wide_deep.py

import numpy as np
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F

from ..wdtypes import *

from ..initializers import Initializer, MultipleInitializer
from ..callbacks import Callback, History, CallbackContainer
from ..metrics import Metric, MultipleMetrics, MetricCallback
from ..losses import FocalLoss

from ._wd_dataset import WideDeepDataset
from ._multiple_optimizer import MultipleOptimizer
from ._multiple_lr_scheduler import MultipleLRScheduler
from ._multiple_transforms import MultipleTransforms
from .deep_dense import dense_layer

from tqdm import tqdm,trange
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader


use_cuda = torch.cuda.is_available()


class WideDeep(nn.Module):
    r""" Main collector class to combine all Wide, DeepDense, DeepText and
    DeepImage models. There are two options to combine these models.
    1) Directly connecting the output of the models to an ouput neuron(s).
    2) Adding a FC-Head on top of the deep models. This FC-Head will combine
    the output form the DeepDense, DeepText and DeepImage and will be then
    connected to the output neuron(s)

    Parameters
    ----------
    wide: nn.Module
        Wide model. I recommend using the Wide class in this package. However,
        can a custom model as long as is  consistent with the required
        architecture.
    deepdense: nn.Module
        'Deep dense' model consisting in a series of categorical features
        represented by embeddings combined with numerical (aka continuous)
        features. I recommend using the DeepDense class in this package.
        However, a custom model as long as is  consistent with the required
        architecture.
    deeptext: nn.Module, Optional
        'Deep text' model for the text input. Must be an object of class
        DeepText or a custom model as long as is consistent with the required
        architecture.
    deepimage: nn.Module, Optional
        'Deep Image' model for the images input. Must be an object of class
        DeepImage or a custom model as long as is consistent with the required
        architecture.
    deephead: nn.Module, Optional
        Dense model consisting in a stack of dense layers. The FC-Head
    head_layers: List, Optional
        Sizes of the stacked dense layers in the fc-head e.g: [128, 64]
    head_dropout: List, Optional
        Dropout between the dense layers. e.g: [0.5, 0.5]
    head_batchnorm: Boolean, Optional
        Whether or not to include batch normalizatin in the dense layers that
        form the texthead
    output_dim: Int
        Size of the final layer. 1 for regression and binary classification or
        'n_class' for multiclass classification

    ** While I recommend using the Wide and DeepDense classes within this
    package when building the corresponding model components, it is very likely
    that the user will want to use custom text and image models. That is perfectly
    possible. Simply, build them and pass them as the corresponding parameters.
    Note that the custom models MUST return a last layer of activations (i.e. not
    the final prediction) so that  these activations are collected by WideDeep and
    combined accordingly. In  addition, the models MUST also contain an attribute
    'output_dim' with the size of these last layers of activations.

    Attributes
    ----------
    deephead: nn.Sequential
        stack of dense layers comprising the FC-Head (aka imagehead) can be
        custom designed

    ** The remaining attributes that will be set as we compile and run the model are
        discussed within the corresponding methods.

    Example
    --------
    >>> import torch
    >>> from pytorch_widedeep.models import Wide, DeepDense, DeepText, DeepImage, WideDeep
    >>>
    >>> X_wide = torch.empty(5, 5).random_(2)
    >>> wide = Wide(wide_dim=X_wide.size(0), output_dim=1)
    >>>
    >>> X_deep = torch.cat((torch.empty(5, 4).random_(4), torch.rand(5, 1)), axis=1)
    >>> colnames = ['a', 'b', 'c', 'd', 'e']
    >>> embed_input = [(u,i,j) for u,i,j in zip(colnames[:4], [4]*4, [8]*4)]
    >>> deep_column_idx = {k:v for v,k in enumerate(colnames)}
    >>> deepdense = DeepDense(hidden_layers=[8,4], deep_column_idx=deep_column_idx, embed_input=embed_input)
    >>>
    >>> X_text = torch.cat((torch.zeros([5,1]), torch.empty(5, 4).random_(1,4)), axis=1)
    >>> deeptext = DeepText(vocab_size=4, hidden_dim=4, n_layers=1, padding_idx=0, embed_dim=4)
    >>>
    >>> X_img = torch.rand((5,3,224,224))
    >>> deepimage = DeepImage(head_layers=[512, 64, 8])
    >>>
    >>> model = WideDeep(wide=wide, deepdense=deepdense, deeptext=deeptext, deepimage=deepimage, output_dim=1)
    >>> input_dict = {'wide':X_wide, 'deepdense':X_deep, 'deeptext':X_text, 'deepimage':X_img}
    >>> model(X=input_dict)
    tensor([[-0.3779],
            [-0.5247],
            [-0.2773],
            [-0.2888],
            [-0.2010]], grad_fn=<AddBackward0>)
    """
    def __init__(self,
        wide:nn.Module,
        deepdense:nn.Module,
        output_dim:int=1,
        deeptext:Optional[nn.Module]=None,
        deepimage:Optional[nn.Module]=None,
        deephead:Optional[nn.Module]=None,
        head_layers:Optional[List]=None,
        head_dropout:Optional[List]=None,
        head_batchnorm:Optional[bool]=None):

