._mode] = valid_terms - - def transform(self, input_: list) -> list: - """Transform a list of tokens by filtering out unwanted words.""" - valid_terms = self._context[self._mode] - return list(filter(lambda token: token in valid_terms, input_)) - - @classmethod - def _tf(cls, list_of_tokens: list) -> dict: - stats = collections.Counter() - for tokens in list_of_tokens: - stats.update(tokens) - return stats - - @classmethod - def _df(cls, list_of_tokens: list) -> dict: - stats = collections.Counter() - for tokens in list_of_tokens: - stats.update(set(tokens)) - return stats - - @classmethod - def _idf(cls, list_of_tokens: list) -> dict: - num_docs = len(list_of_tokens) - stats = cls._df(list_of_tokens) - for key, val in stats.most_common(): - stats[key] = np.log((1 + num_docs) / (1 + val)) + 1 - return stats diff --git a/matchzoo/preprocessors/units/lemmatization.py b/matchzoo/preprocessors/units/lemmatization.py deleted file mode 100644 index 23c05b02..00000000 --- a/matchzoo/preprocessors/units/lemmatization.py +++ /dev/null @@ -1,18 +0,0 @@ -import nltk - -from .unit import Unit - - -class Lemmatization(Unit): - """Process unit for token lemmatization.""" - - def transform(self, input_: list) -> list: - """ - Lemmatization a sequence of tokens. - - :param input_: list of tokens to be lemmatized. - - :return tokens: list of lemmatizd tokens. - """ - lemmatizer = nltk.WordNetLemmatizer() - return [lemmatizer.lemmatize(token, pos='v') for token in input_] diff --git a/matchzoo/preprocessors/units/lowercase.py b/matchzoo/preprocessors/units/lowercase.py deleted file mode 100644 index 1dabb670..00000000 --- a/matchzoo/preprocessors/units/lowercase.py +++ /dev/null @@ -1,15 +0,0 @@ -from .unit import Unit - - -class Lowercase(Unit): - """Process unit for text lower case.""" - - def transform(self, input_: list) -> list: - """ - Convert list of tokens to lower case. - - :param input_: list of tokens. - - :return tokens: lower-cased list of tokens. - """ - return [token.lower() for token in input_] diff --git a/matchzoo/preprocessors/units/matching_histogram.py b/matchzoo/preprocessors/units/matching_histogram.py deleted file mode 100644 index 3746bad9..00000000 --- a/matchzoo/preprocessors/units/matching_histogram.py +++ /dev/null @@ -1,60 +0,0 @@ -import numpy as np - -from .unit import Unit - - -class MatchingHistogram(Unit): - """ - MatchingHistogramUnit Class. - - :param bin_size: The number of bins of the matching histogram. - :param embedding_matrix: The word embedding matrix applied to calculate - the matching histogram. - :param normalize: Boolean, normalize the embedding or not. - :param mode: The type of the historgram, it should be one of 'CH', 'NG', - or 'LCH'. - - Examples: - >>> embedding_matrix = np.array([[1.0, -1.0], [1.0, 2.0], [1.0, 3.0]]) - >>> text_left = [0, 1] - >>> text_right = [1, 2] - >>> histogram = MatchingHistogram(3, embedding_matrix, True, 'CH') - >>> histogram.transform([text_left, text_right]) - [[3.0, 1.0, 1.0], [1.0, 2.0, 2.0]] - - """ - - def __init__(self, bin_size: int = 30, embedding_matrix=None, - normalize=True, mode: str = 'LCH'): - """The constructor.""" - self._hist_bin_size = bin_size - self._embedding_matrix = embedding_matrix - if normalize: - self._normalize_embedding() - self._mode = mode - - def _normalize_embedding(self): - """Normalize the embedding matrix.""" - l2_norm = np.sqrt( - (self._embedding_matrix * self._embedding_matrix).sum(axis=1) - ) - self._embedding_matrix = \ - self._embedding_matrix / l2_norm[:, np.newaxis] - - def transform(self, input_: list) -> list: - """Transform the input text.""" - text_left, text_right = input_ - matching_hist = np.ones((len(text_left), self._hist_bin_size), - dtype=np.float32) - embed_left = self._embedding_matrix[text_left] - embed_right = self._embedding_matrix[text_right] - matching_matrix = embed_left.dot(np.transpose(embed_right)) - for (i, j), value in np.ndenumerate(matching_matrix): - bin_index = int((value + 1.) / 2. * (self._hist_bin_size - 1.)) - matching_hist[i][bin_index] += 1.0 - if self._mode == 'NH': - matching_sum = matching_hist.sum(axis=1) - matching_hist = matching_hist / matching_sum[:, np.newaxis] - elif self._mode == 'LCH': - matching_hist = np.log(matching_hist) - return matching_hist.tolist() diff --git a/matchzoo/preprocessors/units/ngram_letter.py b/matchzoo/preprocessors/units/ngram_letter.py deleted file mode 100644 index a957f337..00000000 --- a/matchzoo/preprocessors/units/ngram_letter.py +++ /dev/null @@ -1,60 +0,0 @@ -from .unit import Unit - - -class NgramLetter(Unit): - """ - Process unit for n-letter generation. - - Triletter is used in :class:`DSSMModel`. - This processor is expected to execute before `Vocab` - has been created. - - Examples: - >>> triletter = NgramLetter() - >>> rv = triletter.transform(['hello', 'word']) - >>> len(rv) - 9 - >>> rv - ['#he', 'hel', 'ell', 'llo', 'lo#', '#wo', 'wor', 'ord', 'rd#'] - >>> triletter = NgramLetter(reduce_dim=False) - >>> rv = triletter.transform(['hello', 'word']) - >>> len(rv) - 2 - >>> rv - [['#he', 'hel', 'ell', 'llo', 'lo#'], ['#wo', 'wor', 'ord', 'rd#']] - - """ - - def __init__(self, ngram: int = 3, reduce_dim: bool = True): - """ - Class initialization. - - :param ngram: By default use 3-gram (tri-letter). - :param reduce_dim: Reduce to 1-D list for sentence representation. - """ - self._ngram = ngram - self._reduce_dim = reduce_dim - - def transform(self, input_: list) -> list: - """ - Transform token into tri-letter. - - For example, `word` should be represented as `#wo`, - `wor`, `ord` and `rd#`. - - :param input_: list of tokens to be transformed. - - :return n_letters: generated n_letters. - """ - n_letters = [] - for token in input_: - token = '#' + token + '#' - token_ngram = [] - while len(token) >= self._ngram: - token_ngram.append(token[:self._ngram]) - token = token[1:] - if self._reduce_dim: - n_letters.extend(token_ngram) - else: - n_letters.append(token_ngram) - return n_letters diff --git a/matchzoo/preprocessors/units/punc_removal.py b/matchzoo/preprocessors/units/punc_removal.py deleted file mode 100644 index af55d582..00000000 --- a/matchzoo/preprocessors/units/punc_removal.py +++ /dev/null @@ -1,18 +0,0 @@ -import string - -from .unit import Unit - - -class PuncRemoval(Unit): - """Process unit for remove punctuations.""" - - def transform(self, input_: list) -> list: - """ - Remove punctuations from list of tokens. - - :param input_: list of toekns. - - :return rv: tokens without punctuation. - """ - table = str.maketrans({key: None for key in string.punctuation}) - return [item.translate(table) for item in input_] diff --git a/matchzoo/preprocessors/units/stateful_unit.py b/matchzoo/preprocessors/units/stateful_unit.py deleted file mode 100644 index 423075dc..00000000 --- a/matchzoo/preprocessors/units/stateful_unit.py +++ /dev/null @@ -1,36 +0,0 @@ -import abc -import typing - -from .unit import Unit - - -class StatefulUnit(Unit, metaclass=abc.ABCMeta): - """ - Unit with inner state. - - Usually need to be fit before transforming. All information gathered in the - fit phrase will be stored into its `context`. - """ - - def __init__(self): - """Initialization.""" - self._context = {} - - @property - def state(self): - """ - Get current context. Same as `unit.context`. - - Deprecated since v2.2.0, and will be removed in the future. - Used `unit.context` instead. - """ - return self._context - - @property - def context(self): - """Get current context. Same as `unit.state`.""" - return self._context - - @abc.abstractmethod - def fit(self, input_: typing.Any): - """Abstract base method, need to be implemented in subclass.""" diff --git a/matchzoo/preprocessors/units/stemming.py b/matchzoo/preprocessors/units/stemming.py deleted file mode 100644 index 83bf4eb9..00000000 --- a/matchzoo/preprocessors/units/stemming.py +++ /dev/null @@ -1,32 +0,0 @@ -import nltk - -from .unit import Unit - - -class Stemming(Unit): - """ - Process unit for token stemming. - - :param stemmer: stemmer to use, `porter` or `lancaster`. - """ - - def __init__(self, stemmer='porter'): - """Initialization.""" - self.stemmer = stemmer - - def transform(self, input_: list) -> list: - """ - Reducing inflected words to their word stem, base or root form. - - :param input_: list of string to be stemmed. - """ - if self.stemmer == 'porter': - porter_stemmer = nltk.stem.PorterStemmer() - return [porter_stemmer.stem(token) for token in input_] - elif self.stemmer == 'lancaster' or self.stemmer == 'krovetz': - lancaster_stemmer = nltk.stem.lancaster.LancasterStemmer() - return [lancaster_stemmer.stem(token) for token in input_] - else: - raise ValueError( - 'Not supported supported stemmer type: {}'.format( - self.stemmer)) diff --git a/matchzoo/preprocessors/units/stop_removal.py b/matchzoo/preprocessors/units/stop_removal.py deleted file mode 100644 index ad5ff234..00000000 --- a/matchzoo/preprocessors/units/stop_removal.py +++ /dev/null @@ -1,45 +0,0 @@ -import nltk - -from .unit import Unit - - -class StopRemoval(Unit): - """ - Process unit to remove stop words. - - Example: - >>> unit = StopRemoval() - >>> unit.transform(['a', 'the', 'test']) - ['test'] - >>> type(unit.stopwords) - - """ - - def __init__(self, lang: str = 'english'): - """Initialization.""" - self._lang = lang - self._stop = nltk.corpus.stopwords.words(self._lang) - - def transform(self, input_: list) -> list: - """ - Remove stopwords from list of tokenized tokens. - - :param input_: list of tokenized tokens. - :param lang: language code for stopwords. - - :return tokens: list of tokenized tokens without stopwords. - """ - return [token - for token - in input_ - if token not in self._stop] - - @property - def stopwords(self) -> list: - """ - Get stopwords based on language. - - :params lang: language code. - :return: list of stop words. - """ - return self._stop diff --git a/matchzoo/preprocessors/units/tokenize.py b/matchzoo/preprocessors/units/tokenize.py deleted file mode 100644 index befdcc56..00000000 --- a/matchzoo/preprocessors/units/tokenize.py +++ /dev/null @@ -1,126 +0,0 @@ -import nltk -from matchzoo.utils.bert_utils import is_chinese_char, \ - whitespace_tokenize, run_split_on_punc - -from .unit import Unit - - -class Tokenize(Unit): - """Process unit for text tokenization.""" - - def transform(self, input_: str) -> list: - """ - Process input data from raw terms to list of tokens. - - :param input_: raw textual input. - - :return tokens: tokenized tokens as a list. - """ - return nltk.word_tokenize(input_) - - -class ChineseTokenize(Unit): - """Process unit for text containing Chinese tokens.""" - - def transform(self, input_: str) -> str: - """ - Process input data from raw terms to processed text. - - :param input_: raw textual input. - - :return output: text with at least one blank between adjacent - Chinese tokens. - """ - output = [] - for char in input_: - cp = ord(char) - if is_chinese_char(cp): - output.append(" ") - output.append(char) - output.append(" ") - else: - output.append(char) - return "".join(output) - - -class BasicTokenize(Unit): - """Process unit for text tokenization.""" - - def transform(self, input_: str) -> list: - """ - Process input data from raw terms to list of tokens. - - :param input_: raw textual input. - - :return tokens: tokenized tokens as a list. - """ - orig_tokens = whitespace_tokenize(input_) - split_tokens = [] - for token in orig_tokens: - split_tokens.extend(run_split_on_punc(token)) - - output_tokens = whitespace_tokenize(" ".join(split_tokens)) - return output_tokens - - -class WordPieceTokenize(Unit): - """Process unit for text tokenization.""" - - def __init__(self, vocab: dict, max_input_chars_per_word: int = 200): - """Initialization.""" - self.vocab = vocab - self.unk_token = '[UNK]' - self.max_input_chars_per_word = max_input_chars_per_word - - def transform(self, input_: list) -> list: - """ - Tokenizes a piece of text into its word pieces. - - This uses a greedy longest-match-first algorithm to perform - tokenization using the given vocabulary. - - For example: - >>> input_list = ["unaffable"] - >>> vocab = {"un": 0, "##aff": 1, "##able":2} - >>> wordpiece_unit = WordPieceTokenize(vocab) - >>> output = wordpiece_unit.transform(input_list) - >>> golden_output = ["un", "##aff", "##able"] - >>> assert output == golden_output - - :param input_: token list. - - :return tokens: A list of wordpiece tokens. - """ - output_tokens = [] - for token in input_: - chars = list(token) - token_length = len(chars) - if token_length > self.max_input_chars_per_word: - output_tokens.append(self.unk_token) - continue - - unknown_suffix = False - start = 0 - sub_tokens = [] - while start < token_length: - end = token_length - cur_substr = None - while start < end: - substr = "".join(chars[start:end]) - if start > 0: - substr = "##" + substr - if substr in self.vocab: - cur_substr = substr - break - end -= 1 - if cur_substr is None: - unknown_suffix = True - break - sub_tokens.append(cur_substr) - start = end - - if unknown_suffix: - output_tokens.append(self.unk_token) - else: - output_tokens.extend(sub_tokens) - return output_tokens diff --git a/matchzoo/preprocessors/units/unit.py b/matchzoo/preprocessors/units/unit.py deleted file mode 100644 index 4dd3390e..00000000 --- a/matchzoo/preprocessors/units/unit.py +++ /dev/null @@ -1,10 +0,0 @@ -import abc -import typing - - -class Unit(metaclass=abc.ABCMeta): - """Process unit do not persive state (i.e. do not need fit).""" - - @abc.abstractmethod - def transform(self, input_: typing.Any): - """Abstract base method, need to be implemented in subclass.""" diff --git a/matchzoo/preprocessors/units/vocabulary.py b/matchzoo/preprocessors/units/vocabulary.py deleted file mode 100644 index 711e4d50..00000000 --- a/matchzoo/preprocessors/units/vocabulary.py +++ /dev/null @@ -1,110 +0,0 @@ -from .stateful_unit import StatefulUnit - - -class Vocabulary(StatefulUnit): - """ - Vocabulary class. - - :param pad_value: The string value for the padding position. - :param oov_value: The string value for the out-of-vocabulary terms. - - Examples: - >>> vocab = Vocabulary(pad_value='[PAD]', oov_value='[OOV]') - >>> vocab.fit(['A', 'B', 'C', 'D', 'E']) - >>> term_index = vocab.state['term_index'] - >>> term_index # doctest: +SKIP - {'[PAD]': 0, '[OOV]': 1, 'D': 2, 'A': 3, 'B': 4, 'C': 5, 'E': 6} - >>> index_term = vocab.state['index_term'] - >>> index_term # doctest: +SKIP - {0: '[PAD]', 1: '[OOV]', 2: 'D', 3: 'A', 4: 'B', 5: 'C', 6: 'E'} - - >>> term_index['out-of-vocabulary-term'] - 1 - >>> index_term[0] - '[PAD]' - >>> index_term[42] - Traceback (most recent call last): - ... - KeyError: 42 - >>> a_index = term_index['A'] - >>> c_index = term_index['C'] - >>> vocab.transform(['C', 'A', 'C']) == [c_index, a_index, c_index] - True - >>> vocab.transform(['C', 'A', '[OOV]']) == [c_index, a_index, 1] - True - >>> indices = vocab.transform(list('ABCDDZZZ')) - >>> ' '.join(vocab.state['index_term'][i] for i in indices) - 'A B C D D [OOV] [OOV] [OOV]' - - """ - - def __init__(self, pad_value: str = '', oov_value: str = ''): - """Vocabulary unit initializer.""" - super().__init__() - self._pad = pad_value - self._oov = oov_value - self._context['term_index'] = self.TermIndex() - self._context['index_term'] = dict() - - class TermIndex(dict): - """Map term to index.""" - - def __missing__(self, key): - """Map out-of-vocabulary terms to index 1.""" - return 1 - - def fit(self, tokens: list): - """Build a :class:`TermIndex` and a :class:`IndexTerm`.""" - self._context['term_index'][self._pad] = 0 - self._context['term_index'][self._oov] = 1 - self._context['index_term'][0] = self._pad - self._context['index_term'][1] = self._oov - terms = set(tokens) - for index, term in enumerate(terms): - self._context['term_index'][term] = index + 2 - self._context['index_term'][index + 2] = term - - def transform(self, input_: list) -> list: - """Transform a list of tokens to corresponding indices.""" - return [self._context['term_index'][token] for token in input_] - - -class BertVocabulary(StatefulUnit): - """ - Vocabulary class. - - :param pad_value: The string value for the padding position. - :param oov_value: The string value for the out-of-vocabulary terms. - - Examples: - >>> vocab = BertVocabulary(pad_value='[PAD]', oov_value='[UNK]') - >>> indices = vocab.transform(list('ABCDDZZZ')) - - """ - - def __init__(self, pad_value: str = '[PAD]', oov_value: str = '[UNK]'): - """Vocabulary unit initializer.""" - super().__init__() - self._pad = pad_value - self._oov = oov_value - self._context['term_index'] = self.TermIndex() - self._context['index_term'] = {} - - class TermIndex(dict): - """Map term to index.""" - - def __missing__(self, key): - """Map out-of-vocabulary terms to index 100 .""" - return 100 - - def fit(self, vocab_path: str): - """Build a :class:`TermIndex` and a :class:`IndexTerm`.""" - with open(vocab_path, 'r', encoding='utf-8') as vocab_file: - for idx, line in enumerate(vocab_file): - term = line.strip() - self._context['term_index'][term] = idx - self._context['index_term'][idx] = term - - def transform(self, input_: list) -> list: - """Transform a list of tokens to corresponding indices.""" - return [self._context['term_index'][token] for token in input_] diff --git a/matchzoo/preprocessors/units/word_exact_match.py b/matchzoo/preprocessors/units/word_exact_match.py deleted file mode 100644 index 717b196d..00000000 --- a/matchzoo/preprocessors/units/word_exact_match.py +++ /dev/null @@ -1,72 +0,0 @@ -import numpy as np -import pandas - -from .unit import Unit - - -class WordExactMatch(Unit): - """ - WordExactUnit Class. - - Process unit to get a binary match list of two word index lists. The - word index list is the word representation of a text. - - Examples: - >>> input_ = pandas.DataFrame({ - ... 'text_left':[[1, 2, 3],[4, 5, 7, 9]], - ... 'text_right':[[5, 3, 2, 7],[2, 3, 5]]} - ... ) - >>> left_word_exact_match = WordExactMatch( - ... fixed_length_text=5, - ... match='text_left', to_match='text_right' - ... ) - >>> left_out = input_.apply(left_word_exact_match.transform, axis=1) - >>> left_out[0] - [0.0, 1.0, 1.0, 0.0, 0.0] - >>> left_out[1] - [0.0, 1.0, 0.0, 0.0, 0.0] - >>> right_word_exact_match = WordExactMatch( - ... fixed_length_text=5, - ... match='text_right', to_match='text_left' - ... ) - >>> right_out = input_.apply(right_word_exact_match.transform, axis=1) - >>> right_out[0] - [0.0, 1.0, 1.0, 0.0, 0.0] - >>> right_out[1] - [0.0, 0.0, 1.0, 0.0, 0.0] - - """ - - def __init__( - self, - fixed_length_text: int, - match: str, - to_match: str - ): - """ - Class initialization. - - :param fixed_length_text: fixed length of the text. - :param match: the 'match' column name. - :param to_match: the 'to_match' column name. - """ - self._fixed_length_text = fixed_length_text - self._match = match - self._to_match = to_match - - def transform(self, input_) -> list: - """ - Transform two word index lists into a binary match list. - - :param input_: a dataframe include 'match' column and - 'to_match' column. - - :return: a binary match result list of two word index lists. - """ - match_length = len(input_[self._match]) - match_binary = np.zeros((self._fixed_length_text)) - for i in range(min(self._fixed_length_text, match_length)): - if input_[self._match][i] in set(input_[self._to_match]): - match_binary[i] = 1 - - return match_binary.tolist() diff --git a/matchzoo/preprocessors/units/word_hashing.py b/matchzoo/preprocessors/units/word_hashing.py deleted file mode 100644 index 805c1ba3..00000000 --- a/matchzoo/preprocessors/units/word_hashing.py +++ /dev/null @@ -1,71 +0,0 @@ -import collections - -import numpy as np - -from .unit import Unit - - -class WordHashing(Unit): - """ - Word-hashing layer for DSSM-based models. - - The input of :class:`WordHashingUnit` should be a list of word - sub-letter list extracted from one document. The output of is - the word-hashing representation of this document. - - :class:`NgramLetterUnit` and :class:`VocabularyUnit` are two - essential prerequisite of :class:`WordHashingUnit`. - - Examples: - >>> letters = [['#te', 'tes','est', 'st#'], ['oov']] - >>> word_hashing = WordHashing( - ... term_index={ - ... '_PAD': 0, 'OOV': 1, 'st#': 2, '#te': 3, 'est': 4, 'tes': 5 - ... }) - >>> hashing = word_hashing.transform(letters) - >>> hashing[0] - [0.0, 0.0, 1.0, 1.0, 1.0, 1.0] - >>> hashing[1] - [0.0, 1.0, 0.0, 0.0, 0.0, 0.0] - - """ - - def __init__( - self, - term_index: dict, - ): - """ - Class initialization. - - :param term_index: term-index mapping generated by - :class:`VocabularyUnit`. - :param dim_triletter: dimensionality of tri_leltters. - """ - self._term_index = term_index - - def transform(self, input_: list) -> list: - """ - Transform list of :attr:`letters` into word hashing layer. - - :param input_: list of `tri_letters` generated by - :class:`NgramLetterUnit`. - :return: Word hashing representation of `tri-letters`. - """ - if any([isinstance(elem, list) for elem in input_]): - # The input shape for CDSSM is - # [[word1 ngram, ngram], [word2, ngram, ngram], ...]. - hashing = np.zeros((len(input_), len(self._term_index))) - for idx, word in enumerate(input_): - counted_letters = collections.Counter(word) - for key, value in counted_letters.items(): - letter_id = self._term_index.get(key, 1) - hashing[idx, letter_id] = value - else: - # The input shape for DSSM model [ngram, ngram, ...]. - hashing = np.zeros(len(self._term_index)) - counted_letters = collections.Counter(input_) - for key, value in counted_letters.items(): - letter_id = self._term_index.get(key, 1) - hashing[letter_id] = value - - return hashing.tolist() diff --git a/matchzoo/tasks/__init__.py b/matchzoo/tasks/__init__.py deleted file mode 100644 index 3911b0d0..00000000 --- a/matchzoo/tasks/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .classification import Classification -from .ranking import Ranking diff --git a/matchzoo/tasks/classification.py b/matchzoo/tasks/classification.py deleted file mode 100644 index a9f24c85..00000000 --- a/matchzoo/tasks/classification.py +++ /dev/null @@ -1,59 +0,0 @@ -"""Classification task.""" - -from matchzoo.engine.base_task import BaseTask - - -class Classification(BaseTask): - """Classification task. - - Examples: - >>> classification_task = Classification(num_classes=2) - >>> classification_task.metrics = ['precision'] - >>> classification_task.num_classes - 2 - >>> classification_task.output_shape - (2,) - >>> classification_task.output_dtype - - >>> print(classification_task) - Classification Task with 2 classes - - """ - - def __init__(self, num_classes: int = 2, **kwargs): - """Classification task.""" - super().__init__(**kwargs) - if not isinstance(num_classes, int): - raise TypeError("Number of classes must be an integer.") - if num_classes < 2: - raise ValueError("Number of classes can't be smaller than 2") - self._num_classes = num_classes - - @property - def num_classes(self) -> int: - """:return: number of classes to classify.""" - return self._num_classes - - @classmethod - def list_available_losses(cls) -> list: - """:return: a list of available losses.""" - return ['categorical_crossentropy'] - - @classmethod - def list_available_metrics(cls) -> list: - """:return: a list of available metrics.""" - return ['acc'] - - @property - def output_shape(self) -> tuple: - """:return: output shape of a single sample of the task.""" - return self._num_classes, - - @property - def output_dtype(self): - """:return: target data type, expect `int` as output.""" - return int - - def __str__(self): - """:return: Task name as string.""" - return f'Classification Task with {self._num_classes} classes' diff --git a/matchzoo/tasks/ranking.py b/matchzoo/tasks/ranking.py deleted file mode 100644 index ff4cfa13..00000000 --- a/matchzoo/tasks/ranking.py +++ /dev/null @@ -1,43 +0,0 @@ -"""Ranking task.""" - -from matchzoo.engine import base_task - - -class Ranking(base_task.BaseTask): - """Ranking Task. - - Examples: - >>> ranking_task = Ranking() - >>> ranking_task.metrics = ['map', 'ndcg'] - >>> ranking_task.output_shape - (1,) - >>> ranking_task.output_dtype - - >>> print(ranking_task) - Ranking Task - - """ - - @classmethod - def list_available_losses(cls) -> list: - """:return: a list of available losses.""" - return ['mse'] - - @classmethod - def list_available_metrics(cls) -> list: - """:return: a list of available metrics.""" - return ['map'] - - @property - def output_shape(self) -> tuple: - """:return: output shape of a single sample of the task.""" - return 1, - - @property - def output_dtype(self): - """:return: target data type, expect `float` as output.""" - return float - - def __str__(self): - """:return: Task name as string.""" - return 'Ranking Task' diff --git a/matchzoo/utils/__init__.py b/matchzoo/utils/__init__.py deleted file mode 100644 index 63a840db..00000000 --- a/matchzoo/utils/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .one_hot import one_hot -from .tensor_type import TensorType -from .list_recursive_subclasses import list_recursive_concrete_subclasses -from .make_keras_optimizer_picklable import make_keras_optimizer_picklable diff --git a/matchzoo/utils/bert_utils.py b/matchzoo/utils/bert_utils.py deleted file mode 100644 index 8490ddbb..00000000 --- a/matchzoo/utils/bert_utils.py +++ /dev/null @@ -1,94 +0,0 @@ -import unicodedata - - -def is_whitespace(char): - """Checks whether `chars` is a whitespace character.""" - # \t, \n, and \r are technically contorl characters but we treat them - # as whitespace since they are generally considered as such. - return (char == " ") or \ - (char == "\t") or \ - (char == "\n") or \ - (char == "\r") or \ - (unicodedata.category(char) == "Zs") - - -def is_control(char): - """Checks whether `chars` is a control character.""" - # These are technically control characters but we count them as whitespace - # characters. - if char == "\t" or char == "\n" or char == "\r": - return False - cat = unicodedata.category(char) - if cat in ["Cc", "Cf"]: - return True - return False - - -def is_punctuation(char): - """Checks whether `chars` is a punctuation character.""" - cp = ord(char) - # We treat all non-letter/number ASCII as punctuation. - # Characters such as "^", "$", and "`" are not in the Unicode - # Punctuation class but we treat them as punctuation anyways, for - # consistency. - condition = (33 <= cp <= 47) or (58 <= cp <= 64) or \ - (91 <= cp <= 96) or (123 <= cp <= 126) - cat = unicodedata.category(char) - if condition or cat.startswith("P"): - return True - return False - - -def is_chinese_char(cp): - """Checks whether CP is the codepoint of a CJK character.""" - # This defines a "chinese character" as anything in the CJK Unicode block: - # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) - # - # Note that the CJK Unicode block is NOT all Japanese and Korean - # characters, despite its name. The modern Korean Hangul alphabet is a - # different block, as is Japanese Hiragana and Katakana. Those alphabets - # are used to write space-separated words, so they are not treated - # specially and handled like the all of the other languages. - return (0x4E00 <= cp <= 0x9FFF) or \ - (0x3400 <= cp <= 0x4DBF) or \ - (0x20000 <= cp <= 0x2A6DF) or \ - (0x2A700 <= cp <= 0x2B73F) or \ - (0x2B740 <= cp <= 0x2B81F) or \ - (0x2B820 <= cp <= 0x2CEAF) or \ - (0xF900 <= cp <= 0xFAFF) or \ - (0x2F800 <= cp <= 0x2FA1F) - - -def run_strip_accents(text): - """Strips accents from a piece of text.""" - text = unicodedata.normalize("NFD", text) - output = [char for char in text if not unicodedata.category(char) == 'Mn'] - return "".join(output) - - -def run_split_on_punc(text): - """Splits punctuation on a piece of text.""" - chars = list(text) - i = 0 - start_new_word = True - output = [] - while i < len(chars): - char = chars[i] - if is_punctuation(char): - output.append([char]) - start_new_word = True - else: - if start_new_word: - output.append([]) - start_new_word = False - output[-1].append(char) - i += 1 - - return ["".join(x) for x in output] - - -def whitespace_tokenize(text): - """Runs basic whitespace cleaning and splitting on a piece of text.""" - text = text.strip() - tokens = text.split() - return tokens diff --git a/matchzoo/utils/list_recursive_subclasses.py b/matchzoo/utils/list_recursive_subclasses.py deleted file mode 100644 index 9ce53901..00000000 --- a/matchzoo/utils/list_recursive_subclasses.py +++ /dev/null @@ -1,17 +0,0 @@ -import inspect - - -def list_recursive_concrete_subclasses(base): - """List all concrete subclasses of `base` recursively.""" - return _filter_concrete(_bfs(base)) - - -def _filter_concrete(classes): - return list(filter(lambda c: not inspect.isabstract(c), classes)) - - -def _bfs(base): - return base.__subclasses__() + sum([ - _bfs(subclass) - for subclass in base.__subclasses__() - ], []) diff --git a/matchzoo/utils/make_keras_optimizer_picklable.py b/matchzoo/utils/make_keras_optimizer_picklable.py deleted file mode 100644 index c45edba4..00000000 --- a/matchzoo/utils/make_keras_optimizer_picklable.py +++ /dev/null @@ -1,19 +0,0 @@ -import keras - - -def make_keras_optimizer_picklable(): - """ - Fix https://github.com/NTMC-Community/MatchZoo/issues/726. - - This function changes how keras behaves, use with caution. - """ - def __getstate__(self): - return keras.optimizers.serialize(self) - - def __setstate__(self, state): - optimizer = keras.optimizers.deserialize(state) - self.__dict__ = optimizer.__dict__ - - cls = keras.optimizers.Optimizer - cls.__getstate__ = __getstate__ - cls.