Base latent model class

Base class for latent class choice models.

BaseLatentClassModel

Base Class to work with Mixtures of models.

Source code in choice_learn/models/latent_class_base_model.py

class BaseLatentClassModel:
    """Base Class to work with Mixtures of models."""

    def __init__(
        self,
        n_latent_classes,
        model_class,
        model_parameters,
        fit_method,
        epochs,
        batch_size=128,
        optimizer=None,
        add_exit_choice=False,
        tolerance=1e-6,
        lr=0.001,
    ):
        """Instantiate of the model mixture.

        Parameters
        ----------
        n_latent_classes : int
            Number of latent classes
        model_class : BaseModel
            class of models to get a mixture of
        model_parameters : dict
            hyper-parameters of the models
        fit_method : str
            Method to estimate the parameters: "EM", "MLE".
            "EM" for Expectation-Maximization, "MLE" for Maximum Likelihood Estimation
        epochs : int
            Number of epochs to train the model.
        optimizer: str, optional
            Name of the tf.keras.optimizers to be used if one is used, by default None
        add_exit_choice : bool, optional
            Whether or not to add an exit choice, by default False
        tolerance: float, optional
            Tolerance for the L-BFGS optimizer if applied, by default 1e-6
        lr: float, optional
            Learning rate for the optimizer if applied, by default 0.001
        """
        self.n_latent_classes = n_latent_classes
        if isinstance(model_parameters, list):
            if not len(model_parameters) == n_latent_classes:
                raise ValueError(
                    """If you specify a list of hyper-parameters, it means that you want to use\
                    different hyper-parameters for each latent class. In this case, the length\
                        of the list must be equal to the number of latent classes."""
                )
            self.model_parameters = model_parameters
        else:
            self.model_parameters = [model_parameters] * n_latent_classes
        self.model_class = model_class
        self.fit_method = fit_method

        self.epochs = epochs
        self.add_exit_choice = add_exit_choice
        self.tolerance = tolerance
        self.optimizer = optimizer
        self.lr = lr
        self.batch_size = batch_size

        self.loss = tf_ops.CustomCategoricalCrossEntropy(from_logits=False, label_smoothing=0.0)
        self.exact_nll = tf_ops.CustomCategoricalCrossEntropy(
            from_logits=False,
            label_smoothing=0.0,
            sparse=False,
            axis=-1,
            epsilon=1e-10,
            name="exact_categorical_crossentropy",
            reduction="sum_over_batch_size",
        )
        self.instantiated = False

    @property
    def trainable_weights(self):
        """Return trainable weights.

        Returns
        -------
        list
           list of trainable weights.
        """
        weights = [self.latent_logits]
        for model in self.models:
            weights += model.trainable_weights
        return weights

    def instantiate(self, **kwargs):
        """Instantiate the model."""
        init_logit = tf.Variable(
            tf.random_normal_initializer(0.0, 0.08, seed=42)(shape=(self.n_latent_classes - 1,)),
            name="Latent-Logits",
        )
        self.latent_logits = init_logit
        self.models = [self.model_class(**mp) for mp in self.model_parameters]
        for model in self.models:
            model.instantiate(**kwargs)

    # @tf.function
    def batch_predict(
        self,
        shared_features_by_choice,
        items_features_by_choice,
        available_items_by_choice,
        choices,
        sample_weight=None,
    ):
        """Represent one prediction (Probas + Loss) for one batch of a ChoiceDataset.

        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, )
        sample_weight : np.ndarray, optional
            List samples weights to apply during the gradient descent to the batch elements,
            by default None

        Returns
        -------
        tf.Tensor (1, )
            Value of NegativeLogLikelihood loss for the batch
        tf.Tensor (batch_size, n_items)
            Probabilities for each product to be chosen for each choice
        """
        # Compute utilities from features
        utilities = self.compute_batch_utility(
            shared_features_by_choice,
            items_features_by_choice,
            available_items_by_choice,
            choices,
        )

        latent_probabilities = self.get_latent_classes_weights()
        # Compute probabilities from utilities & availabilties
        probabilities = []
        for i, class_utilities in enumerate(utilities):
            class_probabilities = tf_ops.softmax_with_availabilities(
                items_logit_by_choice=class_utilities,
                available_items_by_choice=available_items_by_choice,
                normalize_exit=self.add_exit_choice,
                axis=-1,
            )
            probabilities.append(class_probabilities * latent_probabilities[i])
        # Summing over the latent classes
        probabilities = tf.reduce_sum(probabilities, axis=0)

        # Compute loss from probabilities & actual choices
        # batch_loss = self.loss(probabilities, c_batch, sample_weight=sample_weight)
        batch_loss = {
            "optimized_loss": self.loss(
                y_pred=probabilities,
                y_true=tf.one_hot(choices, depth=probabilities.shape[1]),
                sample_weight=sample_weight,
            ),
            "NegativeLogLikelihood": self.exact_nll(
                y_pred=probabilities,
                y_true=tf.one_hot(choices, depth=probabilities.shape[1]),
                sample_weight=sample_weight,
            ),
        }
        return batch_loss, probabilities

    def compute_batch_utility(
        self,
        shared_features_by_choice,
        items_features_by_choice,
        available_items_by_choice,
        choices,
    ):
        """Latent class computation of utility.

        It computes the utility for each of the latent models and stores them in a list.

        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, )

        Returns
        -------
        list of np.ndarray
            List of:
                Utility of each product for each choice.
                Shape must be (n_choices, n_items)
            for each of the latent models.
        """
        utilities = []
        # Iterates over latent models
        for model in self.models:
            model_utilities = model.compute_batch_utility(
                shared_features_by_choice=shared_features_by_choice,
                items_features_by_choice=items_features_by_choice,
                available_items_by_choice=available_items_by_choice,
                choices=choices,
            )
            utilities.append(model_utilities)
        return utilities

    def fit(self, choice_dataset, sample_weight=None, verbose=0):
        """Fit the model on a ChoiceDataset.

