8000 Merge branch 'master' into ordinal_enc_handle_missing · scikit-learn/scikit-learn@5a4dee8 · GitHub
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Merge branch 'master' into ordinal_enc_handle_missing
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‎.travis.yml

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if: type = cron OR commit_message =~ /\[scipy-dev\]/
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install: source build_tools/travis/install.sh
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script: bash build_tools/travis/test_script.sh
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script:
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- bash build_tools/travis/test_script.sh
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- bash build_tools/travis/test_docs.sh
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- bash build_tools/travis/test_pytest_soft_dependency.sh
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after_success: source build_tools/travis/after_success.sh
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notifications:
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webhooks:

‎CONTRIBUTING.md

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@@ -178,17 +178,25 @@ following rules before submitting:
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as your Python, scikit-learn, numpy, and scipy versions. This information
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can be found by running the following code snippet:
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```python
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import platform; print(platform.platform())
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import sys; print("Python", sys.version)
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import numpy; print("NumPy", numpy.__version__)
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import scipy; print("SciPy", scipy.__version__)
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import sklearn; print("Scikit-Learn", sklearn.__version__)
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```
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For scikit-learn >= 0.20:
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```python
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import sklearn; sklearn.show_versions()
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```
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For scikit-learn < 0.20:
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```python
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import platform; print(platform.platform())
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import sys; print("Python", sys.version)
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import numpy; print("NumPy", numpy.__version__)
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import scipy; print("SciPy", scipy.__version__)
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import sklearn; print("Scikit-Learn", sklearn.__version__)
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```
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- Please be specific about what estimators and/or functions are involved
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and the shape of the data, as appropriate; please include a
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[reproducible](http://stackoverflow.com/help/mcve) code snippet
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[reproducible](https://stackoverflow.com/help/mcve) code snippet
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or link to a [gist](https://gist.github.com). If an exception is raised,
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please provide the traceback.
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For building the documentation, you will need
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[sphinx](http://sphinx.pocoo.org/),
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[matplotlib](http://matplotlib.org/), and
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[pillow](http://pillow.readthedocs.io/en/latest/).
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[matplotlib](https://matplotlib.org/), and
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[pillow](https://pillow.readthedocs.io/en/latest/).
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When you are writing documentation, it is important to keep a good
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compromise between mathematical and algorithmic details, and g 10000 ive

‎ISSUE_TEMPLATE.md

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<!--
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If your issue is a usage question, submit it here instead:
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- StackOverflow with the scikit-learn tag: http://stackoverflow.com/questions/tagged/scikit-learn
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- StackOverflow with the scikit-learn tag: https://stackoverflow.com/questions/tagged/scikit-learn
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- Mailing List: https://mail.python.org/mailman/listinfo/scikit-learn
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For more information, see User Questions: http://scikit-learn.org/stable/support.html#user-questions
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-->
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#### Versions
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<!--
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Please run the following snippet and paste the output below.
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For scikit-learn >= 0.20:
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import sklearn; sklearn.show_versions()
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For scikit-learn < 0.20:
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import platform; print(platform.platform())
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import sys; print("Python", sys.version)
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import numpy; print("NumPy", numpy.__version__)

‎MANIFEST.in

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recursive-include sklearn *.c *.h *.pyx *.pxd *.pxi
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recursive-include sklearn/datasets *.csv *.csv.gz *.rst *.jpg *.txt *.arff.gz *.json.gz
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include COPYING
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include AUTHORS.rst
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include README.rst
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‎README.rst

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The project was started in 2007 by David Cournapeau as a Google Summer
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of Code project, and since then many volunteers have contributed. See
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the `AUTHORS.rst <AUTHORS.rst>`_ file for a complete list of contributors.
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the `About us <http://scikit-learn.org/dev/about.html#authors>`_ page
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for a list of core contributors.
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It is currently maintained by a team of volunteers.
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Subprograms library. scikit-learn comes with a reference implementation, but
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the system CBLAS will be detected by the build system and used if present.
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CBLAS exists in many implementations; see `Linear algebra libraries
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<http://scikit-learn.org/stable/modules/computational_performance.html#linear-algebra-libraries>`_
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<http://scikit-learn.org/stable/modules/computing#linear-algebra-libraries>`_
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for known issues.
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User installation
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~~~~~~~~~~~~~~~
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- Official source code repo: https://github.com/scikit-learn/scikit-learn
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- Download releases: https://pypi.python.org/pypi/scikit-learn
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- Download releases: https://pypi.org/project/scikit-learn/
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- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues
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Source code
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The project was started in 2007 by David Cournapeau as a Google Summer
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of Code project, and since then many volunteers have contributed. See
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the `AUTHORS.rst <AUTHORS.rst>`_ file for a complete list of contributors.
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the `About us <http://scikit-learn.org/dev/about.html#authors>`_ page
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for a list of core contributors.
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The project is currently maintained by a team of volunteers.
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- Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
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- IRC channel: ``#scikit-learn`` at ``webchat.freenode.net``
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- Stack Overflow: http://stackoverflow.com/questions/tagged/scikit-learn
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- Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
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- Website: http://scikit-learn.org
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‎appveyor.yml

