def create_transformation(self) -> Transformation: return Chain([ AsNumpyArray(field=FieldName.TARGET, expected_ndim=1), AddTimeFeatures( start_field=FieldName.START, target_field=FieldName.TARGET, output_field=FieldName.FEAT_TIME, time_features=self.time_features, pred_length=self.prediction_length, ), SetFieldIfNotPresent(field=FieldName.FEAT_STATIC_CAT, value=[0.0]), AsNumpyArray(field=transform.FieldName.FEAT_STATIC_CAT, expected_ndim=1), 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], instance_length=self.context_length, use_prediction_features=True, prediction_length=self.prediction_length, ), ])
def _create_instance_splitter(self, mode: str): assert mode in ["training", "validation", "test"] return 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], instance_length=self.context_length, use_prediction_features=(mode is not "training"), prediction_length=self.prediction_length, )
def _create_instance_splitter(self, mode: str): assert mode in ["training", "validation", "test"] return 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=(mode != "training"), prediction_length=self.prediction_length, )
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_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