added some more methods to deepar

pts/model/deepar/deepar_estimator.py

@@ -7,10 +7,16 @@ from pts.feature import (
     TimeFeature,
     get_lags_for_frequency,
     time_features_from_frequency_str,
+    Transformation, Chain, 
+    RemoveFields, SetField, AsNumpyArray, 
+    AddObservedValuesIndicator, AddTimeFeatures,
+    AddAgeFeature, VstackFeatures, InstanceSplitter,
 )
+from pts.dataset import FieldName, ExpectedNumInstanceSampler
 from pts.model import PTSEstimator
 from pts.modules import DistributionOutput, StudentTOutput
 
+from .deepar_network import DeepARTrainingNetwork
 
 class DeepAREstimator(PTSEstimator):
     def __init__(
@@ -70,3 +76,105 @@ class DeepAREstimator(PTSEstimator):
         self.history_length = self.context_length + max(self.lags_seq)
 
         self.num_parallel_samples = num_parallel_samples
+
+    def create_transformation(self) -> Transformation:
+        remove_field_names = [FieldName.FEAT_DYNAMIC_CAT]
+        if not self.use_feat_static_real:
+            remove_field_names.append(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 []
+            )
+            + (
+                [
+                    SetField(
+                        output_field=FieldName.FEAT_STATIC_REAL, value=[0.0]
+                    )
+                ]
+                if not self.use_feat_static_real
+                else []
+            )
+            + [
+                AsNumpyArray(
+                    field=FieldName.FEAT_STATIC_CAT,
+                    expected_ndim=1,
+                    dtype=self.dtype,
+                ),
+                AsNumpyArray(
+                    field=FieldName.FEAT_STATIC_REAL,
+                    expected_ndim=1,
+                    dtype=self.dtype,
+                ),
+                AsNumpyArray(
+                    field=FieldName.TARGET,
+                    # in the following line, we add 1 for the time dimension
+                    expected_ndim=1 + len(self.distr_output.event_shape),
+                    dtype=self.dtype,
+                ),
+                AddObservedValuesIndicator(
+                    target_field=FieldName.TARGET,
+                    output_field=FieldName.OBSERVED_VALUES,
+                    dtype=self.dtype,
+                ),
+                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,
+                    dtype=self.dtype,
+                ),
+                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 []
+                    ),
+                ),
+                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,
+                    ],
+                ),
+            ]
+        )
+
+    def create_training_network(self) -> DeepARTrainingNetwork:
+        return DeepARTrainingNetwork(
+            num_layers=self.num_layers,
+            num_cells=self.num_cells,
+            cell_type=self.cell_type,
+            history_length=self.history_length,
+            context_length=self.context_length,
+            prediction_length=self.prediction_length,
+            distr_output=self.distr_output,
+            dropout_rate=self.dropout_rate,
+            cardinality=self.cardinality,
+            embedding_dimension=self.embedding_dimension,
+            lags_seq=self.lags_seq,
+            scaling=self.scaling,
+            dtype=self.dtype,
+        )
+    

pts/model/deepar/deepar_network.py

@@ -4,3 +4,6 @@ import torch.nn as nn
 
 class DeepARNetwork(nn.Module):
     pass
+
+class DeepARTrainingNetwork(DeepARNetwork):
+    pass