# 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.
from typing import List, Optional
import torch
import torch.nn as nn
[docs]class FeatureEmbedder(nn.Module):
def __init__(
self,
cardinalities: List[int],
embedding_dims: List[int],
) -> None:
super().__init__()
self._num_features = len(cardinalities)
self._embedders = nn.ModuleList(
[nn.Embedding(c, d) for c, d in zip(cardinalities, embedding_dims)]
)
[docs] def forward(self, features: torch.Tensor) -> torch.Tensor:
if self._num_features > 1:
# we slice the last dimension, giving an array of length
# self._num_features with shape (N,T) or (N)
cat_feature_slices = torch.chunk(
features, self._num_features, dim=-1
)
else:
cat_feature_slices = [features]
return torch.cat(
[
embed(cat_feature_slice.squeeze(-1))
for embed, cat_feature_slice in zip(
self._embedders, cat_feature_slices
)
],
dim=-1,
)
[docs]class FeatureAssembler(nn.Module):
def __init__(
self,
T: int,
embed_static: Optional[FeatureEmbedder] = None,
embed_dynamic: Optional[FeatureEmbedder] = None,
) -> None:
super().__init__()
self.T = T
self.embeddings = nn.ModuleDict()
if embed_static is not None:
self.embeddings["embed_static"] = embed_static
if embed_dynamic is not None:
self.embeddings["embed_dynamic"] = embed_dynamic
[docs] def forward(
self,
feat_static_cat: torch.Tensor,
feat_static_real: torch.Tensor,
feat_dynamic_cat: torch.Tensor,
feat_dynamic_real: torch.Tensor,
) -> torch.Tensor:
processed_features = [
self.process_static_cat(feat_static_cat),
self.process_static_real(feat_static_real),
self.process_dynamic_cat(feat_dynamic_cat),
self.process_dynamic_real(feat_dynamic_real),
]
return torch.cat(processed_features, dim=-1)
[docs] def process_static_cat(self, feature: torch.Tensor) -> torch.Tensor:
if "embed_static" in self.embeddings:
feature = self.embeddings["embed_static"](feature)
return feature.unsqueeze(1).expand(-1, self.T, -1).float()
[docs] def process_dynamic_cat(self, feature: torch.Tensor) -> torch.Tensor:
if "embed_dynamic" in self.embeddings:
return self.embeddings["embed_dynamic"](feature)
return feature.float()
[docs] def process_static_real(self, feature: torch.Tensor) -> torch.Tensor:
return feature.unsqueeze(1).expand(-1, self.T, -1)
[docs] def process_dynamic_real(self, feature: torch.Tensor) -> torch.Tensor:
return feature