__setstate__ = __setstate__ diff --git a/matchzoo/utils/one_hot.py b/matchzoo/utils/one_hot.py deleted file mode 100644 index 7e3a043b..00000000 --- a/matchzoo/utils/one_hot.py +++ /dev/null @@ -1,9 +0,0 @@ -"""One hot vectors.""" -import numpy as np - - -def one_hot(indices: int, num_classes: int) -> np.ndarray: - """:return: A one-hot encoded vector.""" - vec = np.zeros((num_classes,), dtype=np.int64) - vec[indices] = 1 - return vec diff --git a/matchzoo/utils/tensor_type.py b/matchzoo/utils/tensor_type.py deleted file mode 100644 index 8153cd20..00000000 --- a/matchzoo/utils/tensor_type.py +++ /dev/null @@ -1,4 +0,0 @@ -"""Define Keras tensor type.""" -import typing - -TensorType = typing.Any diff --git a/matchzoo/version.py b/matchzoo/version.py deleted file mode 100644 index 91f5eb84..00000000 --- a/matchzoo/version.py +++ /dev/null @@ -1,3 +0,0 @@ -"""Matchzoo version file.""" - -__version__ = '2.2.0' diff --git a/readthedocs.yml b/readthedocs.yml deleted file mode 100644 index d53ffa68..00000000 --- a/readthedocs.yml +++ /dev/null @@ -1,5 +0,0 @@ -build: - image: latest - -python: - version: 3.6 diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 995b0417..00000000 --- a/requirements.txt +++ /dev/null @@ -1,18 +0,0 @@ -keras == 2.3.0 -tabulate >= 0.8.2 -tensorflow >= 2.0.0 -nltk >= 3.2.3 -numpy >= 1.14 -tqdm >= 4.23.4 -dill >= 0.2.7.1 -hyperopt >= 0.1.1 -pandas == 0.24.2 -networkx >= 2.1 -h5py >= 2.8.0 -coverage >= 4.3.4 -codecov >= 2.0.15 -pytest >= 3.7.4 -pytest-cov >= 2.4.0 -flake8 >= 3.6.0 -flake8_docstrings >= 1.3.0 -pydocstyle == 2.1 diff --git a/setup.py b/setup.py deleted file mode 100644 index f99f2f42..00000000 --- a/setup.py +++ /dev/null @@ -1,64 +0,0 @@ -import io -import os - -from setuptools import setup, find_packages - - -here = os.path.abspath(os.path.dirname(__file__)) - -# Avoids IDE errors, but actual version is read from version.py -__version__ = None -exec(open('matchzoo/version.py').read()) - -short_description = 'Facilitating the design, comparison and sharing of deep text matching models.' - -# Get the long description from the README file -with io.open(os.path.join(here, 'README.md'), encoding='utf-8') as f: - long_description = f.read() - -install_requires = [ - 'keras >= 2.3.0', - 'nltk >= 3.2.3', - 'numpy >= 1.14', - 'tqdm >= 4.19.4', - 'dill >= 0.2.7.1', - 'pandas >= 0.23.1', - 'networkx >= 2.1', - 'h5py >= 2.8.0', - 'hyperopt >= 0.1.1' -] - -extras_requires = { - 'tests': [ - 'coverage >= 4.3.4', - 'codecov >= 2.0.15', - 'pytest >= 3.0.3', - 'pytest-cov >= 2.4.0', - 'flake8 >= 3.6.0', - 'flake8_docstrings >= 1.0.2'], -} - - -setup( - name="MatchZoo", - version=__version__, - author="Yixing Fan, Bo Wang, Zeyi Wang, Liang Pang, Liu Yang, Qinghua Wang, etc.", - author_email="fanyixing@ict.ac.cn", - description=(short_description), - license="Apache 2.0", - keywords="text matching models", - url="https://github.com/NTMC-Community/MatchZoo", - packages=find_packages(), - long_description=long_description, - long_description_content_type='text/markdown', - classifiers=[ - "Development Status :: 3 - Alpha", - 'Environment :: Console', - 'Operating System :: POSIX :: Linux', - 'Topic :: Scientific/Engineering :: Artificial Intelligence', - "License :: OSI Approved :: Apache Software License", - 'Programming Language :: Python :: 3.6' - ], - install_requires=install_requires, - extras_require=extras_requires -) diff --git a/tests/__init__.py b/test similarity index 100% rename from tests/__init__.py rename to test diff --git a/tests/inte_test/__init__.py b/tests/inte_test/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/unit_test/__init__.py b/tests/unit_test/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/unit_test/data_pack/test_datapack.py b/tests/unit_test/data_pack/test_datapack.py deleted file mode 100644 index 55269897..00000000 --- a/tests/unit_test/data_pack/test_datapack.py +++ /dev/null @@ -1,44 +0,0 @@ -import shutil - -import pandas as pd -import pytest - -from matchzoo import DataPack, load_data_pack - - -@pytest.fixture -def data_pack(): - relation = [['qid0', 'did0', 1], ['qid1', 'did1', 0]] - left = [['qid0', [1, 2]], ['qid1', [2, 3]]] - right = [['did0', [2, 3, 4]], ['did1', [3, 4, 5]]] - relation = pd.DataFrame(relation, columns=['id_left', 'id_right', 'label']) - left = pd.DataFrame(left, columns=['id_left', 'text_left']) - left.set_index('id_left', inplace=True) - right = pd.DataFrame(right, columns=['id_right', 'text_right']) - right.set_index('id_right', inplace=True) - return DataPack(relation=relation, - left=left, - right=right) - - -def test_length(data_pack): - num_examples = 2 - assert len(data_pack) == num_examples - - -def test_getter(data_pack): - assert data_pack.relation.iloc[0].values.tolist() == ['qid0', 'did0', 1] - assert data_pack.relation.iloc[1].values.tolist() == ['qid1', 'did1', 0] - assert data_pack.left.loc['qid0', 'text_left'] == [1, 2] - assert data_pack.right.loc['did1', 'text_right'] == [3, 4, 5] - - -def test_save_load(data_pack): - dirpath = '.tmpdir' - data_pack.save(dirpath) - dp = load_data_pack(dirpath) - with pytest.raises(FileExistsError): - data_pack.save(dirpath) - assert len(data_pack) == 2 - assert len(dp) == 2 - shutil.rmtree(dirpath) diff --git a/tests/unit_test/engine/test_base_preprocessor.py b/tests/unit_test/engine/test_base_preprocessor.py deleted file mode 100644 index 304e969c..00000000 --- a/tests/unit_test/engine/test_base_preprocessor.py +++ /dev/null @@ -1,21 +0,0 @@ -import pytest -import shutil - -import matchzoo as mz -from matchzoo.engine.base_preprocessor import BasePreprocessor - - -@pytest.fixture -def base_preprocessor(): - BasePreprocessor.__abstractmethods__ = set() - base_processor = BasePreprocessor() - return base_processor - - -def test_save_load(base_preprocessor): - dirpath = '.tmpdir' - base_preprocessor.save(dirpath) - assert mz.load_preprocessor(dirpath) - with pytest.raises(FileExistsError): - base_preprocessor.save(dirpath) - shutil.rmtree(dirpath) diff --git a/tests/unit_test/engine/test_base_task.py b/tests/unit_test/engine/test_base_task.py deleted file mode 100644 index 846334fc..00000000 --- a/tests/unit_test/engine/test_base_task.py +++ /dev/null @@ -1,7 +0,0 @@ -import pytest -from matchzoo.engine.base_task import BaseTask - - -def test_base_task_instantiation(): - with pytest.raises(TypeError): - BaseTask() diff --git a/tests/unit_test/engine/test_hyper_spaces.py b/tests/unit_test/engine/test_hyper_spaces.py deleted file mode 100644 index fb396bca..00000000 --- a/tests/unit_test/engine/test_hyper_spaces.py +++ /dev/null @@ -1,44 +0,0 @@ -import pytest -import hyperopt.pyll.base - -from matchzoo.engine import hyper_spaces - - -@pytest.fixture(scope='module', params=[ - lambda x: x + 2, - lambda x: x - 2, - lambda x: x * 2, - lambda x: x / 2, - lambda x: x // 2, - lambda x: x ** 2, - lambda x: 2 + x, - lambda x: 2 - x, - lambda x: 2 * x, - lambda x: 2 / x, - lambda x: 2 // x, - lambda x: 2 ** x, - lambda x: -x -]) -def op(request): - return request.param - - -@pytest.fixture(scope='module', params=[ - hyper_spaces.choice(options=[0, 1]), - hyper_spaces.uniform(low=0, high=10), - hyper_spaces.quniform(low=0, high=10, q=2) -]) -def proxy(request): - return request.param - - -def test_init(proxy): - assert isinstance(proxy.convert('label'), hyperopt.pyll.base.Apply) - - -def test_op(proxy, op): - assert isinstance(op(proxy).convert('label'), hyperopt.pyll.base.Apply) - - -def test_str(proxy): - assert isinstance(str(proxy), str) diff --git a/tests/unit_test/engine/test_param_table.py b/tests/unit_test/engine/test_param_table.py deleted file mode 100644 index 3623b551..00000000 --- a/tests/unit_test/engine/test_param_table.py +++ /dev/null @@ -1,37 +0,0 @@ -import pytest - -from matchzoo.engine.param import Param -from matchzoo.engine.param_table import ParamTable -from matchzoo.engine.hyper_spaces import quniform - - -@pytest.fixture -def param_table(): - params = ParamTable() - params.add(Param('ham', 'Parma Ham')) - return params - - -def test_get(param_table): - assert param_table['ham'] == 'Parma Ham' - - -def test_set(param_table): - new_param = Param('egg', 'Over Easy') - param_table.set('egg', new_param) - assert 'egg' in param_table.keys() - - -def test_keys(param_table): - assert 'ham' in param_table.keys() - - -def test_hyper_space(param_table): - new_param = Param( - name='my_param', - value=1, - hyper_space=quniform(low=1, high=5) - ) - param_table.add(new_param) - hyper_space = param_table.hyper_space - assert hyper_space diff --git a/tests/unit_test/models/test_base_model.py b/tests/unit_test/models/test_base_model.py deleted file mode 100644 index e454a087..00000000 --- a/tests/unit_test/models/test_base_model.py +++ /dev/null @@ -1,22 +0,0 @@ -import pytest - -from matchzoo.engine.base_model import BaseModel - - -def test_base_model_abstract_instantiation(): - with pytest.raises(TypeError): - model = BaseModel(BaseModel.get_default_params()) - assert model - - -def test_base_model_concrete_instantiation(): - class MyBaseModel(BaseModel): - def build(self): - self._backend = 'something' - - model = MyBaseModel() - assert model.params - model.guess_and_fill_missing_params() - model.build() - assert model.backend - assert model.params.completed() diff --git a/tests/unit_test/models/test_models.py b/tests/unit_test/models/test_models.py deleted file mode 100644 index 743fe9c0..00000000 --- a/tests/unit_test/models/test_models.py +++ /dev/null @@ -1,108 +0,0 @@ -""" -These tests are simplied because the original verion takes too much time to -run, making CI fails as it reaches the time limit. -""" - -import pytest -import copy -from pathlib import Path -import shutil - -import matchzoo as mz -from keras.backend import clear_session - -@pytest.fixture(scope='module', params=[ - mz.tasks.Ranking(loss=mz.losses.RankCrossEntropyLoss(num_neg=2)), - mz.tasks.Classification(num_classes=2), -]) -def task(request): - return request.param - - -@pytest.fixture(scope='module') -def train_raw(task): - return mz.datasets.toy.load_data('train', task)[:5] - - -@pytest.fixture(scope='module', params=mz.models.list_available()) -def model_class(request): - return request.param - - -@pytest.fixture(scope='module') -def embedding(): - return mz.datasets.toy.load_embedding() - - -@pytest.fixture(scope='module') -def setup(task, model_class, train_raw, embedding): - clear_session() # prevent OOM during CI tests - return mz.auto.prepare( - task=task, - model_class=model_class, - data_pack=train_raw, - embedding=embedding - ) - - -@pytest.fixture(scope='module') -def model(setup): - return setup[0] - - -@pytest.fixture(scope='module') -def preprocessor(setup): - return setup[1] - - -@pytest.fixture(scope='module') -def gen_builder(setup): - return setup[2] - - -@pytest.fixture(scope='module') -def embedding_matrix(setup): - return setup[3] - - -@pytest.fixture(scope='module') -def data(train_raw, preprocessor, gen_builder): - return gen_builder.build(preprocessor.transform(train_raw))[0] - - -@pytest.mark.slow -def test_model_fit_eval_predict(model, data): - x, y = data - batch_size = len(x['id_left']) - assert model.fit(x, y, batch_size=batch_size, verbose=0) - assert model.evaluate(x, y, batch_size=batch_size) - assert model.predict(x, batch_size=batch_size) is not None - - -@pytest.mark.cron -def test_save_load_model(model): - tmpdir = '.matchzoo_test_save_load_tmpdir' - - if Path(tmpdir).exists(): - shutil.rmtree(tmpdir) - - try: - model.save(tmpdir) - assert mz.load_model(tmpdir) - with pytest.raises(FileExistsError): - model.save(tmpdir) - finally: - if Path(tmpdir).exists(): - shutil.rmtree(tmpdir) - - -@pytest.mark.cron -def test_hyper_space(model): - for _ in range(2): - new_params = copy.deepcopy(model.params) - sample = mz.hyper_spaces.sample(new_params.hyper_space) - for key, value in sample.items(): - new_params[key] = value - new_model = new_params['model_class'](params=new_params) - new_model.build() - new_model.compile() diff --git a/tests/unit_test/processor_units/test_processor_units.py b/tests/unit_test/processor_units/test_processor_units.py deleted file mode 100644 index fccd87e0..00000000 --- a/tests/unit_test/processor_units/test_processor_units.py +++ /dev/null @@ -1,168 +0,0 @@ -import pytest -import numpy as np - -from matchzoo.preprocessors import units - - -@pytest.fixture -def raw_input(): - return "This is an Example sentence to BE ! cleaned with digits 31." - - -@pytest.fixture -def list_input(): - return ['this', 'Is', 'a', 'the', 'test', 'lIst', '36', '!', 'input'] - - -@pytest.fixture -def vec_input(): - return np.array([[0, 0, 0, 0, 1], - [0, 0, 0, 1, 0], - [0, 0, 1, 0, 0], - [0, 1, 0, 0, 0], - [1, 0, 0, 0, 0]]) - - -def test_tokenize_unit(raw_input): - tu = units.Tokenize() - out = tu.transform(raw_input) - assert len(out) == 13 - assert 'an' in out - - -def test_lowercase_unit(list_input): - lu = units.Lowercase() - out = lu.transform(list_input) - assert 'is' in out - - -def test_digitremoval_unit(list_input): - du = units.DigitRemoval() - out = du.transform(list_input) - assert 36 not in out - - -def test_puncremoval_unit(list_input): - pu = units.PuncRemoval() - out = pu.transform(list_input) - assert '!' not in out - - -def test_stopremoval_unit(list_input): - su = units.StopRemoval() - out = su.transform(list_input) - assert 'the' not in out - - -def test_stemming_unit(list_input): - su_porter = units.Stemming() - out_porter = su_porter.transform(list_input) - assert 'thi' in out_porter - su_lancaster = units.Stemming(stemmer='lancaster') - out_lancaster = su_lancaster.transform(list_input) - assert 'thi' in out_lancaster - su_not_exist = units.Stemming(stemmer='fake_stemmer') - with pytest.raises(ValueError): - su_not_exist.transform(list_input) - - -def test_lemma_unit(list_input): - lemma = units.Lemmatization() - out = lemma.transform(list_input) - assert 'this' in out - - -def test_ngram_unit(list_input): - ngram = units.NgramLetter() - out = ngram.transform(list_input) - assert '#a#' in out - ngram = units.NgramLetter(reduce_dim=False) - out = ngram.transform(list_input) - assert len(out) == 9 - - -def test_fixedlength_unit(list_input): - fixedlength = units.FixedLength(3) - out = fixedlength.transform([]) - assert list(out) == [0] * 3 - out = fixedlength.transform(list_input) - assert list(out) == ['36', '!', 'input'] - fixedlength = units.FixedLength(3, truncate_mode='post') - out = fixedlength.transform(list_input) - assert list(out) == ['this', 'Is', 'a'] - fixedlength = units.FixedLength(12, pad_value='0', - truncate_mode='pre', pad_mode='pre') - out = fixedlength.transform(list_input) - assert list(out[3:]) == list_input - assert list(out[:3]) == ['0'] * 3 - fixedlength = units.FixedLength(12, pad_value='0', - truncate_mode='pre', pad_mode='post') - out = fixedlength.transform(list_input) - assert list(out[:-3]) == list_input - assert list(out[-3:]) == ['0'] * 3 - - -@pytest.fixture(scope='module', params=['CH', 'NH', 'LCH']) -def hist_mode(request): - return request.param - - -def test_matchinghistogram_unit(hist_mode): - embedding = np.array([[1.0, -1.0], [1.0, 2.0], [1.0, 3.0]]) - text_left = [0, 1] - text_right = [1, 2] - histogram = units.MatchingHistogram(3, embedding, True, hist_mode) - out = histogram.transform([text_left, text_right]) - out = [[round(elem, 2) for elem in list_val] for list_val in out] - if hist_mode == 'CH': - assert out == [[3.0, 1.0, 1.0], [1.0, 2.0, 2.0]] - elif hist_mode == 'NH': - assert out == [[0.6, 0.2, 0.2], [0.2, 0.4, 0.4]] - elif hist_mode == 'LCH': - assert out == [[1.1, 0.0, 0.0], [0.0, 0.69, 0.69]] - else: - assert False - - -import matchzoo as mz - - -def test_this(): - train_data = mz.datasets.toy.load_data() - test_data = mz.datasets.toy.load_data(stage='test') - dssm_preprocessor = mz.preprocessors.DSSMPreprocessor() - train_data_processed = dssm_preprocessor.fit_transform( - train_data, verbose=0) - type(train_data_processed) - test_data_transformed = dssm_preprocessor.transform(test_data) - type(test_data_transformed) - - -import tempfile -import os - - -def test_bert_tokenizer_unit(): - vocab_tokens = [ - "[PAD]", "further", "##more", ",", "under", "the", "micro", "##scope", "neither", - "entity", "contains", "glands", ".", "此", "外", "在", "显", "微", "镜", "下" - ] - raw_text = "furthermore, \r under the microscope \t neither entity \n contains sebaceous glands. 此外, 在显微镜下" - - golden_tokens = ['further', '##more', ',', 'under', 'the', 'micro', '##scope', 'neither', 'entity', 'contains', - '[UNK]', 'glands', '.', '此', '外', ',', '在', '显', '微', '镜', '下'] - - vocab_dict = {} - for idx, token in enumerate(vocab_tokens): - vocab_dict[token] = idx - - clean_unit = units.BertClean() - cleaned_text = clean_unit.transform(raw_text) - chinese_tokenize_unit = units.ChineseTokenize() - chinese_tokenized_text = chinese_tokenize_unit.transform(cleaned_text) - basic_tokenize_unit = units.BasicTokenize() - basic_tokens = basic_tokenize_unit.transform(chinese_tokenized_text) - wordpiece_unit = units.WordPieceTokenize(vocab_dict) - wordpiece_tokens = wordpiece_unit.transform(basic_tokens) - - assert wordpiece_tokens == golden_tokens diff --git a/tests/unit_test/tasks/test_tasks.py b/tests/unit_test/tasks/test_tasks.py deleted file mode 100644 index 1ded8284..00000000 --- a/tests/unit_test/tasks/test_tasks.py +++ /dev/null @@ -1,23 +0,0 @@ -import pytest - -from matchzoo import tasks - - -@pytest.mark.parametrize("task_type", [ - tasks.Ranking, tasks.Classification -]) -def test_task_listings(task_type): - assert task_type.list_available_losses() - assert task_type.list_available_metrics() - - -@pytest.mark.parametrize("arg", [None, -1, 0, 1]) -def test_classification_instantiation_failure(arg): - with pytest.raises(Exception): - tasks.Classification(num_classes=arg) - - -@pytest.mark.parametrize("arg", [2, 10, 2048]) -def test_classification_num_classes(arg): - task = tasks.Classification(num_classes=arg) - assert task.num_classes == arg diff --git a/tests/unit_test/test_data_generator.py b/tests/unit_test/test_data_generator.py deleted file mode 100644 index 723ff73d..00000000 --- a/tests/unit_test/test_data_generator.py +++ /dev/null @@ -1,65 +0,0 @@ -import copy - -import pytest -import keras - -import matchzoo as mz - - -@pytest.fixture(scope='module') -def data_gen(): - return mz.DataGenerator(mz.datasets.toy.load_data()) - - -@pytest.mark.parametrize('attr', [ - 'callbacks', - 'num_neg', - 'num_dup', - 'mode', - 'batch_size', - 'shuffle', - -]) -def test_data_generator_getters_setters(data_gen, attr): - assert hasattr(data_gen, attr) - val = getattr(data_gen, attr) - setattr(data_gen, attr, val) - assert getattr(data_gen, attr) == val - - -def test_resample(): - model = mz.models.Naive() - prpr = model.get_default_preprocessor() - data_raw = mz.datasets.toy.load_data() - data = prpr.fit_transform(data_raw) - model.params.update(prpr.context) - model.params['task'] = mz.tasks.Ranking() - model.build() - model.compile() - - data_gen = mz.DataGenerator( - data_pack=data, - mode='pair', - resample=True, - batch_size=4 - ) - - class CheckResample(keras.callbacks.Callback): - def __init__(self, data_gen): - super().__init__() - self._data_gen = data_gen - self._orig_indices = None - self._flags = [] - - def on_epoch_end(self, epoch, logs=None): - curr_indices = self._data_gen.batch_indices - if not self._orig_indices: - self._orig_indices = copy.deepcopy(curr_indices) - else: - self._flags.append(self._orig_indices != curr_indices) - self._orig_indices = curr_indices - - check_resample = CheckResample(data_gen) - model.fit_generator(data_gen, epochs=5, callbacks=[check_resample]) - assert check_resample._flags - assert all(check_resample._flags) diff --git a/tests/unit_test/test_datasets.py b/tests/unit_test/test_datasets.py deleted file mode 100644 index f94fb5cd..00000000 --- a/tests/unit_test/test_datasets.py +++ /dev/null @@ -1,120 +0,0 @@ -import pytest - -import matchzoo as mz - - -@pytest.mark.cron -def test_load_data(): - train_data = mz.datasets.wiki_qa.load_data('train', task='ranking') - assert len(train_data) == 20360 - train_data, _ = mz.datasets.wiki_qa.load_data('train', - task='classification', - return_classes=True) - assert len(train_data) == 20360 - - dev_data = mz.datasets.wiki_qa.load_data('dev', task='ranking', - filtered=False) - assert len(dev_data) == 2733 - dev_data, tag = mz.datasets.wiki_qa.load_data('dev', task='classification', - filtered=True, - return_classes=True) - assert len(dev_data) == 1126 - assert tag == [False, True] - - test_data = mz.datasets.wiki_qa.load_data('test', task='ranking', - filtered=False) - assert len(test_data) == 6165 - test_data, tag = mz.datasets.wiki_qa.load_data('test', - task='classification', - filtered=True, - return_classes=True) - assert len(test_data) == 2341 - assert tag == [False, True] - - -@pytest.mark.cron -def test_load_snli(): - train_data, classes = mz.datasets.snli.load_data('train', - 'classification', - return_classes=True) - num_samples = 550146 - assert len(train_data) == num_samples - x, y = train_data.unpack() - assert len(x['text_left']) == num_samples - assert len(x['text_right']) == num_samples - assert y.shape == (num_samples, 4) - assert classes == ['entailment', 'contradiction', 'neutral', '-'] - dev_data, classes = mz.datasets.snli.load_data('dev', 'classification', - return_classes=True) - assert len(dev_data) == 10000 - assert classes == ['entailment', 'contradiction', 'neutral', '-'] - test_data, classes = mz.datasets.snli.load_data('test', 'classification', - return_classes=True) - assert len(test_data) == 10000 - assert classes == ['entailment', 'contradiction', 'neutral', '-'] - - train_data = mz.datasets.snli.load_data('train', 'ranking') - x, y = train_data.unpack() - assert len(x['text_left']) == num_samples - assert len(x['text_right']) == num_samples - assert y.shape == (num_samples, 1) - - -@pytest.mark.cron -def test_load_quora_qp(): - train_data = mz.datasets.quora_qp.load_data(task='classification') - assert len(train_data) == 363177 - - dev_data, tag = mz.datasets.quora_qp.load_data( - 'dev', - task='classification', - return_classes=True) - assert tag == [False, True] - assert len(dev_data) == 40371 - x, y = dev_data.unpack() - assert len(x['text_left']) == 40371 - assert len(x['text_right']) == 40371 - assert y.shape == (40371, 2) - - test_data = mz.datasets.quora_qp.load_data('test') - assert len(test_data) == 390965 - - dev_data = mz.datasets.quora_qp.load_data('dev', 'ranking') - x, y = dev_data.unpack() - assert y.shape == (40371, 1) - - -@pytest.mark.cron -def test_load_cqa_ql_16(): - # test load question pairs - train_data = mz.datasets.cqa_ql_16.load_data(task='classification') - assert len(train_data) == 3998 - dev_data, tag = mz.datasets.cqa_ql_16.load_data( - 'dev', - task='classification', - return_classes=True) - assert tag == ['PerfectMatch', 'Relevant', 'Irrelevant'] - assert len(dev_data) == 500 - x, y = dev_data.unpack() - assert y.shape == (500, 3) - test_data = mz.datasets.cqa_ql_16.load_data('test') - assert len(test_data) == 700 - - # test load answer pairs - train_data = mz.datasets.cqa_ql_16.load_data(match_type='answer') - assert len(train_data) == 39980 - test_data = mz.datasets.cqa_ql_16.load_data(stage='test', match_type='answer') - assert len(test_data) == 7000 - - # test load external answer pairs - train_data = mz.datasets.cqa_ql_16.load_data(match_type='external_answer') - assert len(train_data) == 39980 - - # test load rank data - train_data = mz.datasets.cqa_ql_16.load_data(task='ranking') - x, y = train_data.unpack() - assert y.shape == (3998, 1) - - dev_data = mz.datasets.cqa_ql_16.load_data('dev', task='ranking', match_type='answer', target_label='Good') - x, y = dev_data.unpack() - assert y.shape == (5000, 1) diff --git a/tests/unit_test/test_embedding.py b/tests/unit_test/test_embedding.py deleted file mode 100644 index 81a9c9e6..00000000 --- a/tests/unit_test/test_embedding.py +++ /dev/null @@ -1,19 +0,0 @@ -import pytest - -import matchzoo as mz - - -@pytest.fixture -def term_index(): - return {'G': 1, 'C': 2, 'D': 3, 'A': 4, '_PAD': 0} - - -def test_embedding(term_index): - embed = mz.embedding.load_from_file(mz.datasets.embeddings.EMBED_RANK) - matrix = embed.build_matrix(term_index) - assert matrix.shape == (len(term_index), 50) - embed = mz.embedding.load_from_file(mz.datasets.embeddings.EMBED_10_GLOVE, - mode='glove') - matrix = embed.build_matrix(term_index) - assert matrix.shape == (len(term_index), 10) - assert embed.input_dim == 5 diff --git a/tests/unit_test/test_layers.py b/tests/unit_test/test_layers.py deleted file mode 100644 index cda16191..00000000 --- a/tests/unit_test/test_layers.py +++ /dev/null @@ -1,60 +0,0 @@ -import numpy as np -import pytest -from keras import backend as K - -from matchzoo import layers -from matchzoo.contrib.layers import SpatialGRU -from matchzoo.contrib.layers import MatchingTensorLayer - - -def test_matching_layers(): - s1_value = np.array([[[1, 2], [2, 3], [3, 4]], - [[0.1, 0.2], [0.2, 0.3], [0.3, 0.4]] - ]) - s2_value = np.array([[[1, 2], [2, 3]], - [[0.1, 0.2], [0.2, 0.3]] - ]) - s3_value = np.array([[[1, 2], [2, 3]], - [[0.1, 0.2], [0.2, 0.3]], - [[0.1, 0.2], [0.2, 0.3]] - ]) - s1_tensor = K.variable(s1_value) - s2_tensor = K.variable(s2_value) - s3_tensor = K.variable(s3_value) - for matching_type in ['dot', 'mul', 'plus', 'minus', 'concat']: - model = layers.MatchingLayer(matching_type=matching_type)([s1_tensor, s2_tensor]) - ret = K.eval(model) - with pytest.raises(ValueError): - layers.MatchingLayer(matching_type='error') - with pytest.raises(ValueError): - layers.MatchingLayer()([s1_tensor, s3_tensor]) - - -def test_spatial_gru(): - s_value = K.variable(np.array([[[[1, 2], [2, 3], [3, 4]], - [[4, 5], [5, 6], [6, 7]]], - [[[0.1, 0.2], [0.2, 0.3], [0.3, 0.4]], - [[0.4, 0.5], [0.5, 0.6], [0.6, 0.7]]]])) - for direction in ['lt', 'rb']: - model = SpatialGRU(direction=direction) - _ = K.eval(model(s_value)) - with pytest.raises(ValueError): - SpatialGRU(direction='lr')(s_value) - - -def test_matching_tensor_layer(): - s1_value = np.array([[[1, 2], [2, 3], [3, 4]], - [[0.1, 0.2], [0.2, 0.3], [0.3, 0.4]]]) - s2_value = np.array([[[1, 2], [2, 3]], - [[0.1, 0.2], [0.2, 0.3]]]) - s3_value = np.array([[[1, 2], [2, 3]], - [[0.1, 0.2], [0.2, 0.3]], - [[0.1, 0.2], [0.2, 0.3]]]) - s1_tensor = K.variable(s1_value) - s2_tensor = K.variable(s2_value) - s3_tensor = K.variable(s3_value) - for init_diag in [True, False]: - model = MatchingTensorLayer(init_diag=init_diag) - _ = K.eval(model([s1_tensor, s2_tensor])) - with pytest.raises(ValueError): - MatchingTensorLayer()([s1_tensor, s3_tensor]) diff --git a/tests/unit_test/test_losses.py b/tests/unit_test/test_losses.py deleted file mode 100644 index e5bbdfa3..00000000 --- a/tests/unit_test/test_losses.py +++ /dev/null @@ -1,50 +0,0 @@ -import numpy as np -from keras import backend as K - -from matchzoo import losses - - -def test_hinge_loss(): - true_value = K.variable(np.array([[1.2], [1], - [1], [1]])) - pred_value = K.variable(np.array([[1.2], [0.1], - [0], [-0.3]])) - expected_loss = (0 + 1 - 0.3 + 0) / 2.0 - loss = K.eval(losses.RankHingeLoss()(true_value, pred_value)) - assert np.isclose(expected_loss, loss) - expected_loss = (2 + 0.1 - 1.2 + 2 - 0.3 + 0) / 2.0 - loss = K.eval(losses.RankHingeLoss(margin=2)(true_value, pred_value)) - assert np.isclose(expected_loss, loss) - true_value = K.variable(np.array([[1.2], [1], [0.8], - [1], [1], [0.8]])) - pred_value = K.variable(np.array([[1.2], [0.1], [-0.5], - [0], [0], [-0.3]])) - expected_loss = (0 + 1 - 0.15) / 2.0 - loss = K.eval(losses.RankHingeLoss(num_neg=2, margin=1)( - true_value, pred_value)) - assert np.isclose(expected_loss, loss) - - -def test_rank_crossentropy_loss(): - losses.neg_num = 1 - - def softmax(x): - return np.exp(x) / np.sum(np.exp(x), axis=0) - - true_value = K.variable(np.array([[1.], [0.], - [0.], [1.]])) - pred_value = K.variable(np.array([[0.8], [0.1], - [0.8], [0.1]])) - expected_loss = (-np.log(softmax([0.8, 0.1])[0]) - np.log( - softmax([0.8, 0.1])[1])) / 2 - loss = K.eval(losses.RankCrossEntropyLoss()(true_value, pred_value)) - assert np.isclose(expected_loss, loss) - true_value = K.variable(np.array([[1.], [0.], [0.], - [0.], [1.], [0.]])) - pred_value = K.variable(np.array([[0.8], [0.1], [0.1], - [0.8], [0.1], [0.1]])) - expected_loss = (-np.log(softmax([0.8, 0.1, 0.1])[0]) - np.log( - softmax([0.8, 0.1, 0.1])[1])) / 2 - loss = K.eval(losses.RankCrossEntropyLoss(num_neg=2)( - true_value, pred_value)) - assert np.isclose(expected_loss, loss) diff --git a/tests/unit_test/test_metrics.py b/tests/unit_test/test_metrics.py deleted file mode 100644 index 8d0eb65c..00000000 --- a/tests/unit_test/test_metrics.py +++ /dev/null @@ -1,55 +0,0 @@ -import numpy as np - -from matchzoo.engine.base_metric import sort_and_couple -from matchzoo import metrics - - -def test_sort_and_couple(): - l = [0, 1, 2] - s = [0.1, 0.4, 0.2] - c = sort_and_couple(l, s) - assert (c == np.array([(1, 0.4), (2, 0.2), (0, 0.1)])).all() - - -def test_mean_reciprocal_rank(): - label = [0, 1, 2] - score = [0.1, 0.4, 0.2] - assert metrics.MeanReciprocalRank()(label, score) == 1 - - -def test_precision_at_k(): - label = [0, 1, 2] - score = [0.1, 0.4, 0.2] - assert metrics.Precision(k=1)(label, score) == 1. - assert metrics.Precision(k=2)(label, score) == 1. - assert round(metrics.Precision(k=3)(label, score), 2) == 0.67 - - -def test_average_precision(): - label = [0, 1, 2] - score = [0.1, 0.4, 0.2] - assert round(metrics.AveragePrecision()(label, score), 2) == 0.89 - - -def test_mean_average_precision(): - label = [0, 1, 2] - score = [0.1, 0.4, 0.2] - assert metrics.MeanAveragePrecision()(label, score) == 1. - - -def test_dcg_at_k(): - label = [0, 1, 2] - score = [0.1, 0.4, 0.2] - dcg = metrics.DiscountedCumulativeGain - assert round(dcg(k=1)(label, score), 2) == 1.44 - assert round(dcg(k=2)(label, score), 2) == 4.17 - assert round(dcg(k=3)(label, score), 2) == 4.17 - - -def test_ndcg_at_k(): - label = [0, 1, 2] - score = [0.1, 0.4, 0.2] - ndcg = metrics.NormalizedDiscountedCumulativeGain - assert round(ndcg(k=1)(label, score), 2) == 0.33 - assert round(ndcg(k=2)(label, score), 2) == 0.80 - assert round(ndcg(k=3)(label, score), 2) == 0.80 diff --git a/tests/unit_test/test_tuner.py b/tests/unit_test/test_tuner.py deleted file mode 100644 index 50977fa8..00000000 --- a/tests/unit_test/test_tuner.py +++ /dev/null @@ -1,42 +0,0 @@ -import pytest - -import matchzoo as mz - - -@pytest.fixture(scope='module') -def tuner(): - model = mz.models.DenseBaseline() - prpr = model.get_default_preprocessor() - train_raw = mz.datasets.toy.load_data('train') - dev_raw = mz.datasets.toy.load_data('dev') - prpr.fit(train_raw) - model.params.update(prpr.context) - model.guess_and_fill_missing_params() - return mz.auto.Tuner( - params=model.params, - train_data=prpr.transform(train_raw, verbose=0), - test_data=prpr.transform(dev_raw, verbose=0) - ) - - -@pytest.mark.parametrize('attr', [ - 'params', - 'train_data', - 'test_data', - 'fit_kwargs', - 'evaluate_kwargs', - 'metric', - 'mode', - 'num_runs', - 'callbacks', - 'verbose' -]) -def test_getters_setters(tuner, attr): - val = getattr(tuner, attr) - setattr(tuner, attr, val) - assert getattr(tuner, attr) is val - - -def test_tuning(tuner): - tuner.num_runs = 1 - assert tuner.tune() diff --git a/tests/unit_test/test_utils.py b/tests/unit_test/test_utils.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tutorials/data_handling.ipynb b/tutorials/data_handling.ipynb deleted file mode 100644 index 447a547d..00000000 --- a/tutorials/data_handling.ipynb +++ /dev/null @@ -1,3831 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:30.059593Z", - "start_time": "2019-04-03T09:48:27.403792Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.1.0\n" - ] - } - ], - "source": [ - "import matchzoo as mz\n", - "import pandas as pd\n", - "print(mz.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# DataPack" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Structure" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`matchzoo.DataPack` is a MatchZoo native data structure that most MatchZoo data handling processes build upon. 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" - ], - "text/plain": [ - " id_left text_left id_right \\\n", - "0 Q1 how are glacier caves formed? D1-0 \n", - "1 Q1 how are glacier caves formed? D1-1 \n", - "2 Q1 how are glacier caves formed? D1-2 \n", - "3 Q1 how are glacier caves formed? D1-3 \n", - "4 Q1 how are glacier caves formed? D1-4 \n", - "\n", - " text_right label \n", - "0 A partly submerged glacier cave on Perito More... 1.0 \n", - "1 The ice facade is approximately 60 m high 1.0 \n", - "2 Ice formations in the Titlis glacier cave 1.0 \n", - "3 A glacier cave is a cave formed within the ice... 2.0 \n", - "4 Glacier caves are often called ice caves , but... 1.0 " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "frame().head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Slicing a DataPack" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You may use `[]` to slice a `matchzoo.DataPack` similar to slicing a `list`. This also returns a shallow copy of the sliced data like slicing a `list`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:30.181630Z", - "start_time": "2019-04-03T09:48:30.176857Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "data_slice = data_pack[5:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A sliced data pack's `relation` will directly reflect the slicing." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:30.191098Z", - "start_time": "2019-04-03T09:48:30.183339Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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D2-0In physics , circular motion is a movement of ...