        Parameters
        ----------
        choice_dataset : ChoiceDataset
            Dataset to be used for coefficients estimations
        sample_weight : np.ndarray, optional
            sample weights to apply, by default None
        verbose : int, optional
            print level, for debugging, by default 0

        Returns
        -------
        dict
            Fit history
        """
        if self.fit_method.lower() == "em":
            self.minf = np.log(1e-3)
            print("Expectation-Maximization estimation algorithm not well implemented yet.")
            return self._em_fit(
                choice_dataset=choice_dataset, sample_weight=sample_weight, verbose=verbose
            )

        if self.fit_method.lower() == "mle":
            if isinstance(self.optimizer, str):
                if self.optimizer.lower() == "lbfgs" or self.optimizer.lower() == "l-bfgs":
                    return self._fit_with_lbfgs(
                        choice_dataset=choice_dataset, sample_weight=sample_weight, verbose=verbose
                    )

                if self.optimizer.lower() == "adam":
                    self.optimizer = tf.keras.optimizers.Adam(self.lr)
                elif self.optimizer.lower() == "sgd":
                    self.optimizer = tf.keras.optimizers.SGD(self.lr)
                elif self.optimizer.lower() == "adamax":
                    self.optimizer = tf.keras.optimizers.Adamax(self.lr)
                else:
                    print(f"Optimizer {self.optimizer} not implemnted, switching for default Adam")
                    self.optimizer = tf.keras.optimizers.Adam(self.lr)

            return self._fit_with_gd(
                choice_dataset=choice_dataset, sample_weight=sample_weight, verbose=verbose
            )

        raise ValueError(f"Fit method not implemented: {self.fit_method}")

    def evaluate(self, choice_dataset, sample_weight=None, batch_size=-1, mode="eval"):
        """Evaluate the model for each choice and each product of a ChoiceDataset.

        Predicts the probabilities according to the model and computes the Negative-Log-Likelihood
        loss from the actual choices.

        Parameters
        ----------
        choice_dataset : ChoiceDataset
            Dataset on which to apply to prediction

        Returns
        -------
        np.ndarray (n_choices, n_items)
            Choice probabilties for each choice and each product
        """
        batch_losses = []
        for (
            shared_features,
            items_features,
            available_items,
            choices,
        ) in choice_dataset.iter_batch(batch_size=batch_size):
            loss, _ = self.batch_predict(
                shared_features_by_choice=shared_features,
                items_features_by_choice=items_features,
                available_items_by_choice=available_items,
                choices=choices,
                sample_weight=sample_weight,
            )
            if mode == "eval":
                batch_losses.append(loss["NegativeLogLikelihood"])
            elif mode == "optim":
                batch_losses.append(loss["optimized_loss"])
        if batch_size != -1:
            last_batch_size = available_items.shape[0]
            coefficients = tf.concat(
                [tf.ones(len(batch_losses) - 1) * batch_size, [last_batch_size]], axis=0
            )
            batch_losses = tf.multiply(batch_losses, coefficients)
            batch_loss = tf.reduce_sum(batch_losses) / len(choice_dataset)
        else:
            batch_loss = tf.reduce_mean(batch_losses)
        return batch_loss

    def _lbfgs_train_step(self, choice_dataset, sample_weight=None):
        """Create a function required by tfp.optimizer.lbfgs_minimize.

        Parameters
        ----------
        choice_dataset: ChoiceDataset
            Dataset on which to estimate the paramters.
        sample_weight: np.ndarray, optional
            Sample weights to apply, by default None

        Returns
        -------
        function
            with the signature:
                loss_value, gradients = f(model_parameters).
        """
        # obtain the shapes of all trainable parameters in the model
        trainable_weights = []
        w_to_model = []
        w_to_model_indexes = []
        for i, model in enumerate(self.models):
            for j, w in enumerate(model.trainable_weights):
                trainable_weights.append(w)
                w_to_model.append(i)
                w_to_model_indexes.append(j)
        trainable_weights.append(self.latent_logits)
        w_to_model.append(-1)
        w_to_model_indexes.append(-1)
        shapes = tf.shape_n(trainable_weights)
        n_tensors = len(shapes)

        # we'll use tf.dynamic_stitch and tf.dynamic_partition later, so we need to
        # prepare required information first
        count = 0
        idx = []  # stitch indices
        part = []  # partition indices

        for i, shape in enumerate(shapes):
            n = np.product(shape)
            idx.append(tf.reshape(tf.range(count, count + n, dtype=tf.int32), shape))
            part.extend([i] * n)
            count += n

        part = tf.constant(part)

        @tf.function
        def assign_new_model_parameters(params_1d):
            """Update the model's parameters with a 1D tf.Tensor.

            Pararmeters
            -----------
            params_1d: tf.Tensor
                a 1D tf.Tensor representing the model's trainable parameters.
            """
            params = tf.dynamic_partition(params_1d, part, n_tensors)
            for i, (shape, param) in enumerate(zip(shapes, params)):
                if w_to_model[i] != -1:
                    self.models[w_to_model[i]].trainable_weights[w_to_model_indexes[i]].assign(
                        tf.reshape(param, shape)
                    )
                else:
                    self.latent_logits.assign(tf.reshape(param, shape))

        # now create a function that will be returned by this factory
        @tf.function
        def f(params_1d):
            """To be used by tfp.optimizer.lbfgs_minimize.

