Conditional MNL class

Conditional MNL model.

ConditionalLogit

Bases: ChoiceModel

Conditional MNL that has a generic structure. It can be parametrized with a dictionnary.

Arguments:

coefficients: dict or MNLCoefficients Specfication of the model to be estimated.

Source code in choice_learn/models/conditional_logit.py

class ConditionalLogit(ChoiceModel):
    """Conditional MNL that has a generic structure. It can be parametrized with a dictionnary.

    Arguments:
    ----------
    coefficients: dict or MNLCoefficients
        Specfication of the model to be estimated.
    """

    def __init__(
        self,
        coefficients=None,
        add_exit_choice=False,
        optimizer="lbfgs",
        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).
        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.instantiated = False

    def add_coefficients(
        self, feature_name, coefficient_name="", items_indexes=None, items_names=None
    ):
        """Add a coefficient to the model throught the specification of the utility.

        Parameters
        ----------
        feature_name : str
            features name to which the coefficient is associated. It should work with
            the names given in the ChoiceDataset that will be used for parameters estimation.
        coefficient_name : str, optional
            Name given to the coefficient. If not provided, name will be "beta_feature_name".
        items_indexes : list of int, optional
            list of items indexes (in the ChoiceDataset) for which we need to add a coefficient,
            by default None
        items_names : list of str, optional
            list of items names (in the ChoiceDataset) for which we need to add a coefficient,
            by default None

        Raises
        ------
        ValueError
            When names or indexes are both not specified.
        """
        self._add_coefficient(
            coefficient_name=coefficient_name,
            feature_name=feature_name,
            items_indexes=items_indexes,
            items_names=items_names,
            shared=False,
        )

    def add_shared_coefficient(
        self, feature_name, coefficient_name="", items_indexes=None, items_names=None
    ):
        """Add a single, shared coefficient to the model throught the specification of the utility.

        Parameters
        ----------
        feature_name : str
            features name to which the coefficient is associated. It should work with
            the names given in the ChoiceDataset that will be used for parameters estimation.
        coefficient_name : str, optional
            Name given to the coefficient. If not provided, name will be "beta_feature_name".
        items_indexes : list of int, optional
            list of items indexes (in the ChoiceDataset) for which the coefficient will be used,
            by default None
        items_names : list of str, optional
            list of items names (in the ChoiceDataset) for which the coefficient will be used,
            by default None

        Raises
        ------
        ValueError
            When names or indexes are both not specified.
        """
        self._add_coefficient(
            coefficient_name=coefficient_name,
            feature_name=feature_name,
            items_indexes=items_indexes,
            items_names=items_names,
            shared=True,
        )

    def _add_coefficient(self, feature_name, coefficient_name, items_indexes, items_names, shared):
        if self.coefficients is None:
            self.coefficients = MNLCoefficients()
        elif not isinstance(self.coefficients, MNLCoefficients):
            raise ValueError("Cannot add coefficient on top of a dict instantiation.")

        coefficient_name = coefficient_name if coefficient_name else "beta_%s" % feature_name
        add_method = self.coefficients.add_shared if shared else self.coefficients.add
        add_method(
            coefficient_name=coefficient_name,
            feature_name=feature_name,
            items_indexes=items_indexes,
            items_names=items_names,
        )

    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:
            if not isinstance(self.coefficients, MNLCoefficients):
                self._build_coefficients_from_dict(n_items=choice_dataset.get_n_items())
            self._trainable_weights = self._instantiate_tf_weights()

            # Checking that no weight has been attributed to non existing feature in dataset
            dataset_stacked_features_names = []
            if choice_dataset.shared_features_by_choice_names is not None:
                for feat_tuple in choice_dataset.shared_features_by_choice_names:
                    dataset_stacked_features_names.append(feat_tuple)
            if choice_dataset.items_features_by_choice_names is not None:
                for feat_tuple in choice_dataset.items_features_by_choice_names:
                    dataset_stacked_features_names.append(feat_tuple)
            dataset_stacked_features_names = np.concatenate(dataset_stacked_features_names).ravel()

            for feature_with_weight in self.coefficients.features_with_weights:
                if feature_with_weight != "intercept":
                    if feature_with_weight not in dataset_stacked_features_names:
                        raise ValueError(
                            f"""Feature {feature_with_weight} has an attributed coefficient
                            but is not in dataset"""
                        )
            self._store_dataset_features_names(choice_dataset)
            self.instantiated = True

    def _instantiate_tf_weights(self):
        """Instantiate the model from MNLCoefficients object.

        Returns
        -------
        list of tf.Tensor
            List of the weights created coresponding to the specification.
        """
        weights = []
        for weight_nb, weight_name in enumerate(self.coefficients.names):
            n_weights = (
                len(self.coefficients.get(weight_name)["items_indexes"])
                if self.coefficients.get(weight_name)["items_indexes"] is not None
                else len(self.coefficients.get(weight_name)["items_names"])
            )
            weight = tf.Variable(
                tf.random_normal_initializer(0.0, 0.02, seed=42)(shape=(1, n_weights)),
                name=weight_name,
            )
            weights.append(weight)
            self.coefficients._add_tf_weight(weight_name, weight_nb)

        self._trainable_weights = weights

        return weights

    @property
    def trainable_weights(self):
        """Trainable weights of the model."""
        return self._trainable_weights

    def _build_coefficients_from_dict(self, n_items):
        """Build coefficients when they are given as a dictionnay.

