def test_accuracy_metric_4_multilabel_classification(self):
# 100% correct
expected = np.array([[0, 1, 1], [0, 1, 1], [1, 0, 0], [1, 0, 0],
[0, 1, 1], [1, 0, 0], [0, 1, 1], [1, 0, 0],
[0, 1, 1], [1, 0, 0]])
prediction = expected.copy()
score = acc_metric(expected,
prediction.astype(float),
task=MULTILABEL_CLASSIFICATION)
self.assertEqual(1, score)
# 100% incorrect
prediction = (prediction.copy() - 1) * -1
score = acc_metric(expected,
prediction.astype(float),
task=MULTILABEL_CLASSIFICATION)
self.assertAlmostEqual(-1, score)
# Pseudorandom
prediction = np.array([[0.0, 0.0, 0.0], [0.0, 0.0,
0.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [0.0, 0.0,
0.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
score = acc_metric(expected,
prediction,
task=MULTILABEL_CLASSIFICATION)
self.assertAlmostEqual(-0.0666666666, score)
def test_accuracy_metric_4_multiclass_classification(self):
# 100% correct
expected = np.array([1, 1, 0, 0, 1, 0, 2, 0, 2, 1])
prediction = np.array([[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, 1.0,
0.0], [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]])
score = acc_metric(expected,
prediction,
task=MULTICLASS_CLASSIFICATION)
self.assertEqual(1, score)
# 100% incorrect
prediction = (prediction.copy() - 1) * -1
score = acc_metric(expected,
prediction,
task=MULTICLASS_CLASSIFICATION)
self.assertAlmostEqual(-0.5, score)
# Pseudorandom
prediction = np.array([[1.0, 0.0, 0.0], [0.0, 1.0,
0.0], [0.0, 0.0, 1.0],
[1.0, 0.0, 0.0], [0.0, 1.0,
0.0], [0.0, 0.0, 1.0],
[1.0, 0.0, 0.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 1.0], [1.0, 0.0, 0.0]])
score = acc_metric(expected,
prediction,
task=MULTICLASS_CLASSIFICATION)
self.assertAlmostEqual(0.1, score)
def test_accuracy_metric_4_binary_classification(self):
# 100% correct
expected = np.array([0, 1, 1, 1, 0, 0, 1, 1, 1, 0]).reshape((-1, 1))
prediction = expected.copy()
score = acc_metric(expected, prediction)
self.assertEqual(1, score)
# 100% incorrect
prediction = (expected.copy() - 1) * -1
score = acc_metric(expected, prediction)
self.assertAlmostEqual(-1, score)
# Random
prediction = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
score = acc_metric(expected, prediction)
self.assertAlmostEqual(0, score)
def test_accuracy_metric_4_binary_classification(self):
# 100% correct
expected = np.array([0, 1, 1, 1, 0, 0, 1, 1, 1, 0]).reshape((-1, 1))
prediction = expected.copy()
score = acc_metric(expected, prediction)
self.assertEqual(1, score)
# 100% incorrect
prediction = (expected.copy() - 1) * -1
score = acc_metric(expected, prediction)
self.assertAlmostEqual(-1, score)
# Random
prediction = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
score = acc_metric(expected, prediction)
self.assertAlmostEqual(0, score)
def test_accuracy_metric_4_binary_classification(self):
# 100% correct
expected = np.array([0, 1, 1, 1, 0, 0, 1, 1, 1, 0]).reshape((-1, 1))
prediction = np.array([[1., 0.], [0., 1.], [0., 1.], [0., 1.],
[1., 0.], [1., 0.], [0., 1.], [0., 1.],
[0., 1.], [1., 0.]])
score = acc_metric(expected, prediction, task=BINARY_CLASSIFICATION)
self.assertEqual(1, score)
# 100% incorrect
prediction = (prediction.copy() - 1) * -1
score = acc_metric(expected, prediction, task=BINARY_CLASSIFICATION)
self.assertAlmostEqual(-1, score)
# Random
prediction = np.array([[1., 0.], [1., 0.], [1., 0.], [1., 0.], [1., 0.],
[0., 1.], [0., 1.], [0., 1.], [0., 1.], [0., 1.]])
