ranking/tensorflow_ranking/python/keras/model.py at v0.5.3 · tensorflow/ranking

This repository was archived by the owner on May 21, 2025. It is now read-only.
865 lines (712 loc) · 28.3 KB
# Copyright 2023 The TensorFlow Ranking Authors.
# 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.
"""Ranking model utilities and classes in tfr.keras."""
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from absl import logging
import tensorflow as tf
from tensorflow_ranking.python.keras import feature
from tensorflow_ranking.python.keras import layers
TensorLike = tf.types.experimental.TensorLike
TensorDict = Dict[str, TensorLike]
def create_keras_model(network,
                       metrics,
                       optimizer,
                       size_feature_name,
                       list_size=None):
  """Creates a Functional Keras ranking model.
  A mask is inferred from size_feature_name and passed to the network, along
  with feature dictionary as inputs.
    network: (`tfr.keras.network.RankingNetwork`) A ranking network which
      generates a list of scores.
    loss: (`tfr.keras.losses._RankingLoss`) A ranking loss.
    metrics: (list) List of ranking metrics, `tfr.keras.metrics._RankingMetric`
      instances.
    optimizer: (`tf.keras.optimizer.Optimizer`) Optimizer to minimize ranking
      loss.
    size_feature_name: (str) Name of feature for example list sizes. If not
      None, this feature name corresponds to a `tf.int32` Tensor of size
      [batch_size] corresponding to sizes of example lists. If `None`, all
      examples are treated as valid.
    list_size: (int) The list size for example features. If None, use dynamic
      list size. A fixed list size is required for TPU training.
    A compiled ranking Keras model, a `tf.keras.Model` instance.
  # TODO: Support compatibility with TPUs.
  keras_inputs = feature.create_keras_inputs(
      context_feature_columns=network.context_feature_columns,
      example_feature_columns=network.example_feature_columns,
      size_feature_name=size_feature_name,
      list_size=list_size)
  # Create mask from sizes and list_size.
  mask = None
  if size_feature_name is not None:
    mask = feature.GenerateMask(network.example_feature_columns,
                                size_feature_name)(
                                    keras_inputs)
  logits = network(inputs=keras_inputs, mask=mask)
  ranker = tf.keras.Model(inputs=keras_inputs, outputs=logits)
  ranker.compile(optimizer=optimizer, loss=loss, metrics=metrics)
  return ranker
class AbstractModelBuilder(metaclass=abc.ABCMeta):
  """Interface to build a `tf.keras.Model` for ranking.
  The `AbstractModelBuilder` serves as the interface between model building and
  training. The training pipeline just calls the `build()` method to get the
  model constructed in the strategy scope used in the training pipeline, so for
  all variables in the model, optimizers, and metrics. See `ModelFitPipeline` in
  `pipeline.py` for example.
  The `build()` method is to be implemented in a subclass. The simplest example
  is just to define everything inside the build function when you define a
  tf.keras.Model.
  ```python
  class MyModelBuilder(AbstractModelBuilder):
    def build(self) -> tf.keras.Model:
      inputs = ...
      outputs = ...
      return tf.keras.Model(inputs=inputs, outputs=outputs)
  The `MyModelBuilder` should work with `ModelFitPipeline`. To make the model
  building more structured for ranking problems, we also define subclasses like
  `ModelBuilderWithMask` in the following.
  @abc.abstractmethod
  def build(self) -> tf.keras.Model:
    """The build method to be implemented by a subclass."""
    raise NotImplementedError("Calling an abstract method.")
class ModelBuilderWithMask(AbstractModelBuilder, metaclass=abc.ABCMeta):
  """Interface to build a `tf.keras.Model` for ranking with a mask Tensor.
  The `ModelBuilderWithMask` class is an abstract class to build a ranking model
  based on dense Tensors and a mask Tensor to indicate the padded ones.
  All the boilerplate codes related to constructing a `tf.keras.Model` are
  integrated in the ModelBuilder class.
