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# Copyright 2023 The TensorFlow Authors. 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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.
"""Defines an encoder for concatenating input features into a single tensor."""
from typing import Mapping, Sequence
import tensorflow as tf, tf_keras
from official.recommendation.uplift import types
@tf_keras.utils.register_keras_serializable(package="Uplift")
class ConcatFeatures(tf_keras.layers.Layer):
"""Concatenates features into a single dense tensor.
Takes a dictionary of feature tensors as input and concatenates the specified
features into a single tensor. The tensors are concatenated along their last
axis. Sparse and ragged tensors are converted to dense tensors before being
concatenated.
"""
def __init__(self, feature_names: Sequence[str], **kwargs):
"""Initializes a feature concatenation encoder.
Args:
feature_names: names of the input features to concatenate together.
**kwargs: base layer keyword arguments.
"""
super().__init__(**kwargs)
self._feature_names = feature_names
# Validate feature names.
if not feature_names:
raise ValueError(
"feature_names must be a non-empty list of strings but got"
f" {feature_names} instead."
)
if not all(isinstance(name, str) for name in feature_names):
raise TypeError(
"feature_names must be a list of strings, but got types"
f" {list(map(type, feature_names))}"
)
def build(self, input_shapes: Mapping[str, tf.TensorShape]) -> None:
missing_features = set(self._feature_names) - input_shapes.keys()
if missing_features:
raise ValueError(f"Layer inputs is missing features: {missing_features}")
feature_shapes = {
feature_name: tensor_shape
for feature_name, tensor_shape in input_shapes.items()
if feature_name in self._feature_names
}
most_specific_shape = tf.TensorShape(None)
for feature_name, shape in feature_shapes.items():
if not isinstance(shape, tf.TensorShape):
raise TypeError(
f"Got unsupported tensor shape type for feature {feature_name}. The"
" feature tensor must be one of `tf.Tensor`, `tf.SparseTensor` or"
" `tf.RaggedTensor`, with a well defined tensor shape but got shape"
f" {shape} instead."
)
shape = shape[:-1]
if shape.is_subtype_of(most_specific_shape):
most_specific_shape = shape
elif not most_specific_shape.is_subtype_of(shape):
raise ValueError(
"All features from the feature_names set must be tensors with the"
" same shape except for the last dimension, but got features with"
f" incompatible shapes {feature_shapes}"
)
super().build(input_shapes)
def call(self, inputs: types.DictOfTensors) -> tf.Tensor:
features = []
for feature_name, feature in inputs.items():
if feature_name in self._feature_names:
if isinstance(feature, tf.Tensor):
features.append(feature)
elif isinstance(feature, tf.SparseTensor):
features.append(tf.sparse.to_dense(feature))
elif isinstance(feature, tf.RaggedTensor):
features.append(feature.to_tensor())
else:
raise TypeError(
f"Got unsupported tensor type for feature {feature_name}. The"
" feature tensor must be one of `tf.Tensor`, `tf.SparseTensor` or"
f" `tf.RaggedTensor`, but got {feature} instead."
)
return tf.concat(features, axis=-1)
def get_config(self):
config = super().get_config()
config.update({"feature_names": self._feature_names})
return config
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