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‎.travis.yml

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@@ -3,62 +3,60 @@ sudo: false
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language: python
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# Pre-install packages for the ubuntu distribution
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cache:
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apt: true
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directories:
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- $HOME/.cache/pip
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addons:
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apt:
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packages:
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# these only required by the DISTRIB="ubuntu" builds:
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- python-scipy
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- libatlas3gf-base
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- libatlas-dev
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dist: trusty
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env:
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global:
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# Directory where tests are run from
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- TEST_DIR=/tmp/sklearn
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- OMP_NUM_THREADS=4
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- OPENBLAS_NUM_THREADS=4
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matrix:
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matrix:
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include:
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# This environment tests that scikit-learn can be built against
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# versions of numpy, scipy with ATLAS that comes with Ubuntu Trusty 14.04
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- DISTRIB="ubuntu" PYTHON_VERSION="2.7" CYTHON_VERSION="0.23.4"
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COVERAGE=true
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- env: DISTRIB="ubuntu" PYTHON_VERSION="2.7" CYTHON_VERSION="0.23.4"
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COVERAGE=true
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addons:
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apt:
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packages:
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# these only required by the DISTRIB="ubuntu" builds:
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- python-scipy
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- libatlas3gf-base
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- libatlas-dev
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# This environment tests the oldest supported anaconda env
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- DISTRIB="conda" PYTHON_VERSION="2.7" INSTALL_MKL="false"
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NUMPY_VERSION="1.8.2" SCIPY_VERSION="0.13.3" CYTHON_VERSION="0.23.4"
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COVERAGE=true
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# This environment tests the newest supported Anaconda release (4.3.1)
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- env: DISTRIB="conda" PYTHON_VERSION="2.7" INSTALL_MKL="false"
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NUMPY_VERSION="1.8.2" SCIPY_VERSION="0.13.3" CYTHON_VERSION="0.23.5"
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COVERAGE=true
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# This environment tests the newest supported Anaconda release (4.4.0)
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# It also runs tests requiring Pandas.
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- DISTRIB="conda" PYTHON_VERSION="3.6" INSTALL_MKL="true"
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NUMPY_VERSION="1.11.2" SCIPY_VERSION="0.18.1" PANDAS_VERSION="0.19.2"
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CYTHON_VERSION="0.25.2" COVERAGE=true
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- env: DISTRIB="conda" PYTHON_VERSION="3.6.1" INSTALL_MKL="true"
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NUMPY_VERSION="1.12.1" SCIPY_VERSION="0.19.0" PANDAS_VERSION="0.20.1"
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CYTHON_VERSION="0.25.2" COVERAGE=true
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# This environment use pytest to run the tests. It uses the newest
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# supported Anaconda release (4.3.1). It also runs tests requiring Pandas.
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- USE_PYTEST="true" DISTRIB="conda" PYTHON_VERSION="3.6" INSTALL_MKL="true"
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NUMPY_VERSION="1.11.2" SCIPY_VERSION="0.18.1" PANDAS_VERSION="0.19.2"
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CYTHON_VERSION="0.25.2"
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# supported Anaconda release (4.4.0). It also runs tests requiring Pandas.
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# - env: USE_PYTEST="true" DISTRIB="conda" PYTHON_VERSION="3.6.1"
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# INSTALL_MKL="true" NUMPY_VERSION="1.12.1" SCIPY_VERSION="0.19.0"
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# PANDAS_VERSION="0.20.1" CYTHON_VERSION="0.25.2"
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# flake8 linting on diff wrt common ancestor with upstream/master
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- RUN_FLAKE8="true" SKIP_TESTS="true"
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DISTRIB="conda" PYTHON_VERSION="3.5" INSTALL_MKL="true"
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NUMPY_VERSION="1.10.4" SCIPY_VERSION="0.17.0" CYTHON_VERSION="0.23.4"
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matrix:
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allow_failures:
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# allow_failures seems to be keyed on the python version
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# We are using this to allow failures for DISTRIB=scipy-dev-wheels
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- python: 3.5
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include:
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- env: RUN_FLAKE8="true" SKIP_TESTS="true"
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DISTRIB="conda" PYTHON_VERSION="3.5" INSTALL_MKL="true"
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NUMPY_VERSION="1.12.1" SCIPY_VERSION="0.19.0" CYTHON_VERSION="0.23.5"
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# This environment tests scikit-learn against numpy and scipy master
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# installed from their CI wheels in a virtualenv with the Python
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# interpreter provided by travis.
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- python: 3.5
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env: DISTRIB="scipy-dev-wheels"
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# - python: 3.5
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# env: DISTRIB="scipy-dev-wheels"
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# allow_failures:
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# # allow_failures seems to be keyed on the python version
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# # We are using this to allow failures for DISTRIB=scipy-dev-wheels
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# - python: 3.5
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install: source build_tools/travis/install.sh
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script: bash build_tools/travis/test_script.sh

