Nonnegative predictions for deepar-pytorch

src/gluonts/torch/model/deepar/estimator.py

@@ -144,6 +144,10 @@ class DeepAREstimator(PyTorchLightningEstimator):
  Controls the sampling of windows during training.
  validation_sampler
  Controls the sampling of windows during validation.
+ nonnegative_pred_samples
+ Should final prediction samples be non-negative? If yes, an activation
+ function is applied to ensure non-negative. Observe that this is applied
+ only to the final samples and this is not applied during training.
  """
 
  @validated()
@@ -176,6 +180,7 @@ def __init__(
  trainer_kwargs: Optional[Dict[str, Any]] = None,
  train_sampler: Optional[InstanceSampler] = None,
  validation_sampler: Optional[InstanceSampler] = None,
+ nonnegative_pred_samples: bool = False,
  ) -> None:
  default_trainer_kwargs = {
  "max_epochs": 100,
@@ -230,6 +235,7 @@ def __init__(
  self.validation_sampler = validation_sampler or ValidationSplitSampler(
  min_future=prediction_length
  )
+ self.nonnegative_pred_samples = nonnegative_pred_samples
 
  @classmethod
  def derive_auto_fields(cls, train_iter):
@@ -397,6 +403,7 @@ def create_lightning_module(self) -> DeepARLightningModule:
  "scaling": self.scaling,
  "default_scale": self.default_scale,
  "num_parallel_samples": self.num_parallel_samples,
+ "nonnegative_pred_samples": self.nonnegative_pred_samples,
  },
  )
 

src/gluonts/torch/model/deepar/module.py

@@ -86,6 +86,10 @@ class DeepARModel(nn.Module):
  num_parallel_samples
  Number of samples to produce when unrolling the RNN in the prediction
  time range.
+ nonnegative_pred_samples
+ Should final prediction samples be non-negative? If yes, an activation
+ function is applied to ensure non-negative. Observe that this is applied
+ only to the final samples and this is not applied during training.
  """
 
  @validated()
@@ -107,6 +111,7 @@ def __init__(
  scaling: bool = True,
  default_scale: Optional[float] = None,
  num_parallel_samples: int = 100,
+ nonnegative_pred_samples: bool = False,
  ) -> None:
  super().__init__()
 
@@ -154,6 +159,7 @@ def __init__(
  dropout=dropout_rate,
  batch_first=True,
  )
+ self.nonnegative_pred_samples = nonnegative_pred_samples
 
  def describe_inputs(self, batch_size=1) -> InputSpec:
  return InputSpec(
@@ -350,6 +356,24 @@ def output_distribution(
  sliced_params = [p[:, -trailing_n:] for p in params]
  return self.distr_output.distribution(sliced_params, scale=scale)
 
+ def post_process_samples(self, samples: torch.Tensor) -> torch.Tensor:
+ """
+ Method to enforce domain-specific constraints on the generated samples.
+ For example, we can enforce forecasts to be nonnegative.
+ Parameters
+ ----------
+ samples
+ Tensor of samples
+ Returns
+ -------
+ Tensor of processed samples with the same shape.
+ """
+
+ if self.nonnegative_pred_samples:
+ return torch.relu(samples)
+
+ return samples
+
  def forward(
  self,
  feat_static_cat: torch.Tensor,
@@ -451,6 +475,10 @@ def forward(
 
  future_samples_concat = torch.cat(future_samples, dim=1)
 
+ future_samples_concat = self.post_process_samples(
+ future_samples_concat
+ )
+
  return future_samples_concat.reshape(
  (-1, num_parallel_samples, self.prediction_length)
  )

test/torch/model/test_deepar_nonnegative_pred_samples.py

@@ -0,0 +1,42 @@
+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
+
+import pytest
+
+from gluonts.torch import DeepAREstimator
+from gluonts.torch.distributions import StudentTOutput, NormalOutput
+from gluonts.testutil.dummy_datasets import make_dummy_datasets_with_features
+
+
+@pytest.mark.parametrize("datasets", [make_dummy_datasets_with_features()])
+@pytest.mark.parametrize("distr_output", [StudentTOutput(), NormalOutput()])
+def test_deepar_nonnegative_pred_samples(
+ distr_output,
+ datasets,
+):
+ estimator = DeepAREstimator(
+ distr_output=distr_output,
+ nonnegative_pred_samples=True,
+ freq="D",
+ prediction_length=3,
+ trainer_kwargs={"max_epochs": 1},
+ )
+
+ dataset_train, dataset_test = datasets
+ predictor = estimator.train(dataset_train)
+ forecasts = list(predictor.predict(dataset_test))
+
+ assert len(forecasts) == len(dataset_test)
+
+ for forecast in forecasts:
+ assert (forecast.samples >= 0).all()