def test_receive_3D_invalid_shape(caplog):
node = Pipeline(steps=dummy_transformer, fit=True, mode='transform', meta_label=None)
node.i_training_0.data = DummyData(start_date=now()).next(5)
node.update()
assert node._shape == (5, 5)
node._status = 3
node.i_0.data = DummyData(start_date=now()).next(3)
node.update()
assert caplog.record_tuples[0][2] == 'Invalid shape'
assert node._X == None
def test_receive_2D_invalid_shape(caplog):
node = Pipeline(steps=dummy_transformer, fit=True, mode='transform')
node.i_training.data = DummyData(start_date=now(), num_cols=5).next()
node.update()
assert node._shape == 5
node._status = 3
node.i.data = DummyData(start_date=now(), num_cols=3).next()
node.update()
assert caplog.record_tuples[0][2] == 'Invalid shape'
assert node._X == None
def test_fit_success(caplog):
caplog.set_level(logging.DEBUG)
node = Pipeline(steps=dummy_classifier)
node._status = -1 # bypass accumulation
assert hasattr(node._pipeline[0], 'n_classes_') == False
node._X_train = np.array([-1, 1, 1, 1])
node._y_train = np.array([0, 1, 1, 1])
node.i_events.data = make_event('training_starts')
while node._status != 3:
node.update()
assert node._pipeline[0].n_classes_ == 2
assert caplog.record_tuples[0][2] == 'Start training'
assert caplog.record_tuples[1][2].startswith('Model fitted in')
def test_fit_reset(caplog):
caplog.set_level(logging.DEBUG)
node = Pipeline(steps=dummy_classifier, event_reset='reset')
node._status = -1 # bypass accumulation
node._X_train = np.array([-1, 1, 1, 1])
node._y_train = np.array([0, 1, 1, 1])
node.i_events.data = make_event('training_starts')
node.update()
node.i_events.data = make_event('reset')
node.update()
assert caplog.record_tuples[0][2] == 'Start training'
assert caplog.record_tuples[1][2] == 'Reset'
assert node._status == 0
def test_trim_3D(random):
node = Pipeline(steps=dummy_classifier)
node.i_training_0.data = DummyData(start_date='2018-01-01T00:00:00').next()
node.i_training_1.data = DummyData(start_date='2018-01-01T00:00:10').next()
node.i_training_2.data = DummyData(start_date='2018-01-01T00:00:20').next()
node.i_training_3.data = DummyData(start_date='2018-01-01T00:00:30').next()
node.i_training_0.meta = { 'epoch': { 'context': { 'target': 1 }}}
node.i_training_1.meta = { 'epoch': { 'context': { 'target': 2 }}}
node.i_training_2.meta = { 'epoch': { 'context': { 'target': 3 }}}
node.i_training_3.meta = { 'epoch': { 'context': { 'target': 4 }}}
node._accumulation_start = np.datetime64('2017-12-31T00:00:00')
node._accumulation_stop = np.datetime64('2018-01-01T00:01:00')
node._status = 1
node.update()
node._dimensions = 0 # Bypass accumulation
start = np.datetime64('2018-01-01T00:00:05')
stop = np.datetime64('2018-01-01T00:00:25')
node._accumulate(start, stop)
assert len(node._X_train_indices) == 2
assert len(node._X_train) == 2
assert len(node._y_train) == 2
assert node._y_train.tolist() == [2, 3]