‎benchmarks/bench_plot_nmf.py

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@@ -24,7 +24,7 @@
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from sklearn.decomposition.nmf import INTEGER_TYPES, _check_init
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from sklearn.externals.joblib import Memory
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.utils.extmath import fast_dot, safe_sparse_dot, squared_norm
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from sklearn.utils.extmath import safe_sparse_dot, squared_norm
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from sklearn.utils import check_array
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from sklearn.utils.validation import check_is_fitted, check_non_negative
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@@ -99,7 +99,7 @@ def _nls_subproblem(X, W, H, tol, max_iter, alpha=0., l1_ratio=0.,
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http://www.csie.ntu.edu.tw/~cjlin/nmf/
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"""
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WtX = safe_sparse_dot(W.T, X)
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WtW = fast_dot(W.T, W)
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WtW = np.dot(W.T, W)
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# values justified in the paper (alpha is renamed gamma)
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gamma = 1

‎doc/conf.py

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sphinx_gallery_conf = {
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'doc_module': 'sklearn',
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'backreferences_dir': os.path.join('modules', 'generated'),
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'reference_url': {
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'sklearn': None,
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'matplotlib': 'http://matplotlib.org',
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'numpy': 'http://docs.scipy.org/doc/numpy-1.8.1',
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'scipy': 'http://docs.scipy.org/doc/scipy-0.13.3/reference'},
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'expected_failing_examples': [
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'../examples/applications/plot_stock_market.py']
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'scipy': 'http://docs.scipy.org/doc/scipy-0.13.3/reference'}
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}
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‎doc/developers/performance.rst

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@@ -84,38 +84,6 @@ C/C++ generated files are embedded in distributed stable packages. The goal is
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to make it possible to install scikit-learn stable version
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on any machine with Python, Numpy, Scipy and C/C++ compiler.
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Fast matrix multiplications
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===========================
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Matrix multiplications (matrix-matrix and matrix-vector) are usually handled
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using the NumPy function ``np.dot``, but in versions of NumPy before 1.7.2
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this function is suboptimal when the inputs are not both in the C (row-major)
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layout; in that case, the inputs may be implicitly copied to obtain the right
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layout. This obviously consumes memory and takes time.
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The function ``fast_dot`` in ``sklearn.utils.extmath`` offers a fast
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replacement for ``np.dot`` that prevents copies from being made in some cases.
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In all other cases, it dispatches to ``np.dot`` and when the NumPy version is
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new enough, it is in fact an alias for that function, making it a drop-in
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replacement. Example usage of ``fast_dot``::
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>>> import numpy as np
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>>> from sklearn.utils.extmath import fast_dot
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>>> X = np.random.random_sample([2, 10])
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>>> np.allclose(np.dot(X, X.T), fast_dot(X, X.T))
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True
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This function operates optimally on 2-dimensional arrays, both of the same
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dtype, which should be either single or double precision float. If these
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requirements aren't met or the BLAS package is not available, the call is
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silently dispatched to ``numpy.dot``. If you want to be sure when the original
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``numpy.dot`` has been invoked in a situation where it is suboptimal, you can
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activate the related warning::
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>>> import warnings
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>>> from sklearn.exceptions import NonBLASDotWarning
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>>> warnings.simplefilter('always', NonBLASDotWarning) # doctest: +SKIP
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.. _profiling-python-code:
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Profiling Python code
@@ -425,4 +393,3 @@ A sample algorithmic trick: warm restarts for cross validation
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TODO: demonstrate the warm restart tricks for cross validation of linear
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regression with Coordinate Descent.
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‎doc/faq.rst

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-----------------------------------------
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See :ref:`contributing`. Before wanting to add a new algorithm, which is
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usually a major and lengthy undertaking, it is recommended to start with :ref:`known
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issues <easy_issues>`.
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issues <easy_issues>`. Please do not contact the contributors of scikit-learn directly
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regarding contributing to scikit-learn.
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What's the best way to get help on scikit-learn usage?
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--------------------------------------------------------------

‎doc/modules/classes.rst

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:template: function.rst
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base.clone
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config_context
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set_config
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get_config
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.. _cluster_ref:

‎doc/modules/computational_performance.rst

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:ref:`sphx_glr_auto_examples_applications_plot_prediction_latency.py`. This should give
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you an estimate of the order of magnitude of the prediction latency.
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.. topic:: Configuring Scikit-learn for reduced validation overhead
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Scikit-learn does some validation on data that increases the overhead per
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call to ``predict`` and similar functions. In particular, checking that
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features are finite (not NaN or infinite) involves a full pass over the
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data. If you ensure that your data is acceptable, you may suppress
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checking for finiteness by setting the environment variable
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``SKLEARN_ASSUME_FINITE`` to a non-empty string before importing
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scikit-learn, or configure it in Python with :func:`sklearn.set_config`.
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For more control than these global settings, a :func:`config_context`
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allows you to set this configuration within a specified context::
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>>> import sklearn
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>>> with sklearn.config_context(assume_finite=True):
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... pass # do learning/prediction here with reduced validation
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Note that this will affect all uses of
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:func:`sklearn.utils.assert_all_finite` within the context.
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Influence of the Number of Features
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-----------------------------------
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‎doc/modules/cross_validation.rst

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groups generalizes well to the unseen groups. To measure this, we need to
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ensure that all the samples in the validation fold come from groups that are
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not represented at all in the paired training fold.
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The following cross-validation splitters can be used to do that.
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The grouping identifier for the samples is specified via the ``groups``
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parameter.
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Cross validation of time series data
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====================================
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Time series data is characterised by the correlation between observations
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that are near in time (*autocorrelation*). However, classical
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cross-validation techniques such as :class:`KFold` and
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:class:`ShuffleSplit` assume the samples are independent and
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identically distributed, and would result in unreasonable correlation
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between training and testing instances (yielding poor estimates of
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generalisation error) on time series data. Therefore, it is very important
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to evaluate our model for time series data on the "future" observations
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least like those that are used to train the model. To achieve this, one
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Time series data is characterised by the correlation between observations
605+
that are near in time (*autocorrelation*). However, classical
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cross-validation techniques such as :class:`KFold` and
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:class:`ShuffleSplit` assume the samples are independent and
608+
identically distributed, and would result in unreasonable correlation
609+
between training and testing instances (yielding poor estimates of
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generalisation error) on time series data. Therefore, it is very important
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to evaluate our model for time series data on the "future" observations
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least like those that are used to train the model. To achieve this, one
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solution is provided by :class:`TimeSeriesSplit`.
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616616
Time Series Split
617617
-----------------
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:class:`TimeSeriesSplit` is a variation of *k-fold* which
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returns first :math:`k` folds as train set and the :math:`(k+1)` th
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fold as test set. Note that unlike standard cross-validation methods,
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:class:`TimeSeriesSplit` is a variation of *k-fold* which
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returns first :math:`k` folds as train set and the :math:`(k+1)` th
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fold as test set. Note that unlike standard cross-validation methods,
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successive training sets are supersets of those that come before them.
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Also, it adds all surplus data to the first training partition, which
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is always used to train the model.
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This class can be used to cross-validate time series data samples
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This class can be used to cross-validate time series data samples
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that are observed at fixed time intervals.
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Example of 3-split time series cross-validation on a dataset with 6 samples::
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>>> y = np.array([1, 2, 3, 4, 5, 6])
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>>> tscv = TimeSeriesSplit(n_splits=3)
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>>> print(tscv) # doctest: +NORMALIZE_WHITESPACE
637-
TimeSeriesSplit(n_splits=3)
637+
TimeSeriesSplit(max_train_size=None, n_splits=3)
638638
>>> for train, test in tscv.split(X):
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... print("%s %s" % (train, test))
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[0 1 2] [3]

‎doc/modules/grid_search.rst

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@@ -14,7 +14,7 @@ estimator classes. Typical examples include ``C``, ``kernel`` and ``gamma``
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for Support Vector Classifier, ``alpha`` for Lasso, etc.
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It is possible and recommended to search the hyper-parameter space for the
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best :ref:`cross_validation` score.
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best :ref:`cross validation <cross_validation>` score.
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Any parameter provided when constructing an estimator may be optimized in this
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manner. Specifically, to find the names and current values for all parameters

‎doc/modules/model_evaluation.rst

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>>> # and predictions defined below.
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>>> loss = make_scorer(my_custom_loss_func, greater_is_better=False)
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>>> score = make_scorer(my_custom_loss_func, greater_is_better=True)
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>>> ground_truth = [[1, 1]]
176+
>>> ground_truth = [[1], [1]]
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>>> predictions = [0, 1]
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>>> from sklearn.dummy import DummyClassifier
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>>> clf = DummyClassifier(strategy='most_frequent', random_state=0)

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