def test_loss_measure_1(self): """ Test classification_model_performance_loss. default loss (0-1 loss). """ observed = np.array([0, 1, 1, 0, 1, 0, 1]) calculated = np.array([0, 1, 1, 0, 0, 0, 1]) measure = evaluation.classification_model_performance_loss(observed, calculated) self.assertEqual(measure, 1)
def test_loss_measure_1(self): """ Test classification_model_performance_loss. default loss (0-1 loss). """ observed = np.array([0, 1, 1, 0, 1, 0, 1]) calculated = np.array([0, 1, 1, 0, 0, 0, 1]) measure = evaluation.classification_model_performance_loss( observed, calculated) self.assertEqual(measure, 1)
def test_loss_measure_2(self): """ Test classification_model_performance_loss. user defined loss measure - squared loss. """ observed = np.array([0, 1, 0, 1, 0, 2, 1]) calculated = np.array([0, 1, 1, 0, 2, 0, 1]) loss = lambda i, j: (i-j)*(i-j) measure = evaluation.classification_model_performance_loss(observed, calculated, loss) self.assertEqual(measure, 10)
def test_loss_measure_2(self): """ Test classification_model_performance_loss. user defined loss measure - squared loss. """ observed = np.array([0, 1, 0, 1, 0, 2, 1]) calculated = np.array([0, 1, 1, 0, 2, 0, 1]) loss = lambda i, j: (i - j) * (i - j) measure = evaluation.classification_model_performance_loss( observed, calculated, loss) self.assertEqual(measure, 10)