  To be implemented by subclasses:
    * `create_inputs()`: Contains the logic to create `tf.keras.Input` for
    context and example inputs and mask for valid list items.
    * `preprocess()`: Contains the logic to preprocess context and example
    inputs.
    * `score()`: Contains the logic to score examples in list and return
    outputs.
  Example subclass implementation:
  ```python
  class SimpleModelBuilder(ModelBuilderWithMask):
    def __init__(self, context_feature_spec, example_feature_spec,
                 mask_feature_name, name=None):
      self._context_feature_spec = context_feature_spec
      self._example_feature_spec = example_feature_spec
      self._mask_feature_name = mask_feature_name
      self._name = name
    def create_inputs(self):
      context_inputs = {
          name: tf.keras.Input(
              shape=tuple(spec.shape),
              name=name,
              dtype=spec.dtype
          ) for name, spec in self._context_feature_spec.items()
      example_inputs = {
          name: tf.keras.Input(
              shape=(None,) + tuple(spec.shape),
              name=name,
              dtype=spec.dtype
          ) for name, spec in self._example_feature_spec.items()
      mask = tf.keras.Input(
          name=self._mask_feature_name, shape=(None,), dtype=tf.bool)
      return context_inputs, example_inputs, mask
    def preprocess(self, context_inputs, example_inputs, mask):
      context_features = {
          name: tf.math.log1p(
              tf.abs(tensor)) for name, tensor in context_inputs.items()
      example_features = {
          name: tf.math.log1p(
              tf.abs(tensor)) for name, tensor in example_inputs.items()
      return context_features, example_features
    def score(self, context_features, example_features, mask):
      x = tf.concat([tensor for tensor in example_features.values()], -1)
      return tf.keras.layers.Dense(1)(x)
  def __init__(self, mask_feature_name: str, name: Optional[str] = None):
    """Initializes the instance.
      mask_feature_name: name of 2D mask boolean feature.
      name: (optional) name of the Model.
    self._mask_feature_name = mask_feature_name
    self._name = name
    if self._mask_feature_name is None:
      raise ValueError("mask_feature_name cannot be None.")
  @abc.abstractmethod
  def create_inputs(self) -> Tuple[TensorDict, TensorDict, tf.Tensor]:
    """Creates context and example inputs.
    Example usage:
    ```python
    model_builder = SimpleModelBuilder(
        {},
        {"example_feature_1": tf.io.FixedLenFeature(
            shape=(1,), dtype=tf.float32, default_value=0.0)},
        "list_mask", "model_builder")
    context_inputs, example_inputs, mask = model_builder.create_inputs()
    Returns:
      A tuple of
      context_inputs: maps from context feature keys to Keras Input.
      example_inputs: maps from example feature keys to Keras Input.
      mask: Keras Input for the mask feature.
    raise NotImplementedError("Calling an abstract method.")
  @abc.abstractmethod
  def preprocess(
      self,
      context_inputs: TensorDict,
      example_inputs: TensorDict,
      mask: tf.Tensor,
  ) -> Tuple[TensorDict, TensorDict]:
    """Preprocesses context and example inputs.
    Example usage:
    ```python
    model_builder = SimpleModelBuilder(
        {},
        {"example_feature_1": tf.io.FixedLenFeature(
            shape=(1,), dtype=tf.float32, default_value=0.0)},
        "list_mask", "model_builder")
    context_inputs, example_inputs, mask = model_builder.create_inputs()
    context_features, example_features = model_builder.preprocess(
        context_inputs, example_inputs, mask)
      context_inputs: maps context feature keys to `tf.keras.Input`.
      example_inputs: maps example feature keys to `tf.keras.Input`.
      mask: [batch_size, list_size]-tensor of mask for valid examples.
