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# Training Deep Learning Probabilistic Models

## BayesianTrainer

Bases: `BaseBayesianTrainer`

Class to set the of attributes that will be used during the training process.

Both the Bayesian models and the Trainer in this repo are based on the paper: Weight Uncertainty in Neural Networks.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`model` | `BaseBayesianModel` | An object of class `BaseBayesianModel`. See the `Model Components` section here in the docs. | required
`objective` | `str` | Defines the objective, loss or cost function. Param aliases: `loss_function`, `loss_fn`, `loss`, `cost_function`, `cost_fn`, `cost` Possible values are: 'binary', 'multiclass', 'regression' | required
`custom_loss_function` | `Optional[Module]` | If none of the loss functions available suits the user, it is possible to pass a custom loss function. See for example `pytorch_widedeep.losses.FocalLoss` for the required structure of the object or the Examples folder in the repo. | `None`
`optimizer` | `Optional[Optimizer]` | An instance of Pytorch's `Optimizer` object(e.g. `torch.optim.Adam ()`). if no optimizer is passed it will default to `AdamW`. | `None`
`lr_scheduler` | `Optional[LRScheduler]` | An instance of Pytorch's `LRScheduler` object (e.g `torch.optim.lr_scheduler.StepLR(opt, step_size=5)`). | `None`
`callbacks` | `Optional[List[Callback]]` | List with `Callback` objects. The three callbacks available in `pytorch-widedeep` are: `LRHistory`, `ModelCheckpoint` and `EarlyStopping`. This can also be a custom callback. See `pytorch_widedeep.callbacks.Callback` or the Examples folder in the repo. | `None`
`metrics` | `Optional[Union[List[Metric], List[Metric]]]` | List of objects of type `Metric`. Metrics available are: `Accuracy`, `Precision`, `Recall`, `FBetaScore`, `F1Score` and `R2Score`. This can also be a custom metric as long as it is an object of type `Metric`. See `pytorch_widedeep.metrics.Metric` or the Examples folder in the repo List of objects of type `torchmetrics.Metric`. This can be any metric from torchmetrics library Examples classification-metrics>`_. It can also be a torchmetric custom metric as long as it is an object of type`Metric`. See`the instructions | `None`
`verbose` | `int` | Setting it to 0 will print nothing during training. | `1`
`seed` | `int` | Random seed to be used internally for train_test_split | `1`

Other Parameters:

Name | Type | Description
--- | --- | ---
`**kwargs` |  | Other infrequently used arguments that can also be passed as kwargs are: device: `str` string indicating the device. One of 'cpu', 'gpu' or 'mps' if run on a Mac with Apple silicon or AMD GPU(s) num_workers: `int` number of workers to be used internally by the data loaders class_weight: `List[float]` This is the `weight` or `pos_weight` parameter in `CrossEntropyLoss` and `BCEWithLogitsLoss`, depending on whether reducelronplateau_criterion: `str` This sets the criterion that will be used by the lr scheduler to take a step: One of 'loss' or 'metric'. The ReduceLROnPlateau learning rate is a bit particular.

Attributes:

Name | Type | Description
--- | --- | ---
`cyclic_lr` | `bool` | Attribute that indicates if the lr_scheduler is cyclic_lr (i.e. `CyclicLR` or `OneCycleLR`). See `Pytorch schedulers <https://pytorch.org/docs/stable/optim.html>`_.

Source code in `pytorch_widedeep/training/bayesian_trainer.py`

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    class BayesianTrainer(BaseBayesianTrainer):
        r"""Class to set the of attributes that will be used during the
        training process.

        Both the Bayesian models and the Trainer in this repo are based on the paper:
        [Weight Uncertainty in Neural Networks](https://arxiv.org/pdf/1505.05424.pdf).

