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Restructure access to vendored/site Joblib (#11471)
In order to fix #11408, this swaps `joblib` and `_joblib`. It however, allows users to access joblib's `Memory` or `Parallel` functionality without accessing `sklearn.externals._joblib` by importing `Memory`, `Parallel`, etc. into `sklearn.utils`.
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‎.travis.yml

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@@ -38,13 +38,15 @@ matrix:
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NUMPY_VERSION="1.10.4" SCIPY_VERSION="0.16.1" CYTHON_VERSION="0.25.2"
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PILLOW_VERSION="4.0.0" COVERAGE=true
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if: type != cron
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# This environment tests the newest supported Anaconda release (5.0.0)
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# It also runs tests requiring Pandas and PyAMG
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# This environment tests the newest supported Anaconda release.
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# It runs tests requiring pandas and PyAMG.
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# It also runs with the site joblib instead of the vendored copy of joblib.
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- env: DISTRIB="conda" PYTHON_VERSION="3.6.2" INSTALL_MKL="true"
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NUMPY_VERSION="1.14.2" SCIPY_VERSION="1.0.0" PANDAS_VERSION="0.20.3"
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CYTHON_VERSION="0.26.1" PYAMG_VERSION="3.3.2" PILLOW_VERSION="4.3.0"
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COVERAGE=true
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JOBLIB_VERSION="0.12.0" COVERAGE=true
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CHECK_PYTEST_SOFT_DEPENDENCY="true" TEST_DOCSTRINGS="true"
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SKLEARN_SITE_JOBLIB=1
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if: type != cron
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# flake8 linting on diff wrt common ancestor with upstream/master
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- env: RUN_FLAKE8="true" SKIP_TESTS="true"

‎benchmarks/bench_covertype.py

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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.metrics import zero_one_loss
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from sklearn.externals.joblib import Memory
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from sklearn.utils import Memory
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from sklearn.utils import check_array
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# Memoize the data extraction and memory map the resulting

‎benchmarks/bench_mnist.py

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from sklearn.ensemble import ExtraTreesClassifier
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.dummy import DummyClassifier
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from sklearn.externals.joblib import Memory
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from sklearn.utils import Memory
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from sklearn.kernel_approximation import Nystroem
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from sklearn.kernel_approximation import RBFSampler
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from sklearn.metrics import zero_one_loss

‎benchmarks/bench_plot_nmf.py

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from sklearn.decomposition.nmf import _initialize_nmf
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from sklearn.decomposition.nmf import _beta_divergence
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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.utils import Memory
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.utils.extmath import safe_sparse_dot, squared_norm
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from sklearn.utils import check_array

‎benchmarks/bench_rcv1_logreg_convergence.py

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import gc
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import time
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from sklearn.externals.joblib import Memory
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from sklearn.utils import Memory
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from sklearn.linear_model import (LogisticRegression, SGDClassifier)
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from sklearn.datasets import fetch_rcv1
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from sklearn.linear_model.sag import get_auto_step_size

‎benchmarks/bench_saga.py

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from sklearn.datasets import fetch_rcv1, load_iris, load_digits, \
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fetch_20newsgroups_vectorized
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from sklearn.externals.joblib import delayed, Parallel, Memory
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from sklearn.utils import delayed, Parallel, Memory
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import log_loss
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from sklearn.model_selection import train_test_split

‎benchmarks/bench_tsne_mnist.py

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import json
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import argparse
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from sklearn.externals.joblib import Memory
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from sklearn.utils import Memory
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from sklearn.datasets import fetch_mldata
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from sklearn.manifold import TSNE
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from sklearn.neighbors import NearestNeighbors

‎build_tools/travis/install.sh

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TO_INSTALL="$TO_INSTALL pillow=$PILLOW_VERSION"
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fi
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if [[ -n "$JOBLIB_VERSION" ]]; then
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TO_INSTALL="$TO_INSTALL joblib=$JOBLIB_VERSION"
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fi
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conda create -n testenv --yes $TO_INSTALL
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source activate testenv
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‎doc/developers/utilities.rst

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‎doc/glossary.rst

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