        super(WideDeep, self).__init__()

        # The main 5 components of the wide and deep assemble
        self.wide = wide
        self.deepdense = deepdense
        self.deeptext  = deeptext
        self.deepimage = deepimage
        self.deephead = deephead

        if self.deephead is None:
            if head_layers is not None:
                input_dim = self.deepdense.output_dim + self.deeptext.output_dim + self.deepimage.output_dim
                head_layers = [input_dim] + head_layers
                if not head_dropout: head_dropout = [0.] * (len(head_layers)-1)
                self.deephead = nn.Sequential()
                for i in range(1, len(head_layers)):
                    self.deephead.add_module(
                        'head_layer_{}'.format(i-1),
                        dense_layer( head_layers[i-1], head_layers[i], head_dropout[i-1], head_batchnorm))
                self.deephead.add_module('head_out', nn.Linear(head_layers[-1], output_dim))
            else:
                self.deepdense = nn.Sequential(
                    self.deepdense,
                    nn.Linear(self.deepdense.output_dim, output_dim))
                if self.deeptext is not None:
                    self.deeptext = nn.Sequential(
                        self.deeptext,
                        nn.Linear(self.deeptext.output_dim, output_dim))
                if self.deepimage is not None:
                    self.deepimage = nn.Sequential(
                        self.deepimage,
                        nn.Linear(self.deepimage.output_dim, output_dim))

    def forward(self, X:List[Dict[str,Tensor]])->Tensor:
        r"""
        Parameters
        ----------
        X: List
            List of Dict where the keys are the model names (wide, deepdense,
            deeptext and deepimage) and the values are the corresponding
            Tensors
        """
        # Wide output: direct connection to the output neuron(s)
        out = self.wide(X['wide'])

        # Deep output: either connected directly to the output neuron(s) or
        # passed through a head first
        if self.deephead:
            deepside = self.deepdense(X['deepdense'])
            if self.deeptext is not None:
                deepside = torch.cat( [deepside, self.deeptext(X['deeptext'])], axis=1 )
            if self.deepimage is not None:
                deepside = torch.cat( [deepside, self.deepimage(X['deepimage'])], axis=1 )
            deepside_out = self.deephead(deepside)
            return out.add_(deepside_out)
        else:
            out.add_(self.deepdense(X['deepdense']))
            if self.deeptext is not None:
                out.add_(self.deeptext(X['deeptext']))
            if self.deepimage is not None:
                out.add_(self.deepimage(X['deepimage']))
            return out

    def compile(self,
        method:str,
        optimizers:Optional[Union[Optimizer,Dict[str,Optimizer]]]=None,
        lr_schedulers:Optional[Union[LRScheduler,Dict[str,LRScheduler]]]=None,
        initializers:Optional[Dict[str,Initializer]]=None,
        transforms:Optional[List[Transforms]]=None,
        callbacks:Optional[List[Callback]]=None,
        metrics:Optional[List[Metric]]=None,
        class_weight:Optional[Union[float,List[float],Tuple[float]]]=None,
        with_focal_loss:bool=False,
        alpha:float=0.25,
        gamma:float=2,
        verbose:int=1):
        r"""
        Function to set a number of attributes that are used during the training process.

