def create_transformation(self) -> Transformation:
return Chain([
AsNumpyArray(
field=FieldName.TARGET,
expected_ndim=1 + len(self.distr_output.event_shape),
),
# maps the target to (1, T) if the target data is uni
# dimensional
ExpandDimArray(
field=FieldName.TARGET,
axis=0 if self.distr_output.event_shape[0] == 1 else None,
),
AddObservedValuesIndicator(
target_field=FieldName.TARGET,
output_field=FieldName.OBSERVED_VALUES,
),
AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_TIME,
time_features=self.time_features,
pred_length=self.prediction_length,
),
VstackFeatures(
output_field=FieldName.FEAT_TIME,
input_fields=[FieldName.FEAT_TIME],
),
SetFieldIfNotPresent(field=FieldName.FEAT_STATIC_CAT, value=[0.0]),
TargetDimIndicator(
field_name=FieldName.TARGET_DIM_INDICATOR,
target_field=FieldName.TARGET,
),
AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
])
def create_transformation(self) -> Transformation:
remove_field_names = [FieldName.FEAT_DYNAMIC_CAT]
if not self.use_feat_dynamic_real:
remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)
if not self.use_feat_static_real:
remove_field_names.append(FieldName.FEAT_STATIC_REAL)
return Chain([RemoveFields(field_names=remove_field_names)] + (
[SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0]
)] if not self.use_feat_static_cat else []
) + ([SetField(
output_field=FieldName.FEAT_STATIC_REAL, value=[0.0]
)] if not self.use_feat_static_real else []) + [
AsNumpyArray(
field=FieldName
.TARGET,
expected_ndim=1 +
len(self.distr_output
.event_shape),
),
# maps the target to (1, T)
# if the target data is uni dimensional
ExpandDimArray(
field=FieldName.TARGET,
axis=0 if self.distr_output.event_shape[0] == 1 else None,
),
AddObservedValuesIndicator(
target_field=FieldName.TARGET,
output_field=FieldName.OBSERVED_VALUES,
),
AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_TIME,
time_features=self.time_features,
pred_length=self.prediction_length,
),
AddAgeFeature(
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_AGE,
pred_length=self.prediction_length,
log_scale=True,
),
VstackFeatures(
output_field=FieldName.FEAT_TIME,
input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE] +
([FieldName.FEAT_DYNAMIC_REAL] if self.
use_feat_dynamic_real else []),
),
TargetDimIndicator(
field_name="target_dimension_indicator",
target_field=FieldName.TARGET,
),
AsNumpyArray(field=FieldName.FEAT_STATIC_CAT,
expected_ndim=1,
dtype=np.long),
AsNumpyArray(field=FieldName.FEAT_STATIC_REAL, expected_ndim=1),
])
def create_transformation(self) -> Transformation:
remove_field_names = [
FieldName.FEAT_DYNAMIC_CAT,
FieldName.FEAT_STATIC_REAL,
]
if not self.use_feat_dynamic_real:
remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)
return Chain(
[RemoveFields(field_names=remove_field_names)]
+ (
[SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0.0])]
if not self.use_feat_static_cat
else []
)
+ [
AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
# gives target the (1, T) layout
ExpandDimArray(field=FieldName.TARGET, axis=0),
AddObservedValuesIndicator(
target_field=FieldName.TARGET,
output_field=FieldName.OBSERVED_VALUES,
),
# Unnormalized seasonal features
AddTimeFeatures(
time_features=self.issm.time_features(),
pred_length=self.prediction_length,
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=SEASON_INDICATORS_FIELD,
),
AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_TIME,
time_features=self.time_features,
pred_length=self.prediction_length,
),
AddAgeFeature(
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_AGE,
pred_length=self.prediction_length,
log_scale=True,
),
VstackFeatures(
output_field=FieldName.FEAT_TIME,
