estimator

pts/dataset/__init__.py

@@ -9,6 +9,6 @@ from .sampler import (
     UniformSplitSampler,
 )
 from .process import ProcessStartField, ProcessDataEntry
-from .utils import to_pandas
+from .utils import to_pandas, 
 from .stat import ScaleHistogram, calculate_dataset_statistics
 from .artificial import constant_dataset

pts/model/__init__.py

@@ -2,4 +2,4 @@ from .estimator import Estimator, PTSEstimator
 from .forecast import Forecast
 from .predictor import Predictor
 from .quantile import Quantile
-from .utils import get_module_forward_input_names
+from .utils import get_module_forward_input_names, copy_parameters

pts/model/deepar/deepar_estimator.py

@@ -24,8 +24,8 @@ from pts.dataset import FieldName, ExpectedNumInstanceSampler
 from pts.model import PTSEstimator
 from pts.modules import DistributionOutput, StudentTOutput
 
-from .deepar_network import DeepARTrainingNetwork
-
+from .deepar_network import DeepARTrainingNetwork, DeepARPredictionNetwork
+from ..utils import copy_parameters
 
 class DeepAREstimator(PTSEstimator):
     def __init__(
@@ -167,3 +167,27 @@ class DeepAREstimator(PTSEstimator):
             lags_seq=self.lags_seq,
             scaling=self.scaling,
             dtype=self.dtype).to(device)
+
+    def create_predictor(
+        self, transformation: Transformation, trained_network: nn.Module
+    ) -> Predictor:
+        prediction_network = DeepARPredictionNetwork(
+            num_parallel_samples=self.num_parallel_samples,
+            input_size=self.input_size,
+            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).to(trained_network.device)
+
+        copy_parameters(trained_network, prediction_network)
+
+        return RepresentableBlockPredictor()

pts/model/deepar/deepar_network.py

@@ -393,8 +393,7 @@ class DeepARPredictionNetwork(DeepARNetwork):
         return samples.reshape((
                 (-1, self.num_parallel_samples)
                 + (self.prediction_length,)
-                + self.target_shape
-            )
+                + self.target_shape)
         )
 
     # noinspection PyMethodOverriding,PyPep8Naming
@@ -411,7 +410,6 @@ class DeepARPredictionNetwork(DeepARNetwork):
         Predicts samples, all tensors should have NTC layout.
         Parameters
         ----------
-        F
         feat_static_cat : (batch_size, num_features)
         feat_static_real : (batch_size, num_features)
         past_time_feat : (batch_size, history_length, num_features)

pts/model/estimator.py

@@ -79,19 +79,18 @@ class PTSEstimator(Estimator):
         """
         pass
 
-    # @abstractmethod
-    # def create_predictor(
-    #     self, transformation: Transformation, trained_network: nn.Module
-    # ) -> Predictor:
-    #     """
-    #     Create and return a predictor object.
+    @abstractmethod
+    def create_predictor(
+        self, transformation: Transformation, trained_network: nn.Module) -> Predictor:
+        """
+        Create and return a predictor object.
 
-    #     Returns
-    #     -------
-    #     Predictor
-    #         A predictor wrapping a `nn.Module` used for inference.
-    #     """
-    #     pass
+        Returns
+        -------
+        Predictor
+            A predictor wrapping a `nn.Module` used for inference.
+        """
+        pass
 
     def train_model(self, training_data: Dataset) -> TrainOutput:
         transformation = self.create_transformation()
@@ -119,7 +118,7 @@ class PTSEstimator(Estimator):
         return TrainOutput(
             transformation=transformation,
             trained_net=trained_net,
-            predictor=None#self.create_predictor(transformation, trained_net),
+            predictor=self.create_predictor(transformation, trained_net),
         )
 
     def train(self, training_data: Dataset) -> Predictor:

pts/model/predictor.py

@@ -1,6 +1,9 @@
 from abc import ABC, abstractmethod
 from typing import Iterator
 
+import torch
+import torch.nn as nn
+
 from pts.dataset import Dataset
 
 from .forecast import Forecast
@@ -14,3 +17,6 @@ class Predictor(ABC):
     @abstractmethod
     def predict(self, dataset: Dataset, **kwargs) -> Iterator[Forecast]:
         pass
+
+class PTSPredictor(Predictor):
+    BlockType = nn.Module

pts/model/utils.py

@@ -6,3 +6,7 @@ import torch.nn as nn
 def get_module_forward_input_names(module: nn.Module):
     params = inspect.signature(module.forward).parameters
     return list(params)
+
+
+def copy_parameters(net_source: nn.Module, net_dest: nn.Module) -> None:
+    net_dest.load_state_dict(net_source.state_dict())