        Parameters
        ----------
        method: Str
             One of ('regression', 'binary' or 'multiclass')
        optimizers: Optimizer, Dict. Optional, Default=Adam
            Either an optimizers object (e.g. torch.optim.Adam()) or a
            dictionary where there keys are the model's children (i.e. wide,
            deepdense, deeptext, deepimage and/or deephead)  and the values
            are the corresponding optimizers. If multiple optimizers are used
            the  dictionary MUST contain an optimizer per child.
        lr_schedulers: LRScheduler, Dict. Optional. Default=None
            Either a LRScheduler object (e.g
            torch.optim.lr_scheduler.StepLR(opt, step_size=5)) or  dictionary
            where there keys are the model's children (i.e. wide, deepdense,
            deeptext, deepimage and/or deephead)  and the values are the
            corresponding learning rate schedulers.
        initializers: Dict, Optional. Default=None
            Dict where there keys are the model's children (i.e. wide,
            deepdense, deeptext, deepimage and/or deephead) and the values are
            the corresponding initializers.
        transforms: List, Optional. Default=None
            List with torchvision.transforms to be applied to the image
            component of the model
        callbacks: List, Optional. Default=None
            Callbacks available are: ModelCheckpoint, EarlyStopping, and
            LRHistory. The History callback is used by default.
        metrics: List, Optional. Default=None
            Metrics available are: BinaryAccuracy and CategoricalAccuracy
        class_weight: List, Tuple, Float. Optional. Default=None
            Can be one of: float indicating the weight of the minority class
            in binary classification problems (e.g. 9.) or a list or tuple
            with weights for the different classes in multiclass
            classification problems  (e.g. [1., 2., 3.]). The weights do not
            neccesarily need to be normalised. If your loss function uses
            reduction='mean', the loss will be normalized by the sum of the
            corresponding weights for each element. If you are using
            reduction='none', you would have to take care of the normalization
            yourself. See here:
            https://discuss.pytorch.org/t/passing-the-weights-to-crossentropyloss-correctly/14731/10
        with_focal_loss: Boolean, Optional. Default=False
            Whether or not to use the Focal Loss. https://arxiv.org/pdf/1708.02002.pdf
        alpha, gamma: Float. Default=0.25, 2
            Focal Loss parameters. See: https://arxiv.org/pdf/1708.02002.pdf
        verbose: Int
            Setting it to 0 will print nothing during training.

        Attributes
        ----------
        Attributes that are not direct assignations of parameters

        self.cyclic: Boolean
            Indicates if any of the lr_schedulers is CyclicLR or OneCycleLR

        Example
        --------
        Assuming you have already built the model components (wide, deepdense, etc...)

        >>> from pytorch_widedeep.models import WideDeep
        >>> from pytorch_widedeep.initializers import *
        >>> from pytorch_widedeep.callbacks import *
        >>> from pytorch_widedeep.optim import RAdam
        >>> model = WideDeep(wide=wide, deepdense=deepdense, deeptext=deeptext, deepimage=deepimage)
        >>> wide_opt = torch.optim.Adam(model.wide.parameters())
        >>> deep_opt = torch.optim.Adam(model.deepdense.parameters())
        >>> text_opt = RAdam(model.deeptext.parameters())
        >>> img_opt  = RAdam(model.deepimage.parameters())
        >>> wide_sch = torch.optim.lr_scheduler.StepLR(wide_opt, step_size=5)
        >>> deep_sch = torch.optim.lr_scheduler.StepLR(deep_opt, step_size=3)
        >>> text_sch = torch.optim.lr_scheduler.StepLR(text_opt, step_size=5)
        >>> img_sch  = torch.optim.lr_scheduler.StepLR(img_opt, step_size=3)
        >>> optimizers = {'wide': wide_opt, 'deepdense':deep_opt, 'deeptext':text_opt, 'deepimage': img_opt}
        >>> schedulers = {'wide': wide_sch, 'deepdense':deep_sch, 'deeptext':text_sch, 'deepimage': img_sch}
        >>> initializers = {'wide': Uniform, 'deepdense':Normal, 'deeptext':KaimingNormal,
        >>> ... 'deepimage':KaimingUniform}
        >>> transforms = [ToTensor, Normalize(mean=mean, std=std)]
        >>> callbacks = [LRHistory, EarlyStopping, ModelCheckpoint(filepath='model_weights/wd_out.pt')]
        >>> model.compile(method='regression', initializers=initializers, optimizers=optimizers,
        >>> ... lr_schedulers=schedulers, callbacks=callbacks, transforms=transforms)
        """
        self.verbose = verbose
        self.early_stop = False
        self.method = method
        self.with_focal_loss = with_focal_loss
        if self.with_focal_loss:
            self.alpha, self.gamma = alpha, gamma

