8000 raise DeprecationWarnings and FutureWarnings as errors (#11570) · scikit-learn/scikit-learn@8e2c2aa · GitHub
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raise DeprecationWarnings and FutureWarnings as errors (#11570)
Towards #11252. In the end we'd like to make these errors so we can keep this cleaner in the future.
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‎setup.cfg

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@@ -8,6 +8,9 @@ addopts =
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--doctest-modules
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--disable-pytest-warnings
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-rs
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filterwarnings =
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error::DeprecationWarning
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error::FutureWarning
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[wheelhouse_uploader]
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artifact_indexes=

‎sklearn/cluster/tests/test_hierarchical.py

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@@ -7,6 +7,7 @@
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# License: BSD 3 clause
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from tempfile import mkdtemp
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import shutil
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import pytest
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from functools import partial
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import numpy as np
@@ -142,6 +143,8 @@ def test_agglomerative_clustering_wrong_arg_memory():
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assert_raises(ValueError, clustering.fit, X)
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@pytest.mark.filterwarnings("ignore:the behavior of nmi will "
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"change in version 0.22")
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def test_agglomerative_clustering():
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# Check that we obtain the correct number of clusters with
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# agglomerative clustering.
@@ -250,6 +253,8 @@ def test_ward_agglomeration():
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assert_raises(ValueError, agglo.fit, X[:0])
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@pytest.mark.filterwarnings("ignore:the behavior of nmi will "
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"change in version 0.22")
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def test_single_linkage_clustering():
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# Check that we get the correct result in two emblematic cases
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moons, moon_labels = make_moons(noise=0.05, random_state=42)
@@ -311,6 +316,8 @@ def test_scikit_vs_scipy():
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assert_raises(ValueError, _hc_cut, n_leaves + 1, children, n_leaves)
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@pytest.mark.filterwarnings("ignore:the behavior of nmi will "
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"change in version 0.22")
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def test_identical_points():
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# Ensure identical points are handled correctly when using mst with
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# a sparse connectivity matrix

‎sklearn/covariance/tests/test_graphical_lasso.py

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@@ -4,6 +4,7 @@
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import numpy as np
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from scipy import linalg
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import pytest
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_array_less
@@ -151,6 +152,5 @@ def test_deprecated_grid_scores(random_state=1):
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"0.19 and will be removed in 0.21. Use "
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"``grid_scores_`` instead")
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assert_warns_message(DeprecationWarning, depr_message,
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lambda: graphical_lasso.grid_scores)
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assert_equal(graphical_lasso.grid_scores, graphical_lasso.grid_scores_)
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with pytest.warns(DeprecationWarning, match=depr_message):
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assert_equal(graphical_lasso.grid_scores, graphical_lasso.grid_scores_)

‎sklearn/decomposition/tests/test_dict_learning.py

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@@ -58,6 +58,7 @@ def test_dict_learning_overcomplete():
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assert_true(dico.components_.shape == (n_components, n_features))
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@ignore_warnings(category=DeprecationWarning) # positive lars deprecated 0.22
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@pytest.mark.parametrize("transform_algorithm", [
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"lasso_lars",
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"lasso_cd",
@@ -170,6 +171,7 @@ def test_dict_learning_online_shapes():
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assert_equal(np.dot(code, dictionary).shape, X.shape)
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@ignore_warnings(category=DeprecationWarning) # positive lars deprecated 0.22
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@pytest.mark.parametrize("transform_algorithm", [
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"lasso_lars",
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"lasso_cd",
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assert_equal(code.shape, (n_samples, n_components))
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@ignore_warnings(category=DeprecationWarning) # positive lars deprecated 0.22
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@pytest.mark.parametrize("positive", [
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False,
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True,

‎sklearn/decomposition/tests/test_kernel_pca.py

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@@ -1,9 +1,10 @@
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import numpy as np
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import scipy.sparse as sp
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import pytest
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from sklearn.utils.testing import (assert_array_almost_equal, assert_less,
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assert_equal, assert_not_equal,
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assert_raises)
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assert_raises, ignore_warnings)
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from sklearn.decomposition import PCA, KernelPCA
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from sklearn.datasets import make_circles
@@ -172,6 +173,7 @@ def test_kernel_pca_invalid_kernel():
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assert_raises(ValueError, kpca.fit, X_fit)
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@pytest.mark.filterwarnings('ignore: The default of the `iid`') # 0.22
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def test_gridsearch_pipeline():
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# Test if we can do a grid-search to find parameters to separate
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# circles with a perceptron model.
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assert_equal(grid_search.best_score_, 1)
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@pytest.mark.filterwarnings('ignore: The default of the `iid`') # 0.22
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def test_gridsearch_pipeline_precomputed():
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# Test if we can do a grid-search to find parameters to separate
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# circles with a perceptron model using a precomputed kernel.