        Parameters
        ----------
        model: `BaseBayesianModel`
            An object of class `BaseBayesianModel`. See the `Model Components`
            section here in the docs.
        objective: str
            Defines the objective, loss or cost function.<br/>
            Param aliases: `loss_function`, `loss_fn`, `loss`,
            `cost_function`, `cost_fn`, `cost`<br/>
            Possible values are: _'binary'_, _'multiclass'_, _'regression'_
        custom_loss_function: `nn.Module`, optional, default = None
            If none of the loss functions available suits the user, it is possible
            to pass a custom loss function. See for example
            `pytorch_widedeep.losses.FocalLoss` for the required structure of the
            object or the Examples folder in the repo.
        optimizer: `Optimzer`, optional, default= None
            An instance of Pytorch's `Optimizer` object(e.g. `torch.optim.Adam
            ()`). if no optimizer is passed it will default to `AdamW`.
        lr_scheduler: `LRScheduler`, optional, default=None
            An instance of Pytorch's `LRScheduler` object
            (e.g `torch.optim.lr_scheduler.StepLR(opt, step_size=5)`).
        callbacks: List, optional, default=None
            List with `Callback` objects. The three callbacks available in
            `pytorch-widedeep` are: `LRHistory`, `ModelCheckpoint` and
            `EarlyStopping`. This can also be a custom callback. See
            `pytorch_widedeep.callbacks.Callback` or the Examples folder in the
            repo.
        metrics: List, optional, default=None
            - List of objects of type `Metric`. Metrics available are:
              `Accuracy`, `Precision`, `Recall`, `FBetaScore`,
              `F1Score` and `R2Score`. This can also be a custom metric as
              long as it is an object of type `Metric`. See
              `pytorch_widedeep.metrics.Metric` or the Examples folder in the repo
            - List of objects of type `torchmetrics.Metric`. This can be any
              metric from torchmetrics library [Examples](https://lightning.ai/docs/torchmetrics)
              classification-metrics>`_. It can also be a torchmetric custom metric as
              long as it is an object of type `Metric`.
              See `the [instructions]((https://lightning.ai/docs/torchmetrics))
        verbose: int, default=1
            Setting it to 0 will print nothing during training.
        seed: int, default=1
            Random seed to be used internally for train_test_split

        Other Parameters
        ----------------
        **kwargs: dict
            Other infrequently used arguments that can also be passed as kwargs are:

            - **device**: `str`<br/>
                string indicating the device. One of _'cpu'_, _'gpu'_ or 'mps' if
                run on a Mac with Apple silicon or AMD GPU(s)

            - **num_workers**: `int`<br/>
                number of workers to be used internally by the data loaders

            - **class_weight**: `List[float]`<br/>
                This is the `weight` or `pos_weight` parameter in
                `CrossEntropyLoss` and `BCEWithLogitsLoss`, depending on whether

            - **reducelronplateau_criterion**: `str`
                This sets the criterion that will be used by the lr scheduler to
                take a step: One of _'loss'_ or _'metric'_. The ReduceLROnPlateau
                learning rate is a bit particular.

        Attributes
        ----------
        cyclic_lr: bool
            Attribute that indicates if  the lr_scheduler is cyclic_lr
            (i.e. `CyclicLR` or `OneCycleLR`). See `Pytorch schedulers
            <https://pytorch.org/docs/stable/optim.html>`_.
        """

        @alias(  # noqa: C901
            "objective",
            ["loss_function", "loss_fn", "loss", "cost_function", "cost_fn", "cost"],
        )
        def __init__(
            self,
            model: BaseBayesianModel,
            objective: str,
            custom_loss_function: Optional[Module] = None,
            optimizer: Optional[Optimizer] = None,
            lr_scheduler: Optional[LRScheduler] = None,
            callbacks: Optional[List[Callback]] = None,
            metrics: Optional[Union[List[Metric], List[TorchMetric]]] = None,
            verbose: int = 1,
            seed: int = 1,
            **kwargs,
        ):
            super().__init__(
                model=model,
                objective=objective,
                custom_loss_function=custom_loss_function,
                optimizer=optimizer,
                lr_scheduler=lr_scheduler,
                callbacks=callbacks,
                metrics=metrics,
                verbose=verbose,
                seed=seed,
                **kwargs,
            )

        def fit(  # noqa: C901
            self,
            X_tab: np.ndarray,
            target: np.ndarray,
            X_tab_val: Optional[np.ndarray] = None,
            target_val: Optional[np.ndarray] = None,
            val_split: Optional[float] = None,
            n_epochs: int = 1,
            validation_freq: int = 1,
            batch_size: int = 32,
            n_train_samples: int = 2,
            n_val_samples: int = 2,
        ):
            r"""Fit method.

