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Merge branch 'master' into circle-noplot
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‎.gitattributes

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/sklearn/__check_build/_check_build.c -diff
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/sklearn/_isotonic.c -diff
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/sklearn/cluster/_dbscan_inner.cpp -diff
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/sklearn/cluster/_hierarchical.cpp -diff
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/sklearn/cluster/_k_means.c -diff
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/sklearn/cluster/_k_means_elkan.c -diff
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/sklearn/datasets/_svmlight_format.c -diff
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/sklearn/decomposition/_online_lda.c -diff
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/sklearn/decomposition/cdnmf_fast.c -diff
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/sklearn/ensemble/_gradient_boosting.c -diff
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/sklearn/feature_extraction/_hashing.c -diff
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/sklearn/linear_model/cd_fast.c -diff
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/sklearn/linear_model/sgd_fast.c -diff
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/sklearn/linear_model/sag_fast.c -diff
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/sklearn/metrics/pairwise_fast.c -diff
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/sklearn/neighbors/ball_tree.c -diff
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/sklearn/neighbors/kd_tree.c -diff
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/sklearn/svm/liblinear.c -diff
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/sklearn/svm/libsvm.c -diff
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/sklearn/svm/libsvm_sparse.c -diff
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/sklearn/tree/_tree.c -diff
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/sklearn/tree/_utils.c -diff
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/sklearn/utils/arrayfuncs.c -diff
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/sklearn/utils/graph_shortest_path.c -diff
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/sklearn/utils/lgamma.c -diff
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/sklearn/utils/_logistic_sigmoid.c -diff
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/sklearn/utils/murmurhash.c -diff
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/sklearn/utils/seq_dataset.c -diff
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/sklearn/utils/sparsefuncs_fast.c -diff
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/sklearn/utils/weight_vector.c -diff
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/doc/whats_new.rst merge=union

‎build_tools/circle/push_doc.sh

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@@ -24,9 +24,11 @@ MSG="Pushing the docs to $dir/ for branch: $CIRCLE_BRANCH, commit $CIRCLE_SHA1"
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cd $HOME
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if [ ! -d $DOC_REPO ];
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then git clone "git@github.com:scikit-learn/"$DOC_REPO".git";
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then git clone --depth 1 --no-checkout "git@github.com:scikit-learn/"$DOC_REPO".git";
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fi
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cd $DOC_REPO
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git config core.sparseCheckout true
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echo $dir > .git/info/sparse-checkout
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git checkout $CIRCLE_BRANCH
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git reset --hard origin/$CIRCLE_BRANCH
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git rm -rf $dir/ && rm -rf $dir/

‎circle.yml

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- ./build_tools/circle/build_doc.sh:
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timeout: 3600 # seconds
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test:
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# Grep error on the documentation
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override:
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- cat ~/log.txt && if grep -q "Traceback (most recent call last):" ~/log.txt; then false; else true; fi
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# override is needed otherwise nosetests is run by default
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- echo "Documentation has been built in the 'dependencies' step. No additional test to run"
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deployment:
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push:
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branch: /^master$|^[0-9]+\.[0-9]+\.X$/

‎doc/modules/clustering.rst

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.. topic:: Implementation
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The algorithm is non-deterministic, but the core samples will
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always belong to the same clusters (although the labels may be
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different). The non-determinism comes from deciding to which cluster a
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non-core sample belongs. A non-core sample can have a distance lower
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than ``eps`` to two core samples in different clusters. By the
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The DBSCAN algorithm is deterministic, always generating the same clusters
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when given the same data in the same order. However, the results can differ when
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data is provided in a different order. First, even though the core samples
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will always be assigned to the same clusters, the labels of those clusters
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will depend on the order in which those samples are encountered in the data.
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Second and more importantly, the clusters to which non-core samples are assigned
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can differ depending on the data order. This would happen when a non-core sample
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has a distance lower than ``eps`` to two core samples in different clusters. By the
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triangular inequality, those two core samples must be more distant than
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``eps`` from each other, or they would be in the same cluster. The non-core
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sample is assigned to whichever cluster is generated first, where
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the order is determined randomly. Other than the ordering of
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the dataset, the algorithm is deterministic, making the results relatively
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stable between runs on the same data.
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sample is assigned to whichever cluster is generated first in a pass
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through the data, and so the results will depend on the data ordering.
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The current implementation uses ball trees and kd-trees
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to determine the neighborhood of points,