        Parameters
        ----------
        n_items : int
            Number of different items in the assortment. Used to create the right number of weights.
        """
        coefficients = MNLCoefficients()
        for weight_counter, (feature, mode) in enumerate(self.coefficients.items()):
            if mode == "constant":
                coefficients.add_shared(
                    feature + f"_w_{weight_counter}", feature, list(range(n_items))
                )
            elif mode == "item":
                coefficients.add(feature + f"_w_{weight_counter}", feature, list(range(1, n_items)))
            elif mode == "item-full":
                coefficients.add(feature + f"_w_{weight_counter}", feature, list(range(n_items)))
            else:
                raise ValueError(f"Mode {mode} not recognized.")

        self.coefficients = coefficients

    def _store_dataset_features_names(self, choice_dataset):
        """Register the name of the features in the dataset. For later use in utility computation.

        Parameters
        ----------
        dataset : ChoiceDataset
            ChoiceDataset used to fit the model.
        """
        self._shared_features_by_choice_names = choice_dataset.shared_features_by_choice_names
        self._items_features_by_choice_names = choice_dataset.items_features_by_choice_names

    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)
        """
        _ = choices

        n_items = available_items_by_choice.shape[1]
        n_choices = available_items_by_choice.shape[0]
        items_utilities_by_choice = []

        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,)

        # Shared features
        if self._shared_features_by_choice_names is not None:
            for i, feat_tuple in enumerate(self._shared_features_by_choice_names):
                for j, feat in enumerate(feat_tuple):
                    if feat in self.coefficients.features_with_weights:
                        (
                            item_index_list,
                            weight_index_list,
                        ) = self.coefficients.get_weight_item_indexes(feat)
                        for item_index, weight_index in zip(item_index_list, weight_index_list):
                            partial_items_utility_by_choice = tf.zeros((n_choices, n_items))
                            partial_items_utility_by_choice = [
                                tf.zeros(n_choices) for _ in range(n_items)
                            ]

                            for q, idx in enumerate(item_index):
                                if isinstance(idx, list):
                                    for k in idx:
                                        tf.cast(shared_features_by_choice[i][:, j], tf.float32)
                                        compute = tf.multiply(
                                            shared_features_by_choice[i][:, j],
                                            self.trainable_weights[weight_index][:, q],
                                        )
                                        partial_items_utility_by_choice[k] += compute
                                else:
                                    compute = tf.multiply(
                                        tf.cast(shared_features_by_choice[i][:, j], tf.float32),
                                        self.trainable_weights[weight_index][:, q],
                                    )
                                    partial_items_utility_by_choice[idx] += compute

                            items_utilities_by_choice.append(
                                tf.cast(
                                    tf.stack(partial_items_utility_by_choice, axis=1), tf.float32
                                )
                            )
                    elif verbose > 0:
                        logging.info(
                            f"Feature {feat} is in dataset but has no weight assigned\
                                in utility computations"
                        )

        # Items features
        if self._items_features_by_choice_names is not None:
            for i, feat_tuple in enumerate(self._items_features_by_choice_names):
                for j, feat in enumerate(feat_tuple):
                    if feat in self.coefficients.features_with_weights:
                        (
                            item_index_list,
                            weight_index_list,
                        ) = self.coefficients.get_weight_item_indexes(feat)
                        for item_index, weight_index in zip(item_index_list, weight_index_list):
                            partial_items_utility_by_choice = tf.zeros((n_choices, n_items))

                            for q, idx in enumerate(item_index):
                                if isinstance(idx, list):
                                    for k in idx:
                                        partial_items_utility_by_choice = tf.concat(
                                            [
                                                partial_items_utility_by_choice[:, :k],
                                                tf.expand_dims(
                                                    tf.multiply(
                                                        tf.cast(
                                                            items_features_by_choice[i][:, k, j],
                                                            tf.float32,
                                                        ),
                                                        self.trainable_weights[weight_index][:, q],
                                                    ),
                                                    axis=-1,
                                                ),
                                                partial_items_utility_by_choice[:, k + 1 :],
                                            ],
                                            axis=1,
                                        )
                                else:
                                    partial_items_utility_by_choice = tf.concat(
                                        [
                                            partial_items_utility_by_choice[:, :idx],
                                            tf.expand_dims(
                                                tf.multiply(
                                                    tf.cast(
                                                        items_features_by_choice[i][:, idx, j],
                                                        tf.float32,
                                                    ),
                                                    self.trainable_weights[weight_index][:, q],
                                                ),
                                                axis=-1,
                                            ),
                                            partial_items_utility_by_choice[:, idx + 1 :],
                                        ],
                                        axis=1,
                                    )

                            items_utilities_by_choice.append(
                                tf.cast(partial_items_utility_by_choice, tf.float32)
                            )
                    elif verbose > 0:
                        logging.info(
                            f"Feature {feat} is in dataset but has no weight assigned\
                                in utility computations"
                        )

        if "intercept" in self.coefficients.features_with_weights:
            item_index_list, weight_index_list = self.coefficients.get_weight_item_indexes(
                "intercept"
            )

            for item_index, weight_index in zip(item_index_list, weight_index_list):
                partial_items_utility_by_choice = tf.zeros((n_items,))
                for q, idx in enumerate(item_index):
                    partial_items_utility_by_choice = tf.concat(
                        [
                            partial_items_utility_by_choice[:idx],
                            self.trainable_weights[weight_index][:, q],
                            partial_items_utility_by_choice[idx + 1 :],
                        ],
                        axis=0,
                    )

                partial_items_utility_by_choice = tf.stack(
                    [partial_items_utility_by_choice] * n_choices, axis=0
                )

                items_utilities_by_choice.append(
                    tf.cast(partial_items_utility_by_choice, tf.float32)
                )

        return tf.reduce_sum(items_utilities_by_choice, axis=0)

    def fit(self, choice_dataset, get_report=False, **kwargs):
        """Fit function to estimate the parameters.

