Merge branch 'dev' into add_truncate_normal_distribution_for_torch_di…

…stributions

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

@@ -18,7 +18,7 @@
 import numpy as np
 
 from gluonts.core.component import validated
-from gluonts.dataset.common import DataEntry, Dataset
+from gluonts.dataset.common import Dataset
 from gluonts.dataset.field_names import FieldName
 from gluonts.dataset.loader import as_stacked_batches
 from gluonts.itertools import Cyclic
@@ -41,7 +41,7 @@
  InstanceSplitter,
  SetField,
  RemoveFields,
- SimpleTransformation,
+ QuantizeMeanScaled,
  VstackFeatures,
  Identity,
  TestSplitSampler,
@@ -67,70 +67,6 @@
 ]
 
 
-class QuantizeScaled(SimpleTransformation):
- """Rescale and quantize the target variable.
- Requires `past_target_field`, and `future_target_field` to be present.
-
- The mean absolute value of the past_target is used to rescale
- past_target and future_target. Then the bin_edges are used to quantize
- the rescaled target.
-
- The calculated scale is stored in the `scale_field`.
-
- Parameters
- ----------
- bin_edges
- The bin edges for quantization.
- past_target_field, optional
- The field name that contains `past_target`,
- by default "past_target"
- past_observed_values_field, optional
- The field name that contains `past_observed_values`,
- by default "past_observed_values"
- future_target_field, optional
- The field name that contains `future_target`,
- by default "future_target"
- scale_field, optional
- The field name where scale will be stored,
- by default "scale"
- """
-
- @validated()
- def __init__(
- self,
- bin_edges: List[float],
- past_target_field: str = "past_target",
- past_observed_values_field: str = "past_observed_values",
- future_target_field: str = "future_target",
- scale_field: str = "scale",
- ):
- self.bin_edges = np.array(bin_edges)
- self.future_target_field = future_target_field
- self.past_target_field = past_target_field
- self.past_observed_values_field = past_observed_values_field
- self.scale_field = scale_field
-
- def transform(self, data: DataEntry) -> DataEntry:
- target = data[self.past_target_field]
- weights = data.get(
- self.past_observed_values_field, np.ones_like(target)
- )
- m = np.sum(np.abs(target) * weights) / np.sum(weights)
- scale = m if m > 0 else 1.0
- data[self.future_target_field] = np.digitize(
- data[self.future_target_field] / scale,
- bins=self.bin_edges,
- right=False,
- )
- data[self.past_target_field] = np.digitize(
- data[self.past_target_field] / scale,
- bins=self.bin_edges,
- right=False,
- )
- data[self.scale_field] = np.array([scale], dtype=np.float32)
- return data
-
-
 class WaveNetEstimator(PyTorchLightningEstimator):
  @validated()
  def __init__(
@@ -392,7 +328,7 @@ def _create_instance_splitter(self, mode: str):
  FieldName.FEAT_TIME,
  FieldName.OBSERVED_VALUES,
  ],
- ) + QuantizeScaled(bin_edges=self.bin_edges)
+ ) + QuantizeMeanScaled(bin_edges=self.bin_edges)
 
  def create_training_data_loader(
  self,

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

@@ -18,27 +18,7 @@
 
 from gluonts.core.component import validated
 from gluonts.torch.modules.feature import FeatureEmbedder
-
-
-class LookupValues(nn.Module):
- """Lookup bin values from bin indices.
-
- Parameters
- ----------
- bin_values
- Tensor of bin values with shape (num_bins, ).
- """
-
- @validated()
- def __init__(self, bin_values: torch.Tensor):
- super().__init__()
- self.register_buffer("bin_values", bin_values)
-
- def forward(self, indices: torch.Tensor) -> torch.Tensor:
- indices = torch.clamp(indices, 0, self.bin_values.shape[0] - 1)
- return torch.index_select(
- self.bin_values, 0, indices.reshape(-1)
- ).view_as(indices)
+from gluonts.torch.modules.lookup_table import LookupValues
 
 
 class CausalDilatedResidualLayer(nn.Module):

src/gluonts/torch/modules/lookup_table.py

@@ -0,0 +1,38 @@
+# 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 torch
+import torch.nn as nn
+
+from gluonts.core.component import validated
+
+
+class LookupValues(nn.Module):
+ """A lookup table mapping bin indices to values.
+
+ Parameters
+ ----------
+ bin_values
+ Tensor of bin values with shape (num_bins, ).
+ """
+
+ @validated()
+ def __init__(self, bin_values: torch.Tensor):
+ super().__init__()
+ self.register_buffer("bin_values", bin_values)
+
+ def forward(self, indices: torch.Tensor) -> torch.Tensor:
+ indices = torch.clamp(indices, 0, self.bin_values.shape[0] - 1)
+ return torch.index_select(
+ self.bin_values, 0, indices.reshape(-1)
+ ).view_as(indices)

src/gluonts/transform/__init__.py

@@ -41,6 +41,7 @@
  "InstanceSplitter",
  "ListFeatures",
  "MapTransformation",
+ "QuantizeMeanScaled",
  "RemoveFields",
  "RenameFields",
  "SampleTargetDim",
@@ -89,6 +90,7 @@
  VstackFeatures,
  cdf_to_gaussian_forward_transform,
  Valmap,
+ QuantizeMeanScaled,
 )
 from .feature import (
  AddAgeFeature,

src/gluonts/transform/convert.py

@@ -904,3 +904,67 @@ def flatmap_transform(
  if len(times) > 0 or not self.drop_empty:
  data[self.target_field] = [times, sizes]
  yield data
+
+
+class QuantizeMeanScaled(SimpleTransformation):
+ """Rescale and quantize the target variable.
+ Requires `past_target_field`, and `future_target_field` to be present.
+
+ The mean absolute value of the past_target is used to rescale
+ past_target and future_target. Then the bin_edges are used to quantize
+ the rescaled target.
+
+ The calculated scale is stored in the `scale_field`.
+
+ Parameters
+ ----------
+ bin_edges
+ The bin edges for quantization.
+ past_target_field, optional
+ The field name that contains `past_target`,
+ by default "past_target"
+ past_observed_values_field, optional
+ The field name that contains `past_observed_values`,
+ by default "past_observed_values"
+ future_target_field, optional
+ The field name that contains `future_target`,
+ by default "future_target"
+ scale_field, optional
+ The field name where scale will be stored,
+ by default "scale"
+ """
+
+ @validated()
+ def __init__(
+ self,
+ bin_edges: List[float],
+ past_target_field: str = "past_target",
+ past_observed_values_field: str = "past_observed_values",
+ future_target_field: str = "future_target",
+ scale_field: str = "scale",
+ ):
+ self.bin_edges = np.array(bin_edges)
+ self.future_target_field = future_target_field
+ self.past_target_field = past_target_field
+ self.past_observed_values_field = past_observed_values_field
+ self.scale_field = scale_field
+
+ def transform(self, data: DataEntry) -> DataEntry:
+ target = data[self.past_target_field]
+ weights = data.get(
+ self.past_observed_values_field, np.ones_like(target)
+ )
+ m = np.sum(np.abs(target) * weights) / np.sum(weights)
+ scale = m if m > 0 else 1.0
+ data[self.future_target_field] = np.digitize(
+ data[self.future_target_field] / scale,
+ bins=self.bin_edges,
+ right=False,
+ )
+ data[self.past_target_field] = np.digitize(
+ data[self.past_target_field] / scale,
+ bins=self.bin_edges,
+ right=False,
+ )
+ data[self.scale_field] = np.array([scale], dtype=np.float32)
+ return data