def test_multiple_classes():
gt_label = np.array([
[1.0, 0.0, 0.0],
[0.0, 0.0, 1.0],
[1.0, 0.0, 0.0],
[0.0, 0.0, 1.0],
[0.0, 1.0, 0.0],
[0.0, 1.0, 0.0],
[1.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
[0.0, 0.0, 1.0],
[0.0, 1.0, 0.0],
])
preds = np.array([
[0.0, 0.0, 1.0],
[1.0, 0.0, 0.0],
[0.0, 0.0, 1.0],
[0.0, 0.0, 1.0],
[0.0, 0.0, 1.0],
[0.0, 0.0, 1.0],
[0.0, 0.0, 1.0],
[1.0, 0.0, 0.0],
[0.0, 0.0, 1.0],
[0.0, 0.0, 1.0],
])
tensor_gt_label = tf.constant(gt_label, dtype=tf.float32)
tensor_preds = tf.constant(preds, dtype=tf.float32)
# Initialize
mcc = MatthewsCorrelationCoefficient(3)
# Update
mcc.update_state(tensor_gt_label, tensor_preds)
# Check results by comparing to results of scikit-learn matthew implementation.
sklearn_result = sklearn_matthew(gt_label.argmax(axis=1),
preds.argmax(axis=1))
check_results(mcc, sklearn_result)
def test_binary_classes():
gt_label = tf.constant([[1.0], [1.0], [1.0], [0.0]], dtype=tf.float32)
preds = tf.constant([[1.0], [0.0], [1.0], [1.0]], dtype=tf.float32)
# Initialize
mcc = MatthewsCorrelationCoefficient(1)
# Update
mcc.update_state(gt_label, preds)
# Check results
check_results(mcc, [-0.33333334])
def test_reset_states_graph():
gt_label = tf.constant([[1.0], [1.0], [1.0], [0.0]], dtype=tf.float32)
preds = tf.constant([[1.0], [0.0], [1.0], [1.0]], dtype=tf.float32)
mcc = MatthewsCorrelationCoefficient(1)
mcc.update_state(gt_label, preds)
@tf.function
def reset_states():
mcc.reset_states()
reset_states()
# Check results
check_results(mcc, [0])
def test_multiple_classes():
gt_label = tf.constant(
[[1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [1.0, 0.0, 1.0], [0.0, 1.0, 1.0]],
dtype=tf.float32,
)
preds = tf.constant(
[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 1.0], [1.0, 1.0, 0.0]],
dtype=tf.float32,
)
# Initialize
mcc = MatthewsCorrelationCoefficient(3)
mcc.update_state(gt_label, preds)
# Check results
check_results(mcc, [-0.33333334, 1.0, 0.57735026])