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import numpy as np
import pytest
from scipy import sparse
from numpy.testing import assert_array_equal
from numpy.testing import assert_allclose
from sklearn.datasets import load_iris
from sklearn.utils import check_array
from sklearn.utils import _safe_indexing
from sklearn.utils._testing import _convert_container
from sklearn.utils._mocking import CheckingClassifier
@pytest.fixture
def iris():
return load_iris(return_X_y=True)
def _success(x):
return True
def _fail(x):
return False
@pytest.mark.parametrize(
"kwargs",
[
{},
{"check_X": _success},
{"check_y": _success},
{"check_X": _success, "check_y": _success},
],
)
def test_check_on_fit_success(iris, kwargs):
X, y = iris
CheckingClassifier(**kwargs).fit(X, y)
@pytest.mark.parametrize(
"kwargs",
[
{"check_X": _fail},
{"check_y": _fail},
{"check_X": _success, "check_y": _fail},
{"check_X": _fail, "check_y": _success},
{"check_X": _fail, "check_y": _fail},
],
)
def test_check_on_fit_fail(iris, kwargs):
X, y = iris
clf = CheckingClassifier(**kwargs)
with pytest.raises(AssertionError):
clf.fit(X, y)
@pytest.mark.parametrize(
"pred_func", ["predict", "predict_proba", "decision_function", "score"]
)
def test_check_X_on_predict_success(iris, pred_func):
X, y = iris
clf = CheckingClassifier(check_X=_success).fit(X, y)
getattr(clf, pred_func)(X)
@pytest.mark.parametrize(
"pred_func", ["predict", "predict_proba", "decision_function", "score"]
)
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)
@pytest.mark.parametrize("input_type", ["list", "array", "sparse", "dataframe"])
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_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_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)
@pytest.mark.parametrize(
"methods_to_check",
[["predict"], ["predict", "predict_proba"]],
)
@pytest.mark.parametrize(
"predict_method", ["predict", "predict_proba", "decision_function", "score"]
)
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)
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