def test_continuous_time_splitter():
splitter = transform.ContinuousTimeInstanceSplitter(
past_interval_length=1,
future_interval_length=1,
instance_sampler=transform.ContinuousTimePointSampler(),
)
splitter2 = transform.ContinuousTimeInstanceSplitter(
past_interval_length=1,
future_interval_length=1,
instance_sampler=transform.ContinuousTimePointSampler(min_past=1.0),
)
assert equals(splitter, clone(splitter))
assert not equals(splitter, splitter2)
def test_ctsplitter_train_correct(point_process_dataset):
splitter = transform.ContinuousTimeInstanceSplitter(
1,
1,
train_sampler=MockContinuousTimeSampler(
ret_values=[1.01, 1.5, 1.99], num_instances=3
),
)
iter_de = splitter(point_process_dataset, is_train=True)
outputs = list(iter_de)
assert outputs[0]["past_valid_length"] == 2
assert outputs[0]["future_valid_length"] == 3
assert np.allclose(
outputs[0]["past_target"], np.array([[0.19, 0.7], [0, 1]]).T
)
assert np.allclose(
outputs[0]["future_target"], np.array([[0.09, 0.5, 0.3], [2, 0, 1]]).T
)
assert outputs[1]["past_valid_length"] == 2
assert outputs[1]["future_valid_length"] == 4
assert outputs[2]["past_valid_length"] == 3
assert outputs[2]["future_valid_length"] == 3
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(
past_interval_length=2,
future_interval_length=1,
instance_sampler=transform.ContinuousTimeUniformSampler(
num_instances=10,
min_past=2,
min_future=1,
),
freq=to_offset(point_process_dataset.freq),
)
# 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)])
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_train_short_intervals(point_process_dataset):
splitter = transform.ContinuousTimeInstanceSplitter(
0.01,
0.01,
instance_sampler=MockContinuousTimeSampler(
ret_values=[1.01, 1.5, 1.99]),
)
iter_de = splitter(point_process_dataset, is_train=True)
for d in iter_de:
assert d["future_valid_length"] == d["past_valid_length"] == 0
assert np.prod(np.shape(d["past_target"])) == 0
assert np.prod(np.shape(d["future_target"])) == 0
def test_ctsplitter_train_samples_correct_times(point_process_dataset):
splitter = transform.ContinuousTimeInstanceSplitter(
past_interval_length=1.25,
future_interval_length=1.25,
instance_sampler=transform.ContinuousTimeUniformSampler(
num_instances=20,
min_past=1.25,
min_future=1.25,
),
)
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)])
def test_ctsplitter_train_correct_out_count(point_process_dataset):
# produce new TPP data by shuffling existing TS instance
def shuffle_iterator(num_duplications=5):
for entry in point_process_dataset:
for i in range(num_duplications):
d = dict.copy(entry)
d["target"] = np.random.permutation(d["target"].T).T
yield d
splitter = transform.ContinuousTimeInstanceSplitter(
past_interval_length=1,
future_interval_length=1,
instance_sampler=MockContinuousTimeSampler(
ret_values=[1.01, 1.5, 1.99]),
)
iter_de = splitter(shuffle_iterator(), is_train=True)
outputs = list(iter_de)
assert len(outputs) == 5 * 3
def test_ctsplitter_no_train_last_point(point_process_dataset):
splitter = transform.ContinuousTimeInstanceSplitter(
past_interval_length=2,
future_interval_length=1,
instance_sampler=transform.ContinuousTimePredictionSampler(
allow_empty_interval=False,
min_past=2,
),
)
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
)
