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)