        Parameters
        ----------
        choice_dataset : ChoiceDataset
            Choice dataset to use for the estimation.
        get_report: bool, optional
            Whether or not to compute a report of the estimation, by default False

        Returns
        -------
        dict
            dict with fit history.
        """
        self.instantiate(choice_dataset)

        fit = super().fit(choice_dataset=choice_dataset, **kwargs)
        if get_report:
            self.report = self.compute_report(choice_dataset)
        return fit

    def _fit_with_lbfgs(
        self,
        choice_dataset,
        sample_weight=None,
        get_report=False,
        **kwargs,
    ):
        """Specific fit function to estimate the parameters with LBFGS.

        Parameters
        ----------
        choice_dataset : ChoiceDataset
            Choice dataset to use for the estimation.
        sample_weight : int
            Sample weight to use for the estimation, by default None
        get_report: bool, optional
            Whether or not to compute a report of the estimation, by default False

        Returns
        -------
        dict
            dict with fit history.
        """
        self.instantiate(choice_dataset)

        fit = super()._fit_with_lbfgs(
            choice_dataset=choice_dataset,
            sample_weight=sample_weight,
            **kwargs,
        )
        if get_report:
            self.report = self.compute_report(choice_dataset)
        return fit

    def compute_report(self, choice_dataset):
        """Compute a report of the estimated weights.

        Parameters
        ----------
        choice_dataset : ChoiceDataset
            ChoiceDataset used for the estimation of the weights that will be
            used to compute the Std Err of this estimation.

        Returns
        -------
        pandas.DataFrame
            A DF with estimation, Std Err, z_value and p_value for each coefficient.
        """

        def phi(x):
            """Cumulative distribution function for the standard normal distribution."""
            return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0

        weights_std = self.get_weights_std(choice_dataset)

        names = []
        z_values = []
        estimations = []
        p_z = []
        i = 0
        for weight in self.trainable_weights:
            for j in range(weight.shape[1]):
                if weight.shape[1] > 1:
                    names.append(f"{weight.name[:-2]}_{j}")
                else:
                    names.append(f"{weight.name[:-2]}")
                estimations.append(weight.numpy()[0][j])
                z_values.append(weight.numpy()[0][j] / weights_std[i].numpy())
                p_z.append(2 * (1 - phi(tf.math.abs(z_values[-1]).numpy())))
                i += 1

        return pd.DataFrame(
            {
                "Coefficient Name": names,
                "Coefficient Estimation": estimations,
                "Std. Err": weights_std.numpy(),
                "z_value": z_values,
                "P(.>z)": p_z,
            },
        )

    def get_weights_std(self, choice_dataset):
        """Approximates Std Err with Hessian matrix.

        Parameters
        ----------
        choice_dataset : ChoiceDataset
            ChoiceDataset used for the estimation of the weights that will be
            used to compute the Std Err of this estimation.

        Returns
        -------
        tf.Tensor
            Estimation of the Std Err for the weights.
        """
        # Loops of differentiation
        with tf.GradientTape() as tape_1:
            with tf.GradientTape(persistent=True) as tape_2:
                model = self.clone()
                w = tf.concat(self.trainable_weights, axis=1)
                tape_2.watch(w)
                tape_1.watch(w)
                mw = []
                index = 0
                for _w in self.trainable_weights:
                    mw.append(w[:, index : index + _w.shape[1]])
                    index += _w.shape[1]
                model._trainable_weights = mw
                batch = next(choice_dataset.iter_batch(batch_size=-1))
                utilities = model.compute_batch_utility(*batch)
                probabilities = tf.nn.softmax(utilities, axis=-1)
                loss = tf.keras.losses.CategoricalCrossentropy(reduction="sum")(
                    y_pred=probabilities,
                    y_true=tf.one_hot(choice_dataset.choices, depth=probabilities.shape[1]),
                )
            # Compute the Jacobian
            jacobian = tape_2.jacobian(loss, w)
        # Compute the Hessian from the Jacobian
        hessian = tape_1.batch_jacobian(jacobian, w)
        inv_hessian = tf.linalg.inv(tf.squeeze(hessian))
        return tf.sqrt([inv_hessian[i][i] for i in range(len(tf.squeeze(hessian)))])

    def clone(self):
        """Return a clone of the model."""
        clone = ConditionalLogit(
            coefficients=self.coefficients,
            add_exit_choice=self.add_exit_choice,
            optimizer=self.optimizer_name,
        )
        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, "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
        return clone

trainable_weights property

Trainable weights of the model.

__init__(coefficients=None, add_exit_choice=False, optimizer='lbfgs', lr=0.001, **kwargs)

Initialize of Conditional-MNL.