‎sklearn/ensemble/forest.py

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@@ -944,16 +944,17 @@ class labels (multi-output problem).
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>>> X, y = make_classification(n_samples=1000, n_features=4,
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... n_informative=2, n_redundant=0,
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... random_state=0, shuffle=False)
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>>> clf = RandomForestClassifier(max_depth=2, random_state=0)
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>>> clf = RandomForestClassifier(n_estimators=100, max_depth=2,
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... random_state=0)
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>>> clf.fit(X, y)
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RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
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max_depth=2, max_features='auto', max_leaf_nodes=None,
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min_impurity_decrease=0.0, min_impurity_split=None,
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min_samples_leaf=1, min_samples_split=2,
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min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
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min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,
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oob_score=False, random_state=0, verbose=0, warm_start=False)
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>>> print(clf.feature_importances_)
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[0.17287856 0.80608704 0.01884792 0.00218648]
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[0.14205973 0.76664038 0.0282433 0.06305659]
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>>> print(clf.predict([[0, 0, 0, 0]]))
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[1]
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@@ -1188,18 +1189,19 @@ class RandomForestRegressor(ForestRegressor):
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>>>
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>>> X, y = make_regression(n_features=4, n_informative=2,
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... random_state=0, shuffle=False)
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>>> regr = RandomForestRegressor(max_depth=2, random_state=0)
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>>> regr = RandomForestRegressor(max_depth=2, random_state=0,
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... n_estimators=100)
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>>> regr.fit(X, y)
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RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=2,
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max_features='auto', max_leaf_nodes=None,
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min_impurity_decrease=0.0, min_impurity_split=None,
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min_samples_leaf=1, min_samples_split=2,
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min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
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min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,
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oob_score=False, random_state=0, verbose=0, warm_start=False)
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>>> print(regr.feature_importances_)
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[0.17339552 0.81594114 0. 0.01066333]
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[0.18146984 0.81473937 0.00145312 0.00233767]
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>>> print(regr.predict([[0, 0, 0, 0]]))
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[-2.50699856]
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[-8.32987858]
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Notes
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-----

‎sklearn/ensemble/tests/test_bagging.py

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# Author: Gilles Louppe
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# License: BSD 3 clause
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import pytest
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import numpy as np
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from sklearn.base import BaseEstimator
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assert_array_almost_equal(y1, y3)
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@pytest.mark.filterwarnings('ignore: The default of the `iid`') # 0.22
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def test_gridsearch():
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# Check that bagging ensembles can be grid-searched.
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# Transform iris into a binary classification task

‎sklearn/ensemble/tests/test_forest.py

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@@ -439,13 +439,13 @@ def check_oob_score_raise_error(name):
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def test_oob_score_raise_error(name):
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check_oob_score_raise_error(name)
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def check_gridsearch(name):
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forest = FOREST_CLASSIFIERS[name]()
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clf = GridSearchCV(forest, {'n_estimators': (1, 2), 'max_depth': (1, 2)})
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clf.fit(iris.data, iris.target)
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@pytest.mark.filterwarnings('ignore: The default of the `iid`') # 0.22
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@pytest.mark.parametrize('name', FOREST_CLASSIFIERS)
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def test_gridsearch(name):
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# Check that base trees can be grid-searched.

‎sklearn/ensemble/tests/test_partial_dependence.py

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"""
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Testing for the partial dependence module.
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"""
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import pytest
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import numpy as np
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from numpy.testing import assert_array_equal
@@ -103,6 +104,8 @@ def test_partial_dependecy_input():
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assert_raises(ValueError, partial_dependence, clf, [0], grid=grid)
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@pytest.mark.filterwarnings('ignore: Using or importing the ABCs from')
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# matplotlib Python3.7 warning
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@if_matplotlib
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def test_plot_partial_dependence():
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# Test partial dependence plot function.
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assert all(ax.has_data for ax in axs)
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@pytest.mark.filterwarnings('ignore: Using or importing the ABCs from')
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# matplotlib Python3.7 warning
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@if_matplotlib
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def test_plot_partial_dependence_input():
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# Test partial dependence plot function input checks.
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clf, X, [{'foo': 'bar'}])
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@pytest.mark.filterwarnings('ignore: Using or importing the ABCs from')
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# matplotlib Python3.7 warning
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@if_matplotlib
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def test_plot_partial_dependence_multiclass():
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# Test partial dependence plot function on multi-class input.

‎sklearn/ensemble/tests/test_voting_classifier.py

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@@ -371,10 +371,12 @@ def test_set_estimator_none():
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X1 = np.array([[1], [2]])
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y1 = np.array([1, 2])
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eclf1 = VotingClassifier(estimators=[('rf', clf2), ('nb', clf3)],
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voting='soft', weights=[0, 0.5]).fit(X1, y1)
374+
voting='soft', weights=[0, 0.5],
375+
flatten_transform=False).fit(X1, y1)
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eclf2 = VotingClassifier(estimators=[('rf', clf2), ('nb', clf3)],
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voting='soft', weights=[1, 0.5])
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voting='soft', weights=[1, 0.5],
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flatten_transform=False)
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eclf2.set_params(rf=None).fit(X1, y1)
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assert_array_almost_equal(eclf1.transform(X1),
380382
np.array([[[0.7, 0.3], [0.3, 0.7]],

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