def test_classif_sample_weight(): for criterion in ("gini", "entropy"): sk = 0 iv = 0 for X, y, w in _make_classification_datasets(10, sw=True): clf = skClassifTree(criterion=criterion, max_depth=5) clf.fit(X, y, w) y_pred = clf.predict(X) sk += accuracy_score(y, y_pred, sample_weight=w) clf = TreeClassifier(criterion=criterion, max_depth=5) clf.fit(X, y, w) y_pred = clf.predict(X) iv += accuracy_score(y, y_pred, sample_weight=w) sk /= 10 iv /= 10 assert_almost_equal(sk, iv)
def test_classif_max_depth(): for criterion in ("gini", "entropy"): sk = 0 iv = 0 for X, y in _make_classification_datasets(10): clf = skClassifTree(criterion=criterion, max_depth=5, random_state=1) clf.fit(X, y) y_pred = clf.predict(X) sk += np.mean(y == y_pred) clf = TreeClassifier(criterion=criterion, max_depth=5, random_state=1) clf.fit(X, y) y_pred = clf.predict(X) iv += np.mean(y == y_pred) sk /= 10 iv /= 10 assert_almost_equal(sk, iv)