def test_most_frequent_and_prior_strategy():
X = [[0], [0], [0], [0]] # ignored
y = [1, 2, 1, 1]
for strategy in ("most_frequent", "prior"):
clf = DummyClassifier(strategy=strategy, random_state=0)
clf.fit(X, y)
assert_array_equal(clf.predict(X), np.ones(len(X)))
_check_predict_proba(clf, X, y)
if strategy == "prior":
assert_array_almost_equal(clf.predict_proba([X[0]]),
clf.class_prior_.reshape((1, -1)))
else:
assert_array_almost_equal(clf.predict_proba([X[0]]),
clf.class_prior_.reshape((1, -1)) > 0.5)
def test_dummy_classifier_on_3D_array():
X = np.array([[['foo']], [['bar']], [['baz']]])
y = [2, 2, 2]
y_expected = [2, 2, 2]
y_proba_expected = [[1], [1], [1]]
cls = DummyClassifier()
cls.fit(X, y)
y_pred = cls.predict(X)
y_pred_proba = cls.predict_proba(X)
assert_array_equal(y_pred, y_expected)
assert_array_equal(y_pred_proba, y_proba_expected)