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" - ], - "text/plain": [ - " text_right\n", - "id_right \n", - "D2-0 In physics , circular motion is a movement of ...\n", - "D2-1 It can be uniform, with constant angular rate ...\n", - "D2-2 The rotation around a fixed axis of a three-di...\n", - "D2-3 The equations of motion describe the movement ...\n", - "D2-4 Examples of circular motion include: an artifi..." - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_slice.right" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is also possible to slice a frame view object." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:30.221433Z", - "start_time": "2019-04-03T09:48:30.207711Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " id_left text_left id_right \\\n", - "0 Q2 How are the directions of the velocity and for... D2-0 \n", - "1 Q2 How are the directions of the velocity and for... D2-1 \n", - "2 Q2 How are the directions of the velocity and for... D2-2 \n", - "3 Q2 How are the directions of the velocity and for... D2-3 \n", - "4 Q2 How are the directions of the velocity and for... D2-4 \n", - "\n", - " text_right label \n", - "0 In physics , circular motion is a movement of ... 1.0 \n", - "1 It can be uniform, with constant angular rate ... 1.0 \n", - "2 The rotation around a fixed axis of a three-di... 1.0 \n", - "3 The equations of motion describe the movement ... 1.0 \n", - "4 Examples of circular motion include: an artifi... 1.0 " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_pack.frame[5:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And this is equivalent to slicing the data pack first, then the frame, since both of them are based on the `relation` column. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:30.252181Z", - "start_time": "2019-04-03T09:48:30.223605Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " id_left text_left id_right text_right label\n", - "0 True True True True True\n", - "1 True True True True True\n", - "2 True True True True True\n", - "3 True True True True True\n", - "4 True True True True True" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_slice.frame() == data_pack.frame[5:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Slicing is extremely useful for partitioning data for training vs testing." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:30.261839Z", - "start_time": "2019-04-03T09:48:30.253455Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "num_train = int(len(data_pack) * 0.8)\n", - "data_pack.shuffle(inplace=True)\n", - "train_slice = data_pack[:num_train]\n", - "test_slice = data_pack[num_train:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Transforming Texts" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `apply_on_text` to transform texts in a `matchzoo.DataPack`. Check the documentation for more information." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:30.287943Z", - "start_time": "2019-04-03T09:48:30.263323Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with len: 100%|██████████| 1/1 [00:00<00:00, 2347.12it/s]\n", - "Processing text_right with len: 100%|██████████| 5/5 [00:00<00:00, 10270.09it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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0Q2How are the directions of the velocity and for...85D2-0In physics , circular motion is a movement of ...1261.0
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2Q2How are the directions of the velocity and for...85D2-2The rotation around a fixed axis of a three-di...991.0
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" - ], - "text/plain": [ - " id_left text_left left_length \\\n", - "0 Q2 How are the directions of the velocity and for... 85 \n", - "1 Q2 How are the directions of the velocity and for... 85 \n", - "2 Q2 How are the directions of the velocity and for... 85 \n", - "3 Q2 How are the directions of the velocity and for... 85 \n", - "4 Q2 How are the directions of the velocity and for... 85 \n", - "\n", - " id_right text_right right_length \\\n", - "0 D2-0 In physics , circular motion is a movement of ... 126 \n", - "1 D2-1 It can be uniform, with constant angular rate ... 128 \n", - "2 D2-2 The rotation around a fixed axis of a three-di... 99 \n", - "3 D2-3 The equations of motion describe the movement ... 78 \n", - "4 D2-4 Examples of circular motion include: an artifi... 312 \n", - "\n", - " label \n", - "0 1.0 \n", - "1 1.0 \n", - "2 1.0 \n", - "3 1.0 \n", - "4 1.0 " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_slice.apply_on_text(len, rename=('left_length', 'right_length')).frame()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since adding a column indicating text length is a quite common usage, you may simply do:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:30.347241Z", - "start_time": "2019-04-03T09:48:30.322020Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing length_left with len: 100%|██████████| 1/1 [00:00<00:00, 2288.22it/s]\n", - "Processing length_right with len: 100%|██████████| 5/5 [00:00<00:00, 7361.01it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " id_left text_left length_left \\\n", - "0 Q2 How are the directions of the velocity and for... 85 \n", - "1 Q2 How are the directions of the velocity and for... 85 \n", - "2 Q2 How are the directions of the velocity and for... 85 \n", - "3 Q2 How are the directions of the velocity and for... 85 \n", - "4 Q2 How are the directions of the velocity and for... 85 \n", - "\n", - " id_right text_right length_right \\\n", - "0 D2-0 In physics , circular motion is a movement of ... 126 \n", - "1 D2-1 It can be uniform, with constant angular rate ... 128 \n", - "2 D2-2 The rotation around a fixed axis of a three-di... 99 \n", - "3 D2-3 The equations of motion describe the movement ... 78 \n", - "4 D2-4 Examples of circular motion include: an artifi... 312 \n", - "\n", - " label \n", - "0 1.0 \n", - "1 1.0 \n", - "2 1.0 \n", - "3 1.0 \n", - "4 1.0 " - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_slice.append_text_length().frame()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To one-hot encode the labels:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:30.365410Z", - "start_time": "2019-04-03T09:48:30.348612Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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0Q17how much are the harry potter movies worthD17-8According to Rowling, the main theme is death.[0, 1, 0]
1Q2How are the directions of the velocity and for...D2-1It can be uniform, with constant angular rate ...[0, 1, 0]
2Q5how did apollo creed dieD5-1He was played by Carl Weathers .[0, 1, 0]
3Q9how a vul worksD9-1In a VUL, the cash value can be invested in a ...[0, 1, 0]
4Q5how did apollo creed dieD5-6Rocky Balboa is often wrongly credited with po...[0, 1, 0]
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" - ], - "text/plain": [ - " id_left text_left id_right \\\n", - "0 Q17 how much are the harry potter movies worth D17-8 \n", - "1 Q2 How are the directions of the velocity and for... D2-1 \n", - "2 Q5 how did apollo creed die D5-1 \n", - "3 Q9 how a vul works D9-1 \n", - "4 Q5 how did apollo creed die D5-6 \n", - "\n", - " text_right label \n", - "0 According to Rowling, the main theme is death. [0, 1, 0] \n", - "1 It can be uniform, with constant angular rate ... [0, 1, 0] \n", - "2 He was played by Carl Weathers . [0, 1, 0] \n", - "3 In a VUL, the cash value can be invested in a ... [0, 1, 0] \n", - "4 Rocky Balboa is often wrongly credited with po... [0, 1, 0] " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_pack.relation['label'] = data_pack.relation['label'].astype(int)\n", - "data_pack.one_hot_encode_label(num_classes=3).frame().head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building Your own DataPack" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use `matchzoo.pack` to build your own data pack. Check documentation for more information." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:30.385111Z", - "start_time": "2019-04-03T09:48:30.367282Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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2Q8How are epithelial tissues joined together?D8-2In Biology , Tissue is a cellular organization...0
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" - ], - "text/plain": [ - " id_left text_left id_right \\\n", - "0 Q8 How are epithelial tissues joined together? D8-0 \n", - "1 Q8 How are epithelial tissues joined together? D8-1 \n", - "2 Q8 How are epithelial tissues joined together? D8-2 \n", - "3 Q8 How are epithelial tissues joined together? D8-3 \n", - "4 Q8 How are epithelial tissues joined together? D8-4 \n", - "\n", - " text_right label \n", - "0 Cross section of sclerenchyma fibers in plant ... 0 \n", - "1 Microscopic view of a histologic specimen of h... 0 \n", - "2 In Biology , Tissue is a cellular organization... 0 \n", - "3 A tissue is an ensemble of similar cells from ... 0 \n", - "4 Organs are then formed by the functional group... 0 " - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wiki_dev_entailment_rank = mz.datasets.wiki_qa.load_data(stage='dev')\n", - "wiki_dev_entailment_rank.frame().head()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:34.860314Z", - "start_time": "2019-04-03T09:48:30.651265Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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1L-0This church choir sings to the masses as they ...R-1The church is filled with song.[1, 0, 0, 0]
2L-0This church choir sings to the masses as they ...R-2A choir singing at a baseball game.[0, 1, 0, 0]
3L-1A woman with a green headscarf, blue shirt and...R-3The woman is young.[0, 0, 1, 0]
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[1, 0, 0, 0] " - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "snli_test_classification, classes = mz.datasets.snli.load_data(\n", - " stage='test', task='classification', return_classes=True)\n", - "snli_test_classification.frame().head()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:34.864841Z", - "start_time": "2019-04-03T09:48:34.861733Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['entailment', 'contradiction', 'neutral', '-']" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "classes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Preprocessing" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2019-01-11T09:59:51.361693Z", - "start_time": "2019-01-11T09:59:51.359438Z" - } - }, - "source": [ - "## Preprocessors" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`matchzoo.preprocessors` are responsible for transforming data into correct forms that `matchzoo.models`. `BasicPreprocessor` is used for models with common forms, and some other models have customized preprocessors made just for them." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:34.872121Z", - "start_time": "2019-04-03T09:48:34.866900Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[matchzoo.preprocessors.dssm_preprocessor.DSSMPreprocessor,\n", - " matchzoo.preprocessors.naive_preprocessor.NaivePreprocessor,\n", - " matchzoo.preprocessors.basic_preprocessor.BasicPreprocessor,\n", - " matchzoo.preprocessors.cdssm_preprocessor.CDSSMPreprocessor]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mz.preprocessors.list_available()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When in doubt, use the default preprocessor a model class provides." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:34.878403Z", - "start_time": "2019-04-03T09:48:34.875228Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "preprocessor = mz.models.Naive.get_default_preprocessor()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A preprocessor should be used in two steps. First, `fit`, then, `transform`. `fit` collects information into `context`, which includes everything the preprocessor needs to `transform` together with other useful information for later use. `fit` will only change the preprocessor's inner state but not the input data. In contrast, `transform` returns a modified copy of the input data without changing the preprocessor's inner state." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:34.963013Z", - "start_time": "2019-04-03T09:48:34.880336Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 13/13 [00:00<00:00, 1283.21it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 100/100 [00:00<00:00, 4023.97it/s]\n", - "Processing text_right with append: 100%|██████████| 100/100 [00:00<00:00, 98527.23it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 100/100 [00:00<00:00, 33645.95it/s]\n", - "Processing text_right with transform: 100%|██████████| 100/100 [00:00<00:00, 105596.78it/s]\n", - "Processing text_left with extend: 100%|██████████| 13/13 [00:00<00:00, 27962.03it/s]\n", - "Processing text_right with extend: 100%|██████████| 100/100 [00:00<00:00, 106725.29it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 1665/1665 [00:00<00:00, 1289661.34it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "{'filter_unit': ,\n", - " 'vocab_unit': ,\n", - " 'vocab_size': 285,\n", - " 'embedding_input_dim': 285,\n", - " 'input_shapes': [(30,), (30,)]}" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_raw = mz.datasets.toy.load_data('train', 'ranking')\n", - "test_raw = mz.datasets.toy.load_data('test', 'ranking')\n", - "preprocessor.fit(train_raw)\n", - "preprocessor.context" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:35.049852Z", - "start_time": "2019-04-03T09:48:34.965170Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 13/13 [00:00<00:00, 7532.25it/s]\n", - 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{ - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameter \"task\" set to Ranking Task.\n", - "Parameter \"input_shapes\" set to [(30,), (30,)].\n", - "Epoch 1/1\n", - "100/100 [==============================] - 0s 970us/step - loss: 31720.4902\n" - ] - }, - { - "data": { - "text/plain": [ - "{mean_average_precision(0.0): 0.08333333333333333}" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = mz.models.Naive()\n", - "model.guess_and_fill_missing_params()\n", - "model.build()\n", - "model.compile()\n", - "x_train, y_train = train_preprocessed.unpack()\n", - "model.fit(x_train, y_train)\n", - "x_test, y_test = test_preprocessed.unpack()\n", - "model.evaluate(x_test, y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Processor Units" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Preprocessors utilize `mz.processor_units` to transform data. Processor units correspond to specific transformations and you may use them independently to preprocess a data pack. " - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:35.384276Z", - "start_time": "2019-04-03T09:48:35.356163Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " id_left text_left id_right \\\n", - "0 Q1 [how, are, glacier, caves, formed, ?] D1-0 \n", - "1 Q1 [how, are, glacier, caves, formed, ?] D1-1 \n", - "2 Q1 [how, are, glacier, caves, formed, ?] D1-2 \n", - "3 Q1 [how, are, glacier, caves, formed, ?] D1-3 \n", - "4 Q1 [how, are, glacier, caves, formed, ?] D1-4 \n", - "\n", - " text_right label \n", - "0 [a, partly, submerged, glacier, cave, on, peri... 0.0 \n", - "1 [the, ice, facade, is, approximately, 60, m, h... 0.0 \n", - "2 [ice, formations, in, the, titlis, glacier, cave] 0.0 \n", - "3 [a, glacier, cave, is, a, cave, formed, within... 1.0 \n", - "4 [glacier, caves, are, often, called, ice, cave... 0.0 " - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lower_caser = mz.preprocessors.units.Lowercase()\n", - "data_pack.apply_on_text(lower_caser.transform, inplace=True)\n", - "data_pack.frame[:5]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or use `chain_transform` to apply multiple processor units at one time" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:35.516262Z", - "start_time": "2019-04-03T09:48:35.463597Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase: 100%|██████████| 13/13 [00:00<00:00, 6659.25it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase: 100%|██████████| 100/100 [00:00<00:00, 4481.48it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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D1-4 \n", - "\n", - " text_right label \n", - "0 [12, 29, 66, 24, 72, 54, 33, 51, 29, 21, 25, 1... 0.0 \n", - "1 [37, 4, 32, 29, 7, 49, 32, 29, 68, 24, 49, 24,... 0.0 \n", - "2 [8, 49, 32, 29, 68, 1, 72, 70, 24, 54, 7, 1, 2... 0.0 \n", - "3 [12, 29, 11, 33, 24, 49, 7, 32, 72, 29, 49, 24... 1.0 \n", - "4 [6, 33, 24, 49, 7, 32, 72, 29, 49, 24, 14, 32,... 0.0 " - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_pack = mz.datasets.toy.load_data()\n", - "vocab_unit = mz.build_vocab_unit(data_pack)\n", - "data_pack.apply_on_text(vocab_unit.transform).frame[:5]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2019-01-11T14:34:11.732867Z", - "start_time": "2019-01-11T14:34:11.730142Z" - } - }, - "source": [ - "# DataGenerator\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some MatchZoo models (e.g. DRMM, MatchPyramid) require batch-wise information for training so using `fit_generator` instead of using `fit` is necessary. In addition, sometimes your memory just can't hold all transformed data so to delay a part of the preprocessing process is necessary. \n", - "\n", - "MatchZoo provides `DataGenerator` as an alternative. Instead of `fit`, you may do a `fit_generator` that takes a data generator that `unpack` data on the fly.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:35.638900Z", - "start_time": "2019-04-03T09:48:35.620736Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/1\n", - "\r", - "100/100 [==============================] - 0s 18us/step - loss: 30618.9355\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x_train, y_train = train_preprocessed.unpack()\n", - "model.fit(x_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:35.779263Z", - "start_time": "2019-04-03T09:48:35.640340Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/1\n", - "\r", - "1/1 [==============================] - 0s 18ms/step - loss: 29543.1270\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_gen = mz.DataGenerator(train_preprocessed)\n", - "model.fit_generator(data_gen)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data preprocessing of `DSSM` eats a lot of memory, but we can workaround that using the callback hook of `DataGenerator`." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:35.855202Z", - "start_time": "2019-04-03T09:48:35.780878Z" - } - }, - "outputs": [], - "source": [ - "preprocessor = mz.preprocessors.DSSMPreprocessor(with_word_hashing=False)\n", - "data = preprocessor.fit_transform(train_raw, verbose=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:35.983719Z", - "start_time": "2019-04-03T09:48:35.857115Z" - } - }, - "outputs": [], - "source": [ - "dssm = mz.models.DSSM()\n", - "dssm.params['task'] = mz.tasks.Ranking()\n", - "dssm.params.update(preprocessor.context)\n", - "dssm.build()\n", - "dssm.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:35.989452Z", - "start_time": "2019-04-03T09:48:35.985687Z" - } - }, - "outputs": [], - "source": [ - "term_index = preprocessor.context['vocab_unit'].state['term_index']\n", - "hashing_unit = mz.preprocessors.units.WordHashing(term_index)\n", - "data_generator = mz.DataGenerator(\n", - " data,\n", - " batch_size=4,\n", - " callbacks=[\n", - " mz.data_generator.callbacks.LambdaCallback(\n", - " on_batch_data_pack=lambda dp: dp.apply_on_text(\n", - " hashing_unit.transform, inplace=True, verbose=0)\n", - " )\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:37.417180Z", - "start_time": "2019-04-03T09:48:35.991315Z" - }, - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/1\n", - "25/25 [==============================] - 1s 33ms/step - loss: 0.0471\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dssm.fit_generator(data_generator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition, losses like `RankHingeLoss` and `RankCrossEntropyLoss` have to be used with `DataGenerator` with `mode='pair'`, since batch-wise information are needed and computed on the fly." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:37.518125Z", - "start_time": "2019-04-03T09:48:37.419095Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 13/13 [00:00<00:00, 3234.62it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 100/100 [00:00<00:00, 4000.52it/s]\n", - "Processing text_right with append: 100%|██████████| 100/100 [00:00<00:00, 194993.21it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 100/100 [00:00<00:00, 98898.94it/s]\n", - "Processing text_right with transform: 100%|██████████| 100/100 [00:00<00:00, 89698.55it/s]\n", - "Processing text_left with extend: 100%|██████████| 13/13 [00:00<00:00, 28622.55it/s]\n", - "Processing text_right with extend: 100%|██████████| 100/100 [00:00<00:00, 195995.51it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 1665/1665 [00:00<00:00, 2223341.66it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 13/13 [00:00<00:00, 6020.98it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 100/100 [00:00<00:00, 5079.76it/s]\n", - "Processing text_right with transform: 100%|██████████| 100/100 [00:00<00:00, 106373.42it/s]\n", - "Processing text_left with transform: 100%|██████████| 13/13 [00:00<00:00, 21274.27it/s]\n", - "Processing text_right with transform: 100%|██████████| 100/100 [00:00<00:00, 56397.79it/s]\n", - "Processing length_left with len: 100%|██████████| 13/13 [00:00<00:00, 24863.64it/s]\n", - "Processing length_right with len: 100%|██████████| 100/100 [00:00<00:00, 142228.01it/s]\n", - "Processing text_left with transform: 100%|██████████| 13/13 [00:00<00:00, 21526.23it/s]\n", - "Processing text_right with transform: 100%|██████████| 100/100 [00:00<00:00, 84767.66it/s]\n" - ] - } - ], - "source": [ - "num_neg = 4\n", - "task = mz.tasks.Ranking(loss=mz.losses.RankHingeLoss(num_neg=num_neg))\n", - "preprocessor = model.get_default_preprocessor()\n", - "train_processed = preprocessor.fit_transform(train_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:37.588378Z", - "start_time": "2019-04-03T09:48:37.519898Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "model = mz.models.Naive()\n", - "model.params['task'] = task\n", - "model.params.update(preprocessor.context)\n", - "model.build()\n", - "model.compile()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:37.627721Z", - "start_time": "2019-04-03T09:48:37.590091Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "data_gen = mz.DataGenerator(\n", - " train_processed,\n", - " mode='pair',\n", - " num_neg=num_neg,\n", - " num_dup=2,\n", - " batch_size=32\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:48:38.015010Z", - "start_time": "2019-04-03T09:48:37.629412Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/1\n", - "1/1 [==============================] - 0s 222ms/step - loss: 28.1434\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.fit_generator(data_gen)" - ] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "matchzoo", - "language": "python", - "name": "matchzoo" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - }, - "notify_time": "5", - "toc": { - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "toc_cell": false, - "toc_position": {}, - "toc_section_display": "block", - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "position": { - "height": "171px", - "left": "2190px", - "right": "20px", - "top": "120px", - "width": "349px" - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/model_tuning.ipynb b/tutorials/model_tuning.ipynb deleted file mode 100644 index c13f0d57..00000000 --- a/tutorials/model_tuning.ipynb +++ /dev/null @@ -1,1191 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model Tuning" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:21.149966Z", - "start_time": "2019-04-02T15:14:18.754055Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "import matchzoo as mz\n", - "train_raw = mz.datasets.toy.load_data('train')\n", - "dev_raw = mz.datasets.toy.load_data('dev')\n", - "test_raw = mz.datasets.toy.load_data('test')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2019-01-14T03:48:22.915655Z", - "start_time": "2019-01-14T03:48:22.879130Z" - } - }, - "source": [ - "## basic Usage" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A couple things are needed by the tuner:\n", - " - a model with a parameters filled\n", - " - preprocessed training data\n", - " - preprocessed testing data\n", - " \n", - "Since MatchZoo models have pre-defined hyper-spaces, the tuner can start tuning right away once you have the data ready." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### prepare the data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:21.269885Z", - "start_time": "2019-04-02T15:14:21.152321Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "preprocessor = mz.models.DenseBaseline.get_default_preprocessor()\n", - "train = preprocessor.fit_transform(train_raw, verbose=0)\n", - "dev = preprocessor.transform(dev_raw, verbose=0)\n", - "test = preprocessor.transform(test_raw, verbose=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2019-01-14T10:33:22.859605Z", - "start_time": "2019-01-14T10:33:22.857225Z" - } - }, - "source": [ - "### prepare the model" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:21.274582Z", - "start_time": "2019-04-02T15:14:21.271546Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "model = mz.models.DenseBaseline()\n", - "model.params['input_shapes'] = preprocessor.context['input_shapes']\n", - "model.params['task'] = mz.tasks.Ranking()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2019-01-14T10:33:44.646987Z", - "start_time": "2019-01-14T10:33:44.644671Z" - } - }, - "source": [ - "### start tuning" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:25.740906Z", - "start_time": "2019-04-02T15:14:21.278631Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run #1\n", - "Score: 0.08333333333333333\n", - "model_class \n", - "input_shapes [(30,), (30,)]\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 404\n", - "mlp_num_layers 4\n", - "mlp_num_fan_out 88\n", - "mlp_activation_func relu\n", - "\n", - "Run #2\n", - "Score: 0.125\n", - "model_class \n", - "input_shapes [(30,), (30,)]\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 17\n", - "mlp_num_layers 3\n", - "mlp_num_fan_out 96\n", - "mlp_activation_func relu\n", - "\n", - "Run #3\n", - "Score: 0.125\n", - "model_class \n", - "input_shapes [(30,), (30,)]\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 293\n", - "mlp_num_layers 2\n", - "mlp_num_fan_out 120\n", - "mlp_activation_func relu\n", - "\n", - "Run #4\n", - "Score: 0.5\n", - "model_class \n", - "input_shapes [(30,), (30,)]\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 458\n", - "mlp_num_layers 2\n", - "mlp_num_fan_out 56\n", - "mlp_activation_func relu\n", - "\n", - "Run #5\n", - "Score: 0.16666666666666666\n", - "model_class \n", - "input_shapes [(30,), (30,)]\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 313\n", - "mlp_num_layers 2\n", - "mlp_num_fan_out 36\n", - "mlp_activation_func relu\n", - "\n" - ] - } - ], - "source": [ - "tuner = mz.auto.Tuner(\n", - " params=model.params,\n", - " train_data=train,\n", - " test_data=dev,\n", - " num_runs=5\n", - ")\n", - "results = tuner.tune()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### view the best hyper-parameter set" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:25.756749Z", - "start_time": "2019-04-02T15:14:25.743758Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'#': 4,\n", - " 'params': ,\n", - " 'sample': {'mlp_num_fan_out': 56.0,\n", - " 'mlp_num_layers': 2.0,\n", - " 'mlp_num_units': 458.0},\n", - " 'score': 0.5}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results['best']" - ] - }, - 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NameDescriptionValueHyper-Space
0model_classModel class. Used internally for save/load. Ch...<class 'matchzoo.models.dense_baseline.DenseBa...None
1input_shapesDependent on the model and data. Should be set...[(30,), (30,)]None
2taskDecides model output shape, loss, and metrics.Ranking TaskNone
3optimizerNoneadamNone
4with_multi_layer_perceptronA flag of whether a multiple layer perceptron ...TrueNone
5mlp_num_unitsNumber of units in first `mlp_num_layers` layers.458quantitative uniform distribution in [16, 512...
6mlp_num_layersNumber of layers of the multiple layer percetron.2quantitative uniform distribution in [1, 5), ...
7mlp_num_fan_outNumber of units of the layer that connects the...56quantitative uniform distribution in [4, 128)...
8mlp_activation_funcActivation function used in the multiple layer...reluNone
\n", - "
" - ], - "text/plain": [ - " Name \\\n", - "0 model_class \n", - "1 input_shapes \n", - "2 task \n", - "3 optimizer \n", - "4 with_multi_layer_perceptron \n", - "5 mlp_num_units \n", - "6 mlp_num_layers \n", - "7 mlp_num_fan_out \n", - "8 mlp_activation_func \n", - "\n", - " Description \\\n", - "0 Model class. Used internally for save/load. Ch... \n", - "1 Dependent on the model and data. Should be set... \n", - "2 Decides model output shape, loss, and metrics. \n", - "3 None \n", - "4 A flag of whether a multiple layer perceptron ... \n", - "5 Number of units in first `mlp_num_layers` layers. \n", - "6 Number of layers of the multiple layer percetron. \n", - "7 Number of units of the layer that connects the... \n", - "8 Activation function used in the multiple layer... \n", - "\n", - " Value \\\n", - "0 ,\n", - " 'mlp_num_layers': ,\n", - " 'mlp_num_fan_out': }" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.params.hyper_space" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In a `DenseBaseline` model, only `mlp_num_units`, `mlp_num_layers`, and `mlp_num_fan_out` have pre-defined hyper-space. In other words, only these hyper-parameters will change values during a tuning. Other hyper-parameters, like `mlp_activation_func`, are fixed and will not change." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:25.808039Z", - "start_time": "2019-04-02T15:14:25.791727Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "if sampled: {'mlp_num_fan_out': 60.0, 'mlp_num_layers': 4.0, 'mlp_num_units': 146.0} \n", - "\n", - "the built model will have:\n", - "\n", - "model_class \n", - "input_shapes [(30,), (30,)]\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 146\n", - "mlp_num_layers 4\n", - "mlp_num_fan_out 60\n", - "mlp_activation_func relu \n", - "\n", - "\n", - "\n", - "if sampled: {'mlp_num_fan_out': 64.0, 'mlp_num_layers': 1.0, 'mlp_num_units': 436.0} \n", - "\n", - "the built model will have:\n", - "\n", - "model_class \n", - "input_shapes [(30,), (30,)]\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 436\n", - "mlp_num_layers 1\n", - "mlp_num_fan_out 64\n", - "mlp_activation_func relu \n", - "\n", - "\n", - "\n", - "if sampled: {'mlp_num_fan_out': 44.0, 'mlp_num_layers': 4.0, 'mlp_num_units': 48.0} \n", - "\n", - "the built model will have:\n", - "\n", - "model_class \n", - "input_shapes [(30,), (30,)]\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 48\n", - "mlp_num_layers 4\n", - "mlp_num_fan_out 44\n", - "mlp_activation_func relu \n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "def sample_and_build(params):\n", - " sample = mz.hyper_spaces.sample(params.hyper_space)\n", - " print('if sampled:', sample, '\\n')\n", - " params.update(sample)\n", - " print('the built model will have:\\n')\n", - " print(params, '\\n\\n\\n')\n", - "\n", - "for _ in range(3):\n", - " sample_and_build(model.params)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T07:12:19.698787Z", - "start_time": "2019-04-02T07:12:19.693403Z" - }, - "collapsed": true - }, - "source": [ - "This is similar to the process of a tuner sampling model hyper-parameters, but with one key difference: a tuner's hyper-space is **suggestive**. This means the sampling process in a tuner is not truely random but skewed. Scores of the past samples affect future choices: a tuner with more runs knows better about its hyper-space, and take samples in a way that will likely yields better scores.\n", - "\n", - "For more details, consult tuner's backend: [hyperopt](http://hyperopt.github.io/hyperopt/), and the search algorithm tuner uses: [Tree of Parzen Estimators (TPE)](https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hyper-spaces can also be represented in a human-readable format." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:25.813201Z", - "start_time": "2019-04-02T15:14:25.809832Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "quantitative uniform distribution in [16, 512), with a step size of 1\n" - ] - } - ], - "source": [ - "print(model.params.get('mlp_num_units').hyper_space)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:25.830261Z", - "start_time": "2019-04-02T15:14:25.816717Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameHyper-Space
0model_classNone
1input_shapesNone
2taskNone
3optimizerNone
4with_multi_layer_perceptronNone
5mlp_num_unitsquantitative uniform distribution in [16, 512...
6mlp_num_layersquantitative uniform distribution in [1, 5), ...
7mlp_num_fan_outquantitative uniform distribution in [4, 128)...
8mlp_activation_funcNone
\n", - "
" - ], - "text/plain": [ - " Name \\\n", - "0 model_class \n", - "1 input_shapes \n", - "2 task \n", - "3 optimizer \n", - "4 with_multi_layer_perceptron \n", - "5 mlp_num_units \n", - "6 mlp_num_layers \n", - "7 mlp_num_fan_out \n", - "8 mlp_activation_func \n", - "\n", - " Hyper-Space \n", - "0 None \n", - "1 None \n", - "2 None \n", - "3 None \n", - "4 None \n", - "5 quantitative uniform distribution in [16, 512... \n", - "6 quantitative uniform distribution in [1, 5), ... \n", - "7 quantitative uniform distribution in [4, 128)... \n", - "8 None " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.params.to_frame()[['Name', 'Hyper-Space']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### setting hyper-space" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What if I want the tuner to choose `optimizer` among `adam`, `adagrad`, and `rmsprop`?" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:25.835257Z", - "start_time": "2019-04-02T15:14:25.832370Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "model.params.get('optimizer').hyper_space = mz.hyper_spaces.choice(['adam', 'adagrad', 'rmsprop'])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:25.879425Z", - "start_time": "2019-04-02T15:14:25.837305Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'mlp_num_fan_out': 24.0, 'mlp_num_layers': 4.0, 'mlp_num_units': 439.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 112.0, 'mlp_num_layers': 3.0, 'mlp_num_units': 299.0, 'optimizer': 'rmsprop'}\n", - "{'mlp_num_fan_out': 76.0, 'mlp_num_layers': 3.0, 'mlp_num_units': 353.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 120.0, 'mlp_num_layers': 3.0, 'mlp_num_units': 496.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 64.0, 'mlp_num_layers': 2.0, 'mlp_num_units': 290.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 72.0, 'mlp_num_layers': 5.0, 'mlp_num_units': 360.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 68.0, 'mlp_num_layers': 1.0, 'mlp_num_units': 100.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 20.0, 'mlp_num_layers': 2.0, 'mlp_num_units': 511.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 92.0, 'mlp_num_layers': 2.0, 'mlp_num_units': 18.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 128.0, 'mlp_num_layers': 5.0, 'mlp_num_units': 297.0, 'optimizer': 'adagrad'}\n" - ] - } - ], - "source": [ - "for _ in range(10):\n", - " print(mz.hyper_spaces.sample(model.params.hyper_space))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What about setting `mlp_num_layers` to a fixed value of 2?" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:25.886117Z", - "start_time": "2019-04-02T15:14:25.881809Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "model.params['mlp_num_layers'] = 2\n", - "model.params.get('mlp_num_layers').hyper_space = None" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:25.926672Z", - "start_time": "2019-04-02T15:14:25.888999Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'mlp_num_fan_out': 100.0, 'mlp_num_units': 168.0, 'optimizer': 'adam'}\n", - "{'mlp_num_fan_out': 68.0, 'mlp_num_units': 487.0, 'optimizer': 'adam'}\n", - "{'mlp_num_fan_out': 124.0, 'mlp_num_units': 356.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 116.0, 'mlp_num_units': 499.0, 'optimizer': 'rmsprop'}\n", - "{'mlp_num_fan_out': 32.0, 'mlp_num_units': 120.0, 'optimizer': 'rmsprop'}\n", - "{'mlp_num_fan_out': 84.0, 'mlp_num_units': 442.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 92.0, 'mlp_num_units': 142.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 68.0, 'mlp_num_units': 472.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 32.0, 'mlp_num_units': 343.0, 'optimizer': 'adagrad'}\n", - "{'mlp_num_fan_out': 68.0, 'mlp_num_units': 19.0, 'optimizer': 'adam'}\n" - ] - } - ], - "source": [ - "for _ in range(10):\n", - " print(mz.hyper_spaces.sample(model.params.hyper_space))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### using callbacks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To save the model during the tuning process, use `mz.auto.tuner.callbacks.SaveModel`." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:27.885783Z", - "start_time": "2019-04-02T15:14:25.929438Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: `tune` does not affect the tuner's inner state, so\n", - " each new call to `tune` starts fresh. In other words,\n", - " hyperspaces are suggestive only within the same `tune` call.\n", - "Run #1\n", - "Score: 0.07142857142857142\n", - "model_class \n", - "input_shapes [(30,), (30,)]\n", - "task Ranking Task\n", - "optimizer rmsprop\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 360\n", - "mlp_num_layers 2\n", - "mlp_num_fan_out 92\n", - "mlp_activation_func relu\n", - "\n", - "Run #2\n", - "Score: 0.08333333333333333\n", - "model_class \n", - "input_shapes [(30,), (30,)]\n", - "task Ranking Task\n", - "optimizer adagrad\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 99\n", - "mlp_num_layers 2\n", - "mlp_num_fan_out 84\n", - "mlp_activation_func relu\n", - "\n" - ] - } - ], - "source": [ - "tuner.num_runs = 2\n", - "tuner.callbacks.append(mz.auto.tuner.callbacks.SaveModel())\n", - "results = tuner.tune()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This will save all built models to your `mz.USER_TUNED_MODELS_DIR`, and can be loaded by:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:28.378822Z", - "start_time": "2019-04-02T15:14:27.887573Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "best_model_id = results['best']['model_id']\n", - "mz.load_model(mz.USER_TUNED_MODELS_DIR.joinpath(best_model_id))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To load a pre-trained embedding layer into a built model during a tuning process, use `mz.auto.tuner.callbacks.LoadEmbeddingMatrix`." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:28.468787Z", - "start_time": "2019-04-02T15:14:28.380895Z" - } - }, - "outputs": [], - "source": [ - "toy_embedding = mz.datasets.toy.load_embedding()\n", - "preprocessor = mz.models.DUET.get_default_preprocessor()\n", - "train = preprocessor.fit_transform(train_raw, verbose=0)\n", - "dev = preprocessor.transform(dev_raw, verbose=0)\n", - "params = mz.models.DUET.get_default_params()\n", - "params['task'] = mz.tasks.Ranking()\n", - "params.update(preprocessor.context)\n", - "params['embedding_output_dim'] = toy_embedding.output_dim" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:28.508952Z", - "start_time": "2019-04-02T15:14:28.474349Z" - } - }, - "outputs": [], - "source": [ - "embedding_matrix = toy_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])\n", - "load_embedding_matrix_callback = mz.auto.tuner.callbacks.LoadEmbeddingMatrix(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:31.489312Z", - "start_time": "2019-04-02T15:14:28.517138Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Run #1\n", - "Score: 0.125\n", - "model_class \n", - "input_shapes [(30,), (30,)]\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_embedding True\n", - "embedding_input_dim 285\n", - "embedding_output_dim 2\n", - "embedding_trainable True\n", - "lm_filters 32\n", - "lm_hidden_sizes [32]\n", - "dm_filters 32\n", - "dm_kernel_size 3\n", - "dm_q_hidden_size 32\n", - "dm_d_mpool 3\n", - "dm_hidden_sizes [32]\n", - "padding same\n", - "activation_func relu\n", - "dropout_rate 0.14\n", - "\n" - ] - } - ], - "source": [ - "tuner = mz.auto.tuner.Tuner(\n", - " params=params,\n", - " train_data=train,\n", - " test_data=dev,\n", - " num_runs=1\n", - ")\n", - "tuner.callbacks.append(load_embedding_matrix_callback)\n", - "results = tuner.tune()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### make your own callbacks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To build your own callbacks, inherit `mz.auto.tuner.callbacks.Callback` and overrides corresponding methods.\n", - "\n", - "A run proceeds in the following way:\n", - "\n", - "- run start (callback)\n", - "- build model\n", - "- build end (callback)\n", - "- fit and evaluate model\n", - "- collect result\n", - "- run end (callback)\n", - "\n", - "This process is repeated for `num_runs` times in a tuner." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For example, say I want to verify if my embedding matrix is correctly loaded." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:31.494610Z", - "start_time": "2019-04-02T15:14:31.490880Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "class ValidateEmbedding(mz.auto.tuner.callbacks.Callback):\n", - " def __init__(self, embedding_matrix):\n", - " self._matrix = embedding_matrix\n", - " \n", - " def on_build_end(self, tuner, model):\n", - " loaded_matrix = model.get_embedding_layer().get_weights()[0]\n", - " if np.isclose(self._matrix, loaded_matrix).all():\n", - " print(\"Yes! The my embedding is correctly loaded!\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:31.502034Z", - "start_time": "2019-04-02T15:14:31.496765Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "validate_embedding_matrix_callback = ValidateEmbedding(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-02T15:14:35.199659Z", - "start_time": "2019-04-02T15:14:31.503862Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Yes! The my embedding is correctly loaded!\n", - "Run #1\n", - "Score: 0.08333333333333333\n", - "model_class \n", - "input_shapes [(30,), (30,)]\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_embedding True\n", - "embedding_input_dim 285\n", - "embedding_output_dim 2\n", - "embedding_trainable True\n", - "lm_filters 32\n", - "lm_hidden_sizes [32]\n", - "dm_filters 32\n", - "dm_kernel_size 3\n", - "dm_q_hidden_size 32\n", - "dm_d_mpool 3\n", - "dm_hidden_sizes [32]\n", - "padding same\n", - "activation_func relu\n", - "dropout_rate 0.44\n", - "\n" - ] - } - ], - "source": [ - "tuner = mz.auto.tuner.Tuner(\n", - " params=params,\n", - " train_data=train,\n", - " test_data=dev,\n", - " num_runs=1,\n", - " callbacks=[load_embedding_matrix_callback, validate_embedding_matrix_callback]\n", - ")\n", - "tuner.callbacks.append(load_embedding_matrix_callback)\n", - "results = tuner.tune()" - ] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "matchzoo", - "language": "python", - "name": "matchzoo" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - }, - "toc": { - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "toc_cell": false, - "toc_position": { - "height": "1679px", - "left": "0px", - "right": "909px", - "top": "161px", - "width": "171px" - }, - "toc_section_display": "none", - "toc_window_display": false - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/models.ipynb b/tutorials/models.ipynb deleted file mode 100644 index c4cb171a..00000000 --- a/tutorials/models.ipynb +++ /dev/null @@ -1,79 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Under Construction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Refer to 'tutorials/wikiqa' for model walkthroughs. " - ] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "matchzoo", - "language": "python", - "name": "matchzoo" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - }, - "toc": { - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "toc_cell": false, - "toc_position": {}, - "toc_section_display": "block", - "toc_window_display": false - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/quick_start.ipynb b/tutorials/quick_start.ipynb deleted file mode 100644 index 4d01496f..00000000 --- a/tutorials/quick_start.ipynb +++ /dev/null @@ -1,1689 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"logo\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MatchZoo Quick Start" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.074291Z", - "start_time": "2019-04-03T09:55:17.600013Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.1.0\n" - ] - } - ], - "source": [ - "import matchzoo as mz\n", - "print(mz.__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define Task" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are two types of tasks available in MatchZoo. `mz.tasks.Ranking` and `mz.tasks.Classification`. We will use a ranking task for this demo." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.084876Z", - "start_time": "2019-04-03T09:55:20.076831Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ranking Task\n" - ] - } - ], - "source": [ - "task = mz.tasks.Ranking()\n", - "print(task)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare Data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.118794Z", - "start_time": "2019-04-03T09:55:20.087416Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "train_raw = mz.datasets.toy.load_data(stage='train', task=task)\n", - "test_raw = mz.datasets.toy.load_data(stage='test', task=task)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.135138Z", - "start_time": "2019-04-03T09:55:20.123222Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "matchzoo.data_pack.data_pack.DataPack" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(train_raw)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`DataPack` is a MatchZoo native data structure that most MatchZoo data handling processes build upon. A `DataPack` is consists of three `pandas.DataFrame`:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.150774Z", - "start_time": "2019-04-03T09:55:20.138602Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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D1-4 \n", - "\n", - " text_right label \n", - "0 A partly submerged glacier cave on Perito More... 0.0 \n", - "1 The ice facade is approximately 60 m high 0.0 \n", - "2 Ice formations in the Titlis glacier cave 0.0 \n", - "3 A glacier cave is a cave formed within the ice... 1.0 \n", - "4 Glacier caves are often called ice caves , but... 0.0 " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_raw.frame().head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, using such `pandas.DataFrame` consumes much more memory if there are many duplicates in the texts, and that is the exact reason why we use `DataPack`. For more details about data handling, consult `matchzoo/tutorials/data_handling.ipynb`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocessing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "MatchZoo preprocessors are used to convert a raw `DataPack` into a `DataPack` that ready to be fed into a model. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.210082Z", - "start_time": "2019-04-03T09:55:20.207488Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "preprocessor = mz.preprocessors.BasicPreprocessor()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are two steps to use a preprocessor. First, `fit`. Then, `transform`. `fit` will only changes the preprocessor's inner state but not the input `DataPack`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.273214Z", - "start_time": "2019-04-03T09:55:20.212140Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 13/13 [00:00<00:00, 1194.41it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 100/100 [00:00<00:00, 4426.94it/s]\n", - "Processing text_right with append: 100%|██████████| 100/100 [00:00<00:00, 160516.80it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 100/100 [00:00<00:00, 69742.33it/s]\n", - "Processing text_right with transform: 100%|██████████| 100/100 [00:00<00:00, 96067.43it/s]\n", - "Processing text_left with extend: 100%|██████████| 13/13 [00:00<00:00, 14364.05it/s]\n", - "Processing text_right with extend: 100%|██████████| 100/100 [00:00<00:00, 129854.61it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 1665/1665 [00:00<00:00, 1703712.16it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessor.fit(train_raw)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`fit` will gather useful information into its `context`, which will be used later in a `transform` or used to set hyper-parameters of a model." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.278189Z", - "start_time": "2019-04-03T09:55:20.274519Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'filter_unit': ,\n", - " 'vocab_unit': ,\n", - " 'vocab_size': 285,\n", - " 'embedding_input_dim': 285,\n", - " 'input_shapes': [(30,), (30,)]}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessor.context" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once `fit`, the preprocessor has enough information to `transform`. `transform` will not change the preprocessor's inner state and the input `DataPack`, but return a transformed `DataPack`." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.369369Z", - "start_time": "2019-04-03T09:55:20.280596Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 13/13 [00:00<00:00, 6229.40it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 100/100 [00:00<00:00, 4721.45it/s]\n", - "Processing text_right with transform: 100%|██████████| 100/100 [00:00<00:00, 38168.20it/s]\n", - "Processing text_left with transform: 100%|██████████| 13/13 [00:00<00:00, 20127.70it/s]\n", - "Processing text_right with transform: 100%|██████████| 100/100 [00:00<00:00, 106158.04it/s]\n", - "Processing length_left with len: 100%|██████████| 13/13 [00:00<00:00, 20568.07it/s]\n", - "Processing length_right with len: 100%|██████████| 100/100 [00:00<00:00, 146398.05it/s]\n", - "Processing text_left with transform: 100%|██████████| 13/13 [00:00<00:00, 24954.67it/s]\n", - "Processing text_right with transform: 100%|██████████| 100/100 [00:00<00:00, 66010.45it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 3/3 [00:00<00:00, 1892.74it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 20/20 [00:00<00:00, 3610.80it/s]\n", - "Processing text_right with transform: 100%|██████████| 20/20 [00:00<00:00, 32948.19it/s]\n", - "Processing text_left with transform: 100%|██████████| 3/3 [00:00<00:00, 6275.77it/s]\n", - "Processing text_right with transform: 100%|██████████| 20/20 [00:00<00:00, 35833.44it/s]\n", - "Processing length_left with len: 100%|██████████| 3/3 [00:00<00:00, 1872.74it/s]\n", - "Processing length_right with len: 100%|██████████| 20/20 [00:00<00:00, 36776.01it/s]\n", - "Processing text_left with transform: 100%|██████████| 3/3 [00:00<00:00, 3333.22it/s]\n", - "Processing text_right with transform: 100%|██████████| 20/20 [00:00<00:00, 23838.04it/s]\n" - ] - } - ], - "source": [ - "train_processed = preprocessor.transform(train_raw)\n", - "test_processed = preprocessor.transform(test_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.383522Z", - "start_time": "2019-04-03T09:55:20.371811Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " text_left length_left\n", - "id_left \n", - "Q1 [263, 117, 232, 112, 21, 0, 0, 0, 0, 0, 0, 0, ... 5\n", - "Q2 [263, 117, 89, 194, 22, 89, 225, 186, 195, 105... 15\n", - "Q5 [263, 275, 268, 236, 158, 0, 0, 0, 0, 0, 0, 0,... 5\n", - "Q6 [263, 101, 157, 89, 50, 37, 274, 141, 0, 0, 0,... 8\n", - "Q7 [263, 102, 63, 58, 164, 3, 38, 222, 0, 0, 0, 0... 8" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_processed.left.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, `text_left` is already in sequence form that nerual networks love." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just to make sure we have the correct sequence:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.390512Z", - "start_time": "2019-04-03T09:55:20.385506Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Orig Text: [263, 117, 232, 112, 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", - "Transformed Indices: [263, 117, 232, 112, 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n", - "Transformed Indices Meaning: how_are_glacier_caves_formed_________________________\n" - ] - } - ], - "source": [ - "vocab_unit = preprocessor.context['vocab_unit']\n", - "print('Orig Text:', train_processed.left.loc['Q1']['text_left'])\n", - "sequence = train_processed.left.loc['Q1']['text_left']\n", - "print('Transformed Indices:', sequence)\n", - "print('Transformed Indices Meaning:',\n", - " '_'.join([vocab_unit.state['index_term'][i] for i in sequence]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For more details about preprocessing, consult `matchzoo/tutorials/data_handling.ipynb`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "MatchZoo provides many built-in text matching models." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.396497Z", - "start_time": "2019-04-03T09:55:20.392055Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[matchzoo.models.naive.Naive,\n", - " matchzoo.models.dssm.DSSM,\n", - " matchzoo.models.cdssm.CDSSM,\n", - " matchzoo.models.dense_baseline.DenseBaseline,\n", - " matchzoo.models.arci.ArcI,\n", - " matchzoo.models.arcii.ArcII,\n", - " matchzoo.models.match_pyramid.MatchPyramid,\n", - " matchzoo.models.knrm.KNRM,\n", - " matchzoo.models.duet.DUET,\n", - " matchzoo.models.drmmtks.DRMMTKS,\n", - " matchzoo.models.drmm.DRMM,\n", - " matchzoo.models.anmm.ANMM,\n", - " matchzoo.models.mvlstm.MVLSTM,\n", - " matchzoo.contrib.models.match_lstm.MatchLSTM,\n", - " matchzoo.models.conv_knrm.ConvKNRM]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mz.models.list_available()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's use `mz.models.DenseBaseline` for our demo." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.403308Z", - "start_time": "2019-04-03T09:55:20.399279Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "model = mz.models.DenseBaseline()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The model is initialized with a hyper parameter table, in which values are partially filled. To view parameters and their values, use `print`." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.408216Z", - "start_time": "2019-04-03T09:55:20.405224Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model_class \n", - "input_shapes None\n", - "task None\n", - "optimizer adam\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 256\n", - "mlp_num_layers 3\n", - "mlp_num_fan_out 64\n", - "mlp_activation_func relu\n" - ] - } - ], - "source": [ - "print(model.params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`to_frame` gives you more informartion in addition to just names and values." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.431258Z", - "start_time": "2019-04-03T09:55:20.420657Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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NameDescriptionValue
0model_classModel class. Used internally for save/load. Ch...<class 'matchzoo.models.dense_baseline.DenseBa...