    Returns:
      A tuple of
      context_features: maps from context feature keys to [batch_size,
        feature_dims]-tensors of preprocessed context features.
      example_features: maps from example feature keys to [batch_size,
        list_size, feature_dims]-tensors of preprocessed example features.
    raise NotImplementedError("Calling an abstract method.")
  @abc.abstractmethod
  def score(
      self,
      context_features: TensorDict,
      example_features: TensorDict,
      mask: tf.Tensor,
  ) -> Union[TensorLike, TensorDict]:
    """Scores all examples and returns outputs.
    Example usage:
    ```python
    model_builder = SimpleModelBuilder(
        {},
        {"example_feature_1": tf.io.FixedLenFeature(
            shape=(1,), dtype=tf.float32, default_value=0.0)},
        "list_mask", "model_builder")
    context_inputs, example_inputs, mask = model_builder.create_inputs()
    context_features, example_features = model_builder.preprocess(
        context_inputs, example_inputs, mask)
    scores = model_builder.score(context_features, example_features)
      context_features: maps from context feature keys to [batch_size,
        feature_dims]-tensors of preprocessed context features.
      example_features: maps from example feature keys to [batch_size,
        list_size, feature_dims]-tensors of preprocessed example features.
      mask: [batch_size, list_size]-tensor of mask for valid examples.
    Returns:
      A [batch_size, list_size]-tensor of logits or a dict mapping task name to
      logits in the multi-task setting.
    raise NotImplementedError("Calling an abstract method.")
  def build(self) -> tf.keras.Model:
    """Builds a Keras Model for Ranking Pipeline.
    Example usage:
    ```python
    model_builder = SimpleModelBuilder(
        {},
        {"example_feature_1": tf.io.FixedLenFeature(
            shape=(1,), dtype=tf.float32, default_value=0.0)},
        "list_mask", "model_builder")
    model = model_builder.build()
    Returns:
      A `tf.keras.Model`.
    context_inputs, example_inputs, mask = self.create_inputs()
    logging.info("Context features: %s", context_inputs)
    logging.info("Example features: %s", example_inputs)
    context_features, example_features = self.preprocess(
        context_inputs, example_inputs, mask)
    outputs = self.score(context_features, example_features, mask)
    return tf.keras.Model(
        inputs=dict(
            list(context_inputs.items()) + list(example_inputs.items()) +
            [(self._mask_feature_name, mask)]),
        outputs=outputs,
        name=self._name)
class ModelBuilder(ModelBuilderWithMask):
  """Builds a `tf.keras.Model`.
  This class implements the `ModelBuilderWithMask` by delegating the class
  behaviors to the following implementors that can be specified by callers:
     * input_creator: A callable or a class like `InputCreator` to implement
     `create_inputs`.
     * preprocessor: A callable or a class like `Preprocessor` to implement
     `preprocess`.
     * scorer: A callable or a class like `Scorer` to implement `score`.
  Users can subclass those implementor classes and pass the objects into this
  class to build a `tf.keras.Model`.
  Example usage:
  ```python
  model_builder = ModelBuilder(
      input_creator=FeatureSpecInputCreator(
          {},
          {"example_feature_1": tf.io.FixedLenFeature(
              shape=(1,), dtype=tf.float32, default_value=0.0)}),
      preprocessor=PreprocessorWithSpec(),
      scorer=DNNScorer(hidden_layer_dims=[16]),
      mask_feature_name="list_mask",
      name="model_builder")
  def __init__(
      self,
      input_creator: Callable[[], Tuple[TensorDict, TensorDict]],
      preprocessor: Callable[[TensorDict, TensorDict, tf.Tensor],
                             Tuple[TensorDict, TensorDict]],
      scorer: Callable[[TensorDict, TensorDict, tf.Tensor], Union[TensorLike,
      mask_feature_name: str,
      name: Optional[str] = None,
    """Initializes the instance.
      input_creator: A callable or a class like `InputCreator` to implement
        `create_inputs`.
      preprocessor: A callable or a class like `Preprocessor` to implement
        `preprocess`.