            This function is created by function_factory.

            Parameters
            ----------
            params_1d: tf.Tensor
                a 1D tf.Tensor.

            Returns
            -------
            tf.Tensor
                A scalar loss and the gradients w.r.t. the `params_1d`.
            tf.Tensor
                A 1D tf.Tensor representing the gradients w.r.t. the `params_1d`.
            """
            # use GradientTape so that we can calculate the gradient of loss w.r.t. parameters
            with tf.GradientTape() as tape:
                # update the parameters in the model
                assign_new_model_parameters(params_1d)
                # calculate the loss
                loss_value = self.evaluate(
                    choice_dataset, sample_weight=sample_weight, batch_size=-1, mode="optim"
                )
            # calculate gradients and convert to 1D tf.Tensor
            grads = tape.gradient(loss_value, trainable_weights)
            grads = tf.dynamic_stitch(idx, grads)

            # print out iteration & loss
            f.iter.assign_add(1)

            # store loss value so we can retrieve later
            tf.py_function(f.history.append, inp=[loss_value], Tout=[])

            return loss_value, grads

        # store these information as members so we can use them outside the scope
        f.iter = tf.Variable(0)
        f.idx = idx
        f.part = part
        f.shapes = shapes
        f.assign_new_model_parameters = assign_new_model_parameters
        f.history = []
        return f

    def _fit_with_lbfgs(self, choice_dataset, sample_weight=None, verbose=0):
        """Fit function for L-BFGS optimizer.

        Replaces the .fit method when the optimizer is set to L-BFGS.

        Parameters
        ----------
        choice_dataset : ChoiceDataset
            Dataset to be used for coefficients estimations
        epochs : int
            Maximum number of epochs allowed to reach minimum
        sample_weight : np.ndarray, optional
            Sample weights to apply, by default None
        verbose : int, optional
            print level, for debugging, by default 0

        Returns
        -------
        dict
            Fit history
        """
        # Only import tensorflow_probability if LBFGS optimizer is used, avoid unnecessary
        # dependency
        import tensorflow_probability as tfp

        epochs = self.epochs
        func = self._lbfgs_train_step(choice_dataset, sample_weight=sample_weight)

        # convert initial model parameters to a 1D tf.Tensor
        init = []
        for model in self.models:
            for w in model.trainable_weights:
                init.append(w)
        init.append(self.latent_logits)
        init_params = tf.dynamic_stitch(func.idx, init)

        # train the model with L-BFGS solver
        results = tfp.optimizer.lbfgs_minimize(
            value_and_gradients_function=func,
            initial_position=init_params,
            max_iterations=epochs,
            tolerance=-1,
            f_absolute_tolerance=self.tolerance,
            f_relative_tolerance=-1,
            x_tolerance=-1,
        )

        # after training, the final optimized parameters are still in results.position
        # so we have to manually put them back to the model
        func.assign_new_model_parameters(results.position)
        if verbose > 0:
            print("L-BFGS Opimization finished:")
            print("---------------------------------------------------------------")
            print("Number of iterations:", results[2].numpy())
            print("Algorithm converged before reaching max iterations:", results[0].numpy())
        return func.history, results

    # @tf.function
    def train_step(
        self,
        shared_features_by_choice,
        items_features_by_choice,
        available_items_by_choice,
        choices,
        sample_weight=None,
    ):
        """Represent one training step (= one gradient descent step) of the model.

        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_batch : np.ndarray
            Choices
            Shape must be (n_choices, )
        sample_weight : np.ndarray, optional
            List samples weights to apply during the gradient descent to the batch elements,
            by default None

        Returns
        -------
        tf.Tensor
            Value of NegativeLogLikelihood loss for the batch
        """
        with tf.GradientTape() as tape:
            utilities = self.compute_batch_utility(
                shared_features_by_choice=shared_features_by_choice,
                items_features_by_choice=items_features_by_choice,
                available_items_by_choice=available_items_by_choice,
                choices=choices,
            )

            latent_probabilities = self.get_latent_classes_weights()
            # Compute probabilities from utilities & availabilties
            probabilities = []
            for i, class_utilities in enumerate(utilities):
                class_probabilities = tf_ops.softmax_with_availabilities(
                    items_logit_by_choice=class_utilities,
                    available_items_by_choice=available_items_by_choice,
                    normalize_exit=self.add_exit_choice,
                    axis=-1,
                )
                probabilities.append(class_probabilities * latent_probabilities[i])
            # Summing over the latent classes
            probabilities = tf.reduce_sum(probabilities, axis=0)
            # Negative Log-Likelihood
            neg_loglikelihood = self.loss(
                y_pred=probabilities,
                y_true=tf.one_hot(choices, depth=probabilities.shape[1]),
                sample_weight=sample_weight,
            )
            # if self.regularization is not None:
            #     regularization = tf.reduce_sum(
            #         [self.regularizer(w) for w in self.trainable_weights]
            #     )
            #     neg_loglikelihood += regularization

        grads = tape.gradient(neg_loglikelihood, self.trainable_weights)
        self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
        return neg_loglikelihood

    def _fit_with_gd(
        self,
        choice_dataset,
        sample_weight=None,
        val_dataset=None,
        verbose=0,
    ):
        """Train the model with a ChoiceDataset.

