def test_checking_classifier(iris, input_type):
# Check that the CheckingClassifier outputs what we expect
X, y = iris
X = _convert_container(X, input_type)
clf = CheckingClassifier()
clf.fit(X, y)
assert_array_equal(clf.classes_, np.unique(y))
assert len(clf.classes_) == 3
assert clf.n_features_in_ == 4
y_pred = clf.predict(X)
assert_array_equal(y_pred, np.zeros(y_pred.size, dtype=int))
assert clf.score(X) == pytest.approx(0)
clf.set_params(foo_param=10)
assert clf.fit(X, y).score(X) == pytest.approx(1)
y_proba = clf.predict_proba(X)
assert y_proba.shape == (150, 3)
assert_allclose(y_proba[:, 0], 1)
assert_allclose(y_proba[:, 1:], 0)
y_decision = clf.decision_function(X)
assert y_decision.shape == (150, 3)
assert_allclose(y_decision[:, 0], 1)
assert_allclose(y_decision[:, 1:], 0)
# check the shape in case of binary classification
first_2_classes = np.logical_or(y == 0, y == 1)
X = _safe_indexing(X, first_2_classes)
y = _safe_indexing(y, first_2_classes)
clf.fit(X, y)
y_proba = clf.predict_proba(X)
assert y_proba.shape == (100, 2)
assert_allclose(y_proba[:, 0], 1)
assert_allclose(y_proba[:, 1], 0)
y_decision = clf.decision_function(X)
assert y_decision.shape == (100,)
assert_allclose(y_decision, 0)
def test_check_X_on_predict_fail(iris, pred_func):
X, y = iris
clf = CheckingClassifier(check_X=_success).fit(X, y)
clf.set_params(check_X=_fail)
with pytest.raises(AssertionError):
getattr(clf, pred_func)(X)