input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
+ (
[FieldName.FEAT_DYNAMIC_REAL]
if self.use_feat_dynamic_real
else []
),
),
]
)
def create_transformation(self) -> Transformation:
remove_field_names = [FieldName.FEAT_DYNAMIC_CAT]
if not self.use_feat_dynamic_real:
remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)
return Chain([
RemoveFields(field_names=remove_field_names),
AsNumpyArray(
field=FieldName.TARGET,
expected_ndim=2,
),
# maps the target to (1, T)
# if the target data is uni dimensional
ExpandDimArray(
field=FieldName.TARGET,
axis=None,
),
AddObservedValuesIndicator(
target_field=FieldName.TARGET,
output_field=FieldName.OBSERVED_VALUES,
),
AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_TIME,
time_features=self.time_features,
pred_length=self.prediction_length,
),
VstackFeatures(
output_field=FieldName.FEAT_TIME,
input_fields=[FieldName.FEAT_TIME] +
([FieldName.FEAT_DYNAMIC_REAL]
if self.use_feat_dynamic_real else []),
),
SetFieldIfNotPresent(field=FieldName.FEAT_STATIC_CAT, value=[0]),
TargetDimIndicator(
field_name="target_dimension_indicator",
target_field=FieldName.TARGET,
),
AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
])
def create_transformation(self) -> Transformation:
remove_field_names = [
FieldName.FEAT_DYNAMIC_CAT,
FieldName.FEAT_STATIC_REAL,
]
if not self.use_feat_dynamic_real:
remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)
return Chain(
[RemoveFields(field_names=remove_field_names)]
+ (
[SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0.0])]
if not self.use_feat_static_cat
else []
)
+ [
AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
# gives target the (1, T) layout
ExpandDimArray(field=FieldName.TARGET, axis=0),
AddObservedValuesIndicator(
target_field=FieldName.TARGET,
output_field=FieldName.OBSERVED_VALUES,
),
# Unnormalized seasonal features
AddTimeFeatures(
time_features=CompositeISSM.seasonal_features(self.freq),
pred_length=self.prediction_length,
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=SEASON_INDICATORS_FIELD,
),
AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_TIME,
time_features=self.time_features,
pred_length=self.prediction_length,
),
AddAgeFeature(
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_AGE,
pred_length=self.prediction_length,
log_scale=True,
),
VstackFeatures(
output_field=FieldName.FEAT_TIME,
input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
+ (
[FieldName.FEAT_DYNAMIC_REAL]
if self.use_feat_dynamic_real
else []
),
),
CanonicalInstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
instance_sampler=TestSplitSampler(),
time_series_fields=[
FieldName.FEAT_TIME,
SEASON_INDICATORS_FIELD,
FieldName.OBSERVED_VALUES,
],
allow_target_padding=True,
instance_length=self.past_length,
use_prediction_features=True,
prediction_length=self.prediction_length,
),
]
)
def create_transformation(self) -> Transformation:
def use_marginal_transformation(
marginal_transformation: bool, ) -> Transformation:
if marginal_transformation:
return CDFtoGaussianTransform(
target_field=FieldName.TARGET,
observed_values_field=FieldName.OBSERVED_VALUES,
max_context_length=self.conditioning_length,
target_dim=self.target_dim,
)
else:
return RenameFields({
f"past_{FieldName.TARGET}":
f"past_{FieldName.TARGET}_cdf",
f"future_{FieldName.TARGET}":
f"future_{FieldName.TARGET}_cdf",
})
return Chain([
AsNumpyArray(
field=FieldName.TARGET,
expected_ndim=1 + len(self.distr_output.event_shape),
),
# maps the target to (1, T) if the target data is uni
# dimensional
ExpandDimArray(
field=FieldName.TARGET,
axis=0 if self.distr_output.event_shape[0] == 1 else None,
),