        Parameters
        ----------
        choice_dataset : ChoiceDataset
            Input data in the form of a ChoiceDataset
        sample_weight : np.ndarray, optional
            Sample weight to apply, by default None
        val_dataset : ChoiceDataset, optional
            Test ChoiceDataset to evaluate performances on test at each epoch, by default None
        verbose : int, optional
            print level, for debugging, by default 0
        epochs : int, optional
            Number of epochs, default is None, meaning we use self.epochs
        batch_size : int, optional
            Batch size, default is None, meaning we use self.batch_size

        Returns
        -------
        dict:
            Different metrics values over epochs.
        """
        if hasattr(self, "instantiated"):
            if not self.instantiated:
                raise ValueError("Model not instantiated. Please call .instantiate() first.")
        epochs = self.epochs
        batch_size = self.batch_size

        losses_history = {"train_loss": []}
        t_range = tqdm.trange(epochs, position=0)

        # self.callbacks.on_train_begin()

        # Iterate of epochs
        for epoch_nb in t_range:
            # self.callbacks.on_epoch_begin(epoch_nb)
            t_start = time.time()
            train_logs = {"train_loss": []}
            val_logs = {"val_loss": []}
            epoch_losses = []

            if sample_weight is not None:
                if verbose > 0:
                    inner_range = tqdm.tqdm(
                        choice_dataset.iter_batch(
                            shuffle=True, sample_weight=sample_weight, batch_size=batch_size
                        ),
                        total=int(len(choice_dataset) / np.max([1, batch_size])),
                        position=1,
                        leave=False,
                    )
                else:
                    inner_range = choice_dataset.iter_batch(
                        shuffle=True, sample_weight=sample_weight, batch_size=batch_size
                    )

                for batch_nb, (
                    (
                        shared_features_batch,
                        items_features_batch,
                        available_items_batch,
                        choices_batch,
                    ),
                    weight_batch,
                ) in enumerate(inner_range):
                    # self.callbacks.on_train_batch_begin(batch_nb)

                    neg_loglikelihood = self.train_step(
                        shared_features_batch,
                        items_features_batch,
                        available_items_batch,
                        choices_batch,
                        sample_weight=weight_batch,
                    )

                    train_logs["train_loss"].append(neg_loglikelihood)

                    # temps_logs = {k: tf.reduce_mean(v) for k, v in train_logs.items()}
                    # self.callbacks.on_train_batch_end(batch_nb, logs=temps_logs)

                    # Optimization Steps
                    epoch_losses.append(neg_loglikelihood)

                    if verbose > 0:
                        inner_range.set_description(
                            f"Epoch Negative-LogLikeliHood: {np.sum(epoch_losses):.4f}"
                        )

            # In this case we do not need to batch the sample_weights
            else:
                if verbose > 0:
                    inner_range = tqdm.tqdm(
                        choice_dataset.iter_batch(shuffle=True, batch_size=batch_size),
                        total=int(len(choice_dataset) / np.max([batch_size, 1])),
                        position=1,
                        leave=False,
                    )
                else:
                    inner_range = choice_dataset.iter_batch(shuffle=True, batch_size=batch_size)
                for batch_nb, (
                    shared_features_batch,
                    items_features_batch,
                    available_items_batch,
                    choices_batch,
                ) in enumerate(inner_range):
                    # self.callbacks.on_train_batch_begin(batch_nb)
                    neg_loglikelihood = self.train_step(
                        shared_features_batch,
                        items_features_batch,
                        available_items_batch,
                        choices_batch,
                    )
                    train_logs["train_loss"].append(neg_loglikelihood)
                    # temps_logs = {k: tf.reduce_mean(v) for k, v in train_logs.items()}
                    # self.callbacks.on_train_batch_end(batch_nb, logs=temps_logs)

                    # Optimization Steps
                    epoch_losses.append(neg_loglikelihood)

                    if verbose > 0:
                        inner_range.set_description(
                            f"Epoch Negative-LogLikeliHood: {np.sum(epoch_losses):.4f}"
                        )

            # Take into account last batch that may have a differnt length into account for
            # the computation of the epoch loss.
            if batch_size != -1:
                last_batch_size = available_items_batch.shape[0]
                coefficients = tf.concat(
                    [tf.ones(len(epoch_losses) - 1) * batch_size, [last_batch_size]], axis=0
                )
                epoch_lossses = tf.multiply(epoch_losses, coefficients)
                epoch_loss = tf.reduce_sum(epoch_lossses) / len(choice_dataset)
            else:
                epoch_loss = tf.reduce_mean(epoch_losses)
            losses_history["train_loss"].append(epoch_loss)
            print_loss = losses_history["train_loss"][-1].numpy()
            desc = f"Epoch {epoch_nb} Train Loss {print_loss:.4f}"
            if verbose > 1:
                print(
                    f"Loop {epoch_nb} Time:",
                    f"{time.time() - t_start:.4f}",
                    f"Loss: {print_loss:.4f}",
                )

            # Test on val_dataset if provided
            if val_dataset is not None:
                test_losses = []
                for batch_nb, (
                    shared_features_batch,
                    items_features_batch,
                    available_items_batch,
                    choices_batch,
                ) in enumerate(val_dataset.iter_batch(shuffle=False, batch_size=batch_size)):
                    # self.callbacks.on_batch_begin(batch_nb)
                    # self.callbacks.on_test_batch_begin(batch_nb)
                    test_losses.append(
                        self.batch_predict(
                            shared_features_batch,
                            items_features_batch,
                            available_items_batch,
                            choices_batch,
                        )[0]["optimized_loss"]
                    )
                    val_logs["val_loss"].append(test_losses[-1])
                    # temps_logs = {k: tf.reduce_mean(v) for k, v in val_logs.items()}
                    # self.callbacks.on_test_batch_end(batch_nb, logs=temps_logs)

                test_loss = tf.reduce_mean(test_losses)
                if verbose > 1:
                    print("Test Negative-LogLikelihood:", test_loss.numpy())
                    desc += f", Test Loss {np.round(test_loss.numpy(), 4)}"
                losses_history["test_loss"] = losses_history.get("test_loss", []) + [
                    test_loss.numpy()
                ]
                train_logs = {**train_logs, **val_logs}

            # temps_logs = {k: tf.reduce_mean(v) for k, v in train_logs.items()}
            # self.callbacks.on_epoch_end(epoch_nb, logs=temps_logs)
            # if self.stop_training:
            #     print("Early Stopping taking effect")
            #     break
            t_range.set_description(desc)
            t_range.refresh()

        # temps_logs = {k: tf.reduce_mean(v) for k, v in train_logs.items()}
        # self.callbacks.on_train_end(logs=temps_logs)
        return losses_history

    def _nothing(self, inputs):
        """_summary_.