        if isinstance(class_weight, float):
            self.class_weight = torch.tensor([1.-class_weight, class_weight])
        elif isinstance(class_weight, (List, Tuple)):
            self.class_weight =  torch.tensor(class_weight)
        else:
            self.class_weight = None

        if initializers is not None:
            self.initializer = MultipleInitializer(initializers, verbose=self.verbose)
            self.initializer.apply(self)

        if optimizers is not None:
            if isinstance(optimizers, Optimizer):
                self.optimizer = optimizers
            elif len(optimizers)>1:
                opt_names = list(optimizers.keys())
                mod_names = [n  for n, c in self.named_children()]
                for mn in mod_names: assert mn in opt_names, "No optimizer found for {}".format(mn)
                self.optimizer = MultipleOptimizer(optimizers)
        else:
            self.optimizer = torch.optim.Adam(self.parameters())

        if lr_schedulers is not None:
            if isinstance(lr_schedulers, LRScheduler):
                self.lr_scheduler = lr_schedulers
                self.cyclic = 'cycl' in self.lr_scheduler.__class__.__name__.lower()
            elif len(lr_schedulers) > 1:
                self.lr_scheduler = MultipleLRScheduler(lr_schedulers)
                scheduler_names = [sc.__class__.__name__.lower() for _,sc in self.lr_scheduler._schedulers.items()]
                self.cyclic = any(['cycl' in sn for sn in scheduler_names])
        else:
            self.lr_scheduler, self.cyclic = None, False

        if transforms is not None:
            self.transforms = MultipleTransforms(transforms)()
        else:
            self.transforms = None

        self.history = History()
        self.callbacks = [self.history]
        if callbacks is not None:
            for callback in callbacks:
                if isinstance(callback, type): callback = callback()
                self.callbacks.append(callback)

        if metrics is not None:
            self.metric = MultipleMetrics(metrics)
            self.callbacks += [MetricCallback(self.metric)]
        else:
            self.metric = None

        self.callback_container = CallbackContainer(self.callbacks)
        self.callback_container.set_model(self)

        if use_cuda:
            self.cuda()

    def _activation_fn(self, inp:Tensor) -> Tensor:
        if self.method == 'regression':
            return inp
        if self.method == 'binary':
            return torch.sigmoid(inp)
        if self.method == 'multiclass':
            return F.softmax(inp, dim=1)

    def _loss_fn(self, y_pred:Tensor, y_true:Tensor) -> Tensor:
        if self.with_focal_loss:
            return FocalLoss(self.alpha, self.gamma)(y_pred, y_true)
        if self.method == 'regression':
            return F.mse_loss(y_pred, y_true.view(-1, 1))
        if self.method == 'binary':
            return F.binary_cross_entropy(y_pred, y_true.view(-1, 1), weight=self.class_weight)
        if self.method == 'multiclass':
            return F.cross_entropy(y_pred, y_true, weight=self.class_weight)

    def _training_step(self, data:Dict[str, Tensor], target:Tensor, batch_idx:int):
        self.train()
        X = {k:v.cuda() for k,v in data.items()} if use_cuda else data
        y = target.float() if self.method != 'multiclass' else target
        y = y.cuda() if use_cuda else y

        self.optimizer.zero_grad()
        y_pred =  self._activation_fn(self.forward(X))
        loss = self._loss_fn(y_pred, y)
        loss.backward()
        self.optimizer.step()