1input_shapesDependent on the model and data. Should be set...None
2taskDecides model output shape, loss, and metrics.None
3optimizerNoneadam
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" - ], - "text/plain": [ - " Name \\\n", - "0 model_class \n", - "1 input_shapes \n", - "2 task \n", - "3 optimizer \n", - "4 with_multi_layer_perceptron \n", - "5 mlp_num_units \n", - "6 mlp_num_layers \n", - "7 mlp_num_fan_out \n", - "8 mlp_activation_func \n", - "\n", - " Description \\\n", - "0 Model class. Used internally for save/load. Ch... \n", - "1 Dependent on the model and data. Should be set... \n", - "2 Decides model output shape, loss, and metrics. \n", - "3 None \n", - "4 A flag of whether a multiple layer perceptron ... \n", - "5 Number of units in first `mlp_num_layers` layers. \n", - "6 Number of layers of the multiple layer percetron. \n", - "7 Number of units of the layer that connects the... \n", - "8 Activation function used in the multiple layer... \n", - "\n", - " Value \n", - "0 \n", - "input_shapes None\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 3\n", - "mlp_num_layers 3\n", - "mlp_num_fan_out 64\n", - "mlp_activation_func relu\n" - ] - } - ], - "source": [ - "model.params['task'] = task\n", - "model.params['mlp_num_units'] = 3\n", - "print(model.params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that we are still missing `input_shapes`, and that information is store in the preprocessor." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.448541Z", - "start_time": "2019-04-03T09:55:20.445714Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[(30,), (30,)]\n" - ] - } - ], - "source": [ - "print(preprocessor.context['input_shapes'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We may use `update` to load a preprocessor's context into a model's hyper-parameter table." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.453045Z", - "start_time": "2019-04-03T09:55:20.450104Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "model.params.update(preprocessor.context)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have a completed hyper-parameter table." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.458269Z", - "start_time": "2019-04-03T09:55:20.454854Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.params.completed()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With all parameters filled in, we can now build and compile the model." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.563119Z", - "start_time": "2019-04-03T09:55:20.459804Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "model.build()\n", - "model.compile()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "MatchZoo models are wrapped over keras models, and the `backend` property of a model gives you the actual keras model built." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.571750Z", - "start_time": "2019-04-03T09:55:20.564862Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 30) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 30) 0 \n", - "__________________________________________________________________________________________________\n", - "concatenate_1 (Concatenate) (None, 60) 0 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) (None, 3) 183 concatenate_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_2 (Dense) (None, 3) 12 dense_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_3 (Dense) (None, 3) 12 dense_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_4 (Dense) (None, 64) 256 dense_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_5 (Dense) (None, 1) 65 dense_4[0][0] \n", - "==================================================================================================\n", - "Total params: 528\n", - "Trainable params: 528\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model.backend.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2018-12-10T13:28:54.044570Z", - "start_time": "2018-12-10T13:28:54.038325Z" - } - }, - "source": [ - "For more details about models, consult `matchzoo/tutorials/models.ipynb`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train, Evaluate, Predict" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A `DataPack` can `unpack` itself into data that can be directly used to train a MatchZoo model." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:20.605059Z", - "start_time": "2019-04-03T09:55:20.574058Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "x, y = train_processed.unpack()\n", - "test_x, test_y = test_processed.unpack()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:21.281367Z", - "start_time": "2019-04-03T09:55:20.606828Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/5\n", - "100/100 [==============================] - 0s 3ms/step - loss: 2.7378\n", - "Epoch 2/5\n", - "100/100 [==============================] - 0s 63us/step - loss: 0.3293\n", - "Epoch 3/5\n", - "100/100 [==============================] - 0s 58us/step - loss: 0.3964\n", - "Epoch 4/5\n", - "100/100 [==============================] - 0s 60us/step - loss: 0.2228\n", - "Epoch 5/5\n", - "100/100 [==============================] - 0s 69us/step - loss: 0.1432\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.fit(x, y, batch_size=32, epochs=5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An alternative to train a model is to use a `DataGenerator`. This is useful for delaying expensive preprocessing steps or doing real-time data augmentation. For some models that needs dynamic batch-wise information, using a `DataGenerator` is required. For more details about `DataGenerator`, consult `matchzoo/tutorials/data_handling.ipynb`." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:21.285679Z", - "start_time": "2019-04-03T09:55:21.282881Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "data_generator = mz.DataGenerator(train_processed, batch_size=32)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:21.944886Z", - "start_time": "2019-04-03T09:55:21.287913Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/5\n", - "4/4 [==============================] - 0s 14ms/step - loss: 0.1749\n", - "Epoch 2/5\n", - "4/4 [==============================] - 0s 32ms/step - loss: 0.1149\n", - "Epoch 3/5\n", - "4/4 [==============================] - 0s 30ms/step - loss: 0.0773\n", - "Epoch 4/5\n", - "4/4 [==============================] - 0s 31ms/step - loss: 0.0625\n", - "Epoch 5/5\n", - "4/4 [==============================] - 0s 30ms/step - loss: 0.0984\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.fit_generator(data_generator, epochs=5, use_multiprocessing=True, workers=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:21.996760Z", - "start_time": "2019-04-03T09:55:21.947722Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{mean_average_precision(0.0): 0.16666666666666666}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.evaluate(test_x, test_y)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:55:22.005670Z", - "start_time": "2019-04-03T09:55:21.998800Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-0.00927439],\n", - " [ 0.06645402],\n", - " [ 0.00299546],\n", - " [ 0.06593451],\n", - " [ 0.19827756],\n", - " [-0.00519839],\n", - " [-0.04881426],\n", - " [-0.07771388],\n", - " [-0.04881426],\n", - " [-0.04881426],\n", - " [-0.04881426],\n", - " [-0.04881426],\n", - " [-0.04881426],\n", - " [-0.07235113],\n", - " [-0.04881426],\n", - " [-0.04881426],\n", - " [-0.04881426],\n", - " [-0.04881426],\n", - " [-0.04881426],\n", - " [-0.08091632]], dtype=float32)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.predict(test_x)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2019-01-27T15:38:33.485790Z", - "start_time": "2019-01-27T15:38:33.483430Z" - } - }, - "source": [ - "## A Shortcut to Preprocessing and Model Building" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since data preprocessing and model building are laborious and special setups of some models makes this even worse, MatchZoo provides `prepare`, a unified interface that handles interaction among data, model, and preprocessor automatically.\n", - "\n", - "More specifically, `prepare` does these following things:\n", - " - create a default preprocessor of the model class (if not given one)\n", - " - fit the preprocessor using the raw data\n", - " - create an embedding matrix\n", - " - instantiate a model and fill in hype-parameters\n", - " - build the model\n", - " - instantiate a `DataGeneratorBuilder` that will build a correctly formed `DataGenerator` given a `DataPack`\n", - " \n", - "It also does many special handling for specific models, but we will not go into the details of that here." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:56:29.684941Z", - "start_time": "2019-04-03T09:55:22.008020Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 0s 139ms/step - loss: 62835.0703\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 1s 556ms/step - loss: 0.0507\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 2s 2s/step - loss: 0.1703\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 0s 458ms/step - loss: 259.2346\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 1s 617ms/step - loss: 0.0480\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 1s 717ms/step - loss: 0.0546\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 1s 559ms/step - loss: 0.0518\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 1s 1s/step - loss: 9780.9414\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 1s 1s/step - loss: 1.4414\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 1s 1s/step - loss: 0.0691\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 1s 1s/step - loss: 0.0770\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 1s 953ms/step - loss: 0.0463\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 3s 3s/step - loss: 0.0487\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 5s 5s/step - loss: 0.0475\n", - "\n", - "\n", - "Epoch 1/1\n", - "1/1 [==============================] - 7s 7s/step - loss: 540.5453\n", - "\n" - ] - } - ], - "source": [ - "for model_class in mz.models.list_available():\n", - " print(model_class)\n", - " model, preprocessor, data_generator_builder, embedding_matrix = mz.auto.prepare(\n", - " task=task,\n", - " model_class=model_class,\n", - " data_pack=train_raw,\n", - " )\n", - " train_processed = preprocessor.transform(train_raw, verbose=0)\n", - " test_processed = preprocessor.transform(test_raw, verbose=0)\n", - " train_gen = data_generator_builder.build(train_processed)\n", - " test_gen = data_generator_builder.build(test_processed)\n", - " model.fit_generator(train_gen, epochs=1)\n", - " model.evaluate_generator(test_gen)\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save and Load the Model" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-03T09:56:35.125254Z", - "start_time": "2019-04-03T09:56:29.686705Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "model.save('my-model')\n", - "loaded_model = mz.load_model('my-model')" - ] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "matchzoo", - "language": "python", - "name": "matchzoo" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - }, - "toc": { - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "toc_cell": false, - "toc_position": {}, - "toc_section_display": "block", - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/quick_start_chart.png b/tutorials/quick_start_chart.png deleted file mode 100644 index ef1e6ad7..00000000 Binary files a/tutorials/quick_start_chart.png and /dev/null differ diff --git a/tutorials/quora/esim.ipynb b/tutorials/quora/esim.ipynb deleted file mode 100644 index 4bdf927a..00000000 --- a/tutorials/quora/esim.ipynb +++ /dev/null @@ -1,675 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run ./tutorials/wikiqa/init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from keras.optimizers import Adam\n", - "from keras.utils import to_categorical\n", - "\n", - "import matchzoo as mz\n", - "from matchzoo.contrib.models.esim import ESIM" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def load_filtered_data(preprocessor, data_type):\n", - " assert ( data_type in ['train', 'dev', 'test'])\n", - " data_pack = mz.datasets.wiki_qa.load_data(data_type, task='ranking')\n", - "\n", - " if data_type == 'train':\n", - " X, Y = preprocessor.fit_transform(data_pack).unpack()\n", - " else:\n", - " X, Y = preprocessor.transform(data_pack).unpack()\n", - "\n", - " new_idx = []\n", - " for i in range(Y.shape[0]):\n", - " if X[\"length_left\"][i] == 0 or X[\"length_right\"][i] == 0:\n", - " continue\n", - " new_idx.append(i)\n", - " new_idx = np.array(new_idx)\n", - " print(\"Removed empty data. Found \", (Y.shape[0] - new_idx.shape[0]))\n", - "\n", - " for k in X.keys():\n", - " X[k] = X[k][new_idx]\n", - " Y = Y[new_idx]\n", - "\n", - " pos_idx = (Y == 1)[:, 0]\n", - " pos_qid = X[\"id_left\"][pos_idx]\n", - " keep_idx_bool = np.array([ qid in pos_qid for qid in X[\"id_left\"]])\n", - " keep_idx = np.arange(keep_idx_bool.shape[0])\n", - " keep_idx = keep_idx[keep_idx_bool]\n", - " print(\"Removed questions with no pos label. Found \", (keep_idx_bool == 0).sum())\n", - "\n", - " print(\"shuffling...\")\n", - " np.random.shuffle(keep_idx)\n", - " for k in X.keys():\n", - " X[k] = X[k][keep_idx]\n", - " Y = Y[keep_idx]\n", - "\n", - " return X, Y, preprocessor" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "fixed_length_left = 10\n", - "fixed_length_right = 40\n", - "batch_size = 32\n", - "epochs = 5" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 10798.93it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:02<00:00, 8019.65it/s]\n", - "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 1415354.12it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 226166.63it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 233892.08it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 782897.32it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 1175423.27it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 358408/358408 [00:00<00:00, 4845654.07it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 15108.05it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:02<00:00, 8129.15it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 222548.25it/s]\n", - "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 324738.11it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 122413.67it/s]\n", - "Processing length_left with len: 100%|██████████| 2118/2118 [00:00<00:00, 821484.73it/s]\n", - "Processing length_right with len: 100%|██████████| 18841/18841 [00:00<00:00, 1319786.92it/s]\n", - "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 200871.36it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 180842.83it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removed empty data. Found 91\n", - "Removed questions with no pos label. Found 11642\n", - "shuffling...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 296/296 [00:00<00:00, 15853.43it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2708/2708 [00:00<00:00, 8318.22it/s]\n", - "Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 232964.32it/s]\n", - "Processing text_left with transform: 100%|██████████| 296/296 [00:00<00:00, 200892.23it/s]\n", - "Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 231808.96it/s]\n", - "Processing length_left with len: 100%|██████████| 296/296 [00:00<00:00, 562279.88it/s]\n", - "Processing length_right with len: 100%|██████████| 2708/2708 [00:00<00:00, 1159470.73it/s]\n", - "Processing text_left with transform: 100%|██████████| 296/296 [00:00<00:00, 183357.55it/s]\n", - "Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 178815.40it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removed empty data. Found 8\n", - "Removed questions with no pos label. Found 1595\n", - "shuffling...\n" - ] - } - ], - "source": [ - "# prepare data\n", - "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=fixed_length_left,\n", - " fixed_length_right=fixed_length_right,\n", - " remove_stop_words=False,\n", - " filter_low_freq=10)\n", - "\n", - "train_X, train_Y, preprocessor = load_filtered_data(preprocessor, 'train')\n", - "val_X, val_Y, _ = load_filtered_data(preprocessor, 'dev')\n", - "pred_X, pred_Y = val_X, val_Y\n", - "# pred_X, pred_Y, _ = load_filtered_data(preprocessor, 'test') # no prediction label for quora dataset\n", - "\n", - "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'], initializer=lambda: 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 10) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 40) 0 \n", - "__________________________________________________________________________________________________\n", - "embedding (Embedding) multiple 1930500 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_1 (Dropout) multiple 0 embedding[0][0] \n", - " embedding[1][0] \n", - " dense_1[0][0] \n", - " dense_1[1][0] \n", - " dense_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_1 (Lambda) multiple 0 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "bidirectional_1 (Bidirectional) multiple 1442400 dropout_1[0][0] \n", - " dropout_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_2 (Lambda) (None, 10, 1) 0 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_3 (Lambda) (None, 40, 1) 0 lambda_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_1 (Multiply) (None, 10, 600) 0 bidirectional_1[0][0] \n", - " lambda_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_2 (Multiply) (None, 40, 600) 0 bidirectional_1[1][0] \n", - " lambda_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_4 (Lambda) (None, 10, 1) 0 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_5 (Lambda) (None, 1, 40) 0 lambda_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "dot_1 (Dot) (None, 10, 40) 0 multiply_1[0][0] \n", - " multiply_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_3 (Multiply) (None, 10, 40) 0 lambda_4[0][0] \n", - " lambda_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "permute_1 (Permute) (None, 40, 10) 0 dot_1[0][0] \n", - " multiply_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "atten_mask (Lambda) multiple 0 dot_1[0][0] \n", - " multiply_3[0][0] \n", - " permute_1[0][0] \n", - " permute_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "softmax_1 (Softmax) multiple 0 atten_mask[0][0] \n", - " atten_mask[1][0] \n", - "__________________________________________________________________________________________________\n", - "dot_2 (Dot) (None, 10, 600) 0 softmax_1[0][0] \n", - " multiply_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dot_3 (Dot) (None, 40, 600) 0 softmax_1[1][0] \n", - " multiply_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "subtract_1 (Subtract) (None, 10, 600) 0 multiply_1[0][0] \n", - " dot_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_4 (Multiply) (None, 10, 600) 0 multiply_1[0][0] \n", - " dot_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "subtract_2 (Subtract) (None, 40, 600) 0 multiply_2[0][0] \n", - " dot_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_5 (Multiply) (None, 40, 600) 0 multiply_2[0][0] \n", - " dot_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_1 (Concatenate) (None, 10, 2400) 0 multiply_1[0][0] \n", - " dot_2[0][0] \n", - " subtract_1[0][0] \n", - " multiply_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_2 (Concatenate) (None, 40, 2400) 0 multiply_2[0][0] \n", - " dot_3[0][0] \n", - " subtract_2[0][0] \n", - " multiply_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) multiple 720300 concatenate_1[0][0] \n", - " concatenate_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "bidirectional_2 (Bidirectional) multiple 1442400 dropout_1[2][0] \n", - " dropout_1[3][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_6 (Lambda) (None, 10, 1) 0 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_8 (Lambda) (None, 10, 1) 0 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_10 (Lambda) (None, 40, 1) 0 lambda_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_12 (Lambda) (None, 40, 1) 0 lambda_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_6 (Multiply) (None, 10, 600) 0 bidirectional_2[0][0] \n", - " lambda_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_7 (Multiply) (None, 10, 600) 0 bidirectional_2[0][0] \n", - " lambda_8[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_8 (Multiply) (None, 40, 600) 0 bidirectional_2[1][0] \n", - " lambda_10[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_9 (Multiply) (None, 40, 600) 0 bidirectional_2[1][0] \n", - " lambda_12[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_7 (Lambda) (None, 600) 0 multiply_6[0][0] \n", - " lambda_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_9 (Lambda) (None, 600) 0 multiply_7[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_11 (Lambda) (None, 600) 0 multiply_8[0][0] \n", - " lambda_10[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_13 (Lambda) (None, 600) 0 multiply_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_3 (Concatenate) (None, 1200) 0 lambda_7[0][0] \n", - " lambda_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_4 (Concatenate) (None, 1200) 0 lambda_11[0][0] \n", - " lambda_13[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_5 (Concatenate) (None, 2400) 0 concatenate_3[0][0] \n", - " concatenate_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_2 (Dense) (None, 300) 720300 concatenate_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_3 (Dense) (None, 1) 301 dropout_1[4][0] \n", - "==================================================================================================\n", - "Total params: 6,256,201\n", - "Trainable params: 4,325,701\n", - "Non-trainable params: 1,930,500\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model = ESIM()\n", - "model.params['task'] = mz.tasks.Ranking()\n", - "model.params['mask_value'] = 0\n", - "model.params['input_shapes'] = [[fixed_length_left, ],\n", - " [fixed_length_right, ]]\n", - "model.params['lstm_dim'] = 300\n", - "model.params['embedding_input_dim'] = preprocessor.context['vocab_size']\n", - "model.params['embedding_output_dim'] = 300\n", - "model.params['embedding_trainable'] = False\n", - "model.params['dropout_rate'] = 0.5\n", - "\n", - "model.params['mlp_num_units'] = 300\n", - "model.params['mlp_num_layers'] = 0\n", - "model.params['mlp_num_fan_out'] = 300\n", - "model.params['mlp_activation_func'] = 'tanh'\n", - "model.params['optimizer'] = Adam(lr=4e-4)\n", - "\n", - "model.guess_and_fill_missing_params()\n", - "model.build()\n", - "\n", - "model.compile()\n", - "model.backend.summary() # not visualize" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 8627 samples, validate on 1130 samples\n", - "Epoch 1/5\n", - "8627/8627 [==============================] - 48s 6ms/step - loss: 0.1073 - val_loss: 0.0984\n", - "Validation: mean_average_precision(0.0): 0.6222655981584554\n", - "Epoch 2/5\n", - "8627/8627 [==============================] - 44s 5ms/step - loss: 0.0994 - val_loss: 0.0974\n", - "Validation: mean_average_precision(0.0): 0.640342571890191\n", - "Epoch 3/5\n", - "8627/8627 [==============================] - 44s 5ms/step - loss: 0.0944 - val_loss: 0.0981\n", - "Validation: mean_average_precision(0.0): 0.633281742507933\n", - "Epoch 4/5\n", - "8627/8627 [==============================] - 44s 5ms/step - loss: 0.0915 - val_loss: 0.0898\n", - "Validation: mean_average_precision(0.0): 0.6479046351993808\n", - "Epoch 5/5\n", - "8627/8627 [==============================] - 44s 5ms/step - loss: 0.0893 - val_loss: 0.0931\n", - "Validation: mean_average_precision(0.0): 0.6506805763854636\n" - ] - } - ], - "source": [ - "# run as classification task\n", - "model.load_embedding_matrix(embedding_matrix)\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model,\n", - " x=pred_X,\n", - " y=pred_Y,\n", - " once_every=1,\n", - " batch_size=len(pred_Y))\n", - "\n", - "history = model.fit(x = [train_X['text_left'],\n", - " train_X['text_right']],\n", - " y = train_Y,\n", - " validation_data = (val_X, val_Y),\n", - " batch_size = batch_size,\n", - " epochs = epochs,\n", - " callbacks=[evaluate]\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 10) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 40) 0 \n", - "__________________________________________________________________________________________________\n", - "embedding (Embedding) multiple 1930500 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_1 (Dropout) multiple 0 embedding[0][0] \n", - " embedding[1][0] \n", - " dense_1[0][0] \n", - " dense_1[1][0] \n", - " dense_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_1 (Lambda) multiple 0 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "bidirectional_1 (Bidirectional) multiple 1442400 dropout_1[0][0] \n", - " dropout_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_2 (Lambda) (None, 10, 1) 0 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_3 (Lambda) (None, 40, 1) 0 lambda_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_1 (Multiply) (None, 10, 600) 0 bidirectional_1[0][0] \n", - " lambda_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_2 (Multiply) (None, 40, 600) 0 bidirectional_1[1][0] \n", - " lambda_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_4 (Lambda) (None, 10, 1) 0 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_5 (Lambda) (None, 1, 40) 0 lambda_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "dot_1 (Dot) (None, 10, 40) 0 multiply_1[0][0] \n", - " multiply_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_3 (Multiply) (None, 10, 40) 0 lambda_4[0][0] \n", - " lambda_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "permute_1 (Permute) (None, 40, 10) 0 dot_1[0][0] \n", - " multiply_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "atten_mask (Lambda) multiple 0 dot_1[0][0] \n", - " multiply_3[0][0] \n", - " permute_1[0][0] \n", - " permute_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "softmax_1 (Softmax) multiple 0 atten_mask[0][0] \n", - " atten_mask[1][0] \n", - "__________________________________________________________________________________________________\n", - "dot_2 (Dot) (None, 10, 600) 0 softmax_1[0][0] \n", - " multiply_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dot_3 (Dot) (None, 40, 600) 0 softmax_1[1][0] \n", - " multiply_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "subtract_1 (Subtract) (None, 10, 600) 0 multiply_1[0][0] \n", - " dot_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_4 (Multiply) (None, 10, 600) 0 multiply_1[0][0] \n", - " dot_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "subtract_2 (Subtract) (None, 40, 600) 0 multiply_2[0][0] \n", - " dot_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_5 (Multiply) (None, 40, 600) 0 multiply_2[0][0] \n", - " dot_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_1 (Concatenate) (None, 10, 2400) 0 multiply_1[0][0] \n", - " dot_2[0][0] \n", - " subtract_1[0][0] \n", - " multiply_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_2 (Concatenate) (None, 40, 2400) 0 multiply_2[0][0] \n", - " dot_3[0][0] \n", - " subtract_2[0][0] \n", - " multiply_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) multiple 720300 concatenate_1[0][0] \n", - " concatenate_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "bidirectional_2 (Bidirectional) multiple 1442400 dropout_1[2][0] \n", - " dropout_1[3][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_6 (Lambda) (None, 10, 1) 0 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_8 (Lambda) (None, 10, 1) 0 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_10 (Lambda) (None, 40, 1) 0 lambda_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_12 (Lambda) (None, 40, 1) 0 lambda_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_6 (Multiply) (None, 10, 600) 0 bidirectional_2[0][0] \n", - " lambda_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_7 (Multiply) (None, 10, 600) 0 bidirectional_2[0][0] \n", - " lambda_8[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_8 (Multiply) (None, 40, 600) 0 bidirectional_2[1][0] \n", - " lambda_10[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_9 (Multiply) (None, 40, 600) 0 bidirectional_2[1][0] \n", - " lambda_12[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_7 (Lambda) (None, 600) 0 multiply_6[0][0] \n", - " lambda_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_9 (Lambda) (None, 600) 0 multiply_7[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_11 (Lambda) (None, 600) 0 multiply_8[0][0] \n", - " lambda_10[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_13 (Lambda) (None, 600) 0 multiply_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_3 (Concatenate) (None, 1200) 0 lambda_7[0][0] \n", - " lambda_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_4 (Concatenate) (None, 1200) 0 lambda_11[0][0] \n", - " lambda_13[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_5 (Concatenate) (None, 2400) 0 concatenate_3[0][0] \n", - " concatenate_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_2 (Dense) (None, 300) 720300 concatenate_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_3 (Dense) (None, 2) 602 dropout_1[4][0] \n", - "==================================================================================================\n", - "Total params: 6,256,502\n", - "Trainable params: 4,326,002\n", - "Non-trainable params: 1,930,500\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "# run as classification task\n", - "classification_task = mz.tasks.Classification(num_classes=2)\n", - "classification_task.metrics = 'acc'\n", - "\n", - "model = ESIM()\n", - "model.params['task'] = classification_task\n", - "model.params['mask_value'] = 0\n", - "model.params['input_shapes'] = [[fixed_length_left, ],\n", - " [fixed_length_right, ]]\n", - "model.params['lstm_dim'] = 300\n", - "model.params['embedding_input_dim'] = preprocessor.context['vocab_size']\n", - "model.params['embedding_output_dim'] = 300\n", - "model.params['embedding_trainable'] = False\n", - "model.params['dropout_rate'] = 0.5\n", - "\n", - "model.params['mlp_num_units'] = 300\n", - "model.params['mlp_num_layers'] = 0\n", - "model.params['mlp_num_fan_out'] = 300\n", - "model.params['mlp_activation_func'] = 'tanh'\n", - "model.params['optimizer'] = Adam(lr=4e-4)\n", - "\n", - "model.guess_and_fill_missing_params()\n", - "model.build()\n", - "\n", - "model.compile()\n", - "model.backend.summary() # not visualize" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 8627 samples, validate on 1130 samples\n", - "Epoch 1/5\n", - "8627/8627 [==============================] - 48s 6ms/step - loss: 0.3607 - val_loss: 0.3330\n", - "Validation: categorical_accuracy: 1.0\n", - "Epoch 2/5\n", - "8627/8627 [==============================] - 43s 5ms/step - loss: 0.3273 - val_loss: 0.3490\n", - "Validation: categorical_accuracy: 0.9451327323913574\n", - "Epoch 3/5\n", - "8627/8627 [==============================] - 44s 5ms/step - loss: 0.3096 - val_loss: 0.3498\n", - "Validation: categorical_accuracy: 0.9938052892684937\n", - "Epoch 4/5\n", - "8627/8627 [==============================] - 44s 5ms/step - loss: 0.2970 - val_loss: 0.3170\n", - "Validation: categorical_accuracy: 0.969911515712738\n", - "Epoch 5/5\n", - "8627/8627 [==============================] - 44s 5ms/step - loss: 0.2787 - val_loss: 0.3543\n", - "Validation: categorical_accuracy: 0.8778761029243469\n" - ] - } - ], - "source": [ - "evaluate = mz.callbacks.EvaluateAllMetrics(model,\n", - " x=pred_X,\n", - " y=pred_Y,\n", - " once_every=1,\n", - " batch_size=len(pred_Y))\n", - "\n", - "train_Y = to_categorical(train_Y)\n", - "val_Y = to_categorical(val_Y)\n", - "\n", - "model.load_embedding_matrix(embedding_matrix)\n", - "history = model.fit(x = [train_X['text_left'],\n", - " train_X['text_right']],\n", - " y = train_Y,\n", - " validation_data = (val_X, val_Y),\n", - " batch_size = batch_size,\n", - " epochs = epochs,\n", - " callbacks=[evaluate]\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'categorical_accuracy': 0.8920354}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.evaluate(val_X, val_Y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mz_play", - "language": "python", - "name": "mz_play" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/README.rst b/tutorials/wikiqa/README.rst deleted file mode 100644 index a5521c9a..00000000 --- a/tutorials/wikiqa/README.rst +++ /dev/null @@ -1,111 +0,0 @@ -********************** -WikiQA Best Parameters -********************** - -DRMMTKS -####### - -==== =========================== ========================================= - .. Name Value -==== =========================== ========================================= - 0 model_class - 1 input_shapes [(10,), (100,)] - 2 task Ranking Task - 3 optimizer adadelta - 4 with_embedding True - 5 embedding_input_dim 16674 - 6 embedding_output_dim 100 - 7 embedding_trainable True - 8 with_multi_layer_perceptron True - 9 mlp_num_units 5 - 10 mlp_num_layers 1 - 11 mlp_num_fan_out 1 - 12 mlp_activation_func relu - 13 mask_value -1 - 14 top_k 20 -==== =========================== ========================================= - -MatchPyramid -############ - -==== ==================== ==================================================== - .. Name Value -==== ==================== ==================================================== - 0 model_class - 1 input_shapes [(10,), (40,)] - 2 task Ranking Task - 3 optimizer adam - 4 with_embedding True - 5 embedding_input_dim 16546 - 6 embedding_output_dim 100 - 7 embedding_trainable True - 8 num_blocks 2 - 9 kernel_count [16, 32] - 10 kernel_size [[3, 3], [3, 3]] - 11 activation relu - 12 dpool_size [3, 10] - 13 padding same - 14 dropout_rate 0.1 -==== ==================== ==================================================== - -ArcII -##### - -==== ==================== ===================================== - .. Name Value -==== ==================== ===================================== - 0 model_class - 1 input_shapes [(10,), (100,)] - 2 task Ranking Task - 3 optimizer adam - 4 with_embedding True - 5 embedding_input_dim 16674 - 6 embedding_output_dim 100 - 7 embedding_trainable True - 8 num_blocks 2 - 9 kernel_1d_count 32 - 10 kernel_1d_size 3 - 11 kernel_2d_count [64, 64] - 12 kernel_2d_size [3, 3] - 13 activation relu - 14 pool_2d_size [[3, 3], [3, 3]] - 15 padding same - 16 dropout_rate 0.0 -==== ==================== ===================================== - -MatchLSTM -######### - -==== ==================== ====================================================== - .. Name Value -==== ==================== ====================================================== - 0 model_class - 1 input_shapes [(10,), (40,)] - 2 task Ranking Task - 3 optimizer adadelta - 4 with_embedding True - 5 embedding_input_dim 16674 - 6 embedding_output_dim 100 - 7 embedding_trainable True - 8 lstm_num_units 100 - 9 fc_num_units 100 - 10 dropout_rate 0.5 -==== ==================== ====================================================== - -DSSM -#### - -==== =========================== =================================== - .. Name Value -==== =========================== =================================== - 0 model_class - 1 input_shapes [(9645,), (9645,)] - 2 task Ranking Task - 3 optimizer adam - 4 with_multi_layer_perceptron True - 5 mlp_num_units 300 - 6 mlp_num_layers 3 - 7 mlp_num_fan_out 128 - 8 mlp_activation_func relu -==== =========================== =================================== - diff --git a/tutorials/wikiqa/arci.ipynb b/tutorials/wikiqa/arci.ipynb deleted file mode 100644 index 96daad67..00000000 --- a/tutorials/wikiqa/arci.ipynb +++ /dev/null @@ -1,372 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n", - "/home/fanyixing/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n", - "/home/fanyixing/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 3277.07it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 18841/18841 [00:08<00:00, 2128.88it/s]\n", - "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 443061.44it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 76946.33it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 61087.65it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 357127.07it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 382711.17it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 234249/234249 [00:00<00:00, 1785462.63it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 3638.16it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 18841/18841 [00:08<00:00, 2107.97it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 80677.64it/s]\n", - 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"Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 91550.01it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 99896.44it/s]\n", - "Processing length_left with len: 100%|██████████| 237/237 [00:00<00:00, 183879.03it/s]\n", - "Processing length_right with len: 100%|██████████| 2300/2300 [00:00<00:00, 376464.36it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 44442.71it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 40030.45it/s]\n" - ] - } - ], - "source": [ - "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10, fixed_length_right=100, remove_stop_words=True)\n", - "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n", - "valid_pack_processed = preprocessor.transform(dev_pack_raw)\n", - "test_pack_processed = preprocessor.transform(test_pack_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'filter_unit': ,\n", - " 'vocab_unit': ,\n", - " 'vocab_size': 16546,\n", - " 'embedding_input_dim': 16546,\n", - " 'input_shapes': [(10,), (100,)]}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessor.context" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 10) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 100) 0 \n", - "__________________________________________________________________________________________________\n", - "embedding (Embedding) multiple 4963800 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv1d_1 (Conv1D) (None, 10, 128) 115328 embedding[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv1d_2 (Conv1D) (None, 100, 128) 115328 embedding[1][0] \n", - "__________________________________________________________________________________________________\n", - "max_pooling1d_1 (MaxPooling1D) (None, 2, 128) 0 conv1d_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "max_pooling1d_2 (MaxPooling1D) (None, 25, 128) 0 conv1d_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "flatten_1 (Flatten) (None, 256) 0 max_pooling1d_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "flatten_2 (Flatten) (None, 3200) 0 max_pooling1d_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_1 (Concatenate) (None, 3456) 0 flatten_1[0][0] \n", - " flatten_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_1 (Dropout) (None, 3456) 0 concatenate_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) (None, 100) 345700 dropout_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_2 (Dense) (None, 1) 101 dense_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_3 (Dense) (None, 1) 2 dense_2[0][0] \n", - "==================================================================================================\n", - "Total params: 5,540,259\n", - "Trainable params: 5,540,259\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model = mz.models.ArcI()\n", - "model.params.update(preprocessor.context)\n", - "model.params['task'] = ranking_task\n", - "model.params['embedding_output_dim'] = glove_embedding.output_dim\n", - "model.params['num_blocks'] = 1\n", - "model.params['left_filters'] = [128]\n", - "model.params['left_kernel_sizes'] = [3]\n", - "model.params['left_pool_sizes'] = [4]\n", - "model.params['right_filters'] = [128]\n", - "model.params['right_kernel_sizes'] = [3]\n", - "model.params['right_pool_sizes'] = [4]\n", - "model.params['conv_activation_func']= 'relu'\n", - "model.params['mlp_num_layers'] = 1\n", - "model.params['mlp_num_units'] = 100\n", - "model.params['mlp_num_fan_out'] = 1 \n", - "model.params['mlp_activation_func'] = 'relu' \n", - "model.params['dropout_rate'] = 0.9\n", - "model.params['optimizer'] = 'adadelta'\n", - "model.guess_and_fill_missing_params()\n", - "model.build()\n", - "model.compile()\n", - "model.backend.