def test_chain():
chain = transform.Chain(trans=[
transform.AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
],
pred_length=10,
),
transform.AddAgeFeature(
target_field=FieldName.TARGET,
output_field="age",
pred_length=10,
log_scale=True,
),
transform.AddObservedValuesIndicator(target_field=FieldName.TARGET,
output_field="observed_values"),
])
assert equals(chain, clone(chain))
assert not equals(chain, clone(chain, {"trans": []}))
another_chain = transform.Chain(trans=[
transform.AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
],
pred_length=10,
),
transform.AddAgeFeature(
target_field=FieldName.TARGET,
output_field="age",
pred_length=10,
log_scale=False,
),
transform.AddObservedValuesIndicator(target_field=FieldName.TARGET,
output_field="observed_values"),
])
assert not equals(chain, another_chain)
def test_Transformation():
train_length = 100
ds = gluonts.dataset.common.ListDataset(
[{"start": "2012-01-01", "target": [0.2] * train_length}], freq="1D"
)
pred_length = 10
t = transform.Chain(
trans=[
transform.AddTimeFeatures(
start_field=transform.FieldName.START,
target_field=transform.FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
],
pred_length=pred_length,
),
transform.AddAgeFeature(
target_field=transform.FieldName.TARGET,
output_field="age",
pred_length=pred_length,
log_scale=True,
),
transform.AddObservedValuesIndicator(
target_field=transform.FieldName.TARGET,
output_field="observed_values",
),
transform.VstackFeatures(
output_field="dynamic_feat",
input_fields=["age", "time_feat"],
drop_inputs=True,
),
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.ExpectedNumInstanceSampler(
num_instances=4
),
past_length=train_length,
future_length=pred_length,
time_series_fields=["dynamic_feat", "observed_values"],
),
]
)
assert_serializable(t)
for u in t(iter(ds), is_train=True):
print(u)
def test_forking_sequence_with_features(is_train) -> None:
def make_dataset(N, train_length):
# generates 2 ** N - 1 timeseries with constant increasing values
n = 2 ** N - 1
targets = np.arange(n * train_length).reshape((n, train_length))
return ListDataset(
[
{"start": "2012-01-01", "target": targets[i, :]}
for i in range(n)
],
freq="D",
)
ds = make_dataset(1, 20)
trans = transform.Chain(
trans=[
transform.AddAgeFeature(
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_AGE,
pred_length=10,
),
transform.AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_TIME,
time_features=time_features_from_frequency_str("D"),
pred_length=10,
),
ForkingSequenceSplitter(
train_sampler=TSplitSampler(),
enc_len=5,
dec_len=3,
encoder_series_fields=[
FieldName.FEAT_AGE,
FieldName.FEAT_TIME,
],
decoder_series_fields=[FieldName.FEAT_TIME],
),
]
)
out = trans(iter(ds), is_train=is_train)
transformed_data = next(iter(out))
assert transformed_data["past_target"].shape == (5, 1)
assert transformed_data["past_feat_dynamic_age"].shape == (5, 1)
assert transformed_data["past_time_feat"].shape == (5, 3)
assert transformed_data["future_time_feat"].shape == (5, 3, 3)
if is_train:
assert transformed_data["future_target"].shape == (5, 3)
def test_forking_sequence_splitter() -> None:
len_ts = 20
ds = make_dataset(1, len_ts)
enc_len = 5
dec_len = 3
trans = transform.Chain(
[
transform.AddAgeFeature(
target_field=FieldName.TARGET,
output_field="age",
pred_length=dec_len,
),
ForkingSequenceSplitter(
train_sampler=TSplitSampler(),
enc_len=enc_len,
dec_len=dec_len,
encoder_series_fields=["age"],
),
]
)
out = trans(ds, is_train=True)
transformed_data = next(iter(out))
future_target = np.array(
[
[13.0, 14.0, 15.0],
[14.0, 15.0, 16.0],
[15.0, 16.0, 17.0],
[16.0, 17.0, 18.0],
[17.0, 18.0, 19.0],
]
)
assert (
np.linalg.norm(future_target - transformed_data["future_target"])
< 1e-5
), "the forking sequence target should be computed correctly."
age = np.log10(2.0 + np.arange(len_ts))
assert (
np.linalg.norm(
age[-(enc_len + dec_len) : -dec_len]
- transformed_data["past_age"].flatten()
)
< 1e-5
), "the forking sequence past feature should be computed correctly."