AddObservedValuesIndicator(
target_field=FieldName.TARGET,
output_field=FieldName.OBSERVED_VALUES,
),
AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_TIME,
time_features=self.time_features,
pred_length=self.prediction_length,
),
VstackFeatures(
output_field=FieldName.FEAT_TIME,
input_fields=[FieldName.FEAT_TIME],
),
SetFieldIfNotPresent(field=FieldName.FEAT_STATIC_CAT, value=[0.0]),
TargetDimIndicator(
field_name=FieldName.TARGET_DIM_INDICATOR,
target_field=FieldName.TARGET,
),
AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
InstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
train_sampler=ExpectedNumInstanceSampler(num_instances=1),
past_length=self.history_length,
future_length=self.prediction_length,
time_series_fields=[
FieldName.FEAT_TIME,
FieldName.OBSERVED_VALUES,
],
pick_incomplete=self.pick_incomplete,
),
use_marginal_transformation(self.use_marginal_transformation),
SampleTargetDim(
field_name=FieldName.TARGET_DIM_INDICATOR,
target_field=FieldName.TARGET + "_cdf",
observed_values_field=FieldName.OBSERVED_VALUES,
num_samples=self.target_dim_sample,
shuffle=self.shuffle_target_dim,
),
])
def create_input_transform(
is_train,
prediction_length,
past_length,
use_feat_static_cat,
use_feat_dynamic_real,
freq,
time_features,
extract_tail_chunks_for_train: bool = False,
):
SEASON_INDICATORS_FIELD = "seasonal_indicators"
remove_field_names = [
FieldName.FEAT_DYNAMIC_CAT,
FieldName.FEAT_STATIC_REAL,
]
if not use_feat_dynamic_real:
remove_field_names.append(FieldName.FEAT_DYNAMIC_REAL)
time_features = (
time_features
if time_features is not None
else time_features_from_frequency_str(freq)
)
transform = Chain(
[RemoveFields(field_names=remove_field_names)]
+ (
[SetField(output_field=FieldName.FEAT_STATIC_CAT, value=[0.0])]
if not use_feat_static_cat
else []
)
+ [
AsNumpyArray(field=FieldName.FEAT_STATIC_CAT, expected_ndim=1),
AsNumpyArray(field=FieldName.TARGET, expected_ndim=1),
# gives target the (1, T) layout
ExpandDimArray(field=FieldName.TARGET, axis=0),
AddObservedValuesIndicator(
target_field=FieldName.TARGET,
output_field=FieldName.OBSERVED_VALUES,
),
# Unnormalized seasonal features
AddTimeFeatures(
time_features=CompositeISSM.seasonal_features(freq),
pred_length=prediction_length,
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=SEASON_INDICATORS_FIELD,
),
AddTimeFeatures(
start_field=FieldName.START,
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_TIME,
time_features=time_features,
pred_length=prediction_length,
),
AddAgeFeature(
target_field=FieldName.TARGET,
output_field=FieldName.FEAT_AGE,
pred_length=prediction_length,
log_scale=True,
),
VstackFeatures(
output_field=FieldName.FEAT_TIME,
input_fields=[FieldName.FEAT_TIME, FieldName.FEAT_AGE]
+ (
[FieldName.FEAT_DYNAMIC_REAL]
if use_feat_dynamic_real
else []
),
),
CanonicalInstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
instance_sampler=ExpectedNumInstanceSampler(num_instances=1),
time_series_fields=[
FieldName.FEAT_TIME,
SEASON_INDICATORS_FIELD,
FieldName.OBSERVED_VALUES,
],
allow_target_padding=True,
instance_length=past_length,
use_prediction_features=True,
prediction_length=prediction_length,
)
if (is_train and not extract_tail_chunks_for_train)
else CanonicalInstanceSplitter(
target_field=FieldName.TARGET,
is_pad_field=FieldName.IS_PAD,
start_field=FieldName.START,
forecast_start_field=FieldName.FORECAST_START,
instance_sampler=TestSplitSampler(),
time_series_fields=[
FieldName.FEAT_TIME,
SEASON_INDICATORS_FIELD,
FieldName.OBSERVED_VALUES,
],
allow_target_padding=True,
instance_length=past_length,
use_prediction_features=True,
prediction_length=prediction_length,
),
]
)
return transform