def test_trim_2D(random):
node = Pipeline(steps=dummy_classifier)
data = DummyData(rate=1).next(20)
node._X_train = data.values
node._X_train_indices = np.array(data.index.values, dtype=np.datetime64)
start = np.datetime64('2018-01-01T00:00:05')
stop = np.datetime64('2018-01-01T00:00:15')
node._dimensions = 2
node._accumulate(start, stop)
assert len(node._X_train_indices) == 10
assert len(node._X_train) == 10
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]