      scorer: A callable or a class like `Scorer` to implement `score`.
      mask_feature_name: name of 2D mask boolean feature.
      name: (optional) name of the Model.
    super().__init__(mask_feature_name, name)
    self._input_creator = input_creator
    self._preprocessor = preprocessor
    self._scorer = scorer
  def create_inputs(self) -> Tuple[TensorDict, TensorDict, tf.Tensor]:
    """See `ModelBuilderWithMask`."""
    context_inputs, example_inputs = self._input_creator()
    mask = tf.keras.Input(
        name=self._mask_feature_name, shape=(None,), dtype=tf.bool)
    return context_inputs, example_inputs, mask
  def preprocess(
      self,
      context_inputs: TensorDict,
      example_inputs: TensorDict,
      mask: tf.Tensor,
  ) -> Tuple[TensorDict, TensorDict]:
    """See `ModelBuilderWithMask`."""
    return self._preprocessor(context_inputs, example_inputs, mask)
  def score(
      self,
      context_features: TensorDict,
      example_features: TensorDict,
      mask: tf.Tensor,
  ) -> Union[TensorLike, TensorDict]:
    """See `ModelBuilderWithMask`."""
    return self._scorer(context_features, example_features, mask)
class InputCreator(metaclass=abc.ABCMeta):
  """Interface for input creator.
  The `InputCreator` class is an abstract class to implement `create_inputs` in
  `ModelBuilder` in tfr.keras.
  To be implemented by subclasses:
    * `__call__()`: Contains the logic to create `tf.keras.Input` for context
    and example inputs.
  Example subclass implementation:
  ```python
  class SimpleInputCreator(InputCreator):
    def __call__(self):
      return {}, {"example_feature_1": tf.keras.Input((1,), dtype=tf.float32)}
  @abc.abstractmethod
  def __call__(self) -> Tuple[TensorDict, TensorDict]:
    """Invokes the `InputCreator` instance.
    Returns:
      A tuple of two dicts which map the context and example feature keys to
        the corresponding `tf.keras.Input`.
    raise NotImplementedError("Calling an abstract method.")
class FeatureSpecInputCreator(InputCreator):
  """InputCreator with feature specs.
  Example usage:
  ```python
  input_creator=FeatureSpecInputCreator(
      {"example_feature_1": tf.io.FixedLenFeature(
          shape=(1,), dtype=tf.float32, default_value=0.0)})
  def __init__(
      self,
      context_feature_spec: Dict[str, Union[tf.io.FixedLenFeature,
                                            tf.io.VarLenFeature,
                                            tf.io.RaggedFeature]],
      example_feature_spec: Dict[str, Union[tf.io.FixedLenFeature,
                                            tf.io.VarLenFeature,
                                            tf.io.RaggedFeature]],
    """Initializes the instance.
      context_feature_spec: A dict maps the context feature keys to the
        corresponding context feature specs.
      example_feature_spec: A dict maps the example feature keys to the
        corresponding example feature specs.
    self._context_feature_spec = context_feature_spec
    self._example_feature_spec = example_feature_spec
  def __call__(self) -> Tuple[TensorDict, TensorDict]:
    """See `InputCreator`."""
    def get_keras_input(feature_spec, name, is_example=False):
      if isinstance(feature_spec, tf.io.FixedLenFeature):
        return tf.keras.Input(
            shape=(None,) + tuple(feature_spec.shape)
            if is_example else tuple(feature_spec.shape),
            dtype=feature_spec.dtype,
            name=name)
      elif isinstance(feature_spec, tf.io.VarLenFeature):
        return tf.keras.Input(
            shape=(None, 1) if is_example else (1),
            dtype=feature_spec.dtype,
            name=name,
            sparse=True)
      elif isinstance(feature_spec, tf.io.RaggedFeature):
        return tf.keras.Input(
            shape=(None,) *
            (len(feature_spec.partitions) + 2) if is_example else
            (None,) * (len(feature_spec.partitions) + 1),
            dtype=feature_spec.dtype,
            name=name,
            ragged=True)
      else:
        raise ValueError("{} is not supported.".format(feature_spec))
    context_inputs = {
        name: get_keras_input(spec, name)
        for name, spec in self._context_feature_spec.items()
    example_inputs = {
        name: get_keras_input(spec, name, is_example=True)
        for name, spec in self._example_feature_spec.items()
    return context_inputs, example_inputs
class TypeSpecInputCreator(InputCreator):
  """InputCreator with tensor type specs.