score = acc_metric(expected, prediction, task=BINARY_CLASSIFICATION)
self.assertAlmostEqual(0, score)
def test_accuracy_metric_4_multilabel_classification(self):
# 100% correct
expected = np.array(
[[0, 0, 1, 1, 0, 1, 0, 1, 0, 1], [1, 1, 0, 0, 1, 0, 1, 0, 1, 0], [1, 1, 0, 0, 1, 0, 1, 0, 1, 0]]
)
prediction = expected.copy()
score = acc_metric(expected, prediction)
self.assertEqual(1, score)
# 100% incorrect
prediction = (expected.copy() - 1) * -1
score = acc_metric(expected, prediction)
self.assertAlmostEqual(-1, score)
# Pseudorandom
prediction = np.array(
[[0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]]
)
score = acc_metric(expected, prediction)
self.assertAlmostEqual(-0.0666666666, score)
def test_accuracy_metric_4_multilabel_classification(self):
# 100% correct
expected = np.array([[0, 0, 1, 1, 0, 1, 0, 1, 0, 1],
[1, 1, 0, 0, 1, 0, 1, 0, 1, 0],
[1, 1, 0, 0, 1, 0, 1, 0, 1, 0]])
prediction = expected.copy()
score = acc_metric(expected, prediction)
self.assertEqual(1, score)
# 100% incorrect
prediction = (expected.copy() - 1) * -1
score = acc_metric(expected, prediction)
self.assertAlmostEqual(-1, score)
# Pseudorandom
prediction = np.array([[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]])
score = acc_metric(expected, prediction)
self.assertAlmostEqual(-0.0666666666, score)
def test_accuracy_metric_4_binary_classification(self):
# 100% correct
expected = np.array([0, 1, 1, 1, 0, 0, 1, 1, 1, 0]).reshape((-1, 1))
prediction = np.array([[1., 0.], [0., 1.], [0., 1.], [0.,
1.], [1., 0.],
[1., 0.], [0., 1.], [0., 1.], [0., 1.],
[1., 0.]])
score = acc_metric(expected, prediction, task=BINARY_CLASSIFICATION)
self.assertEqual(1, score)
# 100% incorrect
prediction = (prediction.copy() - 1) * -1
score = acc_metric(expected, prediction, task=BINARY_CLASSIFICATION)
self.assertAlmostEqual(-1, score)
# Random
prediction = np.array([[1., 0.], [1., 0.], [1., 0.], [1.,
0.], [1., 0.],
[0., 1.], [0., 1.], [0., 1.], [0., 1.],
[0., 1.]])
score = acc_metric(expected, prediction, task=BINARY_CLASSIFICATION)
self.assertAlmostEqual(0, score)
def test_accuracy_metric_4_multilabel_classification(self):
# 100% correct
expected = np.array([[0, 1, 1], [0, 1, 1], [1, 0, 0], [1, 0, 0],
[0, 1, 1], [1, 0, 0], [0, 1, 1], [1, 0, 0],
[0, 1, 1], [1, 0, 0]])
prediction = expected.copy()
score = acc_metric(expected, prediction.astype(float),
task=MULTILABEL_CLASSIFICATION)
self.assertEqual(1, score)
# 100% incorrect
prediction = (prediction.copy() - 1) * -1
score = acc_metric(expected, prediction.astype(float),
task=MULTILABEL_CLASSIFICATION)
self.assertAlmostEqual(-1, score)
# Pseudorandom
prediction = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0]])
score = acc_metric(expected, prediction, task=MULTILABEL_CLASSIFICATION)
self.assertAlmostEqual(-0.0666666666, score)
def test_accuracy_metric_4_multiclass_classification(self):
# 100% correct
expected = np.array([1, 1, 0, 0, 1, 0, 2, 0, 2, 1])
prediction = np.array([[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, 1.0, 0.0], [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]])
score = acc_metric(expected, prediction, task=MULTICLASS_CLASSIFICATION)
self.assertEqual(1, score)
# 100% incorrect
prediction = (prediction.copy() - 1) * -1
score = acc_metric(expected, prediction, task=MULTICLASS_CLASSIFICATION)
self.assertAlmostEqual(-0.5, score)
# Pseudorandom
prediction = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0],
[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0],
[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0],
[1.0, 0.0, 0.0]])
score = acc_metric(expected, prediction, task=MULTICLASS_CLASSIFICATION)
self.assertAlmostEqual(0.1, score)
