def test_ctsplitter_train_samples_correct_times(point_process_dataset):
splitter = transform.ContinuousTimeInstanceSplitter(
1.25, 1.25, train_sampler=transform.ContinuousTimeUniformSampler(20))
iter_de = splitter(point_process_dataset, is_train=True)
assert all([(pd.Timestamp("2011-01-01 01:15:00") <= d["forecast_start"] <=
pd.Timestamp("2011-01-01 01:45:00")) for d in iter_de])
def test_ctsplitter_no_train_last_point(point_process_dataset):
splitter = transform.ContinuousTimeInstanceSplitter(
2, 1, train_sampler=transform.ContinuousTimeUniformSampler(num_instances=10),
)
iter_de = splitter(point_process_dataset, is_train=False)
d_out = next(iter(iter_de))
assert "future_target" not in d_out
assert "future_valid_length" not in d_out
assert "past_target" in d_out
assert "past_valid_length" in d_out
assert d_out["past_valid_length"] == 6
assert np.allclose(
[0.1, 0.5, 0.3, 0.3, 0.2, 0.1], d_out["past_target"][..., 0], atol=0.01
)
def test_ctsplitter_mask_sorted(point_process_dataset):
d = next(iter(point_process_dataset))
ia_times = d["target"][0, :]
ts = np.cumsum(ia_times)
splitter = transform.ContinuousTimeInstanceSplitter(
2, 1, train_sampler=transform.ContinuousTimeUniformSampler(num_instances=10),
)
# no boundary conditions
res = splitter._mask_sorted(ts, 1, 2)
assert all([a == b for a, b in zip([2, 3, 4], res)])
# lower bound equal, exclusive of upper bound
res = splitter._mask_sorted(np.array([1, 2, 3, 4, 5, 6]), 1, 2)
assert all([a == b for a, b in zip([0], res)])