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)