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

        if self.metric is not None:
            acc = self.metric(y_pred, y)
            return acc, avg_loss
        else:
            return None, avg_loss

    def _validation_step(self, data:Dict[str, Tensor], target:Tensor, batch_idx:int):

        self.eval()
        with torch.no_grad():
            X = {k:v.cuda() for k,v in data.items()} if use_cuda else data
            y = target.float() if self.method != 'multiclass' else target
            y = y.cuda() if use_cuda else y

            y_pred = self._activation_fn(self.forward(X))
            loss = self._loss_fn(y_pred, y)
            self.valid_running_loss += loss.item()
            avg_loss = self.valid_running_loss/(batch_idx+1)

        if self.metric is not None:
            acc = self.metric(y_pred, y)
            return acc, avg_loss
        else:
            return None, avg_loss

    def _lr_scheduler_step(self, step_location:str):
        r"""
        Function to execute the learning rate schedulers steps.
        If the lr_scheduler is Cyclic (i.e. CyclicLR or OneCycleLR), the step
        must  happen after training each bach durig training. On the other
        hand, if the  scheduler is not Cyclic, is expected to be called after
        validation.

        Parameters
        ----------
        step_location: Str
            Indicates where to run the lr_scheduler step
        """
        if self.lr_scheduler.__class__.__name__ == 'MultipleLRScheduler' and self.cyclic:
            if step_location == 'on_batch_end':
                for model_name, scheduler in self.lr_scheduler._schedulers.items():
                    if 'cycl' in scheduler.__class__.__name__.lower(): scheduler.step()
            elif step_location == 'on_epoch_end':
                for scheduler_name, scheduler in self.lr_scheduler._schedulers.items():
                    if 'cycl' not in scheduler.__class__.__name__.lower(): scheduler.step()
        elif self.cyclic:
            if step_location == 'on_batch_end': self.lr_scheduler.step()
            else: pass
        elif self.lr_scheduler.__class__.__name__ == 'MultipleLRScheduler':
            if step_location == 'on_epoch_end': self.lr_scheduler.step()
            else: pass
        elif step_location == 'on_epoch_end': self.lr_scheduler.step()
        else: pass

    def _train_val_split(self,
        X_wide:Optional[np.ndarray]=None,
        X_deep:Optional[np.ndarray]=None,
        X_text:Optional[np.ndarray]=None,
        X_img:Optional[np.ndarray]=None,
        X_train:Optional[Dict[str,np.ndarray]]=None,
        X_val:Optional[Dict[str,np.ndarray]]=None,
        val_split:Optional[float]=None,
        target:Optional[np.ndarray]=None,
        seed:int=1):
        r"""
        If a validation set (X_val) is passed to the fit method, or val_split
        is specified, the train/val split will happen internally. A number of
        options are allowed in terms of data inputs. For parameter
        information, please, see the .fit() method documentation

        Returns
        -------
        train_set: WideDeepDataset
            WideDeepDataset object that will be loaded through
            torch.utils.data.DataLoader
        eval_set : WideDeepDataset
            WideDeepDataset object that will be loaded through
            torch.utils.data.DataLoader
        """
        # Without validation
        if X_val is None and val_split is None:
            # if a train dictionary is passed, check if text and image datasets
            # are present and instantiate the WideDeepDataset class
            if X_train is not None:
                X_wide, X_deep, target = X_train['X_wide'], X_train['X_deep'], X_train['target']
                if 'X_text' in X_train.keys(): X_text = X_train['X_text']
                if 'X_img' in X_train.keys(): X_img = X_train['X_img']
            X_train={'X_wide': X_wide, 'X_deep': X_deep, 'target': target}
            try: X_train.update({'X_text': X_text})
            except: pass
            try: X_train.update({'X_img': X_img})
            except: pass
            train_set = WideDeepDataset(**X_train, transforms=self.transforms)
            eval_set = None
        # With validation
        else:
            if X_val is not None:
                # if a validation dictionary is passed, then if not train
                # dictionary is passed we build it with the input arrays
                # (either the dictionary or the arrays must be passed)
                if X_train is None:
                    X_train = {'X_wide':X_wide, 'X_deep': X_deep, 'target': target}
                    if X_text is not None: X_train.update({'X_text': X_text})
                    if X_img is not None:  X_train.update({'X_img': X_img})
            else:
                # if a train dictionary is passed, check if text and image
                # datasets are present. The train/val split using val_split
                if X_train is not None:
                    X_wide, X_deep, target = X_train['X_wide'], X_train['X_deep'], X_train['target']
                    if 'X_text' in X_train.keys(): X_text = X_train['X_text']
                    if 'X_img' in X_train.keys(): X_img = X_train['X_img']
                X_tr_wide, X_val_wide, X_tr_deep, X_val_deep, y_tr, y_val = train_test_split(X_wide,
                    X_deep, target, test_size=val_split, random_state=seed)
                X_train = {'X_wide':X_tr_wide, 'X_deep': X_tr_deep, 'target': y_tr}
                X_val = {'X_wide':X_val_wide, 'X_deep': X_val_deep, 'target': y_val}
                try:
                    X_tr_text, X_val_text = train_test_split(X_text, test_size=val_split,
                        random_state=seed)
                    X_train.update({'X_text': X_tr_text}), X_val.update({'X_text': X_val_text})
                except: pass
                try:
                    X_tr_img, X_val_img = train_test_split(X_img, test_size=val_split,
                        random_state=seed)
                    X_train.update({'X_img': X_tr_img}), X_val.update({'X_img': X_val_img})
                except: pass
            # At this point the X_train and X_val dictionaries have been built
            train_set = WideDeepDataset(**X_train, transforms=self.transforms)
            eval_set = WideDeepDataset(**X_val, transforms=self.transforms)
        return train_set, eval_set

