def test_posterior_computation(mocker, classes, prior, cfm, first_class_posterior):
def mock_confusion_matrix(y, y_pred):
return np.array(cfm)
mocker.patch("sklego.meta.subjective_classifier.confusion_matrix", side_effect=mock_confusion_matrix)
mock_estimator = mocker.Mock(RandomForestClassifier())
mock_estimator.classes_ = np.array(classes)
subjective_model = SubjectiveClassifier(mock_estimator, dict(zip(classes, prior)))
subjective_model.fit(np.zeros((10, 10)), np.array([classes[0]] * 10))
assert (
pytest.approx(subjective_model.posterior_matrix_[0, 0], 0.001)
== first_class_posterior
)
assert np.isclose(subjective_model.posterior_matrix_.sum(axis=0), 1).all()
def test_predict_proba(mocker, evidence_type, expected_probas):
def mock_confusion_matrix(y, y_pred):
return np.array([[80, 20], [10, 90]])
mocker.patch("sklego.meta.subjective_classifier.confusion_matrix", side_effect=mock_confusion_matrix)
mock_inner_estimator = mocker.Mock(RandomForestClassifier)
mock_inner_estimator.predict_proba.return_value = np.array(
[[0.8, 0.2], [1, 0], [0.5, 0.5], [0.2, 0.8]]
)
mock_inner_estimator.classes_ = np.array([0, 1])
subjective_model = SubjectiveClassifier(
mock_inner_estimator, {0: 0.8, 1: 0.2}, evidence=evidence_type
)
subjective_model.fit(np.zeros((10, 10)), np.zeros(10))
posterior_probabilities = subjective_model.predict_proba(np.zeros((4, 2)))
assert posterior_probabilities.shape == (4, 2)
assert np.isclose(
posterior_probabilities, np.array(expected_probas), atol=0.01
).all()