def test_get_incremental_score(self):
test_cases = [
# scores to be returned in order by mock of model.score
np.array(
[0, 0, 0.5, 0.3, 0.8, 0.6, 0.9, 0.7, 1, 0.8, 1, 0.9, 1, 1]),
np.array([0, 144, 222, -0.29, 1, 1])
]
for scores in test_cases:
model = RNNModel(3, 8, num_epochs=int(len(scores) / 2))
model.score = MagicMock()
model.score.side_effect = scores
model.spawn_clf = MagicMock()
# mock tensorflow network attributes
model.sess = MagicMock()
model.sess.run = MagicMock()
model.opt = MagicMock()
model.x = MagicMock()
model.y = MagicMock()
model.sequence_lengths = MagicMock()
preprocess.up_sample = MagicMock()
# none of the inputs are actually used due to mocking so just give dummy inputs
train_scores, test_scores = model.incremental_score(
[], [], [], [],
train_sequence_lengths=[],
test_sequence_lengths=[])
self.assertListEqual(
train_scores,
list(scores[[i for i in range(0, len(scores), 2)]]))
self.assertListEqual(
test_scores,
list(scores[[i for i in range(1, len(scores), 2)]]))
def test_score(self):
test_cases = [
(
# True values for y
np.array([1, 0, 0, 0, 1]),
# Predicted values for y (used as return value for mocked prediction method - tested separately)
np.array([0, 1, 0, 0, 1]),
# score
0.6),
(np.array([0, 0, 0, 1, 1, 1, 2, 2,
2]), np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]), 3 / 9),
(np.array([0, 0, 0, 1, 1, 1, 2, 2,
2]), np.array([1, 1, 1, 2, 2, 2, 0, 0, 0]), 0),
(np.array([0, 0, 0, 1, 1, 1, 2, 2,
2]), np.array([0, 0, 0, 1, 1, 1, 2, 2, 2]), 1),
]
for y_true, y_pred, score in test_cases:
model = RNNModel(3, 8)
model.predict = MagicMock()
model.predict.return_value = y_pred
# no params except y_true are used so just input dummy params
self.assertAlmostEqual(
model.score([], y_true, sequence_lengths=[]), score)