        Parameters
        ----------
        inputs : _type_
            _description_

        Returns
        -------
        _type_
            _description_
        """
        latent_probas = tf.clip_by_value(
            self.latent_logits - tf.reduce_max(self.latent_logits), self.minf, 0
        )
        latent_probas = tf.math.exp(latent_probas)
        # latent_probas = tf.math.abs(self.logit_latent_probas)  # alternative implementation
        latent_probas = latent_probas / tf.reduce_sum(latent_probas)
        proba_list = []
        avail = inputs[4]
        for q in range(self.n_latent_classes):
            combined = self.models[q].compute_batch_utility(*inputs)
            combined = tf.clip_by_value(
                combined - tf.reduce_max(combined, axis=1, keepdims=True), self.minf, 0
            )
            combined = tf.keras.layers.Activation(activation=tf.nn.softmax)(combined)
            # combined = tf.keras.layers.Softmax()(combined)
            combined = combined * avail
            combined = latent_probas[q] * tf.math.divide(
                combined, tf.reduce_sum(combined, axis=1, keepdims=True)
            )
            combined = tf.expand_dims(combined, -1)
            proba_list.append(combined)
            # print(combined.get_shape()) # it is useful to print the shape of tensors for debugging

        proba_final = tf.keras.layers.Concatenate(axis=2)(proba_list)
        return tf.math.reduce_sum(proba_final, axis=2, keepdims=False)

    def _expectation(self, choice_dataset):
        predicted_probas = [model.predict_probas(choice_dataset) for model in self.models]
        latent_probabilities = self.get_latent_classes_weights()
        if np.sum(np.isnan(predicted_probas)) > 0:
            print("A NaN values has been found. You should try again to fit with")
            print("smaller tolerance value (for l-bfgs) and epsilon value (in loss computation)")

        latent_model_probas = [
            latent * proba for latent, proba in zip(latent_probabilities, predicted_probas)
        ]
        latent_model_probas = tf.reduce_sum(latent_model_probas, axis=0)
        predicted_probas = [
            latent
            * tf.gather_nd(
                params=proba,
                indices=tf.stack(
                    [tf.range(0, len(choice_dataset), 1), choice_dataset.choices], axis=1
                ),
            )
            for latent, proba in zip(latent_probabilities, predicted_probas)
        ]
        predicted_probas = np.stack(predicted_probas, axis=1)
        loss = self.loss(
            y_pred=latent_model_probas,
            y_true=tf.one_hot(choice_dataset.choices, depth=latent_model_probas.shape[1]),
        )

        return tf.clip_by_value(
            predicted_probas / np.sum(predicted_probas, axis=1, keepdims=True), 1e-10, 1
        ), loss

    def _maximization(self, choice_dataset, verbose=0):
        """Maximize step.

        Parameters
        ----------
        choice_dataset : ChoiceDataset
            dataset to be fitted
        verbose : int, optional
            print level, for debugging, by default 0

        Returns
        -------
        np.ndarray
            latent probabilities resulting of maximization step
        """
        self.models = [self.model_class(**mp) for mp in self.model_parameters]
        # M-step: MNL estimation
        for q in range(self.n_latent_classes):
            self.models[q].fit(choice_dataset, sample_weight=self.weights[:, q], verbose=verbose)

        # M-step: latent probability estimation
        latent_probas = np.sum(self.weights, axis=0)
        return tf.math.log((latent_probas / latent_probas[0])[1:])

    def _em_fit(self, choice_dataset, sample_weight=None, verbose=0):
        """Fit with Expectation-Maximization Algorithm.

        Parameters
        ----------
        choice_dataset: ChoiceDataset
            Dataset to be used for coefficients estimations
        sample_weight : np.ndarray, optional
            sample weights to apply, by default None
        verbose : int, optional
            print level, for debugging, by default 0

        Returns
        -------
        list
            List of logits for each latent class
        list
            List of losses at each epoch
        """
        hist_logits = []
        hist_loss = []
        _ = sample_weight

        # Initialization
        init_sample_weight = np.random.rand(self.n_latent_classes, len(choice_dataset))
        init_sample_weight = init_sample_weight / np.sum(init_sample_weight, axis=0, keepdims=True)
        for i, model in enumerate(self.models):
            # model.instantiate()
            model.fit(choice_dataset, sample_weight=init_sample_weight[i], verbose=verbose)
        for i in tqdm.trange(self.epochs):
            self.weights, loss = self._expectation(choice_dataset)
            self.latent_logits = self._maximization(choice_dataset, verbose=verbose)
            hist_logits.append(self.latent_logits)
            hist_loss.append(loss)
            if np.sum(np.isnan(self.latent_logits)) > 0:
                print("Nan in logits")
                break
        return hist_logits, hist_loss

    def predict_probas(self, choice_dataset, batch_size=-1):
        """Predicts the choice probabilities for each choice and each product of a ChoiceDataset.

