e42bf473dde3f85210ff90b24c53865663abae29,tests/meta/test_adaptive_random_forests.py,,test_adaptive_random_forests_nb,#,62

Before Change

    predictions = []
    true_labels = []
    wait_samples = 100
    correct_predictions = 0

    while cnt < max_samples:
        X, y = stream.next_sample()
        // Test every n samples
        if (cnt % wait_samples == 0) and (cnt != 0):
            predictions.append(int(learner.predict(X)[0]))
            true_labels.append(y[0])
            if np.array_equal(y[0], predictions[-1]):
                correct_predictions += 1

        learner.partial_fit(X, y)
        cnt += 1
    last_version_predictions = [1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0,
                                1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,

After Change

                    "remove_poor_atts=False, split_confidence=0.01, " \
                    "split_criterion="info_gain", stop_mem_management=False, " \
                    "tie_threshold=0.05, warning_detection_method=ADWIN(delta=0.01))"
    info = " ".join([line.strip() for line in learner.get_info().split()])
    assert info == expected_info


def test_adaptive_random_forests_nba():

Instances