    def fit(self,
        X_wide:Optional[np.ndarray]=None,
        X_deep:Optional[np.ndarray]=None,
        X_text:Optional[np.ndarray]=None,
        X_img:Optional[np.ndarray]=None,
        X_train:Optional[Dict[str,np.ndarray]]=None,
        X_val:Optional[Dict[str,np.ndarray]]=None,
        val_split:Optional[float]=None,
        target:Optional[np.ndarray]=None,
        n_epochs:int=1,
        validation_freq:int=1,
        batch_size:int=32,
        patience:int=10,
        seed:int=1):
        r"""
        fit method that must run after calling 'compile'

        Parameters
        ----------
        X_wide: np.ndarray, Optional. Default=None
            One hot encoded wide input.
        X_deep: np.ndarray, Optional. Default=None
            Input for the deepdense model
        X_text: np.ndarray, Optional. Default=None
            Input for the deeptext model
        X_img : np.ndarray, Optional. Default=None
            Input for the deepimage model
        X_train: Dict, Optional. Default=None
            Training dataset for the different model branches.  Keys are
            'X_wide', 'X_deep', 'X_text', 'X_img' and 'target' the values are
            the corresponding matrices e.g X_train = {'X_wide': X_wide,
            'X_wide': X_wide, 'X_text': X_text, 'X_img': X_img}
        X_val: Dict, Optional. Default=None
            Validation dataset for the different model branches.  Keys are
            'X_wide', 'X_deep', 'X_text', 'X_img' and 'target' the values are
            the corresponding matrices e.g X_val = {'X_wide': X_wide,
            'X_wide': X_wide, 'X_text': X_text, 'X_img': X_img}
        val_split: Float, Optional. Default=None
            train/val split
        target: np.ndarray, Optional. Default=None
            target values
        n_epochs: Int, Default=1
        validation_freq: Int, Default=1
        batch_size: Int, Default=32
        patience: Int, Default=10
            Number of epochs without improving the target metric before we
            stop the fit
        seed: Int, Default=1
            Random seed for the train/val split

        **WideDeep assumes that X_wide, X_deep and target ALWAYS exist, while
        X_text and X_img are optional
        **Either X_train or X_wide, X_deep and target must be passed to the
        fit method

        Example
        --------
        Assuming you have already built and compiled the model

        Ex 1. using train input arrays directly and no validation
        >>> model.fit(X_wide=X_wide, X_deep=X_deep, target=target, n_epochs=10, batch_size=256)

        Ex 2: using train input arrays directly and validation with val_split
        >>> model.fit(X_wide=X_wide, X_deep=X_deep, target=target, n_epochs=10, batch_size=256, val_split=0.2)