  Example usage:
  ```python
  input_creator=TypeSpecInputCreator(
      {"example_feature_1": tf.TensorSpec(shape=[None, 1], dtype=tf.float32)},
      example_feature_names=["example_feature_1"])
  def __init__(
      self,
      type_spec: Dict[str, Union[tf.TensorSpec, tf.RaggedTensorSpec]],
      context_feature_names: Optional[List[str]] = None,
      example_feature_names: Optional[List[str]] = None,
    """Initializes the instance.
      type_spec: A dict maps the context and example feature keys to the
        corresponding context and example type specs.
      context_feature_names: A list of context feature keys.
      example_feature_names: A list of example feature keys.
    self._type_spec = type_spec
    self._context_feature_names = context_feature_names or []
    self._example_feature_names = example_feature_names or []
  def __call__(self) -> Tuple[TensorDict, TensorDict]:
    """See `InputCreator`."""
    def get_keras_input(type_spec, name=None):
      """Returns a keras.Input."""
      return tf.keras.Input(
          shape=type_spec.shape[1:],
          dtype=type_spec.dtype,
          ragged=isinstance(type_spec, tf.RaggedTensorSpec),
          name=name)
    context_inputs, example_inputs = {}, {}
    for name, spec in self._type_spec.items():
      k_input = get_keras_input(spec, name)
      if name in self._context_feature_names:
        context_inputs[name] = k_input
      elif name in self._example_feature_names:
        example_inputs[name] = k_input
    return context_inputs, example_inputs
class Preprocessor(metaclass=abc.ABCMeta):
  """Interface for feature preprocessing.
  The `Preprocessor` class is an abstract class to implement `preprocess` in
  `ModelBuilder` in tfr.keras.
  To be implemented by subclasses:
    * `__call__()`: Contains the logic to preprocess context and example inputs.
  Example subclass implementation:
  ```python
  class SimplePreprocessor(Preprocessor):
    def __call__(self, context_inputs, example_inputs, mask):
      context_features = {
          name: tf.math.log1p(
              tf.abs(tensor)) for name, tensor in context_inputs.items()
      example_features = {
          name: tf.math.log1p(
              tf.abs(tensor)) for name, tensor in example_inputs.items()
      return context_features, example_features
  @abc.abstractmethod
  def __call__(
      self,
      context_inputs: TensorDict,
      example_inputs: TensorDict,
      mask: tf.Tensor,
  ) -> Tuple[TensorDict, TensorDict]:
    """Invokes the `Preprocessor` instance.
      context_inputs: maps context feature keys to `tf.keras.Input`.
      example_inputs: maps example feature keys to `tf.keras.Input`.
      mask: [batch_size, list_size]-tensor of mask for valid examples.
    Returns:
      A tuple of two dicts which map the context and example feature keys to
        the corresponding `tf.Tensor`s.
    raise NotImplementedError("Calling an abstract method.")
class PreprocessorWithSpec(Preprocessor):
  """Preprocessing inputs with provided spec.
