8000 Merge branch 'scikit-learn/master' into 'jakirkham/use_ptrs__enet_coo… · scikit-learn/scikit-learn@3b5acf9 · GitHub
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Merge branch 'scikit-learn/master' into 'jakirkham/use_ptrs__enet_coordinate_descent'
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‎doc/developers/contributing.rst

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@@ -140,6 +140,14 @@ feedback:
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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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>>> import sklearn
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>>> sklearn.show_versions() # doctest: +SKIP
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.. note::
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This utility function is only available in scikit-learn v0.20+.
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For previous versions, one has to explicitly run::
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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__)

‎doc/developers/tips.rst

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@@ -121,15 +121,11 @@ Issue: Self-contained example for bug
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Issue: Software versions
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::
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To help diagnose your issue, could you please paste the output of:
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To help diagnose your issue, please paste the output of:
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```py
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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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import sklearn; sklearn.show_versions()
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```
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? Thanks.
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Thanks.
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Issue: Code blocks
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::

‎doc/modules/classes.rst

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@@ -48,6 +48,7 @@ Functions
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config_context
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get_config
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set_config
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show_versions
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.. _calibration_ref:
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‎doc/modules/compose.rst

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@@ -342,7 +342,7 @@ and ``value`` is an estimator object::
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>>> estimators = [('linear_pca', PCA()), ('kernel_pca', KernelPCA())]
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>>> combined = FeatureUnion(estimators)
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>>> combined # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
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FeatureUnion(n_jobs=1,
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FeatureUnion(n_jobs=None,
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transformer_list=[('linear_pca', PCA(copy=True,...)),
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('kernel_pca', KernelPCA(alpha=1.0,...))],
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transformer_weights=None)
@@ -357,7 +357,7 @@ and ignored by setting to ``None``::
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>>> combined.set_params(kernel_pca=None)
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... # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
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FeatureUnion(n_jobs=1,
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FeatureUnion(n_jobs=None,
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transformer_list=[('linear_pca', PCA(copy=True,...)),
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('kernel_pca', None)],
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transformer_weights=None)
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... remainder='drop')
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>>> column_trans.fit(X) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
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ColumnTransformer(n_jobs=1, remainder='drop', sparse_threshold=0.3,
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ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,
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transformer_weights=None,
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transformers=...)
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@@ -496,7 +496,7 @@ above example would be::
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... ('city', CountVectorizer(analyzer=lambda x: [x])),
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... ('title', CountVectorizer()))
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>>> column_trans # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
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ColumnTransformer(n_jobs=1, remainder='drop', sparse_threshold=0.3,
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ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,
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transformer_weights=None,
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transformers=[('countvectorizer-1', ...)
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‎doc/modules/kernel_approximation.rst

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SGDClassifier(alpha=0.0001, average=False, class_weight=None,
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early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,
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l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=5,
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n_iter=None, n_iter_no_change=5, n_jobs=1, penalty='l2',
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n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',
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power_t=0.5, random_state=None, shuffle=True, tol=None,
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validation_fraction=0.1, verbose=0, warm_start=False)
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>>> clf.score(X_features, y)
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1.0
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‎doc/modules/linear_model.rst

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>>> from sklearn import linear_model
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>>> reg = linear_model.LinearRegression()
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>>> reg.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2])
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LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
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... # doctest: +NORMALIZE_WHITESPACE
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LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
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normalize=False)
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>>> reg.coef_
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array([0.5, 0.5])
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‎doc/modules/model_evaluation.rst

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>>> model = svm.SVC()
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>>> cross_val_score(model, X, y, cv=5, scoring='wrong_choice')
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Traceback (most recent call last):
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ValueError: 'wrong_choice' is not a valid scoring value. Valid options are ['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score', 'average_precision', 'balanced_accuracy', 'brier_score_loss', 'completeness_score', 'explained_variance', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'fowlkes_mallows_score', 'homogeneity_score', 'mutual_info_score', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error', 'normalized_mutual_info_score', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc', 'v_measure_score']
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ValueError: 'wrong_choice' is not a valid scoring value. Use sorted(sklearn.metrics.SCORERS.keys()) to get valid options.
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.. note::
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‎doc/modules/sgd.rst

