8000 don't use deprecated auto somewhere · scikit-learn/scikit-learn@9c3055e · GitHub
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amuelleramueller
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don't use deprecated auto somewhere
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+9
-9
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5 files changed

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‎examples/applications/face_recognition.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -105,7 +105,7 @@
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t0 = time()
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param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
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'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
108-
clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid)
108+
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
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clf = clf.fit(X_train_pca, y_train)
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print("done in %0.3fs" % (time() - t0))
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print("Best estimator found by grid search:")

‎sklearn/linear_model/tests/test_logistic.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -481,10 +481,10 @@ def test_logistic_regressioncv_class_weights():
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X, y = make_classification(n_samples=20, n_features=20, n_informative=10,
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random_state=0)
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clf_lbf = LogisticRegressionCV(solver='lbfgs', fit_intercept=False,
484-
class_weight='auto')
484+
class_weight='balanced')
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clf_lbf.fit(X, y)
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clf_lib = LogisticRegressionCV(solver='liblinear', fit_intercept=False,
487-
class_weight='auto')
487+
class_weight='balanced')
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clf_lib.fit(X, y)
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assert_array_almost_equal(clf_lib.coef_, clf_lbf.coef_, decimal=4)
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‎sklearn/linear_model/tests/test_sgd.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -650,7 +650,7 @@ def test_auto_weight(self):
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# make the same prediction using automated class_weight
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clf_auto = self.factory(alpha=0.0001, n_iter=1000,
653-
class_weight="auto", shuffle=False).fit(X, y)
653+
class_weight="balanced", shuffle=False).fit(X, y)
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assert_almost_equal(metrics.f1_score(y, clf_auto.predict(X), average='weighted'), 0.96,
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decimal=1)
656656

@@ -672,13 +672,13 @@ def test_auto_weight(self):
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assert_less(metrics.f1_score(y, y_pred, average='weighted'), 0.96)
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674674
# fit a model with auto class_weight enabled
675-
clf = self.factory(n_iter=1000, class_weight="auto", shuffle=False)
675+
clf = self.factory(n_iter=1000, class_weight="balanced", shuffle=False)
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clf.fit(X_imbalanced, y_imbalanced)
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y_pred = clf.predict(X)
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assert_greater(metrics.f1_score(y, y_pred, average='weighted'), 0.96)
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680680
# fit another using a fit parameter override
681-
clf = self.factory(n_iter=1000, class_weight="auto", shuffle=False)
681+
clf = self.factory(n_iter=1000, class_weight="balanced", shuffle=False)
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clf.fit(X_imbalanced, y_imbalanced)
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y_pred = clf.predict(X)
684684
assert_greater(metrics.f1_score(y, y_pred, average='weighted'), 0.96)

‎sklearn/tests/test_common.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -248,7 +248,7 @@ def test_class_weight_classifiers():
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250250
def test_class_weight_auto_classifiers():
251-
"""Test that class_weight="auto" improves f1-score"""
251+
"""Test that class_weight="balanced" improves f1-score"""
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# This test is broken; its success depends on:
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# * a rare fortuitous RNG seed for make_classification; and

‎sklearn/utils/estimator_checks.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -879,7 +879,7 @@ def check_class_weight_auto_classifiers(name, Classifier, X_train, y_train,
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classifier.fit(X_train, y_train)
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y_pred = classifier.predict(X_test)
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882-
classifier.set_params(class_weight='auto')
882+
classifier.set_params(class_weight='balanced')
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classifier.fit(X_train, y_train)
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y_pred_auto = classifier.predict(X_test)
885885
assert_greater(f1_score(y_test, y_pred_auto, average='weighted'),
@@ -901,7 +901,7 @@ def check_class_weight_auto_linear_classifier(name, Classifier):
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set_random_state(classifier)
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903903
# Let the model compute the class frequencies
904-
classifier.set_params(class_weight='auto')
904+
classifier.set_params(class_weight='balanced')
905905
coef_auto = classifier.fit(X, y).coef_.copy()
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907907
# Count each label occurrence to reweight manually

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