-
Notifications
You must be signed in to change notification settings - Fork 7
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
* ADD: LearningMNL * ADD: foundation of L-MNL notebook
- Loading branch information
1 parent
8810592
commit 85bc388
Showing
10 changed files
with
743 additions
and
25 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,195 @@ | ||
| """ | ||
| Implementation of Enhancing Discrete Choice Models with Representation Learning. | ||
| https://arxiv.org/abs/1812.09747 . | ||
| """ | ||
|
|
||
| import logging | ||
|
|
||
| import tensorflow as tf | ||
|
|
||
| from .conditional_logit import ConditionalLogit | ||
|
|
||
|
|
||
| class LearningMNL(ConditionalLogit): | ||
| """Learning MNL from paper https://arxiv.org/abs/1812.09747 . | ||
| Arguments: | ||
| ---------- | ||
| coefficients: dict or MNLCoefficients | ||
| Specfication of the model to be estimated. | ||
| """ | ||
|
|
||
| def __init__( | ||
| self, | ||
| coefficients=None, | ||
| nn_features=[], | ||
| nn_layers_widths=[10], | ||
| nn_activation="relu", | ||
| add_exit_choice=False, | ||
| optimizer="Adam", | ||
| lr=0.001, | ||
| **kwargs, | ||
| ): | ||
| """Initialize of Conditional-MNL. | ||
| Parameters | ||
| ---------- | ||
| coefficients : dict or MNLCoefficients | ||
| Dictionnary containing the coefficients parametrization of the model. | ||
| The dictionnary must have the following structure: | ||
| {feature_name_1: mode_1, feature_name_2: mode_2, ...} | ||
| mode must be among "constant", "item", "item-full" for now | ||
| (same specifications as torch-choice). | ||
| nn_features: list of str | ||
| List of features names that will be used in the neural network. | ||
| Features used as NN inputs MUST BE shared_features ! | ||
| nn_layers_widths: list of int | ||
| List of integers representing the width of each hidden layer in the neural network. | ||
| add_exit_choice : bool, optional | ||
| Whether or not to normalize the probabilities computation with an exit choice | ||
| whose utility would be 1, by default True | ||
| """ | ||
| super().__init__(add_exit_choice=add_exit_choice, optimizer=optimizer, lr=lr, **kwargs) | ||
| self.coefficients = coefficients | ||
| self.nn_features = nn_features | ||
| self.nn_layers_widths = nn_layers_widths | ||
| self.nn_activation = nn_activation | ||
| self.instantiated = False | ||
|
|
||
| def instantiate(self, choice_dataset): | ||
| """Instantiate the model using the features in the choice_dataset. | ||
| Parameters | ||
| ---------- | ||
| choice_dataset: ChoiceDataset | ||
| Used to match the features names with the model coefficients. | ||
| """ | ||
| if not self.instantiated: | ||
| # Instantiate NN | ||
| nn_input = tf.keras.Input(shape=(len(self.nn_features), 1, 1)) | ||
| nn_output = tf.keras.layers.Conv2D( | ||
| filters=self.nn_layers_widths[0], | ||
| kernel_size=[len(self.nn_features), 1], | ||
| activation="relu", | ||
| padding="valid", | ||
| name="Dense_NN_per_frame", | ||
| )(nn_input) | ||
| nn_output = tf.keras.layers.Dropout(0.2, name="Regularizer")(nn_output) | ||
| # nn_output = tf.reshape(nn_output, (-1, self.nn_layers_widths[0])) | ||
| nn_output = tf.keras.layers.Reshape((self.nn_layers_widths[0],))(nn_output) | ||
|
|
||
| for i in range(len(self.nn_layers_widths) - 1): | ||
| nn_output = tf.keras.layers.Dense( | ||
| units=self.nn_layers_widths[i + 1], activation="relu", name="Dense{}".format(i) | ||
| )(nn_output) | ||
| nn_output = tf.keras.layers.Dropout(0.2, name="Drop{}".format(i))(nn_output) | ||
| nn_output = tf.keras.layers.Dense( | ||
| units=choice_dataset.get_n_items(), name="Output_new_feature" | ||
| )(nn_output) | ||
|
|
||
| # nn_input = tf.keras.Input(shape=(len(self.nn_features), )) | ||
| # x = nn_input | ||
| # for width in self.nn_layers_widths: | ||
| # x = tf.keras.layers.Dense(width, activation=self.nn_activation)(x) | ||
| # x = tf.keras.layers.Dropout(0.2, name="Regularizer")(x) | ||
| # nn_output = tf.keras.layers.Dense(choice_dataset.get_n_items())(x) | ||