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SGDClassifier(alpha=0.0001, average=False, class_weight=None,
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early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,
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l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=5,
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n_iter=None, n_iter_no_change=5, n_jobs=1, penalty='l2',
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n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',
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power_t=0.5, random_state=None, shuffle=True, tol=None,
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validation_fraction=0.1, verbose=0, warm_start=False)
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‎doc/tutorial/statistical_inference/model_selection.rst

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>>> y_diabetes = diabetes.target
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>>> lasso.fit(X_diabetes, y_diabetes)
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LassoCV(alphas=None, copy_X=True, cv=3, eps=0.001, fit_intercept=True,
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max_iter=1000, n_alphas=100, n_jobs=1, normalize=False, positive=False,
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precompute='auto', random_state=None, selection='cyclic', tol=0.0001,
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verbose=False)
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max_iter=1000, n_alphas=100, n_jobs=None, normalize=False,
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positive=False, precompute='auto', random_state=None,
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selection='cyclic', tol=0.0001, verbose=False)
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>>> # The estimator chose automatically its lambda:
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>>> lasso.alpha_ # doctest: +ELLIPSIS
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0.01229...

‎doc/tutorial/statistical_inference/supervised_learning.rst

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>>> knn = KNeighborsClassifier()
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>>> knn.fit(iris_X_train, iris_y_train) # doctest: +NORMALIZE_WHITESPACE
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KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
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metric_params=None, n_jobs=1, n_neighbors=5, p=2,
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metric_params=None, n_jobs=None, n_neighbors=5, p=2,
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weights='uniform')
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>>> knn.predict(iris_X_test)
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array([1, 2, 1, 0, 0, 0, 2, 1, 2, 0])
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>>> from sklearn import linear_model
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>>> regr = linear_model.LinearRegression()
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>>> regr.fit(diabetes_X_train, diabetes_y_train)
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LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
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... # doctest: +NORMALIZE_WHITESPACE
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LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
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normalize=False)
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>>> print(regr.coef_)
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[ 0.30349955 -237.63931533 510.53060544 327.73698041 -814.13170937
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492.81458798 102.84845219 184.60648906 743.51961675 76.09517222]
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>>> # The mean square error
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>>> np.mean((regr.predict(diabetes_X_test)-diabetes_y_test)**2)# doctest: +ELLIPSIS
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>>> np.mean((regr.predict(diabetes_X_test)-diabetes_y_test)**2)
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... # doctest: +ELLIPSIS
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2004.56760268...
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>>> # Explained variance score: 1 is perfect prediction
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>>> from __future__ import print_function
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>>> print([regr.set_params(alpha=alpha
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... ).fit(diabetes_X_train, diabetes_y_train,
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... ).score(diabetes_X_test, diabetes_y_test) for alpha in alphas]) # doctest: +ELLIPSIS
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[0.5851110683883..., 0.5852073015444..., 0.5854677540698..., 0.5855512036503..., 0.5830717085554..., 0.57058999437...]
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... ).score(diabetes_X_test, diabetes_y_test)
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... for alpha in alphas])
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... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
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[0.5851110683883..., 0.5852073015444..., 0.5854677540698...,
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0.5855512036503..., 0.5830717085554..., 0.57058999437...]
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.. note::
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>>> logistic.fit(iris_X_train, iris_y_train)
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LogisticRegression(C=100000.0, class_weight=None, dual=False,
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fit_intercept=True, intercept_scaling=1, max_iter=100,
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multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,
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multi_class='ovr', n_jobs=None, penalty='l2', random_state=None,
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solver='liblinear', tol=0.0001, verbose=0, warm_start=False)
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This is known as :class:`LogisticRegression`.

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