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])\n", - "model.load_embedding_matrix(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "pred_x, pred_y = test_pack_processed.unpack()\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=pred_x, y=pred_y, batch_size=len(pred_y))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num batches: 102\n" - ] - } - ], - "source": [ - "train_generator = mz.DataGenerator(\n", - " train_pack_processed,\n", - " mode='pair',\n", - " num_dup=2,\n", - " num_neg=1,\n", - " batch_size=20\n", - ")\n", - "print('num batches:', len(train_generator))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/30\n", - "102/102 [==============================] - 12s 113ms/step - loss: 0.9915\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 2/30\n", - "102/102 [==============================] - 16s 153ms/step - loss: 0.9609\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6414052424694674 - normalized_discounted_cumulative_gain@5(0.0): 0.6896110630787813 - mean_average_precision(0.0): 0.655464619746667\n", - "Epoch 3/30\n", - "102/102 [==============================] - 15s 147ms/step - loss: 0.9213\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.609449432076622 - normalized_discounted_cumulative_gain@5(0.0): 0.6535863921295467 - mean_average_precision(0.0): 0.6256401326730503\n", - "Epoch 4/30\n", - "102/102 [==============================] - 14s 139ms/step - loss: 0.8644\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5668504718763049 - normalized_discounted_cumulative_gain@5(0.0): 0.6184823173536319 - mean_average_precision(0.0): 0.5887412898235421\n", - "Epoch 5/30\n", - "102/102 [==============================] - 14s 140ms/step - loss: 0.8046\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5665334463767309 - normalized_discounted_cumulative_gain@5(0.0): 0.6176058113896864 - mean_average_precision(0.0): 0.5852152847278305\n", - "Epoch 6/30\n", - "102/102 [==============================] - 15s 148ms/step - loss: 0.8133\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5653296421157549 - normalized_discounted_cumulative_gain@5(0.0): 0.6098561773567537 - mean_average_precision(0.0): 0.5787384301794942\n", - "Epoch 7/30\n", - "102/102 [==============================] - 15s 144ms/step - loss: 0.7223\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5115731066519837 - normalized_discounted_cumulative_gain@5(0.0): 0.5696043849325363 - mean_average_precision(0.0): 0.5309471148833632\n", - "Epoch 8/30\n", - "102/102 [==============================] - 13s 129ms/step - loss: 0.7452\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5654535528839739 - normalized_discounted_cumulative_gain@5(0.0): 0.6150188550188977 - mean_average_precision(0.0): 0.5855908181807582\n", - "Epoch 9/30\n", - "102/102 [==============================] - 15s 149ms/step - loss: 0.6732\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.537504491728362 - normalized_discounted_cumulative_gain@5(0.0): 0.5888879094450309 - mean_average_precision(0.0): 0.556922967842061\n", - "Epoch 10/30\n", - "102/102 [==============================] - 15s 146ms/step - loss: 0.6431\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5294134618498313 - normalized_discounted_cumulative_gain@5(0.0): 0.5900024900110544 - mean_average_precision(0.0): 0.5542640985018126\n", - "Epoch 11/30\n", - "102/102 [==============================] - 14s 134ms/step - loss: 0.5859\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5520633189989336 - normalized_discounted_cumulative_gain@5(0.0): 0.6108468663800319 - mean_average_precision(0.0): 0.5791519476377355\n", - "Epoch 12/30\n", - "102/102 [==============================] - 15s 146ms/step - loss: 0.5602\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.534624128426663 - normalized_discounted_cumulative_gain@5(0.0): 0.5889541538361915 - mean_average_precision(0.0): 0.551507001163675\n", - "Epoch 13/30\n", - "102/102 [==============================] - 15s 144ms/step - loss: 0.5450\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5627233027710186 - normalized_discounted_cumulative_gain@5(0.0): 0.6108236823978452 - mean_average_precision(0.0): 0.584069096207155\n", - "Epoch 14/30\n", - "102/102 [==============================] - 15s 144ms/step - loss: 0.5581\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5513529195409487 - normalized_discounted_cumulative_gain@5(0.0): 0.5999350241763287 - mean_average_precision(0.0): 0.568106916524638\n", - "Epoch 15/30\n", - "102/102 [==============================] - 16s 156ms/step - loss: 0.4980\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5546799339708178 - normalized_discounted_cumulative_gain@5(0.0): 0.6152275605059057 - mean_average_precision(0.0): 0.5795762526981569\n", - "Epoch 16/30\n", - "102/102 [==============================] - 14s 141ms/step - loss: 0.5071\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5600349988760164 - normalized_discounted_cumulative_gain@5(0.0): 0.6072060773562872 - mean_average_precision(0.0): 0.5774453145256668\n", - "Epoch 17/30\n", - "102/102 [==============================] - 14s 140ms/step - loss: 0.4518\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.554905029677223 - normalized_discounted_cumulative_gain@5(0.0): 0.6021378901796073 - mean_average_precision(0.0): 0.5722005742097239\n", - "Epoch 18/30\n", - "102/102 [==============================] - 15s 145ms/step - loss: 0.4292\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5538251855689398 - normalized_discounted_cumulative_gain@5(0.0): 0.6006882253891397 - mean_average_precision(0.0): 0.5649684864479169\n", - "Epoch 19/30\n", - "102/102 [==============================] - 12s 116ms/step - loss: 0.4222\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5502126537672055 - normalized_discounted_cumulative_gain@5(0.0): 0.5933742887299561 - mean_average_precision(0.0): 0.5631647115191418\n", - "Epoch 20/30\n", - "102/102 [==============================] - 14s 142ms/step - loss: 0.3871\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.545929755381746 - normalized_discounted_cumulative_gain@5(0.0): 0.5965961908898312 - mean_average_precision(0.0): 0.5620997683843287\n", - "Epoch 21/30\n", - "102/102 [==============================] - 15s 145ms/step - loss: 0.3485\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.545785357053379 - normalized_discounted_cumulative_gain@5(0.0): 0.6002958901867365 - mean_average_precision(0.0): 0.5651984875273516\n", - "Epoch 22/30\n", - "102/102 [==============================] - 15s 152ms/step - loss: 0.3665\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.554029843099671 - normalized_discounted_cumulative_gain@5(0.0): 0.6048247555957358 - mean_average_precision(0.0): 0.5708237928362012\n", - "Epoch 23/30\n", - "102/102 [==============================] - 13s 128ms/step - loss: 0.3638\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5491694007852175 - normalized_discounted_cumulative_gain@5(0.0): 0.6068284143675057 - mean_average_precision(0.0): 0.568656261259232\n", - "Epoch 24/30\n", - "102/102 [==============================] - 13s 132ms/step - loss: 0.3220\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5501388396528246 - normalized_discounted_cumulative_gain@5(0.0): 0.6082083078502355 - mean_average_precision(0.0): 0.5698885871168076\n", - "Epoch 25/30\n", - "102/102 [==============================] - 14s 134ms/step - loss: 0.3111\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5529613793276678 - normalized_discounted_cumulative_gain@5(0.0): 0.6087770320216022 - mean_average_precision(0.0): 0.5719623784893997\n", - "Epoch 26/30\n", - "102/102 [==============================] - 15s 143ms/step - loss: 0.2875\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5535491988838098 - normalized_discounted_cumulative_gain@5(0.0): 0.607511521411928 - mean_average_precision(0.0): 0.562998351131385\n", - "Epoch 27/30\n", - "102/102 [==============================] - 14s 133ms/step - loss: 0.2849\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5469268899993501 - normalized_discounted_cumulative_gain@5(0.0): 0.6037117043525182 - mean_average_precision(0.0): 0.5606121264001305\n", - "Epoch 28/30\n", - "102/102 [==============================] - 14s 140ms/step - loss: 0.2623\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5550211003938021 - normalized_discounted_cumulative_gain@5(0.0): 0.6047800527522788 - mean_average_precision(0.0): 0.5628610719197386\n", - "Epoch 29/30\n", - "102/102 [==============================] - 14s 141ms/step - loss: 0.2663\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5457115429389982 - normalized_discounted_cumulative_gain@5(0.0): 0.6059713781830973 - mean_average_precision(0.0): 0.5609192818766827\n", - "Epoch 30/30\n", - "102/102 [==============================] - 15s 145ms/step - loss: 0.2661\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5475428253197103 - normalized_discounted_cumulative_gain@5(0.0): 0.610957175825584 - mean_average_precision(0.0): 0.5696311917780938\n" - ] - } - ], - "source": [ - "history = model.fit_generator(train_generator, epochs=30, callbacks=[evaluate], workers=30, use_multiprocessing=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/arcii.ipynb b/tutorials/wikiqa/arcii.ipynb deleted file mode 100644 index 678cf822..00000000 --- a/tutorials/wikiqa/arcii.ipynb +++ /dev/null @@ -1,431 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10, \n", - " fixed_length_right=100, \n", - " remove_stop_words=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 9365.13it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:03<00:00, 5163.33it/s]\n", - "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 848689.58it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 151196.34it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 89439.89it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 706219.56it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 736492.25it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 404432/404432 [00:00<00:00, 3032613.85it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 9550.46it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:03<00:00, 5185.71it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 144261.80it/s]\n", - 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"Processing text_right with transform: 100%|██████████| 1115/1115 [00:00<00:00, 129446.66it/s]\n", - "Processing length_left with len: 100%|██████████| 122/122 [00:00<00:00, 185736.87it/s]\n", - "Processing length_right with len: 100%|██████████| 1115/1115 [00:00<00:00, 583778.42it/s]\n", - "Processing text_left with transform: 100%|██████████| 122/122 [00:00<00:00, 90215.99it/s]\n", - "Processing text_right with transform: 100%|██████████| 1115/1115 [00:00<00:00, 93243.92it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 237/237 [00:00<00:00, 8866.58it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2300/2300 [00:00<00:00, 5131.75it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 147786.31it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 119176.36it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 111582.90it/s]\n", - "Processing length_left with len: 100%|██████████| 237/237 [00:00<00:00, 354132.54it/s]\n", - "Processing length_right with len: 100%|██████████| 2300/2300 [00:00<00:00, 543579.15it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 103730.57it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 89680.20it/s]\n" - ] - } - ], - "source": [ - "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n", - "dev_pack_processed = preprocessor.transform(dev_pack_raw)\n", - "test_pack_processed = preprocessor.transform(test_pack_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'filter_unit': ,\n", - " 'vocab_unit': ,\n", - " 'vocab_size': 16674,\n", - " 'embedding_input_dim': 16674,\n", - " 'input_shapes': [(10,), (100,)]}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessor.context" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model_class \n", - "input_shapes [(10,), (100,)]\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_embedding True\n", - "embedding_input_dim 16674\n", - "embedding_output_dim 100\n", - "embedding_trainable True\n", - "num_blocks 2\n", - "kernel_1d_count 32\n", - "kernel_1d_size 3\n", - "kernel_2d_count [64, 64]\n", - "kernel_2d_size [3, 3]\n", - "activation relu\n", - "pool_2d_size [[3, 3], [3, 3]]\n", - "padding same\n", - "dropout_rate 0.0\n" - ] - } - ], - "source": [ - "model = mz.models.ArcII()\n", - "\n", - "# load `input_shapes` and `embedding_input_dim` (vocab_size)\n", - "model.params.update(preprocessor.context)\n", - "\n", - "model.params['task'] = ranking_task\n", - "model.params['embedding_output_dim'] = 100\n", - "model.params['embedding_trainable'] = True\n", - "model.params['num_blocks'] = 2\n", - "model.params['kernel_1d_count'] = 32\n", - "model.params['kernel_1d_size'] = 3\n", - "model.params['kernel_2d_count'] = [64, 64]\n", - "model.params['kernel_2d_size'] = [3, 3]\n", - "model.params['pool_2d_size'] = [[3, 3], [3, 3]]\n", - "model.params['optimizer'] = 'adam'\n", - "\n", - "model.build()\n", - "model.compile()\n", - "\n", - "print(model.params)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 10) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 100) 0 \n", - "__________________________________________________________________________________________________\n", - "embedding (Embedding) multiple 1667400 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv1d_1 (Conv1D) (None, 10, 32) 9632 embedding[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv1d_2 (Conv1D) (None, 100, 32) 9632 embedding[1][0] \n", - "__________________________________________________________________________________________________\n", - "matching_layer_1 (MatchingLayer (None, 10, 100, 32) 0 conv1d_1[0][0] \n", - " conv1d_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_1 (Conv2D) (None, 10, 100, 64) 18496 matching_layer_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "max_pooling2d_1 (MaxPooling2D) (None, 3, 33, 64) 0 conv2d_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_2 (Conv2D) (None, 3, 33, 64) 36928 max_pooling2d_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "max_pooling2d_2 (MaxPooling2D) (None, 1, 11, 64) 0 conv2d_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "flatten_1 (Flatten) (None, 704) 0 max_pooling2d_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_1 (Dropout) (None, 704) 0 flatten_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) (None, 1) 705 dropout_1[0][0] \n", - "==================================================================================================\n", - "Total params: 1,742,793\n", - "Trainable params: 1,742,793\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model.backend.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "model.load_embedding_matrix(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "test_x, test_y = test_pack_processed[:].unpack()\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=test_x, y=test_y, batch_size=len(test_y))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num batches: 102\n" - ] - } - ], - "source": [ - "train_generator = mz.DataGenerator(\n", - " train_pack_processed,\n", - " mode='pair',\n", - " num_dup=2,\n", - " num_neg=1,\n", - " batch_size=20\n", - ")\n", - "print('num batches:', len(train_generator))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/30\n", - "102/102 [==============================] - 6s 61ms/step - loss: 0.6123\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6165843661519929 - normalized_discounted_cumulative_gain@5(0.0): 0.6639951229938149 - mean_average_precision(0.0): 0.6255013171310638\n", - "Epoch 2/30\n", - "102/102 [==============================] - 11s 107ms/step - loss: 0.3213\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5795022399236163 - normalized_discounted_cumulative_gain@5(0.0): 0.6400033764936961 - mean_average_precision(0.0): 0.5927358064245385\n", - "Epoch 3/30\n", - "102/102 [==============================] - 12s 116ms/step - loss: 0.1556\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5734366624818106 - normalized_discounted_cumulative_gain@5(0.0): 0.6320835504730066 - mean_average_precision(0.0): 0.5873376891991933\n", - "Epoch 4/30\n", - "102/102 [==============================] - 12s 121ms/step - loss: 0.0966\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6047140052395196 - normalized_discounted_cumulative_gain@5(0.0): 0.6646231653619664 - mean_average_precision(0.0): 0.6177779169838045\n", - "Epoch 5/30\n", - "102/102 [==============================] - 12s 117ms/step - loss: 0.0688\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5804830097430296 - normalized_discounted_cumulative_gain@5(0.0): 0.6397107091735946 - mean_average_precision(0.0): 0.5861104257070671\n", - "Epoch 6/30\n", - "102/102 [==============================] - 12s 119ms/step - loss: 0.0741\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5733720686699197 - normalized_discounted_cumulative_gain@5(0.0): 0.6297166660287926 - mean_average_precision(0.0): 0.5793064781802681\n", - "Epoch 7/30\n", - "102/102 [==============================] - 12s 118ms/step - loss: 0.0545\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5612000374349604 - normalized_discounted_cumulative_gain@5(0.0): 0.6275035028226839 - mean_average_precision(0.0): 0.5812912862211518\n", - "Epoch 8/30\n", - "102/102 [==============================] - 13s 129ms/step - loss: 0.0351\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5695536121652267 - normalized_discounted_cumulative_gain@5(0.0): 0.6230819298416224 - mean_average_precision(0.0): 0.5829102115308369\n", - "Epoch 9/30\n", - "102/102 [==============================] - 13s 127ms/step - loss: 0.0546\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5747202712592626 - normalized_discounted_cumulative_gain@5(0.0): 0.6283361484936623 - mean_average_precision(0.0): 0.5858063874997032\n", - "Epoch 10/30\n", - "102/102 [==============================] - 12s 120ms/step - loss: 0.0363\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5608760792872554 - normalized_discounted_cumulative_gain@5(0.0): 0.6165261187651955 - mean_average_precision(0.0): 0.5804023750891116\n", - "Epoch 11/30\n", - "102/102 [==============================] - 12s 121ms/step - loss: 0.0316\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5526488015162425 - normalized_discounted_cumulative_gain@5(0.0): 0.6190121151432394 - mean_average_precision(0.0): 0.5724829930234496\n", - "Epoch 12/30\n", - "102/102 [==============================] - 12s 122ms/step - loss: 0.0286\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5574638704067251 - normalized_discounted_cumulative_gain@5(0.0): 0.6221425949954883 - mean_average_precision(0.0): 0.5790691069305163\n", - "Epoch 13/30\n", - "102/102 [==============================] - 14s 135ms/step - loss: 0.0232\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5530705667805896 - normalized_discounted_cumulative_gain@5(0.0): 0.6045382570104455 - mean_average_precision(0.0): 0.560135777928777\n", - "Epoch 14/30\n", - "102/102 [==============================] - 12s 122ms/step - loss: 0.0232\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5551975290816926 - normalized_discounted_cumulative_gain@5(0.0): 0.6199269060404192 - mean_average_precision(0.0): 0.5695024812501234\n", - "Epoch 15/30\n", - "102/102 [==============================] - 12s 120ms/step - loss: 0.0186\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5798942973253254 - normalized_discounted_cumulative_gain@5(0.0): 0.6326798983491299 - mean_average_precision(0.0): 0.5914300748272545\n", - "Epoch 16/30\n", - "102/102 [==============================] - 12s 120ms/step - loss: 0.0224\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5549313451763698 - normalized_discounted_cumulative_gain@5(0.0): 0.609066461479371 - mean_average_precision(0.0): 0.5696612552826874\n", - "Epoch 17/30\n", - "102/102 [==============================] - 14s 134ms/step - loss: 0.0320\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5362590661756249 - normalized_discounted_cumulative_gain@5(0.0): 0.5960587553325881 - mean_average_precision(0.0): 0.5529867856626977\n", - "Epoch 18/30\n", - "102/102 [==============================] - 13s 123ms/step - loss: 0.0231\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5709133081694716 - normalized_discounted_cumulative_gain@5(0.0): 0.6294306655133045 - mean_average_precision(0.0): 0.5833828714158493\n", - "Epoch 19/30\n", - "102/102 [==============================] - 12s 120ms/step - loss: 0.0210\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5408055010433294 - normalized_discounted_cumulative_gain@5(0.0): 0.6188813340014495 - mean_average_precision(0.0): 0.5639203433881982\n", - "Epoch 20/30\n", - "102/102 [==============================] - 11s 111ms/step - loss: 0.0099\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5857209979914645 - normalized_discounted_cumulative_gain@5(0.0): 0.6369323440919344 - mean_average_precision(0.0): 0.59214541005708\n", - "Epoch 21/30\n", - "102/102 [==============================] - 11s 108ms/step - loss: 0.0314\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5621754667046626 - normalized_discounted_cumulative_gain@5(0.0): 0.6250852206304124 - mean_average_precision(0.0): 0.5746827039867791\n", - "Epoch 22/30\n", - "102/102 [==============================] - 11s 108ms/step - loss: 0.0346\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5245291889962129 - normalized_discounted_cumulative_gain@5(0.0): 0.5957453960380752 - mean_average_precision(0.0): 0.5563949155969331\n", - "Epoch 23/30\n", - "102/102 [==============================] - 11s 104ms/step - loss: 0.0180\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5649023738386006 - normalized_discounted_cumulative_gain@5(0.0): 0.6281559244518438 - mean_average_precision(0.0): 0.5862423847100566\n", - "Epoch 24/30\n", - "102/102 [==============================] - 11s 110ms/step - loss: 0.0326\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5595631369578311 - normalized_discounted_cumulative_gain@5(0.0): 0.6216538205632761 - mean_average_precision(0.0): 0.5770444731020851\n", - "Epoch 25/30\n", - "102/102 [==============================] - 11s 107ms/step - loss: 0.0212\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5611397782325962 - normalized_discounted_cumulative_gain@5(0.0): 0.6275063453800273 - mean_average_precision(0.0): 0.5817784786583774\n", - "Epoch 26/30\n", - "102/102 [==============================] - 11s 110ms/step - loss: 0.0297\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5332907973060383 - normalized_discounted_cumulative_gain@5(0.0): 0.5988259655236242 - mean_average_precision(0.0): 0.5647126123155829\n", - "Epoch 27/30\n", - "102/102 [==============================] - 11s 108ms/step - loss: 0.0244\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5448159675702459 - normalized_discounted_cumulative_gain@5(0.0): 0.5945159262523649 - mean_average_precision(0.0): 0.5558586926930239\n", - "Epoch 28/30\n", - "102/102 [==============================] - 11s 108ms/step - loss: 0.0157\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.570165361029481 - normalized_discounted_cumulative_gain@5(0.0): 0.6316999513916105 - mean_average_precision(0.0): 0.5947381662168211\n", - "Epoch 29/30\n", - "102/102 [==============================] - 11s 107ms/step - loss: 0.0360\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.586639186111628 - normalized_discounted_cumulative_gain@5(0.0): 0.6352015410005084 - mean_average_precision(0.0): 0.596046073559104\n", - "Epoch 30/30\n", - "102/102 [==============================] - 11s 108ms/step - loss: 0.0149\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.57738099248289 - normalized_discounted_cumulative_gain@5(0.0): 0.63122082754378 - mean_average_precision(0.0): 0.5877516720454762\n" - ] - } - ], - "source": [ - "history = model.fit_generator(train_generator, \n", - " epochs=30, \n", - " callbacks=[evaluate], \n", - " workers=30, \n", - " use_multiprocessing=True)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Use this function to update the README.md with a better set of parameters.\n", - "Make sure you delete the correct section of the README.md before calling this function." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "append_params_to_readme(model)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/cdssm.ipynb b/tutorials/wikiqa/cdssm.ipynb deleted file mode 100644 index 84f17bb1..00000000 --- a/tutorials/wikiqa/cdssm.ipynb +++ /dev/null @@ -1,281 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 2118/2118 [00:00<00:00, 5365.33it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 18841/18841 [00:05<00:00, 3205.80it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 310569.71it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 349915.35it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 1614998/1614998 [00:00<00:00, 3031577.24it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 8384.04it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 18841/18841 [00:04<00:00, 3939.48it/s]\n", - "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 125562.34it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 96621.02it/s]\n", - "Processing text_left with chain_transform of NgramLetter => WordHashing: 100%|██████████| 2118/2118 [00:07<00:00, 269.84it/s]\n", - "Processing text_right with chain_transform of NgramLetter => WordHashing: 100%|██████████| 18841/18841 [01:27<00:00, 216.37it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 122/122 [00:00<00:00, 7746.89it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 1115/1115 [00:14<00:00, 77.28it/s] \n", - "Processing text_left with transform: 100%|██████████| 122/122 [00:00<00:00, 63447.62it/s]\n", - "Processing text_right with transform: 100%|██████████| 1115/1115 [00:00<00:00, 88946.88it/s]\n", - "Processing text_left with chain_transform of NgramLetter => WordHashing: 100%|██████████| 122/122 [00:00<00:00, 273.77it/s]\n", - "Processing text_right with chain_transform of NgramLetter => WordHashing: 100%|██████████| 1115/1115 [00:04<00:00, 226.13it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 237/237 [00:00<00:00, 8707.90it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2300/2300 [00:01<00:00, 2237.22it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 101299.30it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 97484.78it/s]\n", - "Processing text_left with chain_transform of NgramLetter => WordHashing: 100%|██████████| 237/237 [00:00<00:00, 269.12it/s]\n", - "Processing text_right with chain_transform of NgramLetter => WordHashing: 100%|██████████| 2300/2300 [00:10<00:00, 212.35it/s]\n" - ] - } - ], - "source": [ - "preprocessor = mz.preprocessors.CDSSMPreprocessor(fixed_length_left=10, fixed_length_right=10)\n", - "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n", - "valid_pack_processed = preprocessor.transform(dev_pack_raw)\n", - "test_pack_processed = preprocessor.transform(test_pack_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 10, 9644) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 10, 9644) 0 \n", - "__________________________________________________________________________________________________\n", - "conv1d_1 (Conv1D) (None, 10, 64) 1851712 text_left[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv1d_2 (Conv1D) (None, 10, 64) 1851712 text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_1 (Dropout) (None, 10, 64) 0 conv1d_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_2 (Dropout) (None, 10, 64) 0 conv1d_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "global_max_pooling1d_1 (GlobalM (None, 64) 0 dropout_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "global_max_pooling1d_2 (GlobalM (None, 64) 0 dropout_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) (None, 64) 4160 global_max_pooling1d_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_3 (Dense) (None, 64) 4160 global_max_pooling1d_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_2 (Dense) (None, 64) 4160 dense_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_4 (Dense) (None, 64) 4160 dense_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "dot_1 (Dot) (None, 1) 0 dense_2[0][0] \n", - " dense_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_5 (Dense) (None, 1) 2 dot_1[0][0] \n", - "==================================================================================================\n", - "Total params: 3,720,066\n", - "Trainable params: 3,720,066\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model = mz.models.CDSSM()\n", - "model.params['input_shapes'] = preprocessor.context['input_shapes']\n", - "model.params['task'] = ranking_task\n", - "model.params['filters'] = 64\n", - "model.params['kernel_size'] = 3\n", - "model.params['strides'] = 1\n", - "model.params['padding'] = 'same'\n", - "model.params['conv_activation_func'] = 'tanh'\n", - "model.params['w_initializer'] = 'glorot_normal'\n", - "model.params['b_initializer'] = 'zeros'\n", - "model.params['mlp_num_layers'] = 1\n", - "model.params['mlp_num_units'] = 64\n", - "model.params['mlp_num_fan_out'] = 64\n", - "model.params['mlp_activation_func'] = 'tanh'\n", - "model.params['dropout_rate'] = 0.8\n", - "model.params['optimizer'] = 'adadelta'\n", - "model.guess_and_fill_missing_params()\n", - "model.build()\n", - "model.compile()\n", - "model.backend.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num batches: 102\n" - ] - } - ], - "source": [ - "pred_x, pred_y = test_pack_processed[:].unpack()\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=pred_x, y=pred_y, batch_size=len(pred_x))\n", - "train_generator = mz.DataGenerator(\n", - " train_pack_processed,\n", - " mode='pair',\n", - " num_dup=2,\n", - " num_neg=1,\n", - " batch_size=20\n", - ")\n", - "print('num batches:', len(train_generator))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n", - "102/102 [==============================] - 65s 635ms/step - loss: 0.8021\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.42304382290227854 - normalized_discounted_cumulative_gain@5(0.0): 0.49915948768338086 - mean_average_precision(0.0): 0.46037758752542035\n", - "Epoch 2/20\n", - "102/102 [==============================] - 45s 445ms/step - loss: 0.5966\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.43781763271520285 - normalized_discounted_cumulative_gain@5(0.0): 0.520097599085372 - mean_average_precision(0.0): 0.4762598411822459\n", - "Epoch 3/20\n", - "102/102 [==============================] - 46s 447ms/step - loss: 0.4992\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44923101748788413 - normalized_discounted_cumulative_gain@5(0.0): 0.5136672947113214 - mean_average_precision(0.0): 0.4803110559647868\n", - "Epoch 4/20\n", - "102/102 [==============================] - 46s 446ms/step - loss: 0.4143\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.467714954371615 - normalized_discounted_cumulative_gain@5(0.0): 0.5353130653753986 - mean_average_precision(0.0): 0.5017560318255102\n", - "Epoch 5/20\n", - "102/102 [==============================] - 46s 451ms/step - loss: 0.3489\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4706511291875292 - normalized_discounted_cumulative_gain@5(0.0): 0.5275832328072992 - mean_average_precision(0.0): 0.5026243479583462\n", - "Epoch 6/20\n", - "102/102 [==============================] - 45s 443ms/step - loss: 0.3231\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4641151831570107 - normalized_discounted_cumulative_gain@5(0.0): 0.5219564667466021 - mean_average_precision(0.0): 0.4934049132027672\n", - "Epoch 7/20\n", - "102/102 [==============================] - 44s 433ms/step - loss: 0.2695\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4760514687477512 - normalized_discounted_cumulative_gain@5(0.0): 0.5285019348702702 - mean_average_precision(0.0): 0.49994736585416333\n", - "Epoch 8/20\n", - "102/102 [==============================] - 47s 457ms/step - loss: 0.2331\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4661781341235399 - normalized_discounted_cumulative_gain@5(0.0): 0.5260071867435453 - mean_average_precision(0.0): 0.4922605321622356\n", - "Epoch 9/20\n", - "102/102 [==============================] - 46s 453ms/step - loss: 0.1942\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4645719968337067 - normalized_discounted_cumulative_gain@5(0.0): 0.5238558790194195 - mean_average_precision(0.0): 0.48851468090847294\n", - "Epoch 10/20\n", - "102/102 [==============================] - 45s 444ms/step - loss: 0.1734\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4600910137969285 - normalized_discounted_cumulative_gain@5(0.0): 0.5320923473092672 - mean_average_precision(0.0): 0.48703092961044614\n", - "Epoch 11/20\n", - "102/102 [==============================] - 47s 464ms/step - loss: 0.1644\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45786306386326225 - normalized_discounted_cumulative_gain@5(0.0): 0.5246949873542252 - mean_average_precision(0.0): 0.48502089087514016\n", - "Epoch 12/20\n", - "102/102 [==============================] - 45s 443ms/step - loss: 0.1560\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4567332855642369 - normalized_discounted_cumulative_gain@5(0.0): 0.528074374789356 - mean_average_precision(0.0): 0.4905494464640722\n", - "Epoch 13/20\n", - "102/102 [==============================] - 45s 440ms/step - loss: 0.1365\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4698836510431016 - normalized_discounted_cumulative_gain@5(0.0): 0.5317255666034969 - mean_average_precision(0.0): 0.49152222966181813\n", - "Epoch 14/20\n", - "102/102 [==============================] - 45s 437ms/step - loss: 0.1263\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.46841048236088156 - normalized_discounted_cumulative_gain@5(0.0): 0.5197949838102164 - mean_average_precision(0.0): 0.4887341126171474\n", - "Epoch 15/20\n", - "102/102 [==============================] - 46s 449ms/step - loss: 0.1208\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4591952265806063 - normalized_discounted_cumulative_gain@5(0.0): 0.5306329604843507 - mean_average_precision(0.0): 0.4956899590808506\n", - "Epoch 16/20\n", - "102/102 [==============================] - 44s 430ms/step - loss: 0.0977\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4698790408918565 - normalized_discounted_cumulative_gain@5(0.0): 0.5355447042513717 - mean_average_precision(0.0): 0.5005823464725863\n", - "Epoch 17/20\n", - "102/102 [==============================] - 44s 436ms/step - loss: 0.0975\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4699380823064665 - normalized_discounted_cumulative_gain@5(0.0): 0.5335843828018585 - mean_average_precision(0.0): 0.4945873841691485\n", - "Epoch 18/20\n", - "102/102 [==============================] - 44s 431ms/step - loss: 0.1070\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4558123512520484 - normalized_discounted_cumulative_gain@5(0.0): 0.5280824209271964 - mean_average_precision(0.0): 0.49009730599920476\n", - "Epoch 19/20\n", - "102/102 [==============================] - 45s 446ms/step - loss: 0.0846\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.47468488947984894 - normalized_discounted_cumulative_gain@5(0.0): 0.5462940161373581 - mean_average_precision(0.0): 0.5050693971440435\n", - "Epoch 20/20\n", - "102/102 [==============================] - 43s 425ms/step - loss: 0.0833\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4571219999869289 - normalized_discounted_cumulative_gain@5(0.0): 0.527098973778668 - mean_average_precision(0.0): 0.4884211445807594\n" - ] - } - ], - "source": [ - "history = model.fit_generator(train_generator, epochs=20, callbacks=[evaluate], workers=1, use_multiprocessing=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/conv_knrm.ipynb b/tutorials/wikiqa/conv_knrm.ipynb deleted file mode 100644 index cd1e5922..00000000 --- a/tutorials/wikiqa/conv_knrm.ipynb +++ /dev/null @@ -1,312 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n", - "/home/fanyixing/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n", - "/home/fanyixing/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 3724.89it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:07<00:00, 2470.05it/s]\n", - "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 445941.69it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 70474.81it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 47716.48it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 342940.70it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 379138.05it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 404415/404415 [00:00<00:00, 1847244.28it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 4067.10it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:07<00:00, 2516.30it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 67851.85it/s]\n", - "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 89496.74it/s]\n", - 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"Processing length_right with len: 100%|██████████| 2300/2300 [00:00<00:00, 361686.38it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 36193.34it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 37573.42it/s]\n" - ] - } - ], - "source": [ - "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10, fixed_length_right=40, remove_stop_words=False)\n", - "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n", - "dev_pack_processed = preprocessor.transform(dev_pack_raw)\n", - "test_pack_processed = preprocessor.transform(test_pack_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'filter_unit': ,\n", - " 'vocab_unit': ,\n", - " 'vocab_size': 16674,\n", - " 'embedding_input_dim': 16674,\n", - " 'input_shapes': [(10,), (40,)]}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessor.context" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "model = mz.models.ConvKNRM()\n", - "model.params.update(preprocessor.context)\n", - "model.params['task'] = ranking_task\n", - "model.params['embedding_output_dim'] = glove_embedding.output_dim\n", - "model.params['embedding_trainable'] = True\n", - "model.params['filters'] = 128 \n", - "model.params['conv_activation_func'] = 'tanh' \n", - "model.params['max_ngram'] = 3\n", - "model.params['use_crossmatch'] = True \n", - "model.params['kernel_num'] = 11\n", - "model.params['sigma'] = 0.1\n", - "model.params['exact_sigma'] = 0.001\n", - "model.params['optimizer'] = 'adadelta'\n", - "model.build()\n", - "model.compile()\n", - "#model.backend.