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divided test for predict_proba into two functions

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Andrew Winterman 2012-11-28 09:43:43 -08:00 committed by Andreas Mueller
parent fd930e37da
commit c4cbffcc13
1 changed files with 48 additions and 29 deletions
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77
sklearn/tests/test_multiclass.py
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@ -143,44 +143,63 @@ def test_ovr_multilabel_dataset():
assert_almost_equal(multilabel_recall(Y_test, Y_pred), recall,
decimal=2)
def test_ovr_predict_proba():
def test_ovr_multilabel_predict_proba():
#shamelessly coppied from test_ovr_multilable_dataset.
base_clf = MultinomialNB(alpha=1)
n_samples = 100
n_classes = 5
for multilabel in (False, True):
for au in (False, True):
if multilabel:
X, Y = datasets.make_multilabel_classification(n_samples=100,
n_features=20,
n_classes=5,
n_labels=3
length=50,
allow_unlabeled=au,
random_state=0)
else:
X,Y = iris.data, iris.target
X_train, Y_train = X[:80], Y[:80]
X_test, Y_test = X[80:], Y[80:]
clf = OneVsRestClassifier(base_clf).fit(X_train, Y_train)
for au in (False, True):
X, Y = datasets.make_multilabel_classification(n_samples=100,
n_features=20,
n_classes=5,
n_labels=3
length=50,
allow_unlabeled=au,
random_state=0)
X_train, Y_train = X[:80], Y[:80]
X_test, Y_test = X[80:], Y[80:]
clf = OneVsRestClassifier(base_clf).fit(X_train, Y_train)
#decision function only estimator. Fails in current implementation.
decision_only_base = OneVsRestClassifier(svm.SVR()).fit(X_train, Y_train)
assert_raises(ValueError, decision_only_base.predict_proba, X_test)
#decision function only estimator. Fails in current implementation.
decision_only_base = OneVsRestClassifier(svm.SVR()).fit(X_train, Y_train)
assert_raises(AttributeError, decision_only_base.predict_proba, X_test)
Y_pred = clf.predict(X_test)
Y_proba = clf.predict_proba(X_test)
Y_pred = clf.predict(X_test)
Y_proba = clf.predict_proba(X_test)
if not multilabel:
assert_almost_equal(Y_proba.sum(axis=1), 1.0)
#predict assigns a label if the probability that the
#sample has the label is greater than than 0.5.
pred = [tuple(l.nonzero()[0]) for l in (Y_proba > 0.5)]
assert_equal(pred, Y_pred)
#predict assigns a label if the probability that the
#sample has the label is greater than than 0.5.
pred = [tuple(l.nonzero()[0]) for l in (Y_proba > 0.5)]
assert_equal(pred, Y_pred)
#I'm also supposed to check for malformed input but it seems like
#that should happen in the base estimator to me
#I'm also supposed to check for malformed input but it seems like
#that should happen in the base estimator to me
def test_ovr_single_label_predict_proba():
#shamelessly coppied from test_ovr_multilable_dataset.
base_clf = MultinomialNB(alpha=1)
n_samples = 100
n_classes = 5
multilabel=False
X,Y = iris.data, iris.target
X_train, Y_train = X[:80], Y[:80]
X_test, Y_test = X[80:], Y[80:]
clf = OneVsRestClassifier(base_clf).fit(X_train, Y_train)
#decision function only estimator. Fails in current implementation.
decision_only_base = OneVsRestClassifier(svm.SVR()).fit(X_train, Y_train)
assert_raises(AttributeError, decision_only_base.predict_proba, X_test)
Y_pred = clf.predict(X_test)
Y_proba = clf.predict_proba(X_test)
assert_almost_equal(Y_proba.sum(axis=1), 1.0)
#predict assigns a label if the probability that the
#sample has the label is greater than than 0.5.
pred = [tuple(l.nonzero()[0]) for l in (Y_proba > 0.5)]
assert_equal(pred, Y_pred)
def test_ovr_gridsearch():
ovr = OneVsRestClassifier(LinearSVC(random_state=0))