def test_checking_classifier_fit_params(iris): # check the error raised when the number of samples is not the one expected X, y = iris clf = CheckingClassifier(expected_fit_params=["sample_weight"]) sample_weight = np.ones(len(X) // 2) with pytest.raises(AssertionError, match="Fit parameter sample_weight"): clf.fit(X, y, sample_weight=sample_weight)
def test_checking_classifier_fit_params(iris): # check the error raised when the number of samples is not the one expected X, y = iris clf = CheckingClassifier(expected_sample_weight=True) sample_weight = np.ones(len(X) // 2) msg = f"sample_weight.shape == ({len(X) // 2},), expected ({len(X)},)!" with pytest.raises(ValueError) as exc: clf.fit(X, y, sample_weight=sample_weight) assert exc.value.args[0] == msg
def test_checking_classifier_methods_to_check(iris, methods_to_check, predict_method): # check that methods_to_check allows to bypass checks X, y = iris clf = CheckingClassifier( check_X=sparse.issparse, methods_to_check=methods_to_check, ) clf.fit(X, y) if predict_method in methods_to_check: with pytest.raises(AssertionError): getattr(clf, predict_method)(X) else: getattr(clf, predict_method)(X)
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_checking_classifier_with_params(iris): X, y = iris X_sparse = sparse.csr_matrix(X) clf = CheckingClassifier(check_X=sparse.issparse) with pytest.raises(AssertionError): clf.fit(X, y) clf.fit(X_sparse, y) clf = CheckingClassifier(check_X=check_array, check_X_params={"accept_sparse": False}) clf.fit(X, y) with pytest.raises(TypeError, match="A sparse matrix was passed"): clf.fit(X_sparse, y)
def test_checking_classifier_with_params(iris): X, y = iris X_sparse = sparse.csr_matrix(X) def check_X_is_sparse(X): if not sparse.issparse(X): raise ValueError("X is not sparse") return True clf = CheckingClassifier(check_X=check_X_is_sparse) with pytest.raises(ValueError, match="X is not sparse"): clf.fit(X, y) clf.fit(X_sparse, y) def _check_array(X, **params): check_array(X, **params) return True clf = CheckingClassifier(check_X=_check_array, check_X_params={"accept_sparse": False}) clf.fit(X, y) with pytest.raises(TypeError, match="A sparse matrix was passed"): clf.fit(X_sparse, y)
def test_check_on_fit_fail(iris, kwargs): X, y = iris clf = CheckingClassifier(**kwargs) with pytest.raises(AssertionError): clf.fit(X, y)
def test_checking_classifier_missing_fit_params(iris): X, y = iris clf = CheckingClassifier(expected_fit_params=["sample_weight"]) with pytest.raises(AssertionError, match="Expected fit parameter"): clf.fit(X, y)
def test_checking_classifier_missing_fit_params(iris): X, y = iris clf = CheckingClassifier(expected_sample_weight=True) err_msg = "Expected sample_weight to be passed" with pytest.raises(AssertionError, match=err_msg): clf.fit(X, y)