| self.nn_model = tf.keras.Model(inputs=nn_input, outputs=nn_output) | ||
|
|
||
| super().instantiate(choice_dataset) | ||
|
|
||
| @property | ||
| def trainable_weights(self): | ||
| """Trainable weights of the model.""" | ||
| return self._trainable_weights + self.nn_model.trainable_variables | ||
|
|
||
| def compute_batch_utility( | ||
| self, | ||
| shared_features_by_choice, | ||
| items_features_by_choice, | ||
| available_items_by_choice, | ||
| choices, | ||
| verbose=1, | ||
| ): | ||
| """Compute the utility when the model is constructed from a MNLCoefficients object. | ||
| Parameters | ||
| ---------- | ||
| shared_features_by_choice : tuple of np.ndarray (choices_features) | ||
| a batch of shared features | ||
| Shape must be (n_choices, n_shared_features) | ||
| items_features_by_choice : tuple of np.ndarray (choices_items_features) | ||
| a batch of items features | ||
| Shape must be (n_choices, n_items_features) | ||
| available_items_by_choice : np.ndarray | ||
| A batch of items availabilities | ||
| Shape must be (n_choices, n_items) | ||
| choices: np.ndarray | ||
| Choices | ||
| Shape must be (n_choices, ) | ||
| verbose : int, optional | ||
| Parametrization of the logging outputs, by default 1 | ||
| Returns | ||
| ------- | ||
| tf.Tensor | ||
| Utilities corresponding of shape (n_choices, n_items) | ||
| """ | ||
| if not isinstance(shared_features_by_choice, tuple): | ||
| shared_features_by_choice = (shared_features_by_choice,) | ||
| if not isinstance(items_features_by_choice, tuple): | ||
| items_features_by_choice = (items_features_by_choice,) | ||
| knowledge_driven_utilities = super().compute_batch_utility( | ||
| shared_features_by_choice, | ||
| items_features_by_choice, | ||
| available_items_by_choice, | ||
| choices, | ||
| verbose=verbose, | ||
| ) | ||
| data_driven_inputs = [] | ||
| if self._shared_features_by_choice_names is not None: | ||
| for nn_feature in self.nn_features: | ||
| for i, feat_tuple in enumerate(self._shared_features_by_choice_names): | ||
| for j, feat in enumerate(feat_tuple): | ||
| if feat == nn_feature: | ||
| data_driven_inputs.append(shared_features_by_choice[i][:, j]) | ||
| else: | ||
| logging.warn("No shared features found in the dataset.") | ||
| data_driven_utilities = self.nn_model( | ||
| tf.expand_dims(tf.expand_dims(tf.stack(data_driven_inputs, axis=1), axis=-1), axis=-1) | ||
| ) | ||
| return knowledge_driven_utilities + data_driven_utilities | ||
|
|
||
| def clone(self): | ||
| """Return a clone of the model.""" | ||
| clone = LearningMNL( | ||
| coefficients=self.coefficients, | ||
| add_exit_choice=self.add_exit_choice, | ||
| optimizer=self.optimizer_name, | ||
| nn_features=self.nn_features, | ||
| nn_layers_widths=self.nn_layers_widths, | ||
| nn_activation=self.nn_activation, | ||
| ) | ||
| if hasattr(self, "history"): | ||
| clone.history = self.history | ||
| if hasattr(self, "is_fitted"): | ||
| clone.is_fitted = self.is_fitted | ||
| if hasattr(self, "instantiated"): | ||
| clone.instantiated = self.instantiated | ||
| clone.loss = self.loss | ||
| clone.label_smoothing = self.label_smoothing | ||
| if hasattr(self, "report"): | ||
| clone.report = self.report | ||
| if hasattr(self, "trainable_weights"): | ||
| clone._trainable_weights = self.trainable_weights | ||
| if hasattr(self, "nn_model"): | ||
| clone.nn_model = self.nn_model | ||
| if hasattr(self, "lr"): | ||
| clone.lr = self.lr | ||
| if hasattr(self, "_shared_features_by_choice_names"): | ||
| clone._shared_features_by_choice_names = self._shared_features_by_choice_names | ||
| if hasattr(self, "_items_features_by_choice_names"): | ||
| clone._items_features_by_choice_names = self._items_features_by_choice_names | ||
| if hasattr(self, "_items_features_by_choice_names"): | ||
| clone._items_features_by_choice_names = self._items_features_by_choice_names | ||
| return clone |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.