Parameters:

Name	Type	Description	Default
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).	None
add_exit_choice	bool	Whether or not to normalize the probabilities computation with an exit choice whose utility would be 1, by default True	False

Source code in choice_learn/models/conditional_logit.py

def __init__(
    self,
    coefficients=None,
    add_exit_choice=False,
    optimizer="lbfgs",
    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).
    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.instantiated = False

add_coefficients(feature_name, coefficient_name='', items_indexes=None, items_names=None)

Add a coefficient to the model throught the specification of the utility.

Parameters:

Name	Type	Description	Default
feature_name	str	features name to which the coefficient is associated. It should work with the names given in the ChoiceDataset that will be used for parameters estimation.	required
coefficient_name	str	Name given to the coefficient. If not provided, name will be "beta_feature_name".	''
items_indexes	list of int	list of items indexes (in the ChoiceDataset) for which we need to add a coefficient, by default None	None
items_names	list of str	list of items names (in the ChoiceDataset) for which we need to add a coefficient, by default None	None

Raises:

Type	Description
ValueError	When names or indexes are both not specified.

Source code in choice_learn/models/conditional_logit.py

def add_coefficients(
    self, feature_name, coefficient_name="", items_indexes=None, items_names=None
):
    """Add a coefficient to the model throught the specification of the utility.

    Parameters
    ----------
    feature_name : str
        features name to which the coefficient is associated. It should work with
        the names given in the ChoiceDataset that will be used for parameters estimation.
    coefficient_name : str, optional
        Name given to the coefficient. If not provided, name will be "beta_feature_name".
    items_indexes : list of int, optional
        list of items indexes (in the ChoiceDataset) for which we need to add a coefficient,
        by default None
    items_names : list of str, optional
        list of items names (in the ChoiceDataset) for which we need to add a coefficient,
        by default None

    Raises
    ------
    ValueError
        When names or indexes are both not specified.
    """
    self._add_coefficient(
        coefficient_name=coefficient_name,
        feature_name=feature_name,
        items_indexes=items_indexes,
        items_names=items_names,
        shared=False,
    )

add_shared_coefficient(feature_name, coefficient_name='', items_indexes=None, items_names=None)

Add a single, shared coefficient to the model throught the specification of the utility.

Parameters:

Name	Type	Description	Default
feature_name	str	features name to which the coefficient is associated. It should work with the names given in the ChoiceDataset that will be used for parameters estimation.	required
coefficient_name	str	Name given to the coefficient. If not provided, name will be "beta_feature_name".	''
items_indexes	list of int	list of items indexes (in the ChoiceDataset) for which the coefficient will be used, by default None	None
items_names	list of str	list of items names (in the ChoiceDataset) for which the coefficient will be used, by default None	None

Raises:

Type	Description
ValueError	When names or indexes are both not specified.

Source code in choice_learn/models/conditional_logit.py

def add_shared_coefficient(
    self, feature_name, coefficient_name="", items_indexes=None, items_names=None
):
    """Add a single, shared coefficient to the model throught the specification of the utility.

    Parameters
    ----------
    feature_name : str
        features name to which the coefficient is associated. It should work with
        the names given in the ChoiceDataset that will be used for parameters estimation.
    coefficient_name : str, optional
        Name given to the coefficient. If not provided, name will be "beta_feature_name".
    items_indexes : list of int, optional
        list of items indexes (in the ChoiceDataset) for which the coefficient will be used,
        by default None
    items_names : list of str, optional
        list of items names (in the ChoiceDataset) for which the coefficient will be used,
        by default None

    Raises
    ------
    ValueError
        When names or indexes are both not specified.
    """
    self._add_coefficient(
        coefficient_name=coefficient_name,
        feature_name=feature_name,
        items_indexes=items_indexes,
        items_names=items_names,
        shared=True,
    )

clone()

Return a clone of the model.

Source code in choice_learn/models/conditional_logit.py

def clone(self):
    """Return a clone of the model."""
    clone = ConditionalLogit(
        coefficients=self.coefficients,
        add_exit_choice=self.add_exit_choice,
        optimizer=self.optimizer_name,
    )
    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, "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
    return clone

compute_batch_utility(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:

Name	Type	Description	Default
shared_features_by_choice	tuple of np.ndarray (choices_features)	a batch of shared features Shape must be (n_choices, n_shared_features)	required
items_features_by_choice	tuple of np.ndarray (choices_items_features)	a batch of items features Shape must be (n_choices, n_items_features)	required
available_items_by_choice	ndarray	A batch of items availabilities Shape must be (n_choices, n_items)	required
choices		Choices Shape must be (n_choices, )	required
verbose	int	Parametrization of the logging outputs, by default 1	1

Returns:

Type	Description
Tensor	Utilities corresponding of shape (n_choices, n_items)

Source code in choice_learn/models/conditional_logit.py

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)
    """
    _ = choices

    n_items = available_items_by_choice.shape[1]
    n_choices = available_items_by_choice.shape[0]
    items_utilities_by_choice = []

    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,)

    # Shared features
    if self._shared_features_by_choice_names is not None:
        for i, feat_tuple in enumerate(self._shared_features_by_choice_names):
            for j, feat in enumerate(feat_tuple):
                if feat in self.coefficients.features_with_weights:
                    (
                        item_index_list,
                        weight_index_list,
                    ) = self.coefficients.get_weight_item_indexes(feat)
                    for item_index, weight_index in zip(item_index_list, weight_index_list):
                        partial_items_utility_by_choice = tf.zeros((n_choices, n_items))
                        partial_items_utility_by_choice = [
                            tf.zeros(n_choices) for _ in range(n_items)
                        ]