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])\n", - "model.load_embedding_matrix(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "pred_x, pred_y = test_pack_processed.unpack()\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=pred_x, y=pred_y, batch_size=len(pred_y))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num batches: 255\n" - ] - } - ], - "source": [ - "train_generator = mz.DataGenerator(\n", - " train_pack_processed,\n", - " mode='pair',\n", - " num_dup=5,\n", - " num_neg=1,\n", - " batch_size=20\n", - ")\n", - "print('num batches:', len(train_generator))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/30\n", - "255/255 [==============================] - 39s 151ms/step - loss: 0.4776\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6046833551308459 - normalized_discounted_cumulative_gain@5(0.0): 0.6665959185697871 - mean_average_precision(0.0): 0.6129317182476187\n", - "Epoch 2/30\n", - "255/255 [==============================] - 42s 165ms/step - loss: 0.1211\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6152022692439065 - normalized_discounted_cumulative_gain@5(0.0): 0.6744661220937255 - mean_average_precision(0.0): 0.6343442709387471\n", - "Epoch 3/30\n", - "255/255 [==============================] - 43s 167ms/step - loss: 0.0620\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5548312155628421 - normalized_discounted_cumulative_gain@5(0.0): 0.6213976803862951 - mean_average_precision(0.0): 0.5710894912635418\n", - "Epoch 4/30\n", - "255/255 [==============================] - 43s 168ms/step - loss: 0.0417\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5954135056241098 - normalized_discounted_cumulative_gain@5(0.0): 0.6578139885107471 - mean_average_precision(0.0): 0.6111818707546555\n", - "Epoch 5/30\n", - "255/255 [==============================] - 42s 165ms/step - loss: 0.0356\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6028110339356899 - normalized_discounted_cumulative_gain@5(0.0): 0.6587081531783964 - mean_average_precision(0.0): 0.609672147851597\n", - "Epoch 6/30\n", - "255/255 [==============================] - 43s 167ms/step - loss: 0.0168\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6162374354841582 - normalized_discounted_cumulative_gain@5(0.0): 0.6730370811514368 - mean_average_precision(0.0): 0.6224984341793723\n", - "Epoch 7/30\n", - "255/255 [==============================] - 44s 171ms/step - loss: 0.0173\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6140235133098497 - normalized_discounted_cumulative_gain@5(0.0): 0.6702416124777638 - mean_average_precision(0.0): 0.6291591558219198\n", - "Epoch 8/30\n", - "255/255 [==============================] - 42s 167ms/step - loss: 0.0105\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6170925436547549 - normalized_discounted_cumulative_gain@5(0.0): 0.6721917042350951 - mean_average_precision(0.0): 0.6319963125925817\n", - "Epoch 9/30\n", - "255/255 [==============================] - 44s 173ms/step - loss: 0.0093\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6128277115636099 - normalized_discounted_cumulative_gain@5(0.0): 0.6723112242287127 - mean_average_precision(0.0): 0.6202358378889221\n", - "Epoch 10/30\n", - "255/255 [==============================] - 44s 172ms/step - loss: 0.0074\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6014563078514086 - normalized_discounted_cumulative_gain@5(0.0): 0.6534368691098246 - mean_average_precision(0.0): 0.6111100251131897\n", - "Epoch 11/30\n", - "255/255 [==============================] - 44s 174ms/step - loss: 0.0040\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6227886270617885 - normalized_discounted_cumulative_gain@5(0.0): 0.671961802050252 - mean_average_precision(0.0): 0.6294700258349492\n", - "Epoch 12/30\n", - "255/255 [==============================] - 43s 170ms/step - loss: 0.0049\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6090539823114218 - normalized_discounted_cumulative_gain@5(0.0): 0.6657954120017808 - mean_average_precision(0.0): 0.6204152162428167\n", - "Epoch 13/30\n", - "255/255 [==============================] - 44s 172ms/step - loss: 0.0045\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.605268576648384 - normalized_discounted_cumulative_gain@5(0.0): 0.6743645292160183 - mean_average_precision(0.0): 0.6225096268464414\n", - "Epoch 14/30\n", - "255/255 [==============================] - 44s 173ms/step - loss: 0.0056\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6094653731796733 - normalized_discounted_cumulative_gain@5(0.0): 0.6626866921233837 - mean_average_precision(0.0): 0.6167465211769009\n", - "Epoch 15/30\n", - "255/255 [==============================] - 44s 174ms/step - loss: 0.0020\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.636181345906811 - normalized_discounted_cumulative_gain@5(0.0): 0.6839535736459206 - mean_average_precision(0.0): 0.6396845610127441\n", - "Epoch 16/30\n", - "255/255 [==============================] - 44s 171ms/step - loss: 0.0020\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6242068770913558 - normalized_discounted_cumulative_gain@5(0.0): 0.6742098356127264 - mean_average_precision(0.0): 0.632390750330996\n", - "Epoch 17/30\n", - "255/255 [==============================] - 44s 173ms/step - loss: 9.0911e-04\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305977929317165 - normalized_discounted_cumulative_gain@5(0.0): 0.6760885220490657 - mean_average_precision(0.0): 0.6345498605597264\n", - "Epoch 18/30\n", - "255/255 [==============================] - 44s 172ms/step - loss: 5.5907e-04\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6302281364338485 - normalized_discounted_cumulative_gain@5(0.0): 0.6801281668512741 - mean_average_precision(0.0): 0.6365815560435815\n", - "Epoch 19/30\n", - "255/255 [==============================] - 44s 174ms/step - loss: 0.0011\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6161750510905308 - normalized_discounted_cumulative_gain@5(0.0): 0.6828588425017452 - mean_average_precision(0.0): 0.6399974866560868\n", - "Epoch 20/30\n", - "255/255 [==============================] - 44s 173ms/step - loss: 5.9813e-04\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6192304771389455 - normalized_discounted_cumulative_gain@5(0.0): 0.6835492157788869 - mean_average_precision(0.0): 0.6339323846467368\n", - "Epoch 21/30\n", - "255/255 [==============================] - 43s 170ms/step - loss: 9.7819e-04\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6306940079499387 - normalized_discounted_cumulative_gain@5(0.0): 0.6811198627999581 - mean_average_precision(0.0): 0.6436540061956451\n", - "Epoch 22/30\n", - "255/255 [==============================] - 44s 171ms/step - loss: 7.8472e-04\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6246823923728032 - normalized_discounted_cumulative_gain@5(0.0): 0.6828731850846279 - mean_average_precision(0.0): 0.6295758423592317\n", - "Epoch 27/30\n", - "255/255 [==============================] - 44s 172ms/step - loss: 3.4582e-04\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6202967663352117 - normalized_discounted_cumulative_gain@5(0.0): 0.6718788743837789 - mean_average_precision(0.0): 0.6227787543285545\n", - "Epoch 28/30\n", - "255/255 [==============================] - 45s 177ms/step - loss: 0.0011\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6221372690184138 - normalized_discounted_cumulative_gain@5(0.0): 0.6775309597373549 - mean_average_precision(0.0): 0.6240701328331634\n", - "Epoch 29/30\n", - "255/255 [==============================] - 45s 177ms/step - loss: 9.9084e-04\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6193374900161305 - normalized_discounted_cumulative_gain@5(0.0): 0.6749664782289659 - mean_average_precision(0.0): 0.6316795270723043\n", - "Epoch 30/30\n", - "255/255 [==============================] - 45s 177ms/step - loss: 4.5966e-04\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6325513939822278 - normalized_discounted_cumulative_gain@5(0.0): 0.6801122164684699 - mean_average_precision(0.0): 0.6351265617879543\n" - ] - } - ], - "source": [ - "history = model.fit_generator(train_generator, epochs=30, callbacks=[evaluate], workers=30, use_multiprocessing=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/drmm.ipynb b/tutorials/wikiqa/drmm.ipynb deleted file mode 100644 index 599d9d70..00000000 --- a/tutorials/wikiqa/drmm.ipynb +++ /dev/null @@ -1,436 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n", - "/home/fanyixing/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n", - "/home/fanyixing/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 3718.40it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:07<00:00, 2381.54it/s]\n", - "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 444321.96it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 66270.86it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 42664.36it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 330095.71it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 373073.88it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 404415/404415 [00:00<00:00, 1847527.98it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 3771.43it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:07<00:00, 2367.53it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 69352.98it/s]\n", - 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"Processing length_left with len: 100%|██████████| 237/237 [00:00<00:00, 145883.48it/s]\n", - "Processing length_right with len: 100%|██████████| 2300/2300 [00:00<00:00, 372769.40it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 35687.87it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 33140.49it/s]\n" - ] - } - ], - "source": [ - "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10, fixed_length_right=100, remove_stop_words=False)\n", - "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n", - "dev_pack_processed = preprocessor.transform(dev_pack_raw)\n", - "test_pack_processed = preprocessor.transform(test_pack_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'filter_unit': ,\n", - " 'vocab_unit': ,\n", - " 'vocab_size': 16674,\n", - " 'embedding_input_dim': 16674,\n", - " 'input_shapes': [(10,), (100,)]}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessor.context" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "ranking_task = mz.tasks.Ranking(loss=mz.losses.RankCrossEntropyLoss(num_neg=10))\n", - "ranking_task.metrics = [\n", - " mz.metrics.NormalizedDiscountedCumulativeGain(k=3),\n", - " mz.metrics.NormalizedDiscountedCumulativeGain(k=5),\n", - " mz.metrics.MeanAveragePrecision()\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 10) 0 \n", - "__________________________________________________________________________________________________\n", - "embedding (Embedding) (None, 10, 300) 5002200 text_left[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) (None, 10, 1) 300 embedding[0][0] \n", - "__________________________________________________________________________________________________\n", - "match_histogram (InputLayer) (None, 10, 30) 0 \n", - "__________________________________________________________________________________________________\n", - "attention_mask (Lambda) (None, 10, 1) 0 dense_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_2 (Dense) (None, 10, 10) 310 match_histogram[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_probs (Lambda) (None, 10, 1) 0 attention_mask[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_3 (Dense) (None, 10, 1) 11 dense_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dot_1 (Dot) (None, 1, 1) 0 attention_probs[0][0] \n", - " dense_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "flatten_1 (Flatten) (None, 1) 0 dot_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_4 (Dense) (None, 1) 2 flatten_1[0][0] \n", - "==================================================================================================\n", - "Total params: 5,002,823\n", - "Trainable params: 5,002,823\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "bin_size = 30\n", - "model = mz.models.DRMM()\n", - "model.params.update(preprocessor.context)\n", - "model.params['input_shapes'] = [[10,], [10, bin_size,]]\n", - "model.params['task'] = ranking_task\n", - "model.params['mask_value'] = 0\n", - "model.params['embedding_output_dim'] = glove_embedding.output_dim\n", - "model.params['mlp_num_layers'] = 1\n", - "model.params['mlp_num_units'] = 10\n", - "model.params['mlp_num_fan_out'] = 1\n", - "model.params['mlp_activation_func'] = 'tanh'\n", - "model.params['optimizer'] = 'adadelta'\n", - "model.build()\n", - "model.compile()\n", - "model.backend.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])\n", - "# normalize the word embedding for fast histogram generating.\n", - "l2_norm = np.sqrt((embedding_matrix*embedding_matrix).sum(axis=1))\n", - "embedding_matrix = embedding_matrix / l2_norm[:, np.newaxis]\n", - "model.load_embedding_matrix(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "hist_callback = mz.data_generator.callbacks.Histogram(embedding_matrix, bin_size=30, hist_mode='LCH')" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "pred_generator = mz.DataGenerator(test_pack_processed, mode='point', callbacks=[hist_callback])\n", - "pred_x, pred_y = pred_generator[:]\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model, \n", - " x=pred_x, \n", - " y=pred_y, \n", - " once_every=1, \n", - " batch_size=len(pred_y),\n", - " model_save_path='./drmm_pretrained_model/'\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num batches: 255\n" - ] - } - ], - "source": [ - "train_generator = mz.DataGenerator(train_pack_processed, mode='pair', num_dup=5, num_neg=10, batch_size=20, \n", - " callbacks=[hist_callback])\n", - "print('num batches:', len(train_generator))\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/30\n", - "255/255 [==============================] - 29s 113ms/step - loss: 2.2520\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.565716344425806 - normalized_discounted_cumulative_gain@5(0.0): 0.6337418608669659 - mean_average_precision(0.0): 0.5867500331707677\n", - "Epoch 2/30\n", - "255/255 [==============================] - 44s 172ms/step - loss: 1.9129\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5985553932954132 - normalized_discounted_cumulative_gain@5(0.0): 0.6538053305162407 - mean_average_precision(0.0): 0.6119736749640002\n", - "Epoch 3/30\n", - "255/255 [==============================] - 44s 172ms/step - loss: 1.5735\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6009435695818752 - normalized_discounted_cumulative_gain@5(0.0): 0.6639328074555028 - mean_average_precision(0.0): 0.6141421880590333\n", - "Epoch 4/30\n", - "255/255 [==============================] - 44s 174ms/step - loss: 1.3388\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.60611654032328 - normalized_discounted_cumulative_gain@5(0.0): 0.657932990992144 - mean_average_precision(0.0): 0.6164241968035815\n", - "Epoch 5/30\n", - "255/255 [==============================] - 44s 173ms/step - loss: 1.2257\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6038327788753987 - normalized_discounted_cumulative_gain@5(0.0): 0.658207699559794 - mean_average_precision(0.0): 0.6165270742594192\n", - "Epoch 6/30\n", - "255/255 [==============================] - 45s 175ms/step - loss: 1.1705\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5996133695926983 - normalized_discounted_cumulative_gain@5(0.0): 0.6492549188602507 - mean_average_precision(0.0): 0.610038711779037\n", - "Epoch 7/30\n", - "255/255 [==============================] - 49s 194ms/step - loss: 1.1245\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.596951218733747 - normalized_discounted_cumulative_gain@5(0.0): 0.6537028205494899 - mean_average_precision(0.0): 0.6118594078618916\n", - "Epoch 8/30\n", - "255/255 [==============================] - 49s 192ms/step - loss: 1.0965\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.607384889011673 - normalized_discounted_cumulative_gain@5(0.0): 0.6591796919495273 - mean_average_precision(0.0): 0.6198677092456238\n", - "Epoch 9/30\n", - "255/255 [==============================] - 50s 196ms/step - loss: 1.0739\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6009170916196322 - normalized_discounted_cumulative_gain@5(0.0): 0.6585982395889274 - mean_average_precision(0.0): 0.61618597649167\n", - "Epoch 10/30\n", - "255/255 [==============================] - 50s 196ms/step - loss: 1.0617\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6033747474109487 - normalized_discounted_cumulative_gain@5(0.0): 0.6578329756640785 - mean_average_precision(0.0): 0.6179567226451466\n", - "Epoch 11/30\n", - "255/255 [==============================] - 51s 199ms/step - loss: 1.0452\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5997577679210652 - normalized_discounted_cumulative_gain@5(0.0): 0.653391959851057 - mean_average_precision(0.0): 0.6107675669593364\n", - "Epoch 12/30\n", - "255/255 [==============================] - 50s 197ms/step - loss: 1.0463\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6099707901807351 - normalized_discounted_cumulative_gain@5(0.0): 0.6576795760995855 - mean_average_precision(0.0): 0.6190800467908035\n", - "Epoch 13/30\n", - "255/255 [==============================] - 48s 189ms/step - loss: 1.0142\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6060017367789287 - normalized_discounted_cumulative_gain@5(0.0): 0.6561934627591615 - mean_average_precision(0.0): 0.6164463654573828\n", - "Epoch 14/30\n", - "255/255 [==============================] - 48s 190ms/step - loss: 1.0166\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5978152368225886 - normalized_discounted_cumulative_gain@5(0.0): 0.6505786036930443 - mean_average_precision(0.0): 0.61033087775462\n", - "Epoch 15/30\n", - "255/255 [==============================] - 49s 192ms/step - loss: 0.9978\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5922250615836083 - normalized_discounted_cumulative_gain@5(0.0): 0.6426732309894715 - mean_average_precision(0.0): 0.6022889901471488\n", - "Epoch 16/30\n", - "255/255 [==============================] - 49s 191ms/step - loss: 1.0025\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5750131412934796 - normalized_discounted_cumulative_gain@5(0.0): 0.6352853673494635 - mean_average_precision(0.0): 0.5900977589685756\n", - "Epoch 17/30\n", - "255/255 [==============================] - 51s 199ms/step - loss: 0.9862\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5758042381295027 - normalized_discounted_cumulative_gain@5(0.0): 0.6346575981857837 - mean_average_precision(0.0): 0.5901888624427178\n", - "Epoch 18/30\n", - "255/255 [==============================] - 49s 193ms/step - loss: 0.9855\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.579621264998872 - normalized_discounted_cumulative_gain@5(0.0): 0.6347686906366622 - mean_average_precision(0.0): 0.5884915666561875\n", - "Epoch 19/30\n", - "255/255 [==============================] - 49s 192ms/step - loss: 0.9766\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5828950659531305 - normalized_discounted_cumulative_gain@5(0.0): 0.6372592629514049 - mean_average_precision(0.0): 0.5918219888593219\n", - "Epoch 20/30\n", - "255/255 [==============================] - 48s 190ms/step - loss: 0.9680\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5825563351056543 - normalized_discounted_cumulative_gain@5(0.0): 0.6391007734169843 - mean_average_precision(0.0): 0.5938890573634718\n", - "Epoch 21/30\n", - "255/255 [==============================] - 49s 191ms/step - loss: 0.9508\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.579128022658977 - normalized_discounted_cumulative_gain@5(0.0): 0.6395338143562516 - mean_average_precision(0.0): 0.5941989088930982\n", - "Epoch 22/30\n", - "255/255 [==============================] - 50s 196ms/step - loss: 0.9522\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5757112036272017 - normalized_discounted_cumulative_gain@5(0.0): 0.6293793465362543 - mean_average_precision(0.0): 0.583203117231657\n", - "Epoch 23/30\n", - "255/255 [==============================] - 52s 203ms/step - loss: 0.9465\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5712280953991604 - normalized_discounted_cumulative_gain@5(0.0): 0.633607426512819 - mean_average_precision(0.0): 0.583670818529751\n", - "Epoch 24/30\n", - "255/255 [==============================] - 51s 200ms/step - loss: 0.9434\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.580920539337657 - normalized_discounted_cumulative_gain@5(0.0): 0.641607241519095 - mean_average_precision(0.0): 0.5944285471787385\n", - "Epoch 25/30\n", - "255/255 [==============================] - 51s 198ms/step - loss: 0.9410\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5804534006493391 - normalized_discounted_cumulative_gain@5(0.0): 0.6365914442161427 - mean_average_precision(0.0): 0.5909732454825581\n", - "Epoch 26/30\n", - "255/255 [==============================] - 48s 190ms/step - loss: 0.9288\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5706608764817882 - normalized_discounted_cumulative_gain@5(0.0): 0.6287030620891625 - mean_average_precision(0.0): 0.5812929865633114\n", - "Epoch 27/30\n", - "255/255 [==============================] - 49s 193ms/step - loss: 0.9390\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5609829297013442 - normalized_discounted_cumulative_gain@5(0.0): 0.6256839520090155 - mean_average_precision(0.0): 0.5787581434265507\n", - "Epoch 28/30\n", - "255/255 [==============================] - 49s 191ms/step - loss: 0.9276\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5719190626216555 - normalized_discounted_cumulative_gain@5(0.0): 0.6307496688555204 - mean_average_precision(0.0): 0.5817515076104938\n", - "Epoch 29/30\n", - "255/255 [==============================] - 49s 193ms/step - loss: 0.9183\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5795917195993303 - normalized_discounted_cumulative_gain@5(0.0): 0.629216010151653 - mean_average_precision(0.0): 0.5871250977359159\n", - "Epoch 30/30\n", - "255/255 [==============================] - 51s 200ms/step - loss: 0.9114\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5699846325745904 - normalized_discounted_cumulative_gain@5(0.0): 0.6288458929530589 - mean_average_precision(0.0): 0.581158143460481\n" - ] - } - ], - "source": [ - "history = model.fit_generator(train_generator, epochs=30, callbacks=[evaluate], workers=30, use_multiprocessing=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 2.1248043 ],\n", - " [-0.7405751 ],\n", - " [ 1.2434225 ],\n", - " [-3.161619 ],\n", - " [-1.5212955 ],\n", - " [-1.3774216 ],\n", - " [-0.43242887],\n", - " [-3.8191142 ],\n", - " [-2.490344 ],\n", - " [ 1.9016179 ]], dtype=float32)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "drmm_model = mz.load_model('./drmm_pretrained_model/16')\n", - "test_generator = mz.DataGenerator(data_pack=dev_pack_processed[:10], mode='point', callbacks=[hist_callback])\n", - "test_x, test_y = test_generator[:]\n", - "prediction = drmm_model.predict(test_x)\n", - "prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "shutil.rmtree('./drmm_pretrained_model/')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/drmmtks.ipynb b/tutorials/wikiqa/drmmtks.ipynb deleted file mode 100644 index 898b2c02..00000000 --- a/tutorials/wikiqa/drmmtks.ipynb +++ /dev/null @@ -1,528 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:35:56.628130Z", - "start_time": "2019-03-20T09:35:45.661384Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:35:56.633000Z", - "start_time": "2019-03-20T09:35:56.630450Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10, fixed_length_right=100, remove_stop_words=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:36:06.249211Z", - "start_time": "2019-03-20T09:35:56.634788Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 9354.30it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:03<00:00, 5242.39it/s]\n", - "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 959307.59it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 144447.70it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 151024.12it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 818306.55it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 742442.92it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 404432/404432 [00:00<00:00, 2591651.03it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 9654.46it/s]\n", - 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"Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 143565.86it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 129280.34it/s]\n", - "Processing length_left with len: 100%|██████████| 237/237 [00:00<00:00, 309383.77it/s]\n", - "Processing length_right with len: 100%|██████████| 2300/2300 [00:00<00:00, 887315.97it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 101288.98it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 85815.05it/s]\n" - ] - } - ], - "source": [ - "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n", - "dev_pack_processed = preprocessor.transform(dev_pack_raw)\n", - "test_pack_processed = preprocessor.transform(test_pack_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:36:06.262937Z", - "start_time": "2019-03-20T09:36:06.253350Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'filter_unit': ,\n", - " 'vocab_unit': ,\n", - " 'vocab_size': 16674,\n", - " 'embedding_input_dim': 16674,\n", - " 'input_shapes': [(10,), (100,)]}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessor.context" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:36:06.413530Z", - "start_time": "2019-03-20T09:36:06.267256Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model_class \n", - "input_shapes [(10,), (100,)]\n", - "task Ranking Task\n", - "optimizer adadelta\n", - "with_embedding True\n", - "embedding_input_dim 16674\n", - "embedding_output_dim 100\n", - "embedding_trainable True\n", - "with_multi_layer_perceptron True\n", - "mlp_num_units 5\n", - "mlp_num_layers 1\n", - "mlp_num_fan_out 1\n", - "mlp_activation_func relu\n", - "mask_value -1\n", - "top_k 20\n" - ] - } - ], - "source": [ - "model = mz.models.DRMMTKS()\n", - "\n", - "# load `input_shapes` and `embedding_input_dim` (vocab_size)\n", - "model.params.update(preprocessor.context)\n", - "\n", - "model.params['task'] = ranking_task\n", - "model.params['mask_value'] = -1\n", - "model.params['embedding_output_dim'] = glove_embedding.output_dim\n", - "model.params['embedding_trainable'] = True\n", - "model.params['top_k'] = 20\n", - "model.params['mlp_num_layers'] = 1\n", - "model.params['mlp_num_units'] = 5\n", - "model.params['mlp_num_fan_out'] = 1\n", - "model.params['mlp_activation_func'] = 'relu'\n", - "model.params['optimizer'] = 'adadelta'\n", - "\n", - "model.build()\n", - "model.compile()\n", - "\n", - "print(model.params)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:36:06.422264Z", - "start_time": "2019-03-20T09:36:06.415605Z" - }, - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 10) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 100) 0 \n", - "__________________________________________________________________________________________________\n", - "embedding (Embedding) multiple 1667400 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "dot_1 (Dot) (None, 10, 100) 0 embedding[0][0] \n", - " embedding[1][0] \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) (None, 10, 1) 100 embedding[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_1 (Lambda) (None, 10, 10) 0 dot_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_mask (Lambda) (None, 10, 1) 0 dense_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_2 (Dense) (None, 10, 5) 55 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_probs (Lambda) (None, 10, 1) 0 attention_mask[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_3 (Dense) (None, 10, 1) 6 dense_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dot_2 (Dot) (None, 1, 1) 0 attention_probs[0][0] \n", - " dense_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "flatten_1 (Flatten) (None, 1) 0 dot_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_4 (Dense) (None, 1) 2 flatten_1[0][0] \n", - "==================================================================================================\n", - "Total params: 1,667,563\n", - "Trainable params: 1,667,563\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model.backend.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:37:59.341616Z", - "start_time": "2019-03-20T09:36:06.425086Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "term_index = preprocessor.context['vocab_unit'].state['term_index']\n", - "embedding_matrix = glove_embedding.build_matrix(term_index)\n", - "l2_norm = np.sqrt((embedding_matrix * embedding_matrix).sum(axis=1))\n", - "embedding_matrix = embedding_matrix / l2_norm[:, np.newaxis]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:37:59.432719Z", - "start_time": "2019-03-20T09:37:59.343309Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "model.load_embedding_matrix(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:37:59.489842Z", - "start_time": "2019-03-20T09:37:59.434509Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "test_x, test_y = test_pack_processed.unpack()\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=test_x, y=test_y, batch_size=len(test_x))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:38:47.716275Z", - "start_time": "2019-03-20T09:38:37.602828Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num batches: 408\n" - ] - } - ], - "source": [ - "train_generator = mz.DataGenerator(\n", - " train_pack_processed,\n", - " mode='pair',\n", - " num_dup=2,\n", - " num_neg=1,\n", - " batch_size=20\n", - ")\n", - "print('num batches:', len(train_generator))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:50:55.901154Z", - "start_time": "2019-03-20T09:38:59.434426Z" - }, - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/30\n", - "408/408 [==============================] - 7s 17ms/step - loss: 0.5841\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5988918016460031 - normalized_discounted_cumulative_gain@5(0.0): 0.6590526112135905 - mean_average_precision(0.0): 0.6149729212603963\n", - "Epoch 2/30\n", - "408/408 [==============================] - 16s 40ms/step - loss: 0.1286\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5986225503033814 - normalized_discounted_cumulative_gain@5(0.0): 0.6573151832954626 - mean_average_precision(0.0): 0.6081435794322055\n", - "Epoch 3/30\n", - "408/408 [==============================] - 17s 41ms/step - loss: 0.0236\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5770594699106932 - normalized_discounted_cumulative_gain@5(0.0): 0.642764019599169 - mean_average_precision(0.0): 0.5915165228479636\n", - "Epoch 4/30\n", - "408/408 [==============================] - 20s 48ms/step - loss: 0.0085\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5630076023551297 - normalized_discounted_cumulative_gain@5(0.0): 0.6314235897212269 - mean_average_precision(0.0): 0.5811220052309326\n", - "Epoch 5/30\n", - "408/408 [==============================] - 21s 52ms/step - loss: 0.0039\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5721875341813376 - normalized_discounted_cumulative_gain@5(0.0): 0.6430346137953176 - mean_average_precision(0.0): 0.5887684999840622\n", - "Epoch 6/30\n", - "408/408 [==============================] - 18s 44ms/step - loss: 0.0025\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5635600485727307 - normalized_discounted_cumulative_gain@5(0.0): 0.636814704018869 - mean_average_precision(0.0): 0.5831658911191673\n", - "Epoch 7/30\n", - "408/408 [==============================] - 19s 47ms/step - loss: 0.0019\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5624051725623126 - normalized_discounted_cumulative_gain@5(0.0): 0.6362646813359831 - mean_average_precision(0.0): 0.5830741350566369\n", - "Epoch 8/30\n", - "408/408 [==============================] - 20s 48ms/step - loss: 0.0020\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5627439034097886 - normalized_discounted_cumulative_gain@5(0.0): 0.6330671467013023 - mean_average_precision(0.0): 0.5806811015299475\n", - "Epoch 9/30\n", - "408/408 [==============================] - 21s 51ms/step - loss: 0.0018\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5679681248986371 - normalized_discounted_cumulative_gain@5(0.0): 0.6363760316713785 - mean_average_precision(0.0): 0.5851179931317684\n", - "Epoch 10/30\n", - "408/408 [==============================] - 22s 54ms/step - loss: 0.0020\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5745461650682577 - normalized_discounted_cumulative_gain@5(0.0): 0.6407437747766428 - mean_average_precision(0.0): 0.5893184033052796\n", - "Epoch 11/30\n", - "408/408 [==============================] - 21s 50ms/step - loss: 0.0020\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5684307665143323 - normalized_discounted_cumulative_gain@5(0.0): 0.634110297384481 - mean_average_precision(0.0): 0.5844463619617193\n", - "Epoch 12/30\n", - "408/408 [==============================] - 20s 48ms/step - loss: 0.0018\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.566182378404992 - normalized_discounted_cumulative_gain@5(0.0): 0.6318466678703829 - mean_average_precision(0.0): 0.583959239462311\n", - "Epoch 13/30\n", - "408/408 [==============================] - 21s 50ms/step - loss: 0.0020\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5671372070359241 - normalized_discounted_cumulative_gain@5(0.0): 0.6288034745152886 - mean_average_precision(0.0): 0.5823057242012007\n", - "Epoch 14/30\n", - "408/408 [==============================] - 24s 59ms/step - loss: 0.0020\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5688445292639432 - normalized_discounted_cumulative_gain@5(0.0): 0.6289506917256232 - mean_average_precision(0.0): 0.5823084213744116\n", - "Epoch 15/30\n", - "408/408 [==============================] - 21s 51ms/step - loss: 0.0016\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5616594491502604 - normalized_discounted_cumulative_gain@5(0.0): 0.6274555733102793 - mean_average_precision(0.0): 0.5803460611842033\n", - "Epoch 16/30\n", - "408/408 [==============================] - 24s 59ms/step - loss: 0.0019\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.56743611685671 - normalized_discounted_cumulative_gain@5(0.0): 0.6279655493568921 - mean_average_precision(0.0): 0.5833285217616007\n", - "Epoch 17/30\n", - "408/408 [==============================] - 21s 51ms/step - loss: 0.0019\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.56743611685671 - normalized_discounted_cumulative_gain@5(0.0): 0.629412929341684 - mean_average_precision(0.0): 0.5831775027001493\n", - "Epoch 18/30\n", - "408/408 [==============================] - 21s 53ms/step - loss: 0.0017\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5616594491502604 - normalized_discounted_cumulative_gain@5(0.0): 0.6230767811708631 - mean_average_precision(0.0): 0.5778751707969239\n", - "Epoch 19/30\n", - "408/408 [==============================] - 21s 51ms/step - loss: 0.0014\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5624755943132025 - normalized_discounted_cumulative_gain@5(0.0): 0.6196682608534165 - mean_average_precision(0.0): 0.5773872198947884\n", - "Epoch 20/30\n", - "408/408 [==============================] - 21s 51ms/step - loss: 0.0017\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.556962625552094 - normalized_discounted_cumulative_gain@5(0.0): 0.6181385580129402 - mean_average_precision(0.0): 0.5762216174819781\n", - "Epoch 21/30\n", - "408/408 [==============================] - 22s 53ms/step - loss: 0.0018\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5532956624869947 - normalized_discounted_cumulative_gain@5(0.0): 0.614101122041995 - mean_average_precision(0.0): 0.570779766786963\n", - "Epoch 22/30\n", - "408/408 [==============================] - 21s 51ms/step - loss: 0.0019\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5580675179872963 - normalized_discounted_cumulative_gain@5(0.0): 0.615310109292882 - mean_average_precision(0.0): 0.5723212571524482\n", - "Epoch 23/30\n", - "408/408 [==============================] - 21s 51ms/step - loss: 0.0017\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.555957813345946 - normalized_discounted_cumulative_gain@5(0.0): 0.6168348059854845 - mean_average_precision(0.0): 0.5745875152731366\n", - "Epoch 24/30\n", - "408/408 [==============================] - 20s 50ms/step - loss: 0.0020\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5545892219654028 - normalized_discounted_cumulative_gain@5(0.0): 0.6161692037842695 - mean_average_precision(0.0): 0.5737084716725741\n", - "Epoch 25/30\n", - "408/408 [==============================] - 21s 51ms/step - loss: 0.0015\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5572513728243542 - normalized_discounted_cumulative_gain@5(0.0): 0.6149353178966624 - mean_average_precision(0.0): 0.5726138530937782\n", - "Epoch 26/30\n", - "408/408 [==============================] - 20s 50ms/step - loss: 0.0019\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5530319635416537 - normalized_discounted_cumulative_gain@5(0.0): 0.6157477651673351 - mean_average_precision(0.0): 0.5719727220729004\n", - "Epoch 27/30\n", - "408/408 [==============================] - 22s 55ms/step - loss: 0.0018\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5535491988838099 - normalized_discounted_cumulative_gain@5(0.0): 0.6157701973864509 - mean_average_precision(0.0): 0.5706400633221479\n", - "Epoch 28/30\n", - "408/408 [==============================] - 24s 58ms/step - loss: 0.0017\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.556713699306524 - normalized_discounted_cumulative_gain@5(0.0): 0.6164860410473986 - mean_average_precision(0.0): 0.5726880826961166\n", - "Epoch 29/30\n", - "408/408 [==============================] - 25s 61ms/step - loss: 0.0015\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5586585674438334 - normalized_discounted_cumulative_gain@5(0.0): 0.6187911692779081 - mean_average_precision(0.0): 0.5763274637089153\n", - "Epoch 30/30\n", - "408/408 [==============================] - 24s 58ms/step - loss: 0.0014\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5642215197801578 - normalized_discounted_cumulative_gain@5(0.0): 0.6253202724847128 - mean_average_precision(0.0): 0.5820402362707493\n" - ] - } - ], - "source": [ - "history = model.fit_generator(train_generator, epochs=30, callbacks=[evaluate], workers=4, use_multiprocessing=True)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Use this function to update the README.md with a better set of parameters.\n", - "Make sure you delete the correct section of the README.md before calling this function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:38:03.829486Z", - "start_time": "2019-03-20T09:35:45.706Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "# append_params_to_readme(model)" - ] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "matchzoo", - "language": "python", - "name": "matchzoo" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - }, - "toc": { - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "toc_cell": false, - "toc_position": {}, - "toc_section_display": "block", - "toc_window_display": false - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/dssm.ipynb b/tutorials/wikiqa/dssm.ipynb deleted file mode 100644 index a7545b85..00000000 --- a/tutorials/wikiqa/dssm.ipynb +++ /dev/null @@ -1,305 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 2118/2118 [00:00<00:00, 3587.72it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 18841/18841 [00:04<00:00, 4528.13it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 592156.77it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 432217.30it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 1614998/1614998 [00:00<00:00, 4239505.32it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 2118/2118 [00:00<00:00, 2709.71it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 18841/18841 [00:11<00:00, 1656.57it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 122/122 [00:00<00:00, 1120.91it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 1115/1115 [00:00<00:00, 1895.34it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 237/237 [00:00<00:00, 1910.44it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 2300/2300 [00:01<00:00, 1630.79it/s]\n" - ] - } - ], - "source": [ - "preprocessor = mz.preprocessors.DSSMPreprocessor()\n", - "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n", - "valid_pack_processed = preprocessor.transform(dev_pack_raw)\n", - "test_pack_processed = preprocessor.transform(test_pack_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'vocab_unit': ,\n", - " 'vocab_size': 9645,\n", - " 'embedding_input_dim': 9645,\n", - " 'input_shapes': [(9645,), (9645,)]}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessor.context" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "ranking_task = mz.tasks.Ranking(loss=mz.losses.RankCrossEntropyLoss(num_neg=4))\n", - "ranking_task.metrics = [\n", - " mz.metrics.NormalizedDiscountedCumulativeGain(k=3),\n", - " mz.metrics.NormalizedDiscountedCumulativeGain(k=5),\n", - " mz.metrics.MeanAveragePrecision()\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 9645) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 9645) 0 \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) (None, 300) 2893800 text_left[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_5 (Dense) (None, 300) 2893800 text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_2 (Dense) (None, 300) 90300 dense_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_6 (Dense) (None, 300) 90300 dense_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_3 (Dense) (None, 300) 90300 dense_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_7 (Dense) (None, 300) 90300 dense_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_4 (Dense) (None, 128) 38528 dense_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_8 (Dense) (None, 128) 38528 dense_7[0][0] \n", - "__________________________________________________________________________________________________\n", - "dot_1 (Dot) (None, 1) 0 dense_4[0][0] \n", - " dense_8[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_9 (Dense) (None, 1) 2 dot_1[0][0] \n", - "==================================================================================================\n", - "Total params: 6,225,858\n", - "Trainable params: 6,225,858\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model = mz.models.DSSM()\n", - "model.params['input_shapes'] = preprocessor.context['input_shapes']\n", - "model.params['task'] = ranking_task\n", - "model.params['mlp_num_layers'] = 3\n", - "model.params['mlp_num_units'] = 300\n", - "model.params['mlp_num_fan_out'] = 128\n", - "model.params['mlp_activation_func'] = 'relu'\n", - "model.guess_and_fill_missing_params()\n", - "model.build()\n", - "model.compile()\n", - "model.backend.summary()\n", - "\n", - "append_params_to_readme(model)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "pred_x, pred_y = test_pack_processed[:].unpack()\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=pred_x, y=pred_y, batch_size=len(pred_x))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING: PairDataGenerator will be deprecated in MatchZoo v2.2. Use `DataGenerator` with callbacks instead.