        Parameters
        ----------
        choice_dataset : ChoiceDataset
            Dataset on which to apply to prediction
        batch_size : int, optional
            Batch size to use for the prediction, by default -1

        Returns
        -------
        np.ndarray (n_choices, n_items)
            Choice probabilties for each choice and each product
        """
        stacked_probabilities = []
        for (
            shared_features,
            items_features,
            available_items,
            choices,
        ) in choice_dataset.iter_batch(batch_size=batch_size):
            _, probabilities = self.batch_predict(
                shared_features_by_choice=shared_features,
                items_features_by_choice=items_features,
                available_items_by_choice=available_items,
                choices=choices,
            )
            stacked_probabilities.append(probabilities)

        return tf.concat(stacked_probabilities, axis=0)

    def get_latent_classes_weights(self):
        """Return the latent classes weights / probabilities from logits.

        Returns
        -------
        np.ndarray (n_latent_classes, )
            Latent classes weights/probabilities
        """
        return tf.nn.softmax(tf.concat([[tf.constant(0.0)], self.latent_logits], axis=0))

trainable_weights property

Return trainable weights.

Returns:

Type	Description
list	list of trainable weights.

__init__(n_latent_classes, model_class, model_parameters, fit_method, epochs, batch_size=128, optimizer=None, add_exit_choice=False, tolerance=1e-06, lr=0.001)

Instantiate of the model mixture.

Parameters:

Name	Type	Description	Default
n_latent_classes	int	Number of latent classes	required
model_class	BaseModel	class of models to get a mixture of	required
model_parameters	dict	hyper-parameters of the models	required
fit_method	str	Method to estimate the parameters: "EM", "MLE". "EM" for Expectation-Maximization, "MLE" for Maximum Likelihood Estimation	required
epochs	int	Number of epochs to train the model.	required
optimizer		Name of the tf.keras.optimizers to be used if one is used, by default None	None
add_exit_choice	bool	Whether or not to add an exit choice, by default False	False
tolerance		Tolerance for the L-BFGS optimizer if applied, by default 1e-6	1e-06
lr		Learning rate for the optimizer if applied, by default 0.001	0.001

Source code in choice_learn/models/latent_class_base_model.py

def __init__(
    self,
    n_latent_classes,
    model_class,
    model_parameters,
    fit_method,
    epochs,
    batch_size=128,
    optimizer=None,
    add_exit_choice=False,
    tolerance=1e-6,
    lr=0.001,
):
    """Instantiate of the model mixture.

    Parameters
    ----------
    n_latent_classes : int
        Number of latent classes
    model_class : BaseModel
        class of models to get a mixture of
    model_parameters : dict
        hyper-parameters of the models
    fit_method : str
        Method to estimate the parameters: "EM", "MLE".
        "EM" for Expectation-Maximization, "MLE" for Maximum Likelihood Estimation
    epochs : int
        Number of epochs to train the model.
    optimizer: str, optional
        Name of the tf.keras.optimizers to be used if one is used, by default None
    add_exit_choice : bool, optional
        Whether or not to add an exit choice, by default False
    tolerance: float, optional
        Tolerance for the L-BFGS optimizer if applied, by default 1e-6
    lr: float, optional
        Learning rate for the optimizer if applied, by default 0.001
    """
    self.n_latent_classes = n_latent_classes
    if isinstance(model_parameters, list):
        if not len(model_parameters) == n_latent_classes:
            raise ValueError(
                """If you specify a list of hyper-parameters, it means that you want to use\
                different hyper-parameters for each latent class. In this case, the length\
                    of the list must be equal to the number of latent classes."""
            )
        self.model_parameters = model_parameters
    else:
        self.model_parameters = [model_parameters] * n_latent_classes
    self.model_class = model_class
    self.fit_method = fit_method

    self.epochs = epochs
    self.add_exit_choice = add_exit_choice
    self.tolerance = tolerance
    self.optimizer = optimizer
    self.lr = lr
    self.batch_size = batch_size

    self.loss = tf_ops.CustomCategoricalCrossEntropy(from_logits=False, label_smoothing=0.0)
    self.exact_nll = tf_ops.CustomCategoricalCrossEntropy(
        from_logits=False,
        label_smoothing=0.0,
        sparse=False,
        axis=-1,
        epsilon=1e-10,
        name="exact_categorical_crossentropy",
        reduction="sum_over_batch_size",
    )
    self.instantiated = False

batch_predict(shared_features_by_choice, items_features_by_choice, available_items_by_choice, choices, sample_weight=None)

Represent one prediction (Probas + Loss) for one batch of a ChoiceDataset.

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
sample_weight	ndarray	List samples weights to apply during the gradient descent to the batch elements, by default None	None

Returns:

Type	Description
Tensor(1)	Value of NegativeLogLikelihood loss for the batch
Tensor(batch_size, n_items)	Probabilities for each product to be chosen for each choice

Source code in choice_learn/models/latent_class_base_model.py

def batch_predict(
    self,
    shared_features_by_choice,
    items_features_by_choice,
    available_items_by_choice,
    choices,
    sample_weight=None,
):
    """Represent one prediction (Probas + Loss) for one batch of a ChoiceDataset.