                        for q, idx in enumerate(item_index):
                            if isinstance(idx, list):
                                for k in idx:
                                    tf.cast(shared_features_by_choice[i][:, j], tf.float32)
                                    compute = tf.multiply(
                                        shared_features_by_choice[i][:, j],
                                        self.trainable_weights[weight_index][:, q],
                                    )
                                    partial_items_utility_by_choice[k] += compute
                            else:
                                compute = tf.multiply(
                                    tf.cast(shared_features_by_choice[i][:, j], tf.float32),
                                    self.trainable_weights[weight_index][:, q],
                                )
                                partial_items_utility_by_choice[idx] += compute

                        items_utilities_by_choice.append(
                            tf.cast(
                                tf.stack(partial_items_utility_by_choice, axis=1), tf.float32
                            )
                        )
                elif verbose > 0:
                    logging.info(
                        f"Feature {feat} is in dataset but has no weight assigned\
                            in utility computations"
                    )

    # Items features
    if self._items_features_by_choice_names is not None:
        for i, feat_tuple in enumerate(self._items_features_by_choice_names):
            for j, feat in enumerate(feat_tuple):
                if feat in self.coefficients.features_with_weights:
                    (
                        item_index_list,
                        weight_index_list,
                    ) = self.coefficients.get_weight_item_indexes(feat)
                    for item_index, weight_index in zip(item_index_list, weight_index_list):
                        partial_items_utility_by_choice = tf.zeros((n_choices, n_items))

                        for q, idx in enumerate(item_index):
                            if isinstance(idx, list):
                                for k in idx:
                                    partial_items_utility_by_choice = tf.concat(
                                        [
                                            partial_items_utility_by_choice[:, :k],
                                            tf.expand_dims(
                                                tf.multiply(
                                                    tf.cast(
                                                        items_features_by_choice[i][:, k, j],
                                                        tf.float32,
                                                    ),
                                                    self.trainable_weights[weight_index][:, q],
                                                ),
                                                axis=-1,
                                            ),
                                            partial_items_utility_by_choice[:, k + 1 :],
                                        ],
                                        axis=1,
                                    )
                            else:
                                partial_items_utility_by_choice = tf.concat(
                                    [
                                        partial_items_utility_by_choice[:, :idx],
                                        tf.expand_dims(
                                            tf.multiply(
                                                tf.cast(
                                                    items_features_by_choice[i][:, idx, j],
                                                    tf.float32,
                                                ),
                                                self.trainable_weights[weight_index][:, q],
                                            ),
                                            axis=-1,
                                        ),
                                        partial_items_utility_by_choice[:, idx + 1 :],
                                    ],
                                    axis=1,
                                )

                        items_utilities_by_choice.append(
                            tf.cast(partial_items_utility_by_choice, tf.float32)
                        )
                elif verbose > 0:
                    logging.info(
                        f"Feature {feat} is in dataset but has no weight assigned\
                            in utility computations"
                    )

    if "intercept" in self.coefficients.features_with_weights:
        item_index_list, weight_index_list = self.coefficients.get_weight_item_indexes(
            "intercept"
        )

        for item_index, weight_index in zip(item_index_list, weight_index_list):
            partial_items_utility_by_choice = tf.zeros((n_items,))
            for q, idx in enumerate(item_index):
                partial_items_utility_by_choice = tf.concat(
                    [
                        partial_items_utility_by_choice[:idx],
                        self.trainable_weights[weight_index][:, q],
                        partial_items_utility_by_choice[idx + 1 :],
                    ],
                    axis=0,
                )

            partial_items_utility_by_choice = tf.stack(
                [partial_items_utility_by_choice] * n_choices, axis=0
            )

            items_utilities_by_choice.append(
                tf.cast(partial_items_utility_by_choice, tf.float32)
            )

    return tf.reduce_sum(items_utilities_by_choice, axis=0)

compute_report(choice_dataset)

Compute a report of the estimated weights.

Parameters:

Name	Type	Description	Default
choice_dataset	ChoiceDataset	ChoiceDataset used for the estimation of the weights that will be used to compute the Std Err of this estimation.	required

Returns:

Type	Description
DataFrame	A DF with estimation, Std Err, z_value and p_value for each coefficient.

Source code in choice_learn/models/conditional_logit.py

def compute_report(self, choice_dataset):
    """Compute a report of the estimated weights.

    Parameters
    ----------
    choice_dataset : ChoiceDataset
        ChoiceDataset used for the estimation of the weights that will be
        used to compute the Std Err of this estimation.

    Returns
    -------
    pandas.DataFrame
        A DF with estimation, Std Err, z_value and p_value for each coefficient.
    """

    def phi(x):
        """Cumulative distribution function for the standard normal distribution."""
        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0

    weights_std = self.get_weights_std(choice_dataset)

    names = []
    z_values = []
    estimations = []
    p_z = []
    i = 0
    for weight in self.trainable_weights:
        for j in range(weight.shape[1]):
            if weight.shape[1] > 1:
                names.append(f"{weight.name[:-2]}_{j}")
            else:
                names.append(f"{weight.name[:-2]}")
            estimations.append(weight.numpy()[0][j])
            z_values.append(weight.numpy()[0][j] / weights_std[i].numpy())
            p_z.append(2 * (1 - phi(tf.math.abs(z_values[-1]).numpy())))
            i += 1

    return pd.DataFrame(
        {
            "Coefficient Name": names,
            "Coefficient Estimation": estimations,
            "Std. Err": weights_std.numpy(),
            "z_value": z_values,
            "P(.>z)": p_z,
        },
    )

fit(choice_dataset, get_report=False, **kwargs)

Fit function to estimate the parameters.