\n" - ] - }, - { - "data": { - "text/plain": [ - "32" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_generator = mz.PairDataGenerator(train_pack_processed, num_dup=1, num_neg=4, batch_size=32, shuffle=True)\n", - "len(train_generator)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n", - "32/32 [==============================] - 7s 215ms/step - loss: 1.3325\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4431849853601904 - normalized_discounted_cumulative_gain@5(0.0): 0.5295386323998266 - mean_average_precision(0.0): 0.48303488812718776\n", - "Epoch 2/20\n", - "32/32 [==============================] - 6s 176ms/step - loss: 1.3159\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4353814849661657 - normalized_discounted_cumulative_gain@5(0.0): 0.5032525911610362 - mean_average_precision(0.0): 0.4776049822282439\n", - "Epoch 3/20\n", - "32/32 [==============================] - 5s 171ms/step - loss: 1.2955\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4088637099689691 - normalized_discounted_cumulative_gain@5(0.0): 0.48351010067595823 - mean_average_precision(0.0): 0.4432379861560312\n", - "Epoch 4/20\n", - "32/32 [==============================] - 6s 173ms/step - loss: 1.2726\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.46569627211992487 - normalized_discounted_cumulative_gain@5(0.0): 0.5305277638291452 - mean_average_precision(0.0): 0.4903964896023526\n", - "Epoch 5/20\n", - "32/32 [==============================] - 6s 172ms/step - loss: 1.2439\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44778538209256513 - normalized_discounted_cumulative_gain@5(0.0): 0.5104380434420628 - mean_average_precision(0.0): 0.47615129143046664\n", - "Epoch 6/20\n", - "32/32 [==============================] - 6s 172ms/step - loss: 1.2202\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4452573045503587 - normalized_discounted_cumulative_gain@5(0.0): 0.5137975378931312 - mean_average_precision(0.0): 0.4742872412051932\n", - "Epoch 7/20\n", - "32/32 [==============================] - 5s 170ms/step - loss: 1.2038\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.41264292792428936 - normalized_discounted_cumulative_gain@5(0.0): 0.4740615140630128 - mean_average_precision(0.0): 0.45294026408574084\n", - "Epoch 8/20\n", - "32/32 [==============================] - 6s 172ms/step - loss: 1.1848\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45527149721829696 - normalized_discounted_cumulative_gain@5(0.0): 0.5229678873030444 - mean_average_precision(0.0): 0.48490323375232625\n", - "Epoch 9/20\n", - "32/32 [==============================] - 5s 171ms/step - loss: 1.1504 3\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4401749964298954 - normalized_discounted_cumulative_gain@5(0.0): 0.5202410581724496 - mean_average_precision(0.0): 0.47967943778482564\n", - "Epoch 10/20\n", - "32/32 [==============================] - 5s 172ms/step - loss: 1.1314\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44883790476151675 - normalized_discounted_cumulative_gain@5(0.0): 0.5215788412779597 - mean_average_precision(0.0): 0.48274548802838624\n", - "Epoch 11/20\n", - "32/32 [==============================] - 6s 173ms/step - loss: 1.1109\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45835958548802597 - normalized_discounted_cumulative_gain@5(0.0): 0.5254562351939174 - mean_average_precision(0.0): 0.48819163523037407\n", - "Epoch 12/20\n", - "32/32 [==============================] - 6s 174ms/step - loss: 1.0915\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4540812972538116 - normalized_discounted_cumulative_gain@5(0.0): 0.502728792326375 - mean_average_precision(0.0): 0.48229166522394096\n", - "Epoch 13/20\n", - "32/32 [==============================] - 6s 173ms/step - loss: 1.0805\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4462255256302118 - normalized_discounted_cumulative_gain@5(0.0): 0.5097488218798687 - mean_average_precision(0.0): 0.4751972950775518\n", - "Epoch 14/20\n", - "32/32 [==============================] - 6s 174ms/step - loss: 1.0575\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4263585495923587 - normalized_discounted_cumulative_gain@5(0.0): 0.5014903707963352 - mean_average_precision(0.0): 0.46364289738480496\n", - "Epoch 15/20\n", - "32/32 [==============================] - 6s 179ms/step - loss: 1.0396\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.43936705108731194 - normalized_discounted_cumulative_gain@5(0.0): 0.5218713927469146 - mean_average_precision(0.0): 0.47233172236473137\n", - "Epoch 16/20\n", - "32/32 [==============================] - 6s 182ms/step - loss: 1.0156\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45080782122574514 - normalized_discounted_cumulative_gain@5(0.0): 0.5181271382497495 - mean_average_precision(0.0): 0.4832342072703635\n", - "Epoch 17/20\n", - "32/32 [==============================] - 6s 175ms/step - loss: 0.9932\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.423108628561739 - normalized_discounted_cumulative_gain@5(0.0): 0.49596605935842625 - mean_average_precision(0.0): 0.4667294180948952\n", - "Epoch 18/20\n", - "32/32 [==============================] - 5s 172ms/step - loss: 0.9800\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4378084124127128 - normalized_discounted_cumulative_gain@5(0.0): 0.5098753091251295 - mean_average_precision(0.0): 0.4734416114488085\n", - "Epoch 19/20\n", - "32/32 [==============================] - 6s 172ms/step - loss: 0.9662\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4504450479915345 - normalized_discounted_cumulative_gain@5(0.0): 0.519107636100811 - mean_average_precision(0.0): 0.48712867088141415\n", - "Epoch 20/20\n", - "32/32 [==============================] - 6s 172ms/step - loss: 0.9512\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45663442312293695 - normalized_discounted_cumulative_gain@5(0.0): 0.5363645153841258 - mean_average_precision(0.0): 0.4956098197015037\n" - ] - } - ], - "source": [ - "history = model.fit_generator(train_generator, epochs=20, callbacks=[evaluate], workers=5, use_multiprocessing=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/duet.ipynb b/tutorials/wikiqa/duet.ipynb deleted file mode 100644 index 609537c0..00000000 --- a/tutorials/wikiqa/duet.ipynb +++ /dev/null @@ -1,373 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n", - "/home/fanyixing/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n", - "/home/fanyixing/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 3594.37it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 18841/18841 [00:11<00:00, 1586.27it/s]\n", - "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 331119.09it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 53611.85it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 42001.20it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 210146.80it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 246073.81it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 234249/234249 [00:00<00:00, 1114012.95it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 2499.93it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 18841/18841 [00:09<00:00, 2037.29it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 87829.63it/s]\n", - 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"Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 15517.48it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 54723.62it/s]\n", - "Processing length_left with len: 100%|██████████| 237/237 [00:00<00:00, 135539.96it/s]\n", - "Processing length_right with len: 100%|██████████| 2300/2300 [00:00<00:00, 281653.07it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 27051.19it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 24465.45it/s]\n" - ] - } - ], - "source": [ - "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10, fixed_length_right=100, remove_stop_words=True)\n", - "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n", - "valid_pack_processed = preprocessor.transform(dev_pack_raw)\n", - "test_pack_processed = preprocessor.transform(test_pack_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 10) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 100) 0 \n", - "__________________________________________________________________________________________________\n", - "embedding (Embedding) multiple 4963800 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv1d_2 (Conv1D) (None, 10, 32) 28832 embedding[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_3 (Dropout) (None, 10, 32) 0 conv1d_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv1d_3 (Conv1D) (None, 100, 32) 28832 embedding[1][0] \n", - "__________________________________________________________________________________________________\n", - "max_pooling1d_1 (MaxPooling1D) (None, 1, 32) 0 dropout_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_4 (Dropout) (None, 100, 32) 0 conv1d_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "reshape_2 (Reshape) (None, 32) 0 max_pooling1d_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "max_pooling1d_2 (MaxPooling1D) (None, 25, 32) 0 dropout_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_1 (Lambda) (None, 10, 100) 0 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_3 (Dense) (None, 32) 1056 reshape_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv1d_4 (Conv1D) (None, 25, 32) 1056 max_pooling1d_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv1d_1 (Conv1D) (None, 10, 32) 320032 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_2 (Lambda) (None, 1, 32) 0 dense_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_5 (Dropout) (None, 25, 32) 0 conv1d_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_1 (Dropout) (None, 10, 32) 0 conv1d_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_3 (Lambda) (None, 25, 32) 0 lambda_2[0][0] \n", - " dropout_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "reshape_1 (Reshape) (None, 320) 0 dropout_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "reshape_3 (Reshape) (None, 800) 0 lambda_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) (None, 32) 10272 reshape_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_4 (Dense) (None, 32) 25632 reshape_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_2 (Dropout) (None, 32) 0 dense_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_6 (Dropout) (None, 32) 0 dense_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_2 (Dense) (None, 1) 33 dropout_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_5 (Dense) (None, 1) 33 dropout_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "add_1 (Add) (None, 1) 0 dense_2[0][0] \n", - " dense_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_6 (Dense) (None, 1) 2 add_1[0][0] \n", - "==================================================================================================\n", - "Total params: 5,379,580\n", - "Trainable params: 5,379,580\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model = mz.models.DUET()\n", - "model.params.update(preprocessor.context)\n", - "model.params['task'] = ranking_task\n", - "model.params['embedding_output_dim'] = 300\n", - "model.params['lm_filters'] = 32\n", - "model.params['lm_hidden_sizes'] = [32]\n", - "model.params['dm_filters'] = 32\n", - "model.params['dm_kernel_size'] = 3\n", - "model.params['dm_d_mpool'] = 4\n", - "model.params['dm_hidden_sizes'] = [32]\n", - "model.params['dropout_rate'] = 0.5\n", - "optimizer = keras.optimizers.Adamax(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0)\n", - "model.params['optimizer'] = 'adagrad'\n", - "model.guess_and_fill_missing_params()\n", - "model.build()\n", - "model.compile()\n", - "model.backend.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])\n", - "model.load_embedding_matrix(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "pred_x, pred_y = test_pack_processed[:].unpack()\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=pred_x, y=pred_y, batch_size=len(pred_y))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num batches: 102\n" - ] - } - ], - "source": [ - "train_generator = mz.DataGenerator(\n", - " train_pack_processed,\n", - " mode='pair',\n", - " num_dup=2,\n", - " num_neg=1,\n", - " batch_size=20\n", - ")\n", - "print('num batches:', len(train_generator))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/30\n", - "102/102 [==============================] - 10s 102ms/step - loss: 1.3443\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5434086104876706 - normalized_discounted_cumulative_gain@5(0.0): 0.5956448102614595 - mean_average_precision(0.0): 0.5563239011406736\n", - "Epoch 2/30\n", - "102/102 [==============================] - 19s 183ms/step - loss: 0.7973\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5409544601384676 - normalized_discounted_cumulative_gain@5(0.0): 0.5963200911770458 - mean_average_precision(0.0): 0.5538297188377527\n", - "Epoch 3/30\n", - "102/102 [==============================] - 18s 172ms/step - loss: 0.7230\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5349731349012871 - normalized_discounted_cumulative_gain@5(0.0): 0.5990249616772886 - mean_average_precision(0.0): 0.5591250854644376\n", - "Epoch 4/30\n", - "102/102 [==============================] - 19s 189ms/step - loss: 0.5881\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5287827044452265 - normalized_discounted_cumulative_gain@5(0.0): 0.5908037158184187 - mean_average_precision(0.0): 0.5466419807557238\n", - "Epoch 5/30\n", - "102/102 [==============================] - 20s 196ms/step - loss: 0.5804\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5240370019809759 - normalized_discounted_cumulative_gain@5(0.0): 0.6006478587685735 - mean_average_precision(0.0): 0.5450772528295846\n", - "Epoch 6/30\n", - "102/102 [==============================] - 18s 172ms/step - loss: 0.5368\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5226797909367127 - normalized_discounted_cumulative_gain@5(0.0): 0.5893317663937131 - mean_average_precision(0.0): 0.5410155727818953\n", - "Epoch 7/30\n", - "102/102 [==============================] - 17s 169ms/step - loss: 0.4807\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4951695408576308 - normalized_discounted_cumulative_gain@5(0.0): 0.5706620204391553 - mean_average_precision(0.0): 0.5178932175787853\n", - "Epoch 8/30\n", - "102/102 [==============================] - 20s 197ms/step - loss: 0.3995\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5056429827777733 - normalized_discounted_cumulative_gain@5(0.0): 0.5743399763446194 - mean_average_precision(0.0): 0.518527984938907\n", - "Epoch 9/30\n", - "102/102 [==============================] - 19s 182ms/step - loss: 0.3581\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5029149072405547 - normalized_discounted_cumulative_gain@5(0.0): 0.5728075884356819 - mean_average_precision(0.0): 0.5230789712170018\n", - "Epoch 10/30\n", - "102/102 [==============================] - 17s 168ms/step - loss: 0.3390\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5221954439731158 - normalized_discounted_cumulative_gain@5(0.0): 0.583142417896642 - mean_average_precision(0.0): 0.5377685049399303\n", - "Epoch 11/30\n", - "102/102 [==============================] - 18s 180ms/step - loss: 0.2994\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5136931363049559 - normalized_discounted_cumulative_gain@5(0.0): 0.5779734005370663 - mean_average_precision(0.0): 0.5308713805567863\n", - "Epoch 12/30\n", - "102/102 [==============================] - 20s 197ms/step - loss: 0.2930\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5144491847286299 - normalized_discounted_cumulative_gain@5(0.0): 0.5764922861029833 - mean_average_precision(0.0): 0.5238341669133789\n", - "Epoch 13/30\n", - "102/102 [==============================] - 18s 172ms/step - loss: 0.2568\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5001641695674516 - normalized_discounted_cumulative_gain@5(0.0): 0.5678059065572373 - mean_average_precision(0.0): 0.5163413086929762\n", - "Epoch 14/30\n", - "102/102 [==============================] - 17s 167ms/step - loss: 0.2333\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.49355785117051687 - normalized_discounted_cumulative_gain@5(0.0): 0.5649387413500839 - mean_average_precision(0.0): 0.5107034360845545\n", - "Epoch 15/30\n", - "102/102 [==============================] - 20s 192ms/step - loss: 0.2004\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5048462698503208 - normalized_discounted_cumulative_gain@5(0.0): 0.5777265739049287 - mean_average_precision(0.0): 0.5251572469500373\n", - "Epoch 16/30\n", - "102/102 [==============================] - 19s 187ms/step - loss: 0.2013\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.49244157839903463 - normalized_discounted_cumulative_gain@5(0.0): 0.5666747321525085 - mean_average_precision(0.0): 0.5174358597173779\n", - "Epoch 17/30\n", - "102/102 [==============================] - 17s 171ms/step - loss: 0.1627\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5008963874519605 - normalized_discounted_cumulative_gain@5(0.0): 0.5693132311623968 - mean_average_precision(0.0): 0.519828449775054\n", - "Epoch 18/30\n", - "102/102 [==============================] - 18s 180ms/step - loss: 0.1676\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4964575973232318 - normalized_discounted_cumulative_gain@5(0.0): 0.5675852335530719 - mean_average_precision(0.0): 0.5214283696480859\n", - "Epoch 19/30\n", - "102/102 [==============================] - 20s 195ms/step - loss: 0.1422\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5012929419262925 - normalized_discounted_cumulative_gain@5(0.0): 0.5641706237691956 - mean_average_precision(0.0): 0.5234524442356135\n", - "Epoch 20/30\n", - "102/102 [==============================] - 17s 167ms/step - loss: 0.1358\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5069445941553911 - normalized_discounted_cumulative_gain@5(0.0): 0.5749811028788108 - mean_average_precision(0.0): 0.5316737183006729\n", - "Epoch 21/30\n", - "102/102 [==============================] - 17s 170ms/step - loss: 0.1279\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.49648582616607223 - normalized_discounted_cumulative_gain@5(0.0): 0.5583586978837978 - mean_average_precision(0.0): 0.5178131860833248\n", - "Epoch 22/30\n", - "102/102 [==============================] - 19s 187ms/step - loss: 0.1256\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4975374414315149 - normalized_discounted_cumulative_gain@5(0.0): 0.5594682616966064 - mean_average_precision(0.0): 0.5193958200237347\n", - "Epoch 23/30\n", - "102/102 [==============================] - 15s 151ms/step - loss: 0.1248\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4982876125317161 - normalized_discounted_cumulative_gain@5(0.0): 0.5591300527258516 - mean_average_precision(0.0): 0.5188557732395174\n", - "Epoch 24/30\n", - "102/102 [==============================] - 15s 152ms/step - loss: 0.1275\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4927392704320665 - normalized_discounted_cumulative_gain@5(0.0): 0.5574523653730236 - mean_average_precision(0.0): 0.5155605033553068\n", - "Epoch 25/30\n", - "102/102 [==============================] - 15s 146ms/step - loss: 0.1103\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.49879007517410123 - normalized_discounted_cumulative_gain@5(0.0): 0.5586164397323847 - mean_average_precision(0.0): 0.5160934561034494\n", - "Epoch 26/30\n", - "102/102 [==============================] - 15s 149ms/step - loss: 0.0892\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4958789980695799 - normalized_discounted_cumulative_gain@5(0.0): 0.5614716756878623 - mean_average_precision(0.0): 0.5169777693899112\n", - "Epoch 27/30\n", - "102/102 [==============================] - 15s 148ms/step - loss: 0.0952\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4954856734956429 - normalized_discounted_cumulative_gain@5(0.0): 0.5589290693398398 - mean_average_precision(0.0): 0.5123821730435948\n", - "Epoch 28/30\n", - "102/102 [==============================] - 15s 150ms/step - loss: 0.0918\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4942614804434666 - normalized_discounted_cumulative_gain@5(0.0): 0.5593891548699772 - mean_average_precision(0.0): 0.5108165708897574\n", - "Epoch 29/30\n", - "102/102 [==============================] - 15s 150ms/step - loss: 0.0925\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.49400667687442384 - normalized_discounted_cumulative_gain@5(0.0): 0.5621486460723 - mean_average_precision(0.0): 0.5123339065745354\n", - "Epoch 30/30\n", - "102/102 [==============================] - 15s 150ms/step - loss: 0.0828\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4966496567299287 - normalized_discounted_cumulative_gain@5(0.0): 0.5533107361861653 - mean_average_precision(0.0): 0.510489040605752\n" - ] - } - ], - "source": [ - "history = model.fit_generator(train_generator, epochs=30, callbacks=[evaluate], workers=30, use_multiprocessing=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/esim.ipynb b/tutorials/wikiqa/esim.ipynb deleted file mode 100644 index 042910bc..00000000 --- a/tutorials/wikiqa/esim.ipynb +++ /dev/null @@ -1,524 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run ./tutorials/wikiqa/init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "from keras.backend.tensorflow_backend import set_session\n", - "config = tf.ConfigProto()\n", - "config.gpu_options.visible_device_list=\"1\"\n", - "config.gpu_options.allow_growth = True # dynamically grow the memory used on the GPU\n", - "sess = tf.Session(config=config)\n", - "set_session(sess) # set this TensorFlow session as the default session for Keras" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def load_filtered_data(preprocessor, data_type):\n", - " assert ( data_type in ['train', 'dev', 'test'])\n", - " data_pack = mz.datasets.wiki_qa.load_data(data_type, task='ranking')\n", - "\n", - " if data_type == 'train':\n", - " X, Y = preprocessor.fit_transform(data_pack).unpack()\n", - " else:\n", - " X, Y = preprocessor.transform(data_pack).unpack()\n", - "\n", - " new_idx = []\n", - " for i in range(Y.shape[0]):\n", - " if X[\"length_left\"][i] == 0 or X[\"length_right\"][i] == 0:\n", - " continue\n", - " new_idx.append(i)\n", - " new_idx = np.array(new_idx)\n", - " print(\"Removed empty data. Found \", (Y.shape[0] - new_idx.shape[0]))\n", - "\n", - " for k in X.keys():\n", - " X[k] = X[k][new_idx]\n", - " Y = Y[new_idx]\n", - "\n", - " pos_idx = (Y == 1)[:, 0]\n", - " pos_qid = X[\"id_left\"][pos_idx]\n", - " keep_idx_bool = np.array([ qid in pos_qid for qid in X[\"id_left\"]])\n", - " keep_idx = np.arange(keep_idx_bool.shape[0])\n", - " keep_idx = keep_idx[keep_idx_bool]\n", - " print(\"Removed questions with no pos label. Found \", (keep_idx_bool == 0).sum())\n", - "\n", - " print(\"shuffling...\")\n", - " np.random.shuffle(keep_idx)\n", - " for k in X.keys():\n", - " X[k] = X[k][keep_idx]\n", - " Y = Y[keep_idx]\n", - "\n", - " return X, Y, preprocessor" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 12754.26it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:02<00:00, 6500.31it/s]\n", - "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 1215206.55it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 185258.28it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 184455.70it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 922581.36it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 1082236.12it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 404432/404432 [00:00<00:00, 3795031.47it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 13650.60it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:02<00:00, 6764.51it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 171037.31it/s]\n", - "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 288623.28it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 90725.37it/s]\n", - "Processing length_left with len: 100%|██████████| 2118/2118 [00:00<00:00, 583636.81it/s]\n", - "Processing length_right with len: 100%|██████████| 18841/18841 [00:00<00:00, 1203693.44it/s]\n", - "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 193145.54it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 134549.60it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removed empty data. Found 38\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 296/296 [00:00<00:00, 14135.26it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 0%| | 0/2708 [00:00 Lowercase => PuncRemoval: 100%|██████████| 2708/2708 [00:00<00:00, 6731.87it/s]\n", - "Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 168473.93it/s]\n", - "Processing text_left with transform: 100%|██████████| 296/296 [00:00<00:00, 204701.40it/s]\n", - "Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 159066.95it/s]\n", - "Processing length_left with len: 100%|██████████| 296/296 [00:00<00:00, 442607.48it/s]\n", - "Processing length_right with len: 100%|██████████| 2708/2708 [00:00<00:00, 1038699.15it/s]\n", - "Processing text_left with transform: 100%|██████████| 296/296 [00:00<00:00, 149130.81it/s]\n", - "Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 140864.36it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 633/633 [00:00<00:00, 12189.39it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removed empty data. Found 2\n", - "Removed questions with no pos label. Found 1601\n", - "shuffling...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 5961/5961 [00:00<00:00, 7064.16it/s]\n", - "Processing text_right with transform: 100%|██████████| 5961/5961 [00:00<00:00, 187399.25it/s]\n", - "Processing text_left with transform: 100%|██████████| 633/633 [00:00<00:00, 259733.36it/s]\n", - "Processing text_right with transform: 100%|██████████| 5961/5961 [00:00<00:00, 160878.23it/s]\n", - "Processing length_left with len: 100%|██████████| 633/633 [00:00<00:00, 688714.51it/s]\n", - "Processing length_right with len: 100%|██████████| 5961/5961 [00:00<00:00, 1166965.98it/s]\n", - "Processing text_left with transform: 100%|██████████| 633/633 [00:00<00:00, 158526.06it/s]\n", - "Processing text_right with transform: 100%|██████████| 5961/5961 [00:00<00:00, 137558.64it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removed empty data. Found 18\n", - "Removed questions with no pos label. Found 3805\n", - "shuffling...\n" - ] - } - ], - "source": [ - "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=20,\n", - " fixed_length_right=40,\n", - " remove_stop_words=False)\n", - "train_X, train_Y, preprocessor = load_filtered_data(preprocessor, 'train')\n", - "val_X, val_Y, _ = load_filtered_data(preprocessor, 'dev')\n", - "pred_X, pred_Y, _ = load_filtered_data(preprocessor, 'test')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 20) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 40) 0 \n", - "__________________________________________________________________________________________________\n", - "embedding (Embedding) multiple 5002500 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_1 (Dropout) multiple 0 embedding[0][0] \n", - " embedding[1][0] \n", - " dense_1[0][0] \n", - " dense_1[1][0] \n", - " dense_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_1 (Lambda) multiple 0 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "bidirectional_1 (Bidirectional) multiple 1442400 dropout_1[0][0] \n", - " dropout_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_2 (Lambda) (None, 20, 1) 0 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_3 (Lambda) (None, 40, 1) 0 lambda_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_1 (Multiply) (None, 20, 600) 0 bidirectional_1[0][0] \n", - " lambda_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_2 (Multiply) (None, 40, 600) 0 bidirectional_1[1][0] \n", - " lambda_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_4 (Lambda) (None, 20, 1) 0 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_5 (Lambda) (None, 1, 40) 0 lambda_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "dot_1 (Dot) (None, 20, 40) 0 multiply_1[0][0] \n", - " multiply_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_3 (Multiply) (None, 20, 40) 0 lambda_4[0][0] \n", - " lambda_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "permute_1 (Permute) (None, 40, 20) 0 dot_1[0][0] \n", - " multiply_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "atten_mask (Lambda) multiple 0 dot_1[0][0] \n", - " multiply_3[0][0] \n", - " permute_1[0][0] \n", - " permute_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "softmax_1 (Softmax) multiple 0 atten_mask[0][0] \n", - " atten_mask[1][0] \n", - "__________________________________________________________________________________________________\n", - "dot_2 (Dot) (None, 20, 600) 0 softmax_1[0][0] \n", - " multiply_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dot_3 (Dot) (None, 40, 600) 0 softmax_1[1][0] \n", - " multiply_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "subtract_1 (Subtract) (None, 20, 600) 0 multiply_1[0][0] \n", - " dot_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_4 (Multiply) (None, 20, 600) 0 multiply_1[0][0] \n", - " dot_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "subtract_2 (Subtract) (None, 40, 600) 0 multiply_2[0][0] \n", - " dot_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_5 (Multiply) (None, 40, 600) 0 multiply_2[0][0] \n", - " dot_3[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_1 (Concatenate) (None, 20, 2400) 0 multiply_1[0][0] \n", - " dot_2[0][0] \n", - " subtract_1[0][0] \n", - " multiply_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_2 (Concatenate) (None, 40, 2400) 0 multiply_2[0][0] \n", - " dot_3[0][0] \n", - " subtract_2[0][0] \n", - " multiply_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) multiple 720300 concatenate_1[0][0] \n", - " concatenate_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "bidirectional_2 (Bidirectional) multiple 1442400 dropout_1[2][0] \n", - " dropout_1[3][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_6 (Lambda) (None, 20, 1) 0 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_8 (Lambda) (None, 20, 1) 0 lambda_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_10 (Lambda) (None, 40, 1) 0 lambda_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_12 (Lambda) (None, 40, 1) 0 lambda_1[1][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_6 (Multiply) (None, 20, 600) 0 bidirectional_2[0][0] \n", - " lambda_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_7 (Multiply) (None, 20, 600) 0 bidirectional_2[0][0] \n", - " lambda_8[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_8 (Multiply) (None, 40, 600) 0 bidirectional_2[1][0] \n", - " lambda_10[0][0] \n", - "__________________________________________________________________________________________________\n", - "multiply_9 (Multiply) (None, 40, 600) 0 bidirectional_2[1][0] \n", - " lambda_12[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_7 (Lambda) (None, 600) 0 multiply_6[0][0] \n", - " lambda_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_9 (Lambda) (None, 600) 0 multiply_7[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_11 (Lambda) (None, 600) 0 multiply_8[0][0] \n", - " lambda_10[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_13 (Lambda) (None, 600) 0 multiply_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_3 (Concatenate) (None, 1200) 0 lambda_7[0][0] \n", - " lambda_9[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_4 (Concatenate) (None, 1200) 0 lambda_11[0][0] \n", - " lambda_13[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_5 (Concatenate) (None, 2400) 0 concatenate_3[0][0] \n", - " concatenate_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_2 (Dense) (None, 300) 720300 concatenate_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_3 (Dense) (None, 2) 602 dropout_1[4][0] \n", - "==================================================================================================\n", - "Total params: 9,328,502\n", - "Trainable params: 4,326,002\n", - "Non-trainable params: 5,002,500\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "from keras.optimizers import Adam\n", - "import matchzoo\n", - "\n", - "model = matchzoo.contrib.models.ESIM()\n", - "\n", - "# update `input_shapes` and `embedding_input_dim`\n", - "# model.params['task'] = mz.tasks.Ranking() \n", - "# or \n", - "model.params['task'] = mz.tasks.Classification(num_classes=2)\n", - "model.params.update(preprocessor.context)\n", - "\n", - "model.params['mask_value'] = 0\n", - "model.params['lstm_dim'] = 300\n", - "model.params['embedding_output_dim'] = 300\n", - "model.params['embedding_trainable'] = False\n", - "model.params['dropout_rate'] = 0.5\n", - "\n", - "model.params['mlp_num_units'] = 300\n", - "model.params['mlp_num_layers'] = 0\n", - "model.params['mlp_num_fan_out'] = 300\n", - "model.params['mlp_activation_func'] = 'tanh'\n", - "model.params['optimizer'] = Adam(lr=1e-4)\n", - "model.guess_and_fill_missing_params()\n", - "model.build()\n", - "model.compile()\n", - "model.backend.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'], initializer=lambda: 0)\n", - "model.load_embedding_matrix(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 8650 samples, validate on 1130 samples\n", - "Epoch 1/10\n", - "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0985 - val_loss: 0.0977\n", - "Validation: mean_average_precision(0.0): 0.6377925262180991\n", - "Epoch 2/10\n", - "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0947 - val_loss: 0.0939\n", - "Validation: mean_average_precision(0.0): 0.6323746460063332\n", - "Epoch 3/10\n", - "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0923 - val_loss: 0.0896\n", - "Validation: mean_average_precision(0.0): 0.6447892278707743\n", - "Epoch 4/10\n", - "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0895 - val_loss: 0.0904\n", - "Validation: mean_average_precision(0.0): 0.6645210508066117\n", - "Epoch 5/10\n", - "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0883 - val_loss: 0.0900\n", - "Validation: mean_average_precision(0.0): 0.6622282952529867\n", - "Epoch 6/10\n", - "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0839 - val_loss: 0.0900\n", - "Validation: mean_average_precision(0.0): 0.6654279587941297\n", - "Epoch 7/10\n", - "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0821 - val_loss: 0.0896\n", - "Validation: mean_average_precision(0.0): 0.6668269018575894\n", - "Epoch 8/10\n", - "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0792 - val_loss: 0.0885\n", - "Validation: mean_average_precision(0.0): 0.6723704781393599\n", - "Epoch 9/10\n", - "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0754 - val_loss: 0.0895\n", - "Validation: mean_average_precision(0.0): 0.6552521148587158\n", - "Epoch 10/10\n", - "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0731 - val_loss: 0.0910\n", - "Validation: mean_average_precision(0.0): 0.6695447388956829\n" - ] - } - ], - "source": [ - "# train as ranking task\n", - "model.params['task'] = mz.tasks.Ranking()\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model,\n", - " x=pred_X,\n", - " y=pred_Y,\n", - " once_every=1,\n", - " batch_size=len(pred_Y))\n", - "history = model.fit(x = [train_X['text_left'],\n", - " train_X['text_right']], # (20360, 1000)\n", - " y = train_Y, # (20360, 2)\n", - " validation_data = (val_X, val_Y),\n", - " callbacks=[evaluate],\n", - " batch_size = 32,\n", - " epochs = 10)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 8650 samples, validate on 1130 samples\n", - "Epoch 1/10\n", - "8650/8650 [==============================] - 68s 8ms/step - loss: 0.3628 - val_loss: 0.3552\n", - "Epoch 2/10\n", - "8650/8650 [==============================] - 63s 7ms/step - loss: 0.3285 - val_loss: 0.3591\n", - "Epoch 3/10\n", - "8650/8650 [==============================] - 63s 7ms/step - loss: 0.3105 - val_loss: 0.3681\n", - "Epoch 4/10\n", - "8650/8650 [==============================] - 64s 7ms/step - loss: 0.3012 - val_loss: 0.3166\n", - "Epoch 5/10\n", - "8650/8650 [==============================] - 64s 7ms/step - loss: 0.2888 - val_loss: 0.2961\n", - "Epoch 6/10\n", - "8650/8650 [==============================] - 64s 7ms/step - loss: 0.2801 - val_loss: 0.3362\n", - "Epoch 7/10\n", - "8650/8650 [==============================] - 64s 7ms/step - loss: 0.2692 - val_loss: 0.3324\n", - "Epoch 8/10\n", - "8650/8650 [==============================] - 64s 7ms/step - loss: 0.2609 - val_loss: 0.3172\n", - "Epoch 9/10\n", - "8650/8650 [==============================] - 58s 7ms/step - loss: 0.2542 - val_loss: 0.3296\n", - "Epoch 10/10\n", - "8650/8650 [==============================] - 53s 6ms/step - loss: 0.2365 - val_loss: 0.3058\n" - ] - } - ], - "source": [ - "# train as classification task \n", - "\n", - "from keras.utils import to_categorical\n", - "train_Y = to_categorical(train_Y)\n", - "val_Y = to_categorical(val_Y)\n", - "\n", - "model.params['task'] = mz.tasks.Classification(num_classes=2)\n", - "\n", - "history = model.fit(x = [train_X['text_left'],\n", - " train_X['text_right']], # (20360, 1000)\n", - " y = train_Y, # (20360, 2)\n", - " validation_data = (val_X, val_Y),\n", - " batch_size = 32,\n", - " epochs = 10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "mz_play", - "language": "python", - "name": "mz_play" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/init.ipynb b/tutorials/wikiqa/init.ipynb deleted file mode 100644 index 5408c079..00000000 --- a/tutorials/wikiqa/init.ipynb +++ /dev/null @@ -1,206 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:24:32.779551Z", - "start_time": "2019-03-20T09:24:30.316404Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n" - ] - } - ], - "source": [ - "import keras\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matchzoo as mz\n", - "import json\n", - "print('matchzoo version', mz.__version__)\n", - "print()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:24:33.363273Z", - "start_time": "2019-03-20T09:24:32.781793Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n" - ] - } - ], - "source": [ - "print('data loading ...')\n", - "train_pack_raw = mz.datasets.wiki_qa.load_data('train', task='ranking')\n", - "dev_pack_raw = mz.datasets.wiki_qa.load_data('dev', task='ranking', filtered=True)\n", - "test_pack_raw = mz.datasets.wiki_qa.load_data('test', task='ranking', filtered=True)\n", - "print('data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:24:33.370082Z", - "start_time": "2019-03-20T09:24:33.365067Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n" - ] - } - ], - "source": [ - "ranking_task = mz.tasks.Ranking(loss=mz.losses.RankHingeLoss())\n", - "ranking_task.metrics = [\n", - " mz.metrics.NormalizedDiscountedCumulativeGain(k=3),\n", - " mz.metrics.NormalizedDiscountedCumulativeGain(k=5),\n", - " mz.metrics.MeanAveragePrecision()\n", - "]\n", - "print(\"`ranking_task` initialized with metrics\", ranking_task.metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:24:42.021896Z", - "start_time": "2019-03-20T09:24:33.374357Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "print(\"loading embedding ...\")\n", - "glove_embedding = mz.datasets.embeddings.load_glove_embedding(dimension=300)\n", - "print(\"embedding loaded as `glove_embedding`\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2019-03-20T09:24:42.033039Z", - "start_time": "2019-03-20T09:24:42.025321Z" - }, - "collapsed": true - }, - "outputs": [], - "source": [ - "def append_params_to_readme(model):\n", - " import tabulate\n", - " \n", - " with open('README.rst', 'a+') as f:\n", - " subtitle = model.params['model_class'].__name__\n", - " line = '#' * len(subtitle)\n", - " subtitle = subtitle + '\\n' + line + '\\n\\n'\n", - " f.write(subtitle)\n", - " \n", - " df = model.params.to_frame()[['Name', 'Value']]\n", - " table = tabulate.tabulate(df, tablefmt='rst', headers='keys') + '\\n\\n'\n", - " f.write(table)" - ] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, - "toc": { - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "toc_cell": false, - "toc_position": {}, - "toc_section_display": "block", - "toc_window_display": false - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/knrm.ipynb b/tutorials/wikiqa/knrm.ipynb deleted file mode 100644 index e37dec61..00000000 --- a/tutorials/wikiqa/knrm.ipynb +++ /dev/null @@ -1,320 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n", - "/home/fanyixing/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n", - "/home/fanyixing/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 3665.85it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:08<00:00, 2124.56it/s]\n", - "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 312816.21it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 58156.99it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 36334.58it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 344069.71it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 301782.94it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 404415/404415 [00:00<00:00, 1562426.96it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 3087.94it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:10<00:00, 1847.91it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 55576.41it/s]\n", - "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 37457.67it/s]\n", - 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"Processing length_right with len: 100%|██████████| 2300/2300 [00:00<00:00, 356197.59it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 19210.92it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 24293.37it/s]\n" - ] - } - ], - "source": [ - "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10, fixed_length_right=40, remove_stop_words=False)\n", - "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n", - "valid_pack_processed = preprocessor.transform(dev_pack_raw)\n", - "test_pack_processed = preprocessor.transform(test_pack_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'filter_unit': ,\n", - " 'vocab_unit': ,\n", - " 'vocab_size': 16674,\n", - " 'embedding_input_dim': 16674,\n", - " 'input_shapes': [(10,), (40,)]}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessor.context" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "model = mz.models.KNRM()\n", - "model.params.update(preprocessor.context)\n", - "model.params['task'] = ranking_task\n", - "model.params['embedding_output_dim'] = glove_embedding.output_dim\n", - "model.params['embedding_trainable'] = True\n", - "model.params['kernel_num'] = 21\n", - "model.params['sigma'] = 0.1\n", - "model.params['exact_sigma'] = 0.001\n", - "model.params['optimizer'] = 'adadelta'\n", - "model.build()\n", - "model.compile()\n", - "#model.backend.