‎doc/modules/grid_search.rst

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Note that it is common that a small subset of those parameters can have a large
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impact on the predictive or computation performance of the model while others
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can be left to their default values. It is recommend to read the docstring of
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can be left to their default values. It is recommended to read the docstring of
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the estimator class to get a finer understanding of their expected behavior,
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possibly by reading the enclosed reference to the literature.
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‎doc/modules/model_evaluation.rst

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you have to predict in average without missing any true one. The best value
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of this metrics is thus the average number of true labels.
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.. note::
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Our implementation's score is 1 greater than the one given in Tsoumakas
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et al., 2010. This extends it to handle the degenerate case in which an
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instance has 0 true labels.
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Formally, given a binary indicator matrix of the ground truth labels
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:math:`y \in \left\{0, 1\right\}^{n_\text{samples} \times n_\text{labels}}` and the
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score associated with each label
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>>> label_ranking_loss(y_true, y_score)
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0.0
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.. topic:: References:
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* Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In
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Data mining and knowledge discovery handbook (pp. 667-685). Springer US.
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.. _regression_metrics:
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Regression metrics

‎doc/modules/model_persistence.rst

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This should make it possible to check that the cross-validation score is in the
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same range as before.
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Since a model internal representation may be different on two different
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architectures, dumping a model on one architecture and loading it on
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another architecture is not supported.
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If you want to know more about these issues and explore other possible
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serialization methods, please refer to this
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`talk by Alex Gaynor <ht 6B28 tp://pyvideo.org/video/2566/pickles-are-for-delis-not-software>`_.

‎doc/whats_new.rst

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do not set attributes on the estimator.
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:issue:`7533` by :user:`Ekaterina Krivich <kiote>`.
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- For sparse matrices, :func:`preprocessing.normalize` with ``return_norm=True``
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will now raise a ``NotImplementedError`` with 'l1' or 'l2' norm and with norm 'max'
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the norms returned will be the same as for dense matrices (:issue:`7771`).
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By `Ang Lu <https://github.com/luang008>`_.
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Bug fixes
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.........
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‎examples/classification/plot_lda_qda.py

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"""
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====================================================================
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Linear and Quadratic Discriminant Analysis with confidence ellipsoid
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Linear and Quadratic Discriminant Analysis with covariance ellipsoid
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====================================================================
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Plot the confidence ellipsoids of each class and decision boundary
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This example plots the covariance ellipsoids of each class and
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decision boundary learned by LDA and QDA. The ellipsoids display
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the double standard deviation for each class. With LDA, the
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standard deviation is the same for all the classes, while each
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class has its own standard deviation with QDA.
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"""
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print(__doc__)
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‎examples/datasets/plot_iris_dataset.py

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# import some data to play with
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iris = datasets.load_iris()
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X = iris.data[:, :2] # we only take the first two features.
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Y = iris.target
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y = iris.target
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x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
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y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
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plt.clf()
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# Plot the training points
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plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
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plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
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plt.xlabel('Sepal length')
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plt.ylabel('Sepal width')
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fig = plt.figure(1, figsize=(8, 6))
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ax = Axes3D(fig, elev=-150, azim=110)
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X_reduced = PCA(n_components=3).fit_transform(iris.data)
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ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,
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ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=y,
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cmap=plt.cm.Paired)
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ax.set_title("First three PCA directions")
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ax.set_xlabel("1st eigenvector")

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