8000 ENH GridSearchCV and cross_val_score check whether the returned score… · amueller/scikit-learn@2d9cb81 · GitHub
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ENH GridSearchCV and cross_val_score check whether the returned score is actually a number, not an array (otherwise cross_val_score returns bogus).
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‎sklearn/cross_validation.py

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@@ -1067,6 +1067,9 @@ def _cross_val_score(estimator, X, y, scorer, train, test, verbose,
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score = estimator.score(X_test, y_test)
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else:
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score = scorer(estimator, X_test, y_test)
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if not isinstance(score, numbers.Number):
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raise ValueError("scoring must return a number, got %s (%s)"
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" instead." % (str(score), type(score)))
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if verbose > 1:
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print("score: %f" % score)
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return score

‎sklearn/grid_search.py

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@@ -10,6 +10,7 @@
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from itertools import product
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import time
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import warnings
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import numbers
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import numpy as np
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@@ -123,6 +124,10 @@ def fit_grid_point(X, y, base_clf, clf_params, train, test, scorer,
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else:
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this_score = clf.score(X_test)
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if not isinstance(this_score, numbers.Number):
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raise ValueError("scoring must return a number, got %s (%s)"
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" instead." % (str(this_score), type(this_score)))
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if verbose > 2:
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msg += ", score=%f" % this_score
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if verbose > 1:

‎sklearn/metrics/tests/test_score_objects.py

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@@ -9,7 +9,8 @@
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from sklearn.linear_model import Ridge, LogisticRegression
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.datasets import make_blobs, load_diabetes
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from sklearn.cross_validation import train_test_split
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from sklearn.cross_validation import train_test_split, cross_val_score
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from sklearn.grid_search import GridSearchCV
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def test_classification_scores():
@@ -74,3 +75,15 @@ def test_unsupervised_scores():
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score1 = scorers['ari'](km, X_test, y_test)
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score2 = adjusted_rand_score(y_test, km.predict(X_test))
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assert_almost_equal(score1, score2)
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def test_raises_on_score_list():
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# test that when a list of scores is returned, we raise proper errors.
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X, y = make_blobs(random_state=0)
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f1_scorer_no_average = AsScorer(f1_score, average=None)
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clf = DecisionTreeClassifier()
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assert_raises(ValueError, cross_val_score, clf, X, y,
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scoring=f1_scorer_no_average)
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grid_search = GridSearchCV(clf, scoring=f1_scorer_no_average,
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param_grid={'max_depth': [1, 2]})
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assert_raises(ValueError, grid_search.fit, X, y)

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