  Transformation including KPL or customized transformation like log1p can be
  defined and passed in `preprocess_spec` with the following example usage:
  ```python
  preprocess_spec = {
      **{name: lambda t: tf.math.log1p(t * tf.sign(t)) * tf.sign(t)
         for name in example_feature_spec.keys()},
      **{name: tf.reduce_mean(
          tf.keras.layers.Embedding(input_dim=10, output_dim=4)(x), axis=-2)
         for name in context_feature_spec.keys()}
  preprocessor = PreprocessorWithSpec(preprocess_spec)
  def __init__(
      self,
      preprocess_spec: Optional[Dict[str, Callable[[Any], Any]]] = None,
      default_value_spec: Optional[Dict[str, float]] = None,
    """Initializes the instance.
      preprocess_spec: maps a feature key to a callable to preprocess a feature.
        Only include those features that need preprocessing.
      default_value_spec: maps a feature key to a default value to convert a
        RaggedTensor to Tensor. Default to 0. if not specified.
    self._preprocess_spec = preprocess_spec or {}
    self._default_value_spec = default_value_spec or {}
  def __call__(
      self,
      context_inputs: TensorDict,
      example_inputs: TensorDict,
      mask: tf.Tensor,
  ) -> Tuple[TensorDict, TensorDict]:
    """See `Preprocessor`."""
    list_size = tf.shape(mask)[1]
    def apply_preprocess(key, value, is_example=False):
      """Applies the preprocessing spec and convert to tf.Tensor."""
      if key in self._preprocess_spec:
        value = self._preprocess_spec[key](value)
      if isinstance(value, tf.RaggedTensor):
        default_value = self._default_value_spec.get(key, 0.)
        s = value.bounding_shape()
        new_shape = tf.concat([s[:1], [list_size], s[2:]],
                              axis=0) if is_example else s
        return value.to_tensor(default_value, shape=new_shape)
      else:
        return value
    context_features = {
        key: apply_preprocess(key, value)
        for key, value in context_inputs.items()
    example_features = {
        key: apply_preprocess(key, value, True)
        for key, value in example_inputs.items()
    return context_features, example_features
class Scorer(metaclass=abc.ABCMeta):
  """Interface for scorer.
  The `Scorer` class is an abstract class to implement `score` in `ModelBuilder`
  in tfr.keras.
  To be implemented by subclasses:
    * `__call__()`: Contains the logic to score based on the context and example
    features.
  Example subclass implementation:
  ```python
  class SimpleScorer(Scorer):
    def __call__(self, context_features, example_features, mask):
      x = tf.concat([tensor for tensor in example_features.values()], -1)
      return tf.keras.layers.Dense(1)(x)
  @abc.abstractmethod
  def __call__(
      self,
      context_features: TensorDict,
      example_features: TensorDict,
      mask: tf.Tensor,
  ) -> Union[TensorLike, TensorDict]:
    """Scores all examples given context and returns logits.
      context_features: maps from context feature keys to [batch_size,
        feature_dims]-tensors of preprocessed context features.
      example_features: maps from example feature keys to [batch_size,
        list_size, feature_dims]-tensors of preprocessed example features.
      mask: [batch_size, list_size]-tensor of mask for valid examples.
    Returns:
      A [batch_size, list_size]-tensor of logits or a dict mapping task name to
      logits in the multi-task setting.
    raise NotImplementedError("Calling an abstract method.")
class UnivariateScorer(Scorer, metaclass=abc.ABCMeta):
  """Interface for univariate scorer.
  The `UnivariateScorer` class is an abstract class to implement `score` in
  `ModelBuilder` in tfr.keras with a univariate scoring function.
  To be implemented by subclasses:
    * `_score_flattened()`: Contains the logic to do the univariate scoring on
    flattened context and example features.
  Example subclass implementation:
  ```python
  class SimpleUnivariateScorer(UnivariateScorer):
    def _score_flattened(self, context_features, example_features):
      x = tf.concat([tensor for tensor in example_features.values()], -1)
      return tf.keras.layers.Dense(1)(x)
  @abc.abstractmethod
  def _score_flattened(
      self,
      context_features: TensorDict,
      example_features: TensorDict,
  ) -> Union[tf.Tensor, TensorDict]:
    """Computes the flattened logits.
      context_features: maps from context feature keys to [batch_size *
        list_size, feature_dims]-tensors of preprocessed context features.
      example_features: maps from example feature keys to [batch_size *
        list_size, feature_dims]-tensors of preprocessed example features.