def test_add_method():
chain = transform.AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
],
pred_length=24,
) + transform.AddAgeFeature(
target_field=FieldName.TARGET,
output_field="age",
pred_length=24,
log_scale=True,
)
assert isinstance(chain, transform.Chain)
def test_AddAgeFeatures(start, target, is_train: bool):
pred_length = 13
t = transform.AddAgeFeature(
pred_length=pred_length,
target_field=FieldName.TARGET,
output_field="age",
log_scale=True,
)
assert_serializable(t)
data = {"start": start, "target": target}
out = t.map_transform(data, is_train=is_train)
expected_length = len(target) + (0 if is_train else pred_length)
assert out["age"].shape[-1] == expected_length
assert np.allclose(
out["age"],
np.log10(2.0 + np.arange(expected_length)).reshape(
(1, expected_length)),
)
def test_multi_dim_transformation(is_train):
train_length = 10
first_dim = np.arange(1, 11, 1).tolist()
first_dim[-1] = "NaN"
second_dim = np.arange(11, 21, 1).tolist()
second_dim[0] = "NaN"
ds = gluonts.dataset.common.ListDataset(
data_iter=[{"start": "2012-01-01", "target": [first_dim, second_dim]}],
freq="1D",
one_dim_target=False,
)
pred_length = 2
# Looks weird - but this is necessary to assert the nan entries correctly.
first_dim[-1] = np.nan
second_dim[0] = np.nan
t = transform.Chain(
trans=[
transform.AddTimeFeatures(
start_field=transform.FieldName.START,
target_field=transform.FieldName.TARGET,
output_field="time_feat",
time_features=[
time_feature.DayOfWeek(),
time_feature.DayOfMonth(),
time_feature.MonthOfYear(),
],
pred_length=pred_length,
),
transform.AddAgeFeature(
target_field=transform.FieldName.TARGET,
output_field="age",
pred_length=pred_length,
log_scale=True,
),
transform.AddObservedValuesIndicator(
target_field=transform.FieldName.TARGET,
output_field="observed_values",
convert_nans=False,
),
transform.VstackFeatures(
output_field="dynamic_feat",
input_fields=["age", "time_feat"],
drop_inputs=True,
),
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.ExpectedNumInstanceSampler(
num_instances=4
),
past_length=train_length,
future_length=pred_length,
time_series_fields=["dynamic_feat", "observed_values"],
output_NTC=False,
),
]
)
assert_serializable(t)
if is_train:
for u in t(iter(ds), is_train=True):
assert_shape(u["past_target"], (2, 10))
assert_shape(u["past_dynamic_feat"], (4, 10))
assert_shape(u["past_observed_values"], (2, 10))
assert_shape(u["future_target"], (2, 2))
assert_padded_array(
u["past_observed_values"],
np.array([[1.0] * 9 + [0.0], [0.0] + [1.0] * 9]),
u["past_is_pad"],
)
assert_padded_array(
u["past_target"],
np.array([first_dim, second_dim]),
u["past_is_pad"],
)
else:
for u in t(iter(ds), is_train=False):
assert_shape(u["past_target"], (2, 10))
assert_shape(u["past_dynamic_feat"], (4, 10))
assert_shape(u["past_observed_values"], (2, 10))
assert_shape(u["future_target"], (2, 0))
assert_padded_array(
u["past_observed_values"],
np.array([[1.0] * 9 + [0.0], [0.0] + [1.0] * 9]),
u["past_is_pad"],
)
assert_padded_array(
u["past_target"],
np.array([first_dim, second_dim]),
u["past_is_pad"],
)
def test_forking_sequence_splitter() -> None:
def make_dataset(N, train_length):
# generates 2 ** N - 1 timeseries with constant increasing values
n = 2 ** N - 1
targets = np.arange(n * train_length).reshape((n, train_length))
return ListDataset(
[
{"start": "2012-01-01", "target": targets[i, :]}
for i in range(n)
],
freq="D",
)
ds = make_dataset(1, 20)
trans = transform.Chain(
trans=[
transform.AddAgeFeature(
target_field=transform.FieldName.TARGET,
output_field="age",
pred_length=10,
),
ForkingSequenceSplitter(
train_sampler=TestSplitSampler(),
time_series_fields=["age"],
enc_len=5,
dec_len=3,
),
]
)
out = trans(iter(ds), is_train=True)
transformed_data = next(iter(out))
future_target = np.array(
[
[13.0, 14.0, 15.0],
[14.0, 15.0, 16.0],
[15.0, 16.0, 17.0],
[16.0, 17.0, 18.0],
[17.0, 18.0, 19.0],
]
)
assert (
np.linalg.norm(future_target - transformed_data["future_target"])
< 1e-5
), "the forking sequence target should be computed correctly."
trans_oob = transform.Chain(
trans=[
transform.AddAgeFeature(
target_field=transform.FieldName.TARGET,
output_field="age",
pred_length=10,
),
ForkingSequenceSplitter(
train_sampler=TestSplitSampler(),
time_series_fields=["age"],
enc_len=20,
dec_len=20,
),
]
)
transformed_data_oob = next(iter(trans_oob(iter(ds), is_train=True)))
assert (
np.sum(transformed_data_oob["future_target"]) - np.sum(np.arange(20))
< 1e-5
), "the forking sequence target should be computed correctly."