Parameters:

Name	Type	Description	Default
choice_dataset	ChoiceDataset	Choice dataset to use for the estimation.	required
get_report		Whether or not to compute a report of the estimation, by default False	False

Returns:

Type	Description
dict	dict with fit history.

Source code in choice_learn/models/conditional_logit.py

def fit(self, choice_dataset, get_report=False, **kwargs):
    """Fit function to estimate the parameters.

    Parameters
    ----------
    choice_dataset : ChoiceDataset
        Choice dataset to use for the estimation.
    get_report: bool, optional
        Whether or not to compute a report of the estimation, by default False

    Returns
    -------
    dict
        dict with fit history.
    """
    self.instantiate(choice_dataset)

    fit = super().fit(choice_dataset=choice_dataset, **kwargs)
    if get_report:
        self.report = self.compute_report(choice_dataset)
    return fit

get_weights_std(choice_dataset)

Approximates Std Err with Hessian matrix.

Parameters:

Name	Type	Description	Default
choice_dataset	ChoiceDataset	ChoiceDataset used for the estimation of the weights that will be used to compute the Std Err of this estimation.	required

Returns:

Type	Description
Tensor	Estimation of the Std Err for the weights.

Source code in choice_learn/models/conditional_logit.py

def get_weights_std(self, choice_dataset):
    """Approximates Std Err with Hessian matrix.

    Parameters
    ----------
    choice_dataset : ChoiceDataset
        ChoiceDataset used for the estimation of the weights that will be
        used to compute the Std Err of this estimation.

    Returns
    -------
    tf.Tensor
        Estimation of the Std Err for the weights.
    """
    # Loops of differentiation
    with tf.GradientTape() as tape_1:
        with tf.GradientTape(persistent=True) as tape_2:
            model = self.clone()
            w = tf.concat(self.trainable_weights, axis=1)
            tape_2.watch(w)
            tape_1.watch(w)
            mw = []
            index = 0
            for _w in self.trainable_weights:
                mw.append(w[:, index : index + _w.shape[1]])
                index += _w.shape[1]
            model._trainable_weights = mw
            batch = next(choice_dataset.iter_batch(batch_size=-1))
            utilities = model.compute_batch_utility(*batch)
            probabilities = tf.nn.softmax(utilities, axis=-1)
            loss = tf.keras.losses.CategoricalCrossentropy(reduction="sum")(
                y_pred=probabilities,
                y_true=tf.one_hot(choice_dataset.choices, depth=probabilities.shape[1]),
            )
        # Compute the Jacobian
        jacobian = tape_2.jacobian(loss, w)
    # Compute the Hessian from the Jacobian
    hessian = tape_1.batch_jacobian(jacobian, w)
    inv_hessian = tf.linalg.inv(tf.squeeze(hessian))
    return tf.sqrt([inv_hessian[i][i] for i in range(len(tf.squeeze(hessian)))])

instantiate(choice_dataset)

Instantiate the model using the features in the choice_dataset.

Parameters:

Name	Type	Description	Default
choice_dataset		Used to match the features names with the model coefficients.	required

Source code in choice_learn/models/conditional_logit.py

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:
        if not isinstance(self.coefficients, MNLCoefficients):
            self._build_coefficients_from_dict(n_items=choice_dataset.get_n_items())
        self._trainable_weights = self._instantiate_tf_weights()

        # Checking that no weight has been attributed to non existing feature in dataset
        dataset_stacked_features_names = []
        if choice_dataset.shared_features_by_choice_names is not None:
            for feat_tuple in choice_dataset.shared_features_by_choice_names:
                dataset_stacked_features_names.append(feat_tuple)
        if choice_dataset.items_features_by_choice_names is not None:
            for feat_tuple in choice_dataset.items_features_by_choice_names:
                dataset_stacked_features_names.append(feat_tuple)
        dataset_stacked_features_names = np.concatenate(dataset_stacked_features_names).ravel()

        for feature_with_weight in self.coefficients.features_with_weights:
            if feature_with_weight != "intercept":
                if feature_with_weight not in dataset_stacked_features_names:
                    raise ValueError(
                        f"""Feature {feature_with_weight} has an attributed coefficient
                        but is not in dataset"""
                    )
        self._store_dataset_features_names(choice_dataset)
        self.instantiated = True

MNLCoefficients

Base class to specify the structure of a cLogit.

Source code in choice_learn/models/conditional_logit.py

class MNLCoefficients:
    """Base class to specify the structure of a cLogit."""

    def __init__(self):
        """Instantiate a MNLCoefficients object."""
        # User interface
        self.coefficients = {}
        # Handled by the model
        self.feature_to_weight = {}

    def add(self, coefficient_name, feature_name, items_indexes=None, items_names=None):
        """Add a coefficient to the model throught the specification of the utility.