            Parameters
            ----------
            X_tab: np.ndarray,
                tabular dataset
            target: np.ndarray
                target values
            X_tab_val: np.ndarray, Optional, default = None
                validation data
            target_val: np.ndarray, Optional, default = None
                validation target values
            val_split: float, Optional. default=None
                An alterative to passing the validation set is to use a train/val
                split fraction via `val_split`
            n_epochs: int, default=1
                number of epochs
            validation_freq: int, default=1
                epochs validation frequency
            batch_size: int, default=32
                batch size
            n_train_samples: int, default=2
                number of samples to average over during the training process.
                See [Weight Uncertainty in Neural Networks](https://arxiv.org/pdf/1505.05424.pdf) for details.
            n_val_samples: int, default=2
                number of samples to average over during the validation process.
                See [Weight Uncertainty in Neural Networks](https://arxiv.org/pdf/1505.05424.pdf) for details.
            """

            self.batch_size = batch_size

            train_set, eval_set = tabular_train_val_split(
                self.seed, self.objective, X_tab, target, X_tab_val, target_val, val_split
            )
            train_loader = DataLoader(
                dataset=train_set, batch_size=batch_size, num_workers=self.num_workers
            )
            train_steps = len(train_loader)

            if eval_set is not None:
                eval_loader = DataLoader(
                    dataset=eval_set,
                    batch_size=batch_size,
                    num_workers=self.num_workers,
                    shuffle=False,
                )
                eval_steps = len(eval_loader)

            self.callback_container.on_train_begin(
                {
                    "batch_size": batch_size,
                    "train_steps": train_steps,
                    "n_epochs": n_epochs,
                }
            )
            for epoch in range(n_epochs):
                epoch_logs: Dict[str, float] = {}
                self.callback_container.on_epoch_begin(epoch, logs=epoch_logs)

                self.train_running_loss = 0.0
                with trange(train_steps, disable=self.verbose != 1) as t:
                    for batch_idx, (X, y) in zip(t, train_loader):
                        t.set_description("epoch %i" % (epoch + 1))
                        train_score, train_loss = self._train_step(
                            X, y, n_train_samples, train_steps, batch_idx
                        )
                        print_loss_and_metric(t, train_loss, train_score)
                        self.callback_container.on_batch_end(batch=batch_idx)
                epoch_logs = save_epoch_logs(epoch_logs, train_loss, train_score, "train")

                on_epoch_end_metric = None
                if eval_set is not None and epoch % validation_freq == (
                    validation_freq - 1
                ):
                    self.callback_container.on_eval_begin()
                    self.valid_running_loss = 0.0
                    with trange(eval_steps, disable=self.verbose != 1) as v:
                        for i, (X, y) in zip(v, eval_loader):
                            v.set_description("valid")
                            val_score, val_loss = self._eval_step(
                                X, y, n_val_samples, train_steps, i
                            )
                            print_loss_and_metric(v, val_loss, val_score)
                    epoch_logs = save_epoch_logs(epoch_logs, val_loss, val_score, "val")

                    if self.reducelronplateau:
                        if self.reducelronplateau_criterion == "loss":
                            on_epoch_end_metric = val_loss
                        else:
                            on_epoch_end_metric = val_score[
                                self.reducelronplateau_criterion
                            ]

                self.callback_container.on_epoch_end(epoch, epoch_logs, on_epoch_end_metric)

                if self.early_stop:
                    self.callback_container.on_train_end(epoch_logs)
                    break

            self.callback_container.on_train_end(epoch_logs)
            self._restore_best_weights()
            self.model.train()

        def predict(  # type: ignore[return]
            self,
            X_tab: np.ndarray,
            n_samples: int = 5,
            return_samples: bool = False,
            batch_size: int = 256,
        ) -> np.ndarray:
            r"""Returns the predictions

            Parameters
            ----------
            X_tab: np.ndarray,
                tabular dataset
            n_samples: int, default=5
                number of samples that will be either returned or averaged to
                produce an overal prediction
            return_samples: bool, default = False
                Boolean indicating whether the n samples will be averaged or directly returned
            batch_size: int, default = 256
                batch size

            Returns
            -------
            np.ndarray:
                array with the predictions
            """

            preds_l = self._predict(X_tab, n_samples, return_samples, batch_size)
            preds = np.hstack(preds_l) if return_samples else np.vstack(preds_l)
            axis = 2 if return_samples else 1

            if self.objective == "regression":
                return preds.squeeze(axis)
            if self.objective == "binary":
                return (preds.squeeze(axis) > 0.5).astype("int")
            if self.objective == "multiclass":
                return np.argmax(preds, axis)

        def predict_proba(  # type: ignore[return]
            self,
            X_tab: np.ndarray,
            n_samples: int = 5,
            return_samples: bool = False,
            batch_size: int = 256,
        ) -> np.ndarray:
            r"""Returns the predicted probabilities

