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_config():
# mcc object
mcc1 = MatthewsCorrelationCoefficient(num_classes=1)
assert mcc1.num_classes == 1
assert mcc1.dtype == tf.float32
# check configure
mcc2 = MatthewsCorrelationCoefficient.from_config(mcc1.get_config())
assert mcc2.num_classes == 1
assert mcc2.dtype == tf.float32
def test_config(self):
# mcc object
mcc1 = MatthewsCorrelationCoefficient(num_classes=1)
self.assertEqual(mcc1.num_classes, 1)
self.assertEqual(mcc1.dtype, tf.float32)
# check configure
mcc2 = MatthewsCorrelationCoefficient.from_config(mcc1.get_config())
self.assertEqual(mcc2.num_classes, 1)
self.assertEqual(mcc2.dtype, tf.float32)
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])
def test_keras_model():
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(64, activation="relu"))
model.add(tf.keras.layers.Dense(64, activation="relu"))
model.add(tf.keras.layers.Dense(1, activation="softmax"))
mcc = MatthewsCorrelationCoefficient(num_classes=1)
model.compile(optimizer="Adam",
loss="binary_crossentropy",
metrics=["accuracy", mcc])
# data preparation
data = np.random.random((10, 1))
labels = np.random.random((10, 1))
labels = np.where(labels > 0.5, 1.0, 0.0)
model.fit(data, labels, epochs=1, batch_size=32, verbose=0)
def initialize_vars(self, n_classes):
mcc = MatthewsCorrelationCoefficient(num_classes=n_classes)
self.evaluate(tf.compat.v1.variables_initializer(mcc.variables))
return mcc