        Parameters
        ----------
        coefficient_name : str
            Name given to the coefficient.
        feature_name : str
            features name to which the coefficient is associated. It should work with
            the names given in the ChoiceDataset that will be used for parameters estimation.
        items_indexes : list of int, optional
            list of items indexes (in the ChoiceDataset) for which we need to add a coefficient,
            by default None
        items_names : list of str, optional
            list of items names (in the ChoiceDataset) for which we need to add a coefficient,
            by default None

        Raises
        ------
        ValueError
            When names or indexes are both not specified.
        """
        if items_indexes is None and items_names is None:
            raise ValueError("Either items_indexes or items_names must be specified")

        if isinstance(items_indexes, int):
            items_indexes = [items_indexes]
        if isinstance(items_names, str):
            items_names = [items_names]

        self.coefficients[coefficient_name] = {
            "feature_name": feature_name,
            "items_indexes": items_indexes,
            "items_names": items_names,
        }

    def add_shared(self, coefficient_name, feature_name, items_indexes=None, items_names=None):
        """Add a single, shared coefficient to the model throught the specification of the utility.

        Parameters
        ----------
        coefficient_name : str
            Name given to the coefficient.
        feature_name : str
            features name to which the coefficient is associated. It should work with
            the names given in the ChoiceDataset that will be used for parameters estimation.
        items_indexes : list of int, optional
            list of items indexes (in the ChoiceDataset) for which the coefficient will be used,
            by default None
        items_names : list of str, optional
            list of items names (in the ChoiceDataset) for which the coefficient will be used,
            by default None

        Raises
        ------
        ValueError
            When names or indexes are both not specified.
        """
        if items_indexes is None and items_names is None:
            raise ValueError("Either items_indexes or items_names must be specified")

        if not coefficient_name:
            coefficient_name = f"beta_{feature_name}"

        if isinstance(items_indexes, int):
            logging.warning(
                "You have added a single index to a shared coefficient. This is not recommended.",
                "Returning to standard add_coefficients method.",
            )
            self.add_coefficients(coefficient_name, feature_name, items_indexes, items_names)

        if isinstance(items_names, str):
            logging.warning(
                "You have added a single name to a shared coefficient. This is not recommended.",
                "Returning to standard add_coefficients method.",
            )
            self.add_coefficients(coefficient_name, feature_name, items_indexes, items_names)

        self.coefficients[coefficient_name] = {
            "feature_name": feature_name,
            "items_indexes": [items_indexes] if items_indexes is not None else None,
            "items_names": items_names if items_names is not None else None,
        }

    def get(self, coefficient_name):
        """Getter of a coefficient specification, from its name.

        Parameters
        ----------
        coefficient_name : str
            Name of the coefficient to get.

        Returns
        -------
        dict
            specification of the coefficient.
        """
        return self.coefficients[coefficient_name]

    def _add_tf_weight(self, weight_name, weight_index):
        """Create the Tensorflow weight corresponding for cLogit.

        Parameters
        ----------
        weight_name : str
            Name of the weight to add.
        weight_index : int
            Index of the weight (in the conditionalMNL) to add.
        """
        if weight_name not in self.coefficients.keys():
            raise ValueError(f"Weight {weight_name} not in coefficients")

        if self.coefficients[weight_name]["feature_name"] in self.feature_to_weight.keys():
            self.feature_to_weight[self.coefficients[weight_name]["feature_name"]].append(
                (
                    weight_name,
                    weight_index,
                )
            )
        else:
            self.feature_to_weight[self.coefficients[weight_name]["feature_name"]] = [
                (
                    weight_name,
                    weight_index,
                ),
            ]

    @property
    def features_with_weights(self):
        """Get a list of the features that have a weight to be estimated.

        Returns
        -------
        dict.keys
            List of the features that have a weight to be estimated.
        """
        return self.feature_to_weight.keys()

    def get_weight_item_indexes(self, feature_name):
        """Get the indexes of the concerned items for a given weight.

        Parameters
        ----------
        feature_name : str
            Features that is concerned by the weight.

        Returns
        -------
        list
            List of indexes of the items concerned by the weight.
        int
            The index of the weight in the conditionalMNL weights.
        """
        weights_info = self.feature_to_weight[feature_name]
        weight_names = [weight_info[0] for weight_info in weights_info]
        weight_indexs = [weight_info[1] for weight_info in weights_info]
        return [
            self.coefficients[weight_name]["items_indexes"] for weight_name in weight_names
        ], weight_indexs

    @property
    def names(self):
        """Returns the list of coefficients.

        Returns
        -------
        dict keys
            List of coefficients in the specification.
        """
        return list(self.coefficients.keys())

features_with_weights property

Get a list of the features that have a weight to be estimated.

Returns:

Type	Description
keys	List of the features that have a weight to be estimated.

names property

Returns the list of coefficients.

Returns:

Type	Description
dict keys	List of coefficients in the specification.

__init__()

Instantiate a MNLCoefficients object.

Source code in choice_learn/models/conditional_logit.py

def __init__(self):
    """Instantiate a MNLCoefficients object."""
    # User interface
    self.coefficients = {}
    # Handled by the model
    self.feature_to_weight = {}

add(coefficient_name, feature_name, items_indexes=None, items_names=None)

Add a coefficient to the model throught the specification of the utility.