    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, )
    sample_weight : np.ndarray, optional
        List samples weights to apply during the gradient descent to the batch elements,
        by default None

    Returns
    -------
    tf.Tensor (1, )
        Value of NegativeLogLikelihood loss for the batch
    tf.Tensor (batch_size, n_items)
        Probabilities for each product to be chosen for each choice
    """
    # Compute utilities from features
    utilities = self.compute_batch_utility(
        shared_features_by_choice,
        items_features_by_choice,
        available_items_by_choice,
        choices,
    )

    latent_probabilities = self.get_latent_classes_weights()
    # Compute probabilities from utilities & availabilties
    probabilities = []
    for i, class_utilities in enumerate(utilities):
        class_probabilities = tf_ops.softmax_with_availabilities(
            items_logit_by_choice=class_utilities,
            available_items_by_choice=available_items_by_choice,
            normalize_exit=self.add_exit_choice,
            axis=-1,
        )
        probabilities.append(class_probabilities * latent_probabilities[i])
    # Summing over the latent classes
    probabilities = tf.reduce_sum(probabilities, axis=0)

    # Compute loss from probabilities & actual choices
    # batch_loss = self.loss(probabilities, c_batch, sample_weight=sample_weight)
    batch_loss = {
        "optimized_loss": self.loss(
            y_pred=probabilities,
            y_true=tf.one_hot(choices, depth=probabilities.shape[1]),
            sample_weight=sample_weight,
        ),
        "NegativeLogLikelihood": self.exact_nll(
            y_pred=probabilities,
            y_true=tf.one_hot(choices, depth=probabilities.shape[1]),
            sample_weight=sample_weight,
        ),
    }
    return batch_loss, probabilities

compute_batch_utility(shared_features_by_choice, items_features_by_choice, available_items_by_choice, choices)

Latent class computation of utility.

It computes the utility for each of the latent models and stores them in a list.

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	ndarray	Choices Shape must be (n_choices, )	required

Returns:

Type	Description
list of np.ndarray	List of: Utility of each product for each choice. Shape must be (n_choices, n_items) for each of the latent models.

Source code in choice_learn/models/latent_class_base_model.py

def compute_batch_utility(
    self,
    shared_features_by_choice,
    items_features_by_choice,
    available_items_by_choice,
    choices,
):
    """Latent class computation of utility.

    It computes the utility for each of the latent models and stores them in a list.

    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, )

    Returns
    -------
    list of np.ndarray
        List of:
            Utility of each product for each choice.
            Shape must be (n_choices, n_items)
        for each of the latent models.
    """
    utilities = []
    # Iterates over latent models
    for model in self.models:
        model_utilities = model.compute_batch_utility(
            shared_features_by_choice=shared_features_by_choice,
            items_features_by_choice=items_features_by_choice,
            available_items_by_choice=available_items_by_choice,
            choices=choices,
        )
        utilities.append(model_utilities)
    return utilities

evaluate(choice_dataset, sample_weight=None, batch_size=-1, mode='eval')

Evaluate the model for each choice and each product of a ChoiceDataset.

Predicts the probabilities according to the model and computes the Negative-Log-Likelihood loss from the actual choices.

Parameters:

Name	Type	Description	Default
choice_dataset	ChoiceDataset	Dataset on which to apply to prediction	required

Returns:

Type	Description
ndarray(n_choices, n_items)	Choice probabilties for each choice and each product

Source code in choice_learn/models/latent_class_base_model.py

def evaluate(self, choice_dataset, sample_weight=None, batch_size=-1, mode="eval"):
    """Evaluate the model for each choice and each product of a ChoiceDataset.

    Predicts the probabilities according to the model and computes the Negative-Log-Likelihood
    loss from the actual choices.

    Parameters
    ----------
    choice_dataset : ChoiceDataset
        Dataset on which to apply to prediction

    Returns
    -------
    np.ndarray (n_choices, n_items)
        Choice probabilties for each choice and each product
    """
    batch_losses = []
    for (
        shared_features,
        items_features,
        available_items,
        choices,
    ) in choice_dataset.iter_batch(batch_size=batch_size):
        loss, _ = self.batch_predict(
            shared_features_by_choice=shared_features,
            items_features_by_choice=items_features,
            available_items_by_choice=available_items,
            choices=choices,
            sample_weight=sample_weight,
        )
        if mode == "eval":
            batch_losses.append(loss["NegativeLogLikelihood"])
        elif mode == "optim":
            batch_losses.append(loss["optimized_loss"])
    if batch_size != -1:
        last_batch_size = available_items.shape[0]
        coefficients = tf.concat(
            [tf.ones(len(batch_losses) - 1) * batch_size, [last_batch_size]], axis=0
        )
        batch_losses = tf.multiply(batch_losses, coefficients)
        batch_loss = tf.reduce_sum(batch_losses) / len(choice_dataset)
    else:
        batch_loss = tf.reduce_mean(batch_losses)
    return batch_loss

fit(choice_dataset, sample_weight=None, verbose=0)

Fit the model on a ChoiceDataset.

Parameters:

Name	Type	Description	Default
choice_dataset	ChoiceDataset	Dataset to be used for coefficients estimations	required
sample_weight	ndarray	sample weights to apply, by default None	None
verbose	int	print level, for debugging, by default 0	0

Returns:

Type	Description
dict	Fit history

Source code in choice_learn/models/latent_class_base_model.py

def fit(self, choice_dataset, sample_weight=None, verbose=0):
    """Fit the model on a ChoiceDataset.

