def test_recall(self): predicted_results = array([[1], [1], [0], [0], [1]]) actual_results = array([[1], [1], [0], [1], [1]]) labels = [0, 1] metric = Recall() evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0.75 expected_0 = 1 self.assertEquals(expected_1, results[1]) self.assertEquals(expected_0, results[0]) predicted_results = array([[0], [0], [1], [0], [0]]) actual_results = array([[1], [1], [0], [1], [1]]) evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0 expected_0 = 0 self.assertEquals(expected_1, results[1]) self.assertEquals(expected_0, results[0])
def test_accuracy(self): predicted_results = array([[1], [1], [0], [1], [0], [1]]) actual_results = array([[1], [1], [1], [0], [0], [1]]) labels = [0, 1] metric = Accuracy() evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1, expected_0 = 0.625, 0.625 self.assertAlmostEqual(expected_1, results[1]) self.assertEqual(expected_0, results[0])
def test_f2_score(self): predicted_results = array([[1], [1], [0], [1], [0], [1]]) actual_results = array([[1], [1], [1], [0], [1], [1]]) labels = [0, 1] metric = F_score(2) evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0.625 expected_0 = 0 self.assertAlmostEqual(expected_1, results[1]) self.assertEqual(expected_0, results[0])
def test_simple_accuracy(self): predicted_results = array([[1], [1], [0], [0]]) actual_results = array([[1], [1], [0], [1]]) labels = [0, 1] metric = SimpleAccuracy() evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0.75 expected_0 = 0.75 self.assertEquals(expected_1, results[1]) self.assertEquals(expected_0, results[0])
def test_fpr(self): predicted_results = array([[1], [1], [0], [1], [0]]) actual_results = array([[0], [0], [0], [1], [0]]) labels = [0, 1] metric = FPR() evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0.5 expected_0 = 0 self.assertEquals(expected_1, results[1]) self.assertEquals(expected_0, results[0])
def test_precision(self): predicted_results = array([[1], [1], [0], [0]]) actual_results = array([[1], [1], [0], [1]]) labels = [0, 1] metric = Precision() evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 1 expected_0 = 0.5 self.assertEquals(expected_1, results[1]) self.assertEquals(expected_0, results[0]) predicted_results = array([[0], [0], [0], [0]]) evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0 expected_0 = 0.25 self.assertEquals(expected_1, results[1]) self.assertEquals(expected_0, results[0])
def create_evaluation(self, predicted_values, real_values, labels): return Evaluation(predicted_values, real_values, labels)