            Parameters
            ----------
            X_tab: np.ndarray,
                tabular dataset
            n_samples: int, default=5
                number of samples that will be either returned or averaged to
                produce an overal prediction
            return_samples: bool, default = False
                Boolean indicating whether the n samples will be averaged or directly returned
            batch_size: int, default = 256
                batch size

            Returns
            -------
            np.ndarray
                array with the probabilities per class
            """
            preds_l = self._predict(X_tab, n_samples, return_samples, batch_size)
            preds = np.hstack(preds_l) if return_samples else np.vstack(preds_l)

            if self.objective == "binary":
                if return_samples:
                    preds = preds.squeeze(2)
                    probs = np.zeros([n_samples, preds.shape[1], 2])
                    for i in range(n_samples):
                        probs[i, :, 0] = 1 - preds[i]
                        probs[i, :, 1] = preds[i]
                else:
                    preds = preds.squeeze(1)
                    probs = np.zeros([preds.shape[0], 2])
                    probs[:, 0] = 1 - preds
                    probs[:, 1] = preds
                return probs
            if self.objective == "multiclass":
                return preds

        def save(
            self,
            path: str,
            save_state_dict: bool = False,
            model_filename: str = "bayesian_model.pt",
        ):
            r"""Saves the model, training and evaluation history to disk

            The `Trainer` class is built so that it 'just' trains a model. With
            that in mind, all the torch related parameters (such as optimizers or
            learning rate schedulers) have to be defined externally and then
            passed to the `Trainer`. As a result, the `Trainer` does not
            generate any attribute or additional data products that need to be
            saved other than the `model` object itself, which can be saved as
            any other torch model (e.g. `torch.save(model, path)`).

            Parameters
            ----------
            path: str
                path to the directory where the model and the feature importance
                attribute will be saved.
            save_state_dict: bool, default = False
                Boolean indicating whether to save directly the model or the
                model's state dictionary
            model_filename: str, Optional, default = "wd_model.pt"
                filename where the model weights will be store
            """

            save_dir = Path(path)
            history_dir = save_dir / "history"
            history_dir.mkdir(exist_ok=True, parents=True)

            # the trainer is run with the History Callback by default
            with open(history_dir / "train_eval_history.json", "w") as teh:
                json.dump(self.history, teh)  # type: ignore[attr-defined]

            has_lr_history = any(
                [clbk.__class__.__name__ == "LRHistory" for clbk in self.callbacks]
            )
            if self.lr_scheduler is not None and has_lr_history:
                with open(history_dir / "lr_history.json", "w") as lrh:
                    json.dump(self.lr_history, lrh)  # type: ignore[attr-defined]

            model_path = save_dir / model_filename
            if save_state_dict:
                torch.save(self.model.state_dict(), model_path)
            else:
                torch.save(self.model, model_path)

        def _train_step(
            self,
            X_tab: Tensor,
            target: Tensor,
            n_samples: int,
            n_batches: int,
            batch_idx: int,
        ):
            self.model.train()

            X = to_device(X_tab, self.device)
            y = target.view(-1, 1).float() if self.objective != "multiclass" else target
            y = to_device(y, self.device)

            self.optimizer.zero_grad()
            y_pred, loss = self.model.sample_elbo(X, y, self.loss_fn, n_samples, n_batches)  # type: ignore[arg-type]

            y_pred = y_pred.mean(dim=0)
            score = self._get_score(y_pred, y)

            loss.backward()
            self.optimizer.step()

            self.train_running_loss += loss.item()
            avg_loss = self.train_running_loss / (batch_idx + 1)

            return score, avg_loss

        def _eval_step(
            self,
            X_tab: Tensor,
            target: Tensor,
            n_samples: int,
            n_batches: int,
            batch_idx: int,
        ):
            self.model.eval()
            with torch.no_grad():
                X = to_device(X_tab, self.device)
                y = target.view(-1, 1).float() if self.objective != "multiclass" else target
                y = to_device(y, self.device)

                y_pred, loss = self.model.sample_elbo(
                    X,  # type: ignore[arg-type]
                    y,
                    self.loss_fn,
                    n_samples,
                    n_batches,
                )
                y_pred = y_pred.mean(dim=0)
                score = self._get_score(y_pred, y)