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global:
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# SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the
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# /E:ON and /V:ON options are not enabled in the batch script interpreter
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# See: http://stackoverflow.com/a/13751649/163740
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# See: https://stackoverflow.com/a/13751649/163740
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CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\build_tools\\appveyor\\run_with_env.cmd"
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WHEELHOUSE_UPLOADER_USERNAME: sklearn-appveyor
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WHEELHOUSE_UPLOADER_SECRET:
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Where-Object pullRequestId -eq $env:APPVEYOR_PULL_REQUEST_NUMBER)[0].buildNumber) { `
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throw "There are newer queued builds for this pull request, failing early." }
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# Install Python (from the official .msi of http://python.org) and pip when
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# Install Python (from the official .msi of https://python.org) and pip when
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# not already installed.
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- "powershell ./build_tools/appveyor/install.ps1"
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- "SET PATH=%PYTHON%;%PYTHON%\\Scripts;%PATH%"

‎benchmarks/bench_covertype.py

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S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07.
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* `"Training Linear SVMs in Linear Time"
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<www.cs.cornell.edu/People/tj/publications/joachims_06a.pdf>`_
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<https://www.cs.cornell.edu/people/tj/publications/joachims_06a.pdf>`_
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T. Joachims - In SIGKDD '06
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[1] http://archive.ics.uci.edu/ml/datasets/Covertype
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[1] https://archive.ics.uci.edu/ml/datasets/Covertype
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"""
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from __future__ import division, print_function

‎benchmarks/bench_feature_expansions.py

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import matplotlib.pyplot as plt
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import numpy as np
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import scipy.sparse as sparse
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from sklearn.preprocessing import PolynomialFeatures
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from time import time
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degree = 2
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trials = 3
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num_rows = 1000
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dimensionalities = np.array([1, 2, 8, 16, 32, 64])
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densities = np.array([0.01, 0.1, 1.0])
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csr_times = {d: np.zeros(len(dimensionalities)) for d in densities}
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dense_times = {d: np.zeros(len(dimensionalities)) for d in densities}
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transform = PolynomialFeatures(degree=degree, include_bias=False,
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interaction_only=False)
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for trial in range(trials):
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for density in densities:
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for dim_index, dim in enumerate(dimensionalities):
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print(trial, density, dim)
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X_csr = sparse.random(num_rows, dim, density).tocsr()
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X_dense = X_csr.toarray()
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# CSR
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t0 = time()
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transform.fit_transform(X_csr)
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csr_times[density][dim_index] += time() - t0
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# Dense
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t0 = time()
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transform.fit_transform(X_dense)
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dense_times[density][dim_index] += time() - t0
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csr_linestyle = (0, (3, 1, 1, 1, 1, 1)) # densely dashdotdotted
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dense_linestyle = (0, ()) # solid
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fig, axes = plt.subplots(nrows=len(densities), ncols=1, figsize=(8, 10))
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for density, ax in zip(densities, axes):
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ax.plot(dimensionalities, csr_times[density] / trials,
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label='csr', linestyle=csr_linestyle)
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ax.plot(dimensionalities, dense_times[density] / trials,
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label='dense', linestyle=dense_linestyle)
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ax.set_title("density %0.2f, degree=%d, n_samples=%d" %
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(density, degree, num_rows))
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ax.legend()
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ax.set_xlabel(& 10000 #39;Dimensionality')
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ax.set_ylabel('Time (seconds)')
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plt.tight_layout()
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plt.show()

‎benchmarks/bench_plot_nmf.py

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----------
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C.-J. Lin. Projected gradient methods for non-negative matrix
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factorization. Neural Computation, 19(2007), 2756-2779.
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http://www.csie.ntu.edu.tw/~cjlin/nmf/
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https://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 = np.dot(W.T, W)

‎benchmarks/bench_plot_randomized_svd.py

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----------
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(1) Finding structure with randomness: Stochastic algorithms for constructing
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approximate matrix decompositions
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Halko, et al., 2009 http://arxiv.org/abs/arXiv:0909.4061
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Halko, et al., 2009 https://arxiv.org/abs/0909.4061
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(2) A randomized algorithm for the decomposition of matrices
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Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert
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MAX_MEMORY = np.int(2e9)
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# The following datasets can be dowloaded manually from:
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# CIFAR 10: http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
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# CIFAR 10: https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
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# SVHN: http://ufldl.stanford.edu/housenumbers/train_32x32.mat
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CIFAR_FOLDER = "./cifar-10-batches-py/"
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SVHN_FOLDER = "./SVHN/"

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