    Parameters
    ----------
    choice_dataset : ChoiceDataset
        Dataset to be used for coefficients estimations
    sample_weight : np.ndarray, optional
        sample weights to apply, by default None
    verbose : int, optional
        print level, for debugging, by default 0

    Returns
    -------
    dict
        Fit history
    """
    if self.fit_method.lower() == "em":
        self.minf = np.log(1e-3)
        print("Expectation-Maximization estimation algorithm not well implemented yet.")
        return self._em_fit(
            choice_dataset=choice_dataset, sample_weight=sample_weight, verbose=verbose
        )

    if self.fit_method.lower() == "mle":
        if isinstance(self.optimizer, str):
            if self.optimizer.lower() == "lbfgs" or self.optimizer.lower() == "l-bfgs":
                return self._fit_with_lbfgs(
                    choice_dataset=choice_dataset, sample_weight=sample_weight, verbose=verbose
                )

            if self.optimizer.lower() == "adam":
                self.optimizer = tf.keras.optimizers.Adam(self.lr)
            elif self.optimizer.lower() == "sgd":
                self.optimizer = tf.keras.optimizers.SGD(self.lr)
            elif self.optimizer.lower() == "adamax":
                self.optimizer = tf.keras.optimizers.Adamax(self.lr)
            else:
                print(f"Optimizer {self.optimizer} not implemnted, switching for default Adam")
                self.optimizer = tf.keras.optimizers.Adam(self.lr)

        return self._fit_with_gd(
            choice_dataset=choice_dataset, sample_weight=sample_weight, verbose=verbose
        )

    raise ValueError(f"Fit method not implemented: {self.fit_method}")

get_latent_classes_weights()

Return the latent classes weights / probabilities from logits.

Returns:

Type	Description
ndarray(n_latent_classes)	Latent classes weights/probabilities

Source code in choice_learn/models/latent_class_base_model.py

def get_latent_classes_weights(self):
    """Return the latent classes weights / probabilities from logits.

    Returns
    -------
    np.ndarray (n_latent_classes, )
        Latent classes weights/probabilities
    """
    return tf.nn.softmax(tf.concat([[tf.constant(0.0)], self.latent_logits], axis=0))

instantiate(**kwargs)

Instantiate the model.

Source code in choice_learn/models/latent_class_base_model.py

def instantiate(self, **kwargs):
    """Instantiate the model."""
    init_logit = tf.Variable(
        tf.random_normal_initializer(0.0, 0.08, seed=42)(shape=(self.n_latent_classes - 1,)),
        name="Latent-Logits",
    )
    self.latent_logits = init_logit
    self.models = [self.model_class(**mp) for mp in self.model_parameters]
    for model in self.models:
        model.instantiate(**kwargs)

predict_probas(choice_dataset, batch_size=-1)

Predicts the choice probabilities for each choice and each product of a ChoiceDataset.

Parameters:

Name	Type	Description	Default
choice_dataset	ChoiceDataset	Dataset on which to apply to prediction	required
batch_size	int	Batch size to use for the prediction, by default -1	-1

Returns:

Type	Description
ndarray(n_choices, n_items)	Choice probabilties for each choice and each product

Source code in choice_learn/models/latent_class_base_model.py

def predict_probas(self, choice_dataset, batch_size=-1):
    """Predicts the choice probabilities for each choice and each product of a ChoiceDataset.

    Parameters
    ----------
    choice_dataset : ChoiceDataset
        Dataset on which to apply to prediction
    batch_size : int, optional
        Batch size to use for the prediction, by default -1

    Returns
    -------
    np.ndarray (n_choices, n_items)
        Choice probabilties for each choice and each product
    """
    stacked_probabilities = []
    for (
        shared_features,
        items_features,
        available_items,
        choices,
    ) in choice_dataset.iter_batch(batch_size=batch_size):
        _, probabilities = self.batch_predict(
            shared_features_by_choice=shared_features,
            items_features_by_choice=items_features,
            available_items_by_choice=available_items,
            choices=choices,
        )
        stacked_probabilities.append(probabilities)

    return tf.concat(stacked_probabilities, axis=0)

train_step(shared_features_by_choice, items_features_by_choice, available_items_by_choice, choices, sample_weight=None)

Represent one training step (= one gradient descent step) of the model.

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_batch	ndarray	Choices Shape must be (n_choices, )	required
sample_weight	ndarray	List samples weights to apply during the gradient descent to the batch elements, by default None	None

Returns:

Type	Description
Tensor	Value of NegativeLogLikelihood loss for the batch

Source code in choice_learn/models/latent_class_base_model.py

def train_step(
    self,
    shared_features_by_choice,
    items_features_by_choice,
    available_items_by_choice,
    choices,
    sample_weight=None,
):
    """Represent one training step (= one gradient descent step) of the model.

    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_batch : np.ndarray
        Choices
        Shape must be (n_choices, )
    sample_weight : np.ndarray, optional
        List samples weights to apply during the gradient descent to the batch elements,
        by default None

    Returns
    -------
    tf.Tensor
        Value of NegativeLogLikelihood loss for the batch
    """
    with tf.GradientTape() as tape:
        utilities = self.compute_batch_utility(
            shared_features_by_choice=shared_features_by_choice,
            items_features_by_choice=items_features_by_choice,
            available_items_by_choice=available_items_by_choice,
            choices=choices,
        )

        latent_probabilities = self.get_latent_classes_weights()
        # Compute probabilities from utilities & availabilties
        probabilities = []
        for i, class_utilities in enumerate(utilities):
            class_probabilities = tf_ops.softmax_with_availabilities(
                items_logit_by_choice=class_utilities,
                available_items_by_choice=available_items_by_choice,
                normalize_exit=self.add_exit_choice,
                axis=-1,
            )
            probabilities.append(class_probabilities * latent_probabilities[i])
        # Summing over the latent classes
        probabilities = tf.reduce_sum(probabilities, axis=0)
        # Negative Log-Likelihood
        neg_loglikelihood = self.loss(
            y_pred=probabilities,
            y_true=tf.one_hot(choices, depth=probabilities.shape[1]),
            sample_weight=sample_weight,
        )
        # if self.regularization is not None:
        #     regularization = tf.reduce_sum(
        #         [self.regularizer(w) for w in self.trainable_weights]
        #     )
        #     neg_loglikelihood += regularization

    grads = tape.gradient(neg_loglikelihood, self.trainable_weights)
    self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
    return neg_loglikelihood