                self.valid_running_loss += loss.item()
                avg_loss = self.valid_running_loss / (batch_idx + 1)

            return score, avg_loss

        def _get_score(self, y_pred, y):
            if self.metric is not None:
                if self.objective == "regression":
                    score = self.metric(y_pred, y)
                if self.objective == "binary":
                    score = self.metric(torch.sigmoid(y_pred), y)
                if self.objective == "multiclass":
                    score = self.metric(F.softmax(y_pred, dim=1), y)
                return score
            else:
                return None

        def _predict(  # noqa: C901
            self,
            X_tab: np.ndarray,
            n_samples: int = 5,
            return_samples: bool = False,
            batch_size: int = 256,
        ) -> List:
            self.batch_size = batch_size

            test_set = TensorDataset(torch.from_numpy(X_tab))
            test_loader = DataLoader(
                dataset=test_set,
                batch_size=self.batch_size,
                num_workers=self.num_workers,
                shuffle=False,
            )
            test_steps = (len(test_loader.dataset) // test_loader.batch_size) + 1  # type: ignore[arg-type]

            preds_l = []
            with torch.no_grad():
                with trange(test_steps, disable=self.verbose != 1) as tt:
                    for _, Xl in zip(tt, test_loader):
                        tt.set_description("predict")

                        try:
                            X = Xl[0].to(self.device)
                        except TypeError:
                            X = Xl[0].to(self.device, dtype=torch.float32)

                        if return_samples:
                            preds = torch.stack([self.model(X) for _ in range(n_samples)])
                        else:
                            self.model.eval()
                            preds = self.model(X)

                        if self.objective == "binary":
                            preds = torch.sigmoid(preds)
                        if self.objective == "multiclass":
                            preds = (
                                F.softmax(preds, dim=2)
                                if return_samples
                                else F.softmax(preds, dim=1)
                            )

                        preds_cpu = preds.cpu().data.numpy()
                        preds_l.append(preds_cpu)

            self.model.train()

            return preds_l

### fit

    fit(
        X_tab,
        target,
        X_tab_val=None,
        target_val=None,
        val_split=None,
        n_epochs=1,
        validation_freq=1,
        batch_size=32,
        n_train_samples=2,
        n_val_samples=2,
    )

Fit method.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`X_tab` | `ndarray` | tabular dataset | required
`target` | `ndarray` | target values | required
`X_tab_val` | `Optional[ndarray]` | validation data | `None`
`target_val` | `Optional[ndarray]` | validation target values | `None`
`val_split` | `Optional[float]` | An alterative to passing the validation set is to use a train/val split fraction via `val_split` | `None`
`n_epochs` | `int` | number of epochs | `1`
`validation_freq` | `int` | epochs validation frequency | `1`
`batch_size` | `int` | batch size | `32`
`n_train_samples` | `int` | number of samples to average over during the training process. See Weight Uncertainty in Neural Networks for details. | `2`
`n_val_samples` | `int` | number of samples to average over during the validation process. See Weight Uncertainty in Neural Networks for details. | `2`

Source code in `pytorch_widedeep/training/bayesian_trainer.py`

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    def fit(  # noqa: C901
        self,
        X_tab: np.ndarray,
        target: np.ndarray,
        X_tab_val: Optional[np.ndarray] = None,
        target_val: Optional[np.ndarray] = None,
        val_split: Optional[float] = None,
        n_epochs: int = 1,
        validation_freq: int = 1,
        batch_size: int = 32,
        n_train_samples: int = 2,
        n_val_samples: int = 2,
    ):
        r"""Fit method.

        Parameters
        ----------
        X_tab: np.ndarray,
            tabular dataset
        target: np.ndarray
            target values
        X_tab_val: np.ndarray, Optional, default = None
            validation data
        target_val: np.ndarray, Optional, default = None
            validation target values
        val_split: float, Optional. default=None
            An alterative to passing the validation set is to use a train/val
            split fraction via `val_split`
        n_epochs: int, default=1
            number of epochs
        validation_freq: int, default=1
            epochs validation frequency
        batch_size: int, default=32
            batch size
        n_train_samples: int, default=2
            number of samples to average over during the training process.
            See [Weight Uncertainty in Neural Networks](https://arxiv.org/pdf/1505.05424.pdf) for details.
        n_val_samples: int, default=2
            number of samples to average over during the validation process.
            See [Weight Uncertainty in Neural Networks](https://arxiv.org/pdf/1505.05424.pdf) for details.
        """