        Ex 3: using train dict and val_split
        >>> X_train = {'X_wide': X_wide, 'X_deep': X_deep, 'target': y}
        >>> model.fit(X_train, n_epochs=10, batch_size=256, val_split=0.2)

        Ex 4: validation using training and validation dicts
        >>> X_train = {'X_wide': X_wide_tr, 'X_deep': X_deep_tr, 'target': y_tr}
        >>> X_val = {'X_wide': X_wide_val, 'X_deep': X_deep_val, 'target': y_val}
        >>> model.fit(X_train=X_train, X_val=X_val n_epochs=10, batch_size=256)
        """

        if X_train is None and (X_wide is None or X_deep is None or target is None):
            raise ValueError(
                "Training data is missing. Either a dictionary (X_train) with "
                "the training dataset or at least 3 arrays (X_wide, X_deep, "
                "target) must be passed to the fit method")

        self.batch_size = batch_size
        train_set, eval_set = self._train_val_split(X_wide, X_deep, X_text, X_img,
            X_train, X_val, val_split, target, seed)
        train_loader = DataLoader(dataset=train_set, batch_size=batch_size, num_workers=8)
        train_steps =  (len(train_loader.dataset) // batch_size) + 1
        self.callback_container.on_train_begin({'batch_size': batch_size,
            'train_steps': train_steps, 'n_epochs': n_epochs})

        for epoch in range(n_epochs):
            # train step...
            epoch_logs={}
            self.callback_container.on_epoch_begin(epoch, logs=epoch_logs)
            self.train_running_loss = 0.
            with trange(train_steps, disable=self.verbose != 1) as t:
                for batch_idx, (data,target) in zip(t, train_loader):
                    t.set_description('epoch %i' % (epoch+1))
                    acc, train_loss = self._training_step(data, target, batch_idx)
                    if acc is not None:
                        t.set_postfix(metrics=acc, loss=train_loss)
                    else:
                        t.set_postfix(loss=np.sqrt(train_loss))
                    if self.lr_scheduler: self._lr_scheduler_step(step_location='on_batch_end')
                    self.callback_container.on_batch_end(batch=batch_idx)
            epoch_logs['train_loss'] = train_loss
            if acc is not None: epoch_logs['train_acc'] = acc['acc']
            # eval step...

            if epoch % validation_freq  == (validation_freq - 1):
                if eval_set is not None:
                    eval_loader = DataLoader(dataset=eval_set, batch_size=batch_size, num_workers=8,
                        shuffle=False)
                    eval_steps =  (len(eval_loader.dataset) // batch_size) + 1
                    self.valid_running_loss = 0.
                    with trange(eval_steps, disable=self.verbose != 1) as v:
                        for i, (data,target) in zip(v, eval_loader):
                            v.set_description('valid')
                            acc, val_loss = self._validation_step(data, target, i)
                            if acc is not None:
                                v.set_postfix(metrics=acc, loss=val_loss)
                            else:
                                v.set_postfix(loss=np.sqrt(val_loss))
                    epoch_logs['val_loss'] = val_loss
                    if acc is not None: epoch_logs['val_acc'] = acc['acc']
            if self.lr_scheduler: self._lr_scheduler_step(step_location='on_epoch_end')
            # log and check if early_stop...
            self.callback_container.on_epoch_end(epoch, epoch_logs)
            if self.early_stop:
                self.callback_container.on_train_end(epoch)
                break
            self.callback_container.on_train_end(epoch)
        self.train()

    def _predict(self, X_wide:np.ndarray, X_deep:np.ndarray, X_text:Optional[np.ndarray]=None,
        X_img:Optional[np.ndarray]=None, X_test:Optional[Dict[str, np.ndarray]]=None)->List:
        r"""
        Hidden method to avoid code repetition in predict and predict_proba.
        For parameter information, please, see the .predict() method
        documentation
        """
        if X_test is not None:
            test_set = WideDeepDataset(**X_test)
        else:
            load_dict = {'X_wide': X_wide, 'X_deep': X_deep}
            if X_text is not None: load_dict.update({'X_text': X_text})
            if X_img is not None:  load_dict.update({'X_img': X_img})
            test_set = WideDeepDataset(**load_dict)

