#
# Copyright (c) 2021, NVIDIA CORPORATION.
#
# 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.
#
from typing import List, Optional
import tensorflow as tf
from merlin_standard_lib import Schema
from merlin_standard_lib.utils.doc_utils import docstring_parameter
from ..tabular.base import (
TABULAR_MODULE_PARAMS_DOCSTRING,
FilterFeatures,
TabularAggregationType,
TabularTransformationType,
)
from .base import InputBlock
[docs]@docstring_parameter(tabular_module_parameters=TABULAR_MODULE_PARAMS_DOCSTRING)
@tf.keras.utils.register_keras_serializable(package="transformers4rec")
class ContinuousFeatures(InputBlock):
"""Input block for continuous features.
Parameters
----------
features: List[str]
List of continuous features to include in this module.
{tabular_module_parameters}
"""
def __init__(
self,
features: List[str],
pre: Optional[TabularTransformationType] = None,
post: Optional[TabularTransformationType] = None,
aggregation: Optional[TabularAggregationType] = None,
schema: Optional[Schema] = None,
name: Optional[str] = None,
**kwargs
):
super().__init__(
pre=pre, post=post, aggregation=aggregation, schema=schema, name=name, **kwargs
)
self.filter_features = FilterFeatures(features)
[docs] @classmethod
def from_features(cls, features, **kwargs):
return cls(features, **kwargs)
[docs] def call(self, inputs, *args, **kwargs):
cont_features = self.filter_features(inputs)
cont_features = {k: tf.expand_dims(v, -1) for k, v in cont_features.items()}
return cont_features
[docs] def compute_call_output_shape(self, input_shapes):
cont_features_sizes = self.filter_features.compute_output_shape(input_shapes)
cont_features_sizes = {
k: tf.TensorShape(list(v) + [1]) for k, v in cont_features_sizes.items()
}
return cont_features_sizes
[docs] def get_config(self):
config = super().get_config()
config["features"] = self.filter_features.to_include
return config
def _get_name(self):
return "ContinuousFeatures"
[docs] def repr_ignore(self) -> List[str]:
return ["filter_features"]