def test_InstanceSplitter(start, target, lead_time: int, is_train: bool,
pick_incomplete: bool):
train_length = 100
pred_length = 13
t = transform.InstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=transform.UniformSplitSampler(p=1.0),
past_length=train_length,
future_length=pred_length,
lead_time=lead_time,
time_series_fields=["some_time_feature"],
pick_incomplete=pick_incomplete,
)
assert_serializable(t)
other_feat = np.arange(len(target) + 100)
data = {
"start": start,
"target": target,
"some_time_feature": other_feat,
"some_other_col": "ABC",
}
if not is_train and not pick_incomplete and len(target) < train_length:
with pytest.raises(AssertionError):
out = list(t.flatmap_transform(data, is_train=is_train))
return
else:
out = list(t.flatmap_transform(data, is_train=is_train))
if is_train:
assert len(out) == max(
0,
len(target) - pred_length - lead_time + 1 -
(0 if pick_incomplete else train_length),
)
else:
assert len(out) == 1
for o in out:
assert "target" not in o
assert "some_time_feature" not in o
assert "some_other_col" in o
assert len(o["past_some_time_feature"]) == train_length
assert len(o["past_target"]) == train_length
if is_train:
assert len(o["future_target"]) == pred_length
assert len(o["future_some_time_feature"]) == pred_length
else:
assert len(o["future_target"]) == 0
assert len(o["future_some_time_feature"]) == pred_length
def test_CanonicalInstanceSplitter(
start,
target,
is_train: bool,
use_prediction_features: bool,
allow_target_padding: bool,
):
train_length = 100
pred_length = 13
t = transform.CanonicalInstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
instance_sampler=(transform.UniformSplitSampler(
p=1.0,
min_past=train_length,
) if is_train else (
transform.ValidationSplitSampler()
if allow_target_padding else transform.TestSplitSampler())),
instance_length=train_length,
prediction_length=pred_length,
time_series_fields=["some_time_feature"],
allow_target_padding=allow_target_padding,
use_prediction_features=use_prediction_features,
)
assert_serializable(t)
other_feat = np.arange(len(target) + 100)
data = {
"start": start,
"target": target,
"some_time_feature": other_feat,
"some_other_col": "ABC",
}
out = list(t.flatmap_transform(data, is_train=is_train))
min_num_instances = 1 if allow_target_padding and not is_train else 0
if is_train:
assert len(out) == max(min_num_instances,
len(target) - train_length + 1)
else:
assert len(out) == 1
for o in out:
assert "target" not in o
assert "future_target" not in o
assert "some_time_feature" not in o
assert "some_other_col" in o
assert len(o["past_some_time_feature"]) == train_length
assert len(o["past_target"]) == train_length
if use_prediction_features and not is_train:
assert len(o["future_some_time_feature"]) == pred_length
def test_CanonicalInstanceSplitter(
start, target, is_train, use_prediction_features, allow_target_padding
):
train_length = 100
pred_length = 13
t = transform.CanonicalInstanceSplitter(
target_field=transform.FieldName.TARGET,
is_pad_field=transform.FieldName.IS_PAD,
start_field=transform.FieldName.START,
forecast_start_field=transform.FieldName.FORECAST_START,
instance_sampler=transform.UniformSplitSampler(p=1.0),
instance_length=train_length,
prediction_length=pred_length,
time_series_fields=['some_time_feature'],
allow_target_padding=allow_target_padding,
use_prediction_features=use_prediction_features,
)
assert_serializable(t)
other_feat = np.arange(len(target) + 100)
data = {
'start': start,
'target': target,
'some_time_feature': other_feat,
'some_other_col': 'ABC',
}
out = list(t.flatmap_transform(data, is_train=is_train))
min_num_instances = 1 if allow_target_padding else 0
if is_train:
assert len(out) == max(
min_num_instances, len(target) - train_length + 1
)
else:
assert len(out) == 1
for o in out:
assert 'target' not in o
assert 'future_target' not in o
assert 'some_time_feature' not in o
assert 'some_other_col' in o
assert len(o['past_some_time_feature']) == train_length
assert len(o['past_target']) == train_length
if use_prediction_features and not is_train:
assert len(o['future_some_time_feature']) == pred_length
def test_InstanceSplitter(start, target, is_train):
train_length = 100
pred_length = 13
t = transform.InstanceSplitter(
target_field=transform.FieldName.TARGET,
is_pad_field=transform.FieldName.IS_PAD,
start_field=transform.FieldName.START,
forecast_start_field=transform.FieldName.FORECAST_START,
train_sampler=transform.UniformSplitSampler(p=1.0),
past_length=train_length,
future_length=pred_length,
time_series_fields=["some_time_feature"],
pick_incomplete=True,
)
assert_serializable(t)
other_feat = np.arange(len(target) + 100)
data = {
"start": start,
"target": target,
"some_time_feature": other_feat,
"some_other_col": "ABC",
}
out = list(t.flatmap_transform(data, is_train=is_train))
if is_train:
assert len(out) == max(0, len(target) - pred_length + 1)
else:
assert len(out) == 1
for o in out:
assert "target" not in o
assert "some_time_feature" not in o
assert "some_other_col" in o
assert len(o["past_some_time_feature"]) == train_length
assert len(o["past_target"]) == train_length
if is_train:
assert len(o["future_target"]) == pred_length
assert len(o["future_some_time_feature"]) == pred_length
else:
assert len(o["future_target"]) == 0
assert len(o["future_some_time_feature"]) == pred_length