        self.batch_size = batch_size

        train_set, eval_set = tabular_train_val_split(
            self.seed, self.objective, X_tab, target, X_tab_val, target_val, val_split
        )
        train_loader = DataLoader(
            dataset=train_set, batch_size=batch_size, num_workers=self.num_workers
        )
        train_steps = len(train_loader)

        if eval_set is not None:
            eval_loader = DataLoader(
                dataset=eval_set,
                batch_size=batch_size,
                num_workers=self.num_workers,
                shuffle=False,
            )
            eval_steps = len(eval_loader)

        self.callback_container.on_train_begin(
            {
                "batch_size": batch_size,
                "train_steps": train_steps,
                "n_epochs": n_epochs,
            }
        )
        for epoch in range(n_epochs):
            epoch_logs: Dict[str, float] = {}
            self.callback_container.on_epoch_begin(epoch, logs=epoch_logs)

            self.train_running_loss = 0.0
            with trange(train_steps, disable=self.verbose != 1) as t:
                for batch_idx, (X, y) in zip(t, train_loader):
                    t.set_description("epoch %i" % (epoch + 1))
                    train_score, train_loss = self._train_step(
                        X, y, n_train_samples, train_steps, batch_idx
                    )
                    print_loss_and_metric(t, train_loss, train_score)
                    self.callback_container.on_batch_end(batch=batch_idx)
            epoch_logs = save_epoch_logs(epoch_logs, train_loss, train_score, "train")

            on_epoch_end_metric = None
            if eval_set is not None and epoch % validation_freq == (
                validation_freq - 1
            ):
                self.callback_container.on_eval_begin()
                self.valid_running_loss = 0.0
                with trange(eval_steps, disable=self.verbose != 1) as v:
                    for i, (X, y) in zip(v, eval_loader):
                        v.set_description("valid")
                        val_score, val_loss = self._eval_step(
                            X, y, n_val_samples, train_steps, i
                        )
                        print_loss_and_metric(v, val_loss, val_score)
                epoch_logs = save_epoch_logs(epoch_logs, val_loss, val_score, "val")

                if self.reducelronplateau:
                    if self.reducelronplateau_criterion == "loss":
                        on_epoch_end_metric = val_loss
                    else:
                        on_epoch_end_metric = val_score[
                            self.reducelronplateau_criterion
                        ]

            self.callback_container.on_epoch_end(epoch, epoch_logs, on_epoch_end_metric)

            if self.early_stop:
                self.callback_container.on_train_end(epoch_logs)
                break

        self.callback_container.on_train_end(epoch_logs)
        self._restore_best_weights()
        self.model.train()

### predict

    predict(
        X_tab, n_samples=5, return_samples=False, batch_size=256
    )

Returns the predictions

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`X_tab` | `ndarray` | tabular dataset | required
`n_samples` | `int` | number of samples that will be either returned or averaged to produce an overal prediction | `5`
`return_samples` | `bool` | Boolean indicating whether the n samples will be averaged or directly returned | `False`
`batch_size` | `int` | batch size | `256`

Returns:

Type | Description
--- | ---
`np.ndarray:` | array with the predictions

Source code in `pytorch_widedeep/training/bayesian_trainer.py`

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    def predict(  # type: ignore[return]
        self,
        X_tab: np.ndarray,
        n_samples: int = 5,
        return_samples: bool = False,
        batch_size: int = 256,
    ) -> np.ndarray:
        r"""Returns the predictions

        Parameters
        ----------
        X_tab: np.ndarray,
            tabular dataset
        n_samples: int, default=5
            number of samples that will be either returned or averaged to
            produce an overal prediction
        return_samples: bool, default = False
            Boolean indicating whether the n samples will be averaged or directly returned
        batch_size: int, default = 256
            batch size

        Returns
        -------
        np.ndarray:
            array with the predictions
        """

        preds_l = self._predict(X_tab, n_samples, return_samples, batch_size)
        preds = np.hstack(preds_l) if return_samples else np.vstack(preds_l)
        axis = 2 if return_samples else 1

        if self.objective == "regression":
            return preds.squeeze(axis)
        if self.objective == "binary":
            return (preds.squeeze(axis) > 0.5).astype("int")
        if self.objective == "multiclass":
            return np.argmax(preds, axis)

### predict_proba

    predict_proba(
        X_tab, n_samples=5, return_samples=False, batch_size=256
    )