Parameters:

Name	Type	Description	Default
coefficient_name	str	Name given to the coefficient.	required
feature_name	str	features name to which the coefficient is associated. It should work with the names given in the ChoiceDataset that will be used for parameters estimation.	required
items_indexes	list of int	list of items indexes (in the ChoiceDataset) for which we need to add a coefficient, by default None	None
items_names	list of str	list of items names (in the ChoiceDataset) for which we need to add a coefficient, by default None	None

Raises:

Type	Description
ValueError	When names or indexes are both not specified.

Source code in choice_learn/models/conditional_logit.py

def add(self, coefficient_name, feature_name, items_indexes=None, items_names=None):
    """Add a coefficient to the model throught the specification of the utility.

    Parameters
    ----------
    coefficient_name : str
        Name given to the coefficient.
    feature_name : str
        features name to which the coefficient is associated. It should work with
        the names given in the ChoiceDataset that will be used for parameters estimation.
    items_indexes : list of int, optional
        list of items indexes (in the ChoiceDataset) for which we need to add a coefficient,
        by default None
    items_names : list of str, optional
        list of items names (in the ChoiceDataset) for which we need to add a coefficient,
        by default None

    Raises
    ------
    ValueError
        When names or indexes are both not specified.
    """
    if items_indexes is None and items_names is None:
        raise ValueError("Either items_indexes or items_names must be specified")

    if isinstance(items_indexes, int):
        items_indexes = [items_indexes]
    if isinstance(items_names, str):
        items_names = [items_names]

    self.coefficients[coefficient_name] = {
        "feature_name": feature_name,
        "items_indexes": items_indexes,
        "items_names": items_names,
    }

add_shared(coefficient_name, feature_name, items_indexes=None, items_names=None)

Add a single, shared coefficient to the model throught the specification of the utility.

Parameters:

Name	Type	Description	Default
coefficient_name	str	Name given to the coefficient.	required
feature_name	str	features name to which the coefficient is associated. It should work with the names given in the ChoiceDataset that will be used for parameters estimation.	required
items_indexes	list of int	list of items indexes (in the ChoiceDataset) for which the coefficient will be used, by default None	None
items_names	list of str	list of items names (in the ChoiceDataset) for which the coefficient will be used, by default None	None

Raises:

Type	Description
ValueError	When names or indexes are both not specified.

Source code in choice_learn/models/conditional_logit.py

def add_shared(self, coefficient_name, feature_name, items_indexes=None, items_names=None):
    """Add a single, shared coefficient to the model throught the specification of the utility.

    Parameters
    ----------
    coefficient_name : str
        Name given to the coefficient.
    feature_name : str
        features name to which the coefficient is associated. It should work with
        the names given in the ChoiceDataset that will be used for parameters estimation.
    items_indexes : list of int, optional
        list of items indexes (in the ChoiceDataset) for which the coefficient will be used,
        by default None
    items_names : list of str, optional
        list of items names (in the ChoiceDataset) for which the coefficient will be used,
        by default None

    Raises
    ------
    ValueError
        When names or indexes are both not specified.
    """
    if items_indexes is None and items_names is None:
        raise ValueError("Either items_indexes or items_names must be specified")

    if not coefficient_name:
        coefficient_name = f"beta_{feature_name}"

    if isinstance(items_indexes, int):
        logging.warning(
            "You have added a single index to a shared coefficient. This is not recommended.",
            "Returning to standard add_coefficients method.",
        )
        self.add_coefficients(coefficient_name, feature_name, items_indexes, items_names)

    if isinstance(items_names, str):
        logging.warning(
            "You have added a single name to a shared coefficient. This is not recommended.",
            "Returning to standard add_coefficients method.",
        )
        self.add_coefficients(coefficient_name, feature_name, items_indexes, items_names)

    self.coefficients[coefficient_name] = {
        "feature_name": feature_name,
        "items_indexes": [items_indexes] if items_indexes is not None else None,
        "items_names": items_names if items_names is not None else None,
    }

get(coefficient_name)

Getter of a coefficient specification, from its name.

Parameters:

Name	Type	Description	Default
coefficient_name	str	Name of the coefficient to get.	required

Returns:

Type	Description
dict	specification of the coefficient.

Source code in choice_learn/models/conditional_logit.py

def get(self, coefficient_name):
    """Getter of a coefficient specification, from its name.

    Parameters
    ----------
    coefficient_name : str
        Name of the coefficient to get.

    Returns
    -------
    dict
        specification of the coefficient.
    """
    return self.coefficients[coefficient_name]

get_weight_item_indexes(feature_name)

Get the indexes of the concerned items for a given weight.

Parameters:

Name	Type	Description	Default
feature_name	str	Features that is concerned by the weight.	required

Returns:

Type	Description
list	List of indexes of the items concerned by the weight.
int	The index of the weight in the conditionalMNL weights.

Source code in choice_learn/models/conditional_logit.py

def get_weight_item_indexes(self, feature_name):
    """Get the indexes of the concerned items for a given weight.

    Parameters
    ----------
    feature_name : str
        Features that is concerned by the weight.

    Returns
    -------
    list
        List of indexes of the items concerned by the weight.
    int
        The index of the weight in the conditionalMNL weights.
    """
    weights_info = self.feature_to_weight[feature_name]
    weight_names = [weight_info[0] for weight_info in weights_info]
    weight_indexs = [weight_info[1] for weight_info in weights_info]
    return [
        self.coefficients[weight_name]["items_indexes"] for weight_name in weight_names
    ], weight_indexs