    Returns:
      A tf.Tensor of size [batch_size * list_size, 1] or a dict maps output
        names to tf.Tensors of size [batch_size * list_size, 1].
    raise NotImplementedError("Calling an abstract method.")
  def __call__(
      self,
      context_features: TensorDict,
      example_features: TensorDict,
      mask: tf.Tensor,
  ) -> Union[tf.Tensor, TensorDict]:
    """See `Scorer`."""
    (flattened_context_features,
     flattened_example_features) = layers.FlattenList()(
         inputs=(context_features, example_features, mask))
    flattened_logits = self._score_flattened(flattened_context_features,
                                             flattened_example_features)
    # Handle a dict of logits for the multi-task setting.
    if isinstance(flattened_logits, dict):
      logits = {
          k: layers.RestoreList(name=k)(inputs=(v, mask))
          for k, v in flattened_logits.items()
      logits = layers.RestoreList()(inputs=(flattened_logits, mask))
    return logits
class DNNScorer(UnivariateScorer):
  """Univariate scorer using DNN.
  Example usage:
  ```python
  scorer=DNNScorer(hidden_layer_dims=[16])
  def __init__(self, **dnn_kwargs):
    """Initializes the instance.
      **dnn_kwargs: A dict of keyward arguments for dense neural network layers.
        Please see `tfr.keras.layers.create_tower` for specific list of keyword
        arguments.
    self._dnn_kwargs = dnn_kwargs
  def _score_flattened(
      self,
      context_features: TensorDict,
      example_features: TensorDict,
  ) -> tf.Tensor:
    """See `UnivariateScorer`."""
    context_input_layer = [
        tf.keras.layers.Flatten()(context_features[name])
        for name in sorted(context_features)
    example_input_layer = [
        tf.keras.layers.Flatten()(example_features[name])
        for name in sorted(example_features)
    input_layer = tf.concat(context_input_layer + example_input_layer, 1)
    flattened_logits = layers.create_tower(**self._dnn_kwargs)(input_layer)
    return flattened_logits
class GAMScorer(UnivariateScorer):
  """Univariate scorer using GAM.
  The scorer implements Neural Generalized Additive Ranking Model, which is an
  additive ranking model.
  See the [paper](https://arxiv.org/abs/2005.02553) for more details.
  Example usage:
  ```python
  scorer=GAMScorer(hidden_layer_dims=[16])
  def __init__(self, **gam_kwargs):
    """Initializes the instance.
      **gam_kwargs: A dict of keyward arguments for GAM layers. Please see
        `tfr.keras.layers.GAMlayer` for specific list of keyword arguments.
    self._gam_kwargs = gam_kwargs
  def _score_flattened(
      self,
      context_features: TensorDict,
      example_features: TensorDict,
  ) -> tf.Tensor:
    """See `UnivariateScorer`."""
    context_inputs = [
        tf.keras.layers.Flatten()(context_features[name])
        for name in sorted(context_features)
    example_inputs = [
        tf.keras.layers.Flatten()(example_features[name])
        for name in sorted(example_features)
    gam_kwargs = self._gam_kwargs
    # TODO: These parameters may be inferred from the call inputs.
    gam_kwargs.update({
        "example_feature_num": len(example_inputs),
        "context_feature_num": len(context_inputs),
    flattened_logits, _, _ = layers.GAMLayer(**gam_kwargs)(
        inputs=(example_inputs, context_inputs))
    return flattened_logits
Provide feedback

Saved searches

Use saved searches to filter your results more quickly

FilesExpand file tree

model.py

Latest commit

History

model.py

File metadata and controls