Returns the predicted probabilities

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`X_tab` | `ndarray` | tabular dataset | required
`n_samples` | `int` | number of samples that will be either returned or averaged to produce an overal prediction | `5`
`return_samples` | `bool` | Boolean indicating whether the n samples will be averaged or directly returned | `False`
`batch_size` | `int` | batch size | `256`

Returns:

Type | Description
--- | ---
`ndarray` | array with the probabilities per class

Source code in `pytorch_widedeep/training/bayesian_trainer.py`

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    def predict_proba(  # type: ignore[return]
        self,
        X_tab: np.ndarray,
        n_samples: int = 5,
        return_samples: bool = False,
        batch_size: int = 256,
    ) -> np.ndarray:
        r"""Returns the predicted probabilities

        Parameters
        ----------
        X_tab: np.ndarray,
            tabular dataset
        n_samples: int, default=5
            number of samples that will be either returned or averaged to
            produce an overal prediction
        return_samples: bool, default = False
            Boolean indicating whether the n samples will be averaged or directly returned
        batch_size: int, default = 256
            batch size

        Returns
        -------
        np.ndarray
            array with the probabilities per class
        """
        preds_l = self._predict(X_tab, n_samples, return_samples, batch_size)
        preds = np.hstack(preds_l) if return_samples else np.vstack(preds_l)

        if self.objective == "binary":
            if return_samples:
                preds = preds.squeeze(2)
                probs = np.zeros([n_samples, preds.shape[1], 2])
                for i in range(n_samples):
                    probs[i, :, 0] = 1 - preds[i]
                    probs[i, :, 1] = preds[i]
            else:
                preds = preds.squeeze(1)
                probs = np.zeros([preds.shape[0], 2])
                probs[:, 0] = 1 - preds
                probs[:, 1] = preds
            return probs
        if self.objective == "multiclass":
            return preds

### save

    save(
        path,
        save_state_dict=False,
        model_filename="bayesian_model.pt",
    )

Saves the model, training and evaluation history to disk

The `Trainer` class is built so that it 'just' trains a model. With that in mind, all the torch related parameters (such as optimizers or learning rate schedulers) have to be defined externally and then passed to the `Trainer`. As a result, the `Trainer` does not generate any attribute or additional data products that need to be saved other than the `model` object itself, which can be saved as any other torch model (e.g. `torch.save(model, path)`).

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`path` | `str` | path to the directory where the model and the feature importance attribute will be saved. | required
`save_state_dict` | `bool` | Boolean indicating whether to save directly the model or the model's state dictionary | `False`
`model_filename` | `str` | filename where the model weights will be store | `'bayesian_model.pt'`

Source code in `pytorch_widedeep/training/bayesian_trainer.py`

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    def save(
        self,
        path: str,
        save_state_dict: bool = False,
        model_filename: str = "bayesian_model.pt",
    ):
        r"""Saves the model, training and evaluation history to disk

        The `Trainer` class is built so that it 'just' trains a model. With
        that in mind, all the torch related parameters (such as optimizers or
        learning rate schedulers) have to be defined externally and then
        passed to the `Trainer`. As a result, the `Trainer` does not
        generate any attribute or additional data products that need to be
        saved other than the `model` object itself, which can be saved as
        any other torch model (e.g. `torch.save(model, path)`).

        Parameters
        ----------
        path: str
            path to the directory where the model and the feature importance
            attribute will be saved.
        save_state_dict: bool, default = False
            Boolean indicating whether to save directly the model or the
            model's state dictionary
        model_filename: str, Optional, default = "wd_model.pt"
            filename where the model weights will be store
        """

        save_dir = Path(path)
        history_dir = save_dir / "history"
        history_dir.mkdir(exist_ok=True, parents=True)

        # the trainer is run with the History Callback by default
        with open(history_dir / "train_eval_history.json", "w") as teh:
            json.dump(self.history, teh)  # type: ignore[attr-defined]

        has_lr_history = any(
            [clbk.__class__.__name__ == "LRHistory" for clbk in self.callbacks]
        )
        if self.lr_scheduler is not None and has_lr_history:
            with open(history_dir / "lr_history.json", "w") as lrh:
                json.dump(self.lr_history, lrh)  # type: ignore[attr-defined]

        model_path = save_dir / model_filename
        if save_state_dict:
            torch.save(self.model.state_dict(), model_path)
        else:
            torch.save(self.model, model_path)