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])\n", - "model.load_embedding_matrix(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "pred_x, pred_y = test_pack_processed.unpack()\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=pred_x, y=pred_y, batch_size=len(pred_x))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num batches: 255\n" - ] - } - ], - "source": [ - "train_generator = mz.DataGenerator(\n", - " train_pack_processed,\n", - " mode='pair',\n", - " num_dup=5,\n", - " num_neg=1,\n", - " batch_size=20\n", - ")\n", - "print('num batches:', len(train_generator))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/30\n", - "255/255 [==============================] - 30s 117ms/step - loss: 1.0630\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.525888722537362 - normalized_discounted_cumulative_gain@5(0.0): 0.5969076581909297 - mean_average_precision(0.0): 0.5458421758049457\n", - "Epoch 2/30\n", - "255/255 [==============================] - 40s 158ms/step - loss: 0.4855\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.540316755776057 - normalized_discounted_cumulative_gain@5(0.0): 0.6183376741777723 - mean_average_precision(0.0): 0.5712611133729898\n", - "Epoch 3/30\n", - "255/255 [==============================] - 39s 155ms/step - loss: 0.3582\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.541616192577938 - normalized_discounted_cumulative_gain@5(0.0): 0.6153837509826408 - mean_average_precision(0.0): 0.567777172721793\n", - "Epoch 4/30\n", - "255/255 [==============================] - 40s 156ms/step - loss: 0.2887\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5584765720489868 - normalized_discounted_cumulative_gain@5(0.0): 0.6194525103413756 - mean_average_precision(0.0): 0.5778673022579046\n", - "Epoch 5/30\n", - "255/255 [==============================] - 41s 162ms/step - loss: 0.2118\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5528840743131206 - normalized_discounted_cumulative_gain@5(0.0): 0.6232796348158448 - mean_average_precision(0.0): 0.5763382056568952\n", - "Epoch 6/30\n", - "255/255 [==============================] - 41s 160ms/step - loss: 0.1721\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5479498178842863 - normalized_discounted_cumulative_gain@5(0.0): 0.6080774335846967 - mean_average_precision(0.0): 0.5628146118070727\n", - "Epoch 7/30\n", - "255/255 [==============================] - 42s 163ms/step - loss: 0.1351\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5448249760251662 - normalized_discounted_cumulative_gain@5(0.0): 0.6050334611320453 - mean_average_precision(0.0): 0.5539922350259576\n", - "Epoch 8/30\n", - "255/255 [==============================] - 41s 161ms/step - loss: 0.1056\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5278279251987681 - normalized_discounted_cumulative_gain@5(0.0): 0.5926550462383007 - mean_average_precision(0.0): 0.546292777345115\n", - "Epoch 9/30\n", - "255/255 [==============================] - 40s 155ms/step - loss: 0.0843\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5260762848714361 - normalized_discounted_cumulative_gain@5(0.0): 0.5997466236266378 - mean_average_precision(0.0): 0.5463433489984417\n", - "Epoch 10/30\n", - "255/255 [==============================] - 39s 151ms/step - loss: 0.0641\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.53545626407713 - normalized_discounted_cumulative_gain@5(0.0): 0.5911736172377866 - mean_average_precision(0.0): 0.5511742297719537\n", - "Epoch 11/30\n", - "255/255 [==============================] - 39s 154ms/step - loss: 0.0506\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5281313957863254 - normalized_discounted_cumulative_gain@5(0.0): 0.5909946137693491 - mean_average_precision(0.0): 0.5455621168460892\n", - "Epoch 12/30\n", - "255/255 [==============================] - 42s 163ms/step - loss: 0.0411\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5295249224151654 - normalized_discounted_cumulative_gain@5(0.0): 0.5882240550416985 - mean_average_precision(0.0): 0.544818346189001\n", - "Epoch 13/30\n", - "255/255 [==============================] - 41s 162ms/step - loss: 0.0356\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5332953580728098 - normalized_discounted_cumulative_gain@5(0.0): 0.5923864923271813 - mean_average_precision(0.0): 0.5449250708396572\n", - "Epoch 14/30\n", - "255/255 [==============================] - 42s 163ms/step - loss: 0.0275\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5344059653683887 - normalized_discounted_cumulative_gain@5(0.0): 0.5852285609827376 - mean_average_precision(0.0): 0.5457076553713109\n", - "Epoch 15/30\n", - "255/255 [==============================] - 42s 163ms/step - loss: 0.0224\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5326101057106915 - normalized_discounted_cumulative_gain@5(0.0): 0.5896572847849786 - mean_average_precision(0.0): 0.5461713501388032\n", - "Epoch 16/30\n", - "255/255 [==============================] - 40s 155ms/step - loss: 0.0189\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5209623555446414 - normalized_discounted_cumulative_gain@5(0.0): 0.5907051989832439 - mean_average_precision(0.0): 0.544006088275105\n", - "Epoch 17/30\n", - "255/255 [==============================] - 39s 153ms/step - loss: 0.0154\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5270528188502702 - normalized_discounted_cumulative_gain@5(0.0): 0.5929241490222487 - mean_average_precision(0.0): 0.5458721064851292\n", - "Epoch 18/30\n", - "255/255 [==============================] - 39s 152ms/step - loss: 0.0134\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.52963998719156 - normalized_discounted_cumulative_gain@5(0.0): 0.59748806088061 - mean_average_precision(0.0): 0.5504225606330331\n", - "Epoch 19/30\n", - "255/255 [==============================] - 41s 161ms/step - loss: 0.0129\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5173295480303289 - normalized_discounted_cumulative_gain@5(0.0): 0.5890029693924619 - mean_average_precision(0.0): 0.5393698842857736\n", - "Epoch 20/30\n", - "255/255 [==============================] - 42s 164ms/step - loss: 0.0136\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5319324815526434 - normalized_discounted_cumulative_gain@5(0.0): 0.599965023036905 - mean_average_precision(0.0): 0.550039654928675\n", - "Epoch 21/30\n", - "255/255 [==============================] - 42s 163ms/step - loss: 0.0108\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5311517097282423 - normalized_discounted_cumulative_gain@5(0.0): 0.5943106559500474 - mean_average_precision(0.0): 0.5487263041947172\n", - "Epoch 22/30\n", - "255/255 [==============================] - 42s 165ms/step - loss: 0.0093\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5340421861939937 - normalized_discounted_cumulative_gain@5(0.0): 0.6020890805193887 - mean_average_precision(0.0): 0.5541712982869859\n", - "Epoch 23/30\n", - "255/255 [==============================] - 40s 157ms/step - loss: 0.0063\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5245100179927665 - normalized_discounted_cumulative_gain@5(0.0): 0.5896176258663225 - mean_average_precision(0.0): 0.5427922534821856\n", - "Epoch 24/30\n", - "255/255 [==============================] - 40s 157ms/step - loss: 0.00841s - loss\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.528425907303436 - normalized_discounted_cumulative_gain@5(0.0): 0.5952097664878763 - mean_average_precision(0.0): 0.5498493585406231\n", - "Epoch 25/30\n", - "255/255 [==============================] - 41s 160ms/step - loss: 0.0071\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5303218965746611 - normalized_discounted_cumulative_gain@5(0.0): 0.5948210455548147 - mean_average_precision(0.0): 0.5505741686543946\n", - "Epoch 26/30\n", - "255/255 [==============================] - 41s 160ms/step - loss: 0.0065\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5223105087495103 - normalized_discounted_cumulative_gain@5(0.0): 0.5901051330822427 - mean_average_precision(0.0): 0.5426582852986084\n", - "Epoch 27/30\n", - "255/255 [==============================] - 42s 164ms/step - loss: 0.0045\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5259626991148388 - normalized_discounted_cumulative_gain@5(0.0): 0.5922912075108971 - mean_average_precision(0.0): 0.5439769673667663\n", - "Epoch 28/30\n", - "255/255 [==============================] - 42s 164ms/step - loss: 0.0039\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5326394392626637 - normalized_discounted_cumulative_gain@5(0.0): 0.5991254533953382 - mean_average_precision(0.0): 0.5509986930248422\n", - "Epoch 29/30\n", - "255/255 [==============================] - 34s 134ms/step - loss: 0.0037\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5250020425449076 - normalized_discounted_cumulative_gain@5(0.0): 0.5952453236394062 - mean_average_precision(0.0): 0.5498564190858788\n", - "Epoch 30/30\n", - "255/255 [==============================] - 35s 138ms/step - loss: 0.0046\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5309288871342496 - normalized_discounted_cumulative_gain@5(0.0): 0.5931766459815412 - mean_average_precision(0.0): 0.5474981722703828\n" - ] - } - ], - "source": [ - "history = model.fit_generator(train_generator, epochs=30, callbacks=[evaluate], workers=30, use_multiprocessing=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/match_lstm.ipynb b/tutorials/wikiqa/match_lstm.ipynb deleted file mode 100644 index 237a87ae..00000000 --- a/tutorials/wikiqa/match_lstm.ipynb +++ /dev/null @@ -1,336 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10, fixed_length_right=40, remove_stop_words=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 5660.56it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:04<00:00, 4467.85it/s]\n", - "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 786301.58it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 122848.89it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 139092.07it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 454679.90it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 669810.24it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 404432/404432 [00:00<00:00, 2422809.59it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 8241.89it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:04<00:00, 4600.64it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 118459.97it/s]\n", - "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 188154.70it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 41857.09it/s]\n", - "Processing length_left with len: 100%|██████████| 2118/2118 [00:00<00:00, 588586.49it/s]\n", - "Processing length_right with len: 100%|██████████| 18841/18841 [00:00<00:00, 725717.97it/s]\n", - "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 114181.33it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 89142.16it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 122/122 [00:00<00:00, 8251.31it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 1115/1115 [00:00<00:00, 4700.14it/s]\n", - "Processing text_right with transform: 100%|██████████| 1115/1115 [00:00<00:00, 119923.30it/s]\n", - "Processing text_left with transform: 100%|██████████| 122/122 [00:00<00:00, 97728.24it/s]\n", - "Processing text_right with transform: 100%|██████████| 1115/1115 [00:00<00:00, 124012.86it/s]\n", - "Processing length_left with len: 100%|██████████| 122/122 [00:00<00:00, 181842.60it/s]\n", - "Processing length_right with len: 100%|██████████| 1115/1115 [00:00<00:00, 544176.05it/s]\n", - "Processing text_left with transform: 100%|██████████| 122/122 [00:00<00:00, 89147.23it/s]\n", - "Processing text_right with transform: 100%|██████████| 1115/1115 [00:00<00:00, 89651.09it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 237/237 [00:00<00:00, 8348.80it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2300/2300 [00:00<00:00, 4593.53it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 131653.35it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 165840.85it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 132096.83it/s]\n", - "Processing length_left with len: 100%|██████████| 237/237 [00:00<00:00, 273842.99it/s]\n", - "Processing length_right with len: 100%|██████████| 2300/2300 [00:00<00:00, 639163.80it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 89064.60it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 86151.49it/s]\n" - ] - } - ], - "source": [ - "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n", - "dev_pack_processed = preprocessor.transform(dev_pack_raw)\n", - "test_pack_processed = preprocessor.transform(test_pack_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'filter_unit': ,\n", - " 'vocab_unit': ,\n", - " 'vocab_size': 16674,\n", - " 'embedding_input_dim': 16674,\n", - " 'input_shapes': [(10,), (40,)]}" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessor.context" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model_class \n", - "input_shapes [(10,), (40,)]\n", - "task Ranking Task\n", - "optimizer adadelta\n", - "with_embedding True\n", - "embedding_input_dim 16674\n", - "embedding_output_dim 100\n", - "embedding_trainable True\n", - "lstm_num_units 100\n", - "fc_num_units 100\n", - "dropout_rate 0.5\n" - ] - } - ], - "source": [ - "model = mz.contrib.models.MatchLSTM()\n", - "model.params.update(preprocessor.context)\n", - "model.params['task'] = ranking_task\n", - "model.params['embedding_output_dim'] = 100\n", - "model.params['embedding_trainable'] = True\n", - "model.params['fc_num_units'] = 100\n", - "model.params['lstm_num_units'] = 100\n", - "model.params['dropout_rate'] = 0.5\n", - "model.params['optimizer'] = 'adadelta'\n", - "model.guess_and_fill_missing_params()\n", - "model.build()\n", - "model.compile()\n", - "print(model.params)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 10) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 40) 0 \n", - "__________________________________________________________________________________________________\n", - "embedding (Embedding) multiple 1667400 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "lstm_left (LSTM) (None, 10, 100) 80400 embedding[0][0] \n", - "__________________________________________________________________________________________________\n", - "lstm_right (LSTM) (None, 40, 100) 80400 embedding[1][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_10 (Lambda) (None, 10, 100) 0 lstm_left[0][0] \n", - " lstm_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "concatenate_4 (Concatenate) (None, 50, 100) 0 lambda_10[0][0] \n", - " lstm_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "lstm_merge (LSTM) (None, 200) 240800 concatenate_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_4 (Dropout) (None, 200) 0 lstm_merge[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_23 (Dense) (None, 100) 20100 dropout_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_24 (Dense) (None, 1) 101 dense_23[0][0] \n", - "==================================================================================================\n", - "Total params: 2,089,201\n", - "Trainable params: 2,089,201\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model.backend.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])\n", - "model.load_embedding_matrix(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "test_x, test_y = test_pack_processed.unpack()\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=test_x, y=test_y, batch_size=len(test_x))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num batches: 102\n" - ] - } - ], - "source": [ - "train_generator = mz.DataGenerator(\n", - " train_pack_processed,\n", - " mode='pair',\n", - " num_dup=2,\n", - " num_neg=1,\n", - " batch_size=20\n", - ")\n", - "print('num batches:', len(train_generator))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10\n", - "102/102 [==============================] - 13s 130ms/step - loss: 0.9022\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.56891574533972 - normalized_discounted_cumulative_gain@5(0.0): 0.6259075966908896 - mean_average_precision(0.0): 0.5895521163084454\n", - "Epoch 2/10\n", - "102/102 [==============================] - 11s 106ms/step - loss: 0.6955\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5876851995953162 - normalized_discounted_cumulative_gain@5(0.0): 0.6407140437458756 - mean_average_precision(0.0): 0.5965985760516177\n", - "Epoch 3/10\n", - "102/102 [==============================] - 11s 105ms/step - loss: 0.6073\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6151453530205596 - normalized_discounted_cumulative_gain@5(0.0): 0.6639169915844698 - mean_average_precision(0.0): 0.6198851976278136\n", - "Epoch 4/10\n", - "102/102 [==============================] - 11s 105ms/step - loss: 0.5805\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6097028553948147 - normalized_discounted_cumulative_gain@5(0.0): 0.6654420380026644 - mean_average_precision(0.0): 0.6240460033736575\n", - "Epoch 5/10\n", - "102/102 [==============================] - 11s 108ms/step - loss: 0.5180\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6133176603089614 - normalized_discounted_cumulative_gain@5(0.0): 0.6538666262678027 - mean_average_precision(0.0): 0.6188266626371615\n", - "Epoch 6/10\n", - "102/102 [==============================] - 11s 107ms/step - loss: 0.4860\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6196634602764765 - normalized_discounted_cumulative_gain@5(0.0): 0.6765955662781967 - mean_average_precision(0.0): 0.6318559407749947\n", - "Epoch 7/10\n", - "102/102 [==============================] - 11s 107ms/step - loss: 0.4297\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5911951516660675 - normalized_discounted_cumulative_gain@5(0.0): 0.6356282828122544 - mean_average_precision(0.0): 0.5974699334878332\n", - "Epoch 8/10\n", - "102/102 [==============================] - 11s 105ms/step - loss: 0.3946\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6316524262499843 - normalized_discounted_cumulative_gain@5(0.0): 0.6774169076547488 - mean_average_precision(0.0): 0.6368686569077484\n", - "Epoch 9/10\n", - "102/102 [==============================] - 11s 106ms/step - loss: 0.3788\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5969334279811508 - normalized_discounted_cumulative_gain@5(0.0): 0.6513736764474628 - mean_average_precision(0.0): 0.607785113937385\n", - "Epoch 10/10\n", - "102/102 [==============================] - 11s 106ms/step - loss: 0.3170\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6266464172038838 - normalized_discounted_cumulative_gain@5(0.0): 0.6722764129615637 - mean_average_precision(0.0): 0.6201359089808456\n" - ] - } - ], - "source": [ - "history = model.fit_generator(train_generator, epochs=10, callbacks=[evaluate], workers=4, use_multiprocessing=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use this function to update the README.md with a better set of parameters. Make sure you delete the correct section of the README.md before calling this function." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "append_params_to_readme(model)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/matchpyramid.ipynb b/tutorials/wikiqa/matchpyramid.ipynb deleted file mode 100644 index 21ee09e6..00000000 --- a/tutorials/wikiqa/matchpyramid.ipynb +++ /dev/null @@ -1,419 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10, \n", - " fixed_length_right=40, \n", - " remove_stop_words=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 8302.01it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 18841/18841 [00:04<00:00, 4319.36it/s]\n", - "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 903668.21it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 195422.81it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 108320.04it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 725009.05it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 860396.98it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 234263/234263 [00:00<00:00, 2861207.82it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 8464.35it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 18841/18841 [00:04<00:00, 4340.04it/s]\n", - 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"Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 208270.89it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 242925.23it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 199443.84it/s]\n", - "Processing length_left with len: 100%|██████████| 237/237 [00:00<00:00, 323689.37it/s]\n", - "Processing length_right with len: 100%|██████████| 2300/2300 [00:00<00:00, 703075.52it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 113696.68it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 119756.92it/s]\n" - ] - } - ], - "source": [ - "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n", - "dev_pack_processed = preprocessor.transform(dev_pack_raw)\n", - "test_pack_processed = preprocessor.transform(test_pack_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model_class \n", - "input_shapes [(10,), (40,)]\n", - "task Ranking Task\n", - "optimizer adam\n", - "with_embedding True\n", - "embedding_input_dim 16546\n", - "embedding_output_dim 100\n", - "embedding_trainable True\n", - "num_blocks 2\n", - "kernel_count [16, 32]\n", - "kernel_size [[3, 3], [3, 3]]\n", - "activation relu\n", - "dpool_size [3, 10]\n", - "padding same\n", - "dropout_rate 0.1\n" - ] - } - ], - "source": [ - "model = mz.models.MatchPyramid()\n", - "\n", - "# load `input_shapes` and `embedding_input_dim` (vocab_size)\n", - "model.params.update(preprocessor.context)\n", - "\n", - "model.params['task'] = ranking_task\n", - "model.params['embedding_output_dim'] = 100\n", - "model.params['embedding_trainable'] = True\n", - "model.params['num_blocks'] = 2\n", - "model.params['kernel_count'] = [16, 32]\n", - "model.params['kernel_size'] = [[3, 3], [3, 3]]\n", - "model.params['dpool_size'] = [3, 10]\n", - "model.params['optimizer'] = 'adam'\n", - "model.params['dropout_rate'] = 0.1\n", - "\n", - "model.build()\n", - "model.compile()\n", - "\n", - "print(model.params)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 10) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 40) 0 \n", - "__________________________________________________________________________________________________\n", - "embedding (Embedding) multiple 1654600 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "matching_layer_1 (MatchingLayer (None, 10, 40, 1) 0 embedding[0][0] \n", - " embedding[1][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_1 (Conv2D) (None, 10, 40, 16) 160 matching_layer_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "conv2d_2 (Conv2D) (None, 10, 40, 32) 4640 conv2d_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dpool_index (InputLayer) (None, 10, 40, 2) 0 \n", - "__________________________________________________________________________________________________\n", - "dynamic_pooling_layer_1 (Dynami (None, 3, 10, 32) 0 conv2d_2[0][0] \n", - " dpool_index[0][0] \n", - "__________________________________________________________________________________________________\n", - "flatten_1 (Flatten) (None, 960) 0 dynamic_pooling_layer_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_1 (Dropout) (None, 960) 0 flatten_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_1 (Dense) (None, 1) 961 dropout_1[0][0] \n", - "==================================================================================================\n", - "Total params: 1,660,361\n", - "Trainable params: 1,660,361\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model.backend.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "model.load_embedding_matrix(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num batches: 102\n" - ] - } - ], - "source": [ - "dpool_callback = mz.data_generator.callbacks.DynamicPooling(\n", - " fixed_length_left=10, \n", - " fixed_length_right=40\n", - ")\n", - "train_generator = mz.DataGenerator(\n", - " train_pack_processed,\n", - " mode='pair',\n", - " num_dup=2,\n", - " num_neg=1,\n", - " batch_size=20,\n", - " callbacks=[dpool_callback]\n", - ")\n", - "print('num batches:', len(train_generator))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "118" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_generator = mz.DataGenerator(\n", - " test_pack_processed,\n", - " batch_size=20,\n", - " callbacks=[dpool_callback]\n", - ")\n", - "len(test_generator)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "test_x, test_y = test_generator[:]\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=test_x, y=test_y, batch_size=len(test_y))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n", - "102/102 [==============================] - 5s 47ms/step - loss: 0.8098\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5543583477044723 - normalized_discounted_cumulative_gain@5(0.0): 0.6116356675515685 - mean_average_precision(0.0): 0.5743161762170562\n", - "Epoch 2/20\n", - "102/102 [==============================] - 10s 95ms/step - loss: 0.5260\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5301739579616542 - normalized_discounted_cumulative_gain@5(0.0): 0.6016159230710824 - mean_average_precision(0.0): 0.5601950549545589\n", - "Epoch 3/20\n", - "102/102 [==============================] - 10s 101ms/step - loss: 0.3829\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5092929494151631 - normalized_discounted_cumulative_gain@5(0.0): 0.591478646606495 - mean_average_precision(0.0): 0.5441527086702361\n", - "Epoch 4/20\n", - "102/102 [==============================] - 10s 96ms/step - loss: 0.3304\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4812004958713687 - normalized_discounted_cumulative_gain@5(0.0): 0.5596264451275563 - mean_average_precision(0.0): 0.5088711984093643\n", - "Epoch 5/20\n", - "102/102 [==============================] - 11s 109ms/step - loss: 0.2227\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.508961151215818 - normalized_discounted_cumulative_gain@5(0.0): 0.5842189791253107 - mean_average_precision(0.0): 0.5374359920620746\n", - "Epoch 6/20\n", - "102/102 [==============================] - 11s 108ms/step - loss: 0.1847\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4934803343084 - normalized_discounted_cumulative_gain@5(0.0): 0.5644795486607259 - mean_average_precision(0.0): 0.5198714294860981\n", - "Epoch 7/20\n", - "102/102 [==============================] - 11s 108ms/step - loss: 0.1371\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5107068648352034 - normalized_discounted_cumulative_gain@5(0.0): 0.5784800117656429 - mean_average_precision(0.0): 0.5308213418764718\n", - "Epoch 8/20\n", - "102/102 [==============================] - 10s 97ms/step - loss: 0.1038\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.506959204392066 - normalized_discounted_cumulative_gain@5(0.0): 0.5816987880270651 - mean_average_precision(0.0): 0.5314363380052429\n", - "Epoch 9/20\n", - "102/102 [==============================] - 10s 95ms/step - loss: 0.0863\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.49359062647045354 - normalized_discounted_cumulative_gain@5(0.0): 0.5634597428623797 - mean_average_precision(0.0): 0.5230596215775214\n", - "Epoch 10/20\n", - "102/102 [==============================] - 10s 99ms/step - loss: 0.0791\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.514243097562575 - normalized_discounted_cumulative_gain@5(0.0): 0.575757696009702 - mean_average_precision(0.0): 0.5351664532621346\n", - "Epoch 11/20\n", - "102/102 [==============================] - 11s 110ms/step - loss: 0.0486\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5149547148083139 - normalized_discounted_cumulative_gain@5(0.0): 0.5730596976101693 - mean_average_precision(0.0): 0.533951748706618\n", - "Epoch 12/20\n", - "102/102 [==============================] - 11s 108ms/step - loss: 0.0417\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5089291208562943 - normalized_discounted_cumulative_gain@5(0.0): 0.5729989997270044 - mean_average_precision(0.0): 0.5339051698603664\n", - "Epoch 13/20\n", - "102/102 [==============================] - 11s 104ms/step - loss: 0.0293\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.494068153864542 - normalized_discounted_cumulative_gain@5(0.0): 0.5605928322073352 - mean_average_precision(0.0): 0.5139849024134501\n", - "Epoch 14/20\n", - "102/102 [==============================] - 9s 90ms/step - loss: 0.0279\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4987296535086411 - normalized_discounted_cumulative_gain@5(0.0): 0.5671151052462753 - mean_average_precision(0.0): 0.5215687290635513\n", - "Epoch 15/20\n", - "102/102 [==============================] - 10s 95ms/step - loss: 0.0250\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5098806065082092 - normalized_discounted_cumulative_gain@5(0.0): 0.5740280134728454 - mean_average_precision(0.0): 0.5333708142044251\n", - "Epoch 16/20\n", - "102/102 [==============================] - 11s 108ms/step - loss: 0.0182\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5153947436725983 - normalized_discounted_cumulative_gain@5(0.0): 0.5762000806926193 - mean_average_precision(0.0): 0.5298641458868614\n", - "Epoch 17/20\n", - "102/102 [==============================] - 11s 108ms/step - loss: 0.0178\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5134634304473057 - normalized_discounted_cumulative_gain@5(0.0): 0.56980318132021 - mean_average_precision(0.0): 0.5282068879797845\n", - "Epoch 18/20\n", - "102/102 [==============================] - 11s 110ms/step - loss: 0.0145\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5176147404760021 - normalized_discounted_cumulative_gain@5(0.0): 0.581247024416785 - mean_average_precision(0.0): 0.5377934773692993\n", - "Epoch 19/20\n", - "102/102 [==============================] - 9s 92ms/step - loss: 0.0172\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.49962649604962145 - normalized_discounted_cumulative_gain@5(0.0): 0.56731906503144 - mean_average_precision(0.0): 0.5253291982119823\n", - "Epoch 20/20\n", - "102/102 [==============================] - 9s 92ms/step - loss: 0.0181\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.5087417209853161 - normalized_discounted_cumulative_gain@5(0.0): 0.5729943933841338 - mean_average_precision(0.0): 0.5320246681539154\n" - ] - } - ], - "source": [ - "history = model.fit_generator(train_generator, epochs=20, callbacks=[evaluate], workers=30, use_multiprocessing=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{normalized_discounted_cumulative_gain@3(0.0): 0.5087417209853161,\n", - " normalized_discounted_cumulative_gain@5(0.0): 0.5729943933841338,\n", - " mean_average_precision(0.0): 0.5320246681539154}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.evaluate(test_x, test_y, batch_size=5)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "Use this function to update the README.md with a better set of parameters.\n", - "Make sure you delete the correct section of the README.md before calling this function." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "append_params_to_readme(model)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tutorials/wikiqa/mvlstm.ipynb b/tutorials/wikiqa/mvlstm.ipynb deleted file mode 100644 index 91fcd0e6..00000000 --- a/tutorials/wikiqa/mvlstm.ipynb +++ /dev/null @@ -1,340 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n", - "/home/fanyixing/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n", - "/home/fanyixing/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matchzoo version 2.1.0\n", - "\n", - "data loading ...\n", - "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n", - "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n", - "loading embedding ...\n", - "embedding loaded as `glove_embedding`\n" - ] - } - ], - "source": [ - "%run init.ipynb" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 3684.04it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:07<00:00, 2461.23it/s]\n", - "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 461585.85it/s]\n", - "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 71870.86it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 38605.84it/s]\n", - "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 283592.53it/s]\n", - "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 346305.69it/s]\n", - "Building Vocabulary from a datapack.: 100%|██████████| 404415/404415 [00:00<00:00, 1310721.01it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 4026.58it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:08<00:00, 2330.13it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 62427.97it/s]\n", - "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 76941.04it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 60446.57it/s]\n", - "Processing length_left with len: 100%|██████████| 2118/2118 [00:00<00:00, 336141.06it/s]\n", - "Processing length_right with len: 100%|██████████| 18841/18841 [00:00<00:00, 395438.78it/s]\n", - "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 44951.02it/s]\n", - "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 37867.11it/s]\n", - "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 122/122 [00:00<00:00, 4134.42it/s]\n", - "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 1115/1115 [00:00<00:00, 2569.53it/s]\n", - "Processing text_right with transform: 100%|██████████| 1115/1115 [00:00<00:00, 54590.38it/s]\n", - "Processing text_left with transform: 100%|██████████| 122/122 [00:00<00:00, 76373.89it/s]\n", - "Processing text_right with transform: 100%|██████████| 1115/1115 [00:00<00:00, 7920.67it/s]\n", - 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"Processing length_right with len: 100%|██████████| 2300/2300 [00:00<00:00, 367123.31it/s]\n", - "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 41574.66it/s]\n", - "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 37033.81it/s]\n" - ] - } - ], - "source": [ - "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=10, fixed_length_right=40, remove_stop_words=False)\n", - "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n", - "valid_pack_processed = preprocessor.transform(dev_pack_raw)\n", - "test_pack_processed = preprocessor.transform(test_pack_raw)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "text_left (InputLayer) (None, 10) 0 \n", - "__________________________________________________________________________________________________\n", - "text_right (InputLayer) (None, 40) 0 \n", - "__________________________________________________________________________________________________\n", - "embedding (Embedding) multiple 5002200 text_left[0][0] \n", - " text_right[0][0] \n", - "__________________________________________________________________________________________________\n", - "bidirectional_3 (Bidirectional) (None, 10, 100) 140400 embedding[0][0] \n", - "__________________________________________________________________________________________________\n", - "bidirectional_4 (Bidirectional) (None, 40, 100) 140400 embedding[1][0] \n", - "__________________________________________________________________________________________________\n", - "dot_2 (Dot) (None, 10, 40) 0 bidirectional_3[0][0] \n", - " bidirectional_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "reshape_2 (Reshape) (None, 400) 0 dot_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "lambda_4 (Lambda) (None, 20) 0 reshape_2[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_5 (Dense) (None, 10) 210 lambda_4[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_6 (Dense) (None, 10) 110 dense_5[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_7 (Dense) (None, 5) 55 dense_6[0][0] \n", - "__________________________________________________________________________________________________\n", - "dropout_2 (Dropout) (None, 5) 0 dense_7[0][0] \n", - "__________________________________________________________________________________________________\n", - "dense_8 (Dense) (None, 1) 6 dropout_2[0][0] \n", - "==================================================================================================\n", - "Total params: 5,283,381\n", - "Trainable params: 5,283,381\n", - "Non-trainable params: 0\n", - "__________________________________________________________________________________________________\n" - ] - } - ], - "source": [ - "model = mz.models.MVLSTM()\n", - "model.params.update(preprocessor.context)\n", - "model.params['task'] = ranking_task\n", - "model.params['embedding_output_dim'] = 300\n", - "model.params['lstm_units'] = 50\n", - "model.params['top_k'] = 20\n", - "model.params['mlp_num_layers'] = 2\n", - "model.params['mlp_num_units'] = 10\n", - "model.params['mlp_num_fan_out'] = 5\n", - "model.params['mlp_activation_func'] = 'relu'\n", - "model.params['dropout_rate'] = 0.5\n", - "model.params['optimizer'] = 'adadelta'\n", - "model.guess_and_fill_missing_params()\n", - "model.build()\n", - "model.compile()\n", - "model.backend.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'])\n", - "model.load_embedding_matrix(embedding_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "pred_x, pred_y = test_pack_processed.unpack()\n", - "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=pred_x, y=pred_y, batch_size=len(pred_y))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num batches: 102\n" - ] - } - ], - "source": [ - "train_generator = mz.DataGenerator(\n", - " train_pack_processed,\n", - " mode='pair',\n", - " num_dup=2,\n", - " num_neg=1,\n", - " batch_size=20\n", - ")\n", - "print('num batches:', len(train_generator))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/30\n", - "102/102 [==============================] - 24s 235ms/step - loss: 1.0025\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.595728243469325 - normalized_discounted_cumulative_gain@5(0.0): 0.6453087410611237 - mean_average_precision(0.0): 0.6055350793503939\n", - "Epoch 2/30\n", - "102/102 [==============================] - 21s 208ms/step - loss: 1.0002\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6343952744869739 - normalized_discounted_cumulative_gain@5(0.0): 0.6803687365482167 - mean_average_precision(0.0): 0.6365944334270842\n", - "Epoch 3/30\n", - "102/102 [==============================] - 22s 213ms/step - loss: 0.9999\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6321468863776336 - normalized_discounted_cumulative_gain@5(0.0): 0.6792345599265245 - mean_average_precision(0.0): 0.6351879636661841\n", - "Epoch 4/30\n", - "102/102 [==============================] - 21s 208ms/step - loss: 0.9999\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6039431841160746 - normalized_discounted_cumulative_gain@5(0.0): 0.6476652529714161 - mean_average_precision(0.0): 0.6115127602489608\n", - "Epoch 5/30\n", - "102/102 [==============================] - 22s 214ms/step - loss: 1.0003\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6279274770949332 - normalized_discounted_cumulative_gain@5(0.0): 0.6782797312955924 - mean_average_precision(0.0): 0.6316855781831582\n", - "Epoch 6/30\n", - "102/102 [==============================] - 21s 211ms/step - loss: 0.9999\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 7/30\n", - "102/102 [==============================] - 22s 219ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 8/30\n", - "102/102 [==============================] - 22s 216ms/step - loss: 1.0001\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 9/30\n", - "102/102 [==============================] - 22s 215ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 10/30\n", - "102/102 [==============================] - 23s 221ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 11/30\n", - "102/102 [==============================] - 22s 215ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 12/30\n", - "102/102 [==============================] - 22s 218ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 13/30\n", - "102/102 [==============================] - 22s 218ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 14/30\n", - "102/102 [==============================] - 22s 217ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 15/30\n", - "102/102 [==============================] - 23s 227ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 16/30\n", - "102/102 [==============================] - 26s 258ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 17/30\n", - "102/102 [==============================] - 25s 241ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 18/30\n", - "102/102 [==============================] - 27s 260ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 19/30\n", - "102/102 [==============================] - 25s 249ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 20/30\n", - "102/102 [==============================] - 25s 245ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 21/30\n", - "102/102 [==============================] - 28s 272ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 22/30\n", - "102/102 [==============================] - 25s 242ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 23/30\n", - "102/102 [==============================] - 26s 255ms/step - loss: 1.00001s - loss: \n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 24/30\n", - "102/102 [==============================] - 26s 256ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 25/30\n", - "102/102 [==============================] - 25s 241ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 26/30\n", - "102/102 [==============================] - 28s 274ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 27/30\n", - "102/102 [==============================] - 25s 245ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 28/30\n", - "102/102 [==============================] - 26s 255ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 29/30\n", - "102/102 [==============================] - 26s 255ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n", - "Epoch 30/30\n", - "102/102 [==============================] - 24s 238ms/step - loss: 1.0000\n", - "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.6305896279538845 - normalized_discounted_cumulative_gain@5(0.0): 0.6776773015027755 - mean_average_precision(0.0): 0.633078259024834\n" - ] - } - ], - "source": [ - "history = model.fit_generator(train_generator, epochs=30, callbacks=[evaluate], workers=30, use_multiprocessing=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - 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