def test_f1_non_zero_one_input(self):
# Data
actual = ['a', 'b', 'a', 'a']
predicted = ['a', 'b', 'a', 'a']
# Metric
metric = BinaryClassificationMetrics.F1()
# Score
with self.assertRaises(ValueError):
metric.get_score(actual, predicted)
def test_f1_non_binary_input(self):
# Data
actual = [0, 1, 2, 0, 0, 0]
predicted = [0, 0, 0, 0, 0, 0]
# Metric
metric = BinaryClassificationMetrics.F1()
# Score
with self.assertRaises(ValueError):
metric.get_score(actual, predicted)
def test_f1_numpy(self):
# Data
actual = np.array([1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1])
predicted = np.array([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0])
# Metric
metric = BinaryClassificationMetrics.F1()
# Score
score = metric.get_score(actual, predicted)
self.assertEqual(score, 0.6666666666666666)
def test_f1_pandas(self):
# Data
actual = pd.Series([1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1])
predicted = pd.Series([1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0])
# Metric
metric = BinaryClassificationMetrics.F1()
# Score
score = metric.get_score(actual, predicted)
self.assertEqual(score, 0.6666666666666666)
def test_f1(self):
# Data
actual = [1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1]
predicted = [1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0]
# Metric
metric = BinaryClassificationMetrics.F1()
# Score
score = metric.get_score(actual, predicted)
self.assertEqual(score, 0.6666666666666666)