        test_loader = torch.utils.data.DataLoader(dataset=test_set,
            batch_size=self.batch_size,shuffle=False)
        test_steps =  (len(test_loader.dataset) // test_loader.batch_size) + 1

        self.eval()
        preds_l = []
        with torch.no_grad():
            with trange(test_steps, disable=self.verbose != 1) as t:
                for i, data in zip(t, test_loader):
                    t.set_description('predict')
                    X = {k:v.cuda() for k,v in data.items()} if use_cuda else data
                    preds = self._activation_fn(self.forward(X)).cpu().data.numpy()
                    preds_l.append(preds)
        self.train()
        return preds_l

    def predict(self, X_wide:np.ndarray, X_deep:np.ndarray, X_text:Optional[np.ndarray]=None,
        X_img:Optional[np.ndarray]=None, X_test:Optional[Dict[str, np.ndarray]]=None)->np.ndarray:
        r"""
        fit method that must run after calling 'compile'

        Parameters
        ----------
        X_wide: np.ndarray, Optional. Default=None
            One hot encoded wide input.
        X_deep: np.ndarray, Optional. Default=None
            Input for the deepdense model
        X_text: np.ndarray, Optional. Default=None
            Input for the deeptext model
        X_img : np.ndarray, Optional. Default=None
            Input for the deepimage model
        X_test: Dict, Optional. Default=None
            Testing dataset for the different model branches.  Keys are
            'X_wide', 'X_deep', 'X_text', 'X_img' and 'target' the values are
            the corresponding matrices e.g X_train = {'X_wide': X_wide,
            'X_wide': X_wide, 'X_text': X_text, 'X_img': X_img}

        **WideDeep assumes that X_wide, X_deep and target ALWAYS exist, while
        X_text and X_img are optional

        Returns
        -------
        preds: np.array with the predicted target for the test dataset.
        """
        preds_l = self._predict(X_wide, X_deep, X_text, X_img, X_test)
        if self.method == "regression":
            return np.vstack(preds_l).squeeze(1)
        if self.method == "binary":
            preds = np.vstack(preds_l).squeeze(1)
            return (preds > 0.5).astype('int')
        if self.method == "multiclass":
            preds = np.vstack(preds_l)
            return np.argmax(preds, 1)

    def predict_proba(self, X_wide:np.ndarray, X_deep:np.ndarray, X_text:Optional[np.ndarray]=None,
        X_img:Optional[np.ndarray]=None, X_test:Optional[Dict[str, np.ndarray]]=None)->np.ndarray:
        """
        Returns
        -------
        preds: np.ndarray
            Predicted probabilities of target for the test dataset for  binary
            and multiclass methods
        """
        preds_l = self._predict(X_wide, X_deep, X_text, X_img, X_test)
        if self.method == "binary":
            preds = np.vstack(preds_l).squeeze(1)
            probs = np.zeros([preds.shape[0],2])
            probs[:,0] = 1-preds
            probs[:,1] = preds
            return probs
        if self.method == "multiclass":
            return np.vstack(preds_l)

    def get_embeddings(self, col_name:str,
        cat_encoding_dict:Dict[str,Dict[str,int]]) -> Dict[str,np.ndarray]:
        """
        Get the learned embeddings for the categorical features passed through deepdense.

        Parameters
        ----------
        col_name: str,
            Column name of the feature we want to get the embeddings for
        cat_encoding_dict: Dict
            Categorical encodings. The function is designed to take the
            'encoding_dict' attribute from the DeepPreprocessor class. Any
            Dict with the same structure can be used

        Returns
        -------
        cat_embed_dict: Dict
            Categorical levels of the col_name feature and the corresponding
            embeddings

        Example:
        -------
        Assuming we have already train the model:

        >>> model.get_embeddings(col_name='education', cat_encoding_dict=deep_preprocessor.encoding_dict)
        {'11th': array([-0.42739448, -0.22282735,  0.36969638,  0.4445322 ,  0.2562272 ,
        0.11572784, -0.01648579,  0.09027119,  0.0457597 , -0.28337458], dtype=float32),
         'HS-grad': array([-0.10600474, -0.48775527,  0.3444158 ,  0.13818645, -0.16547225,
        0.27409762, -0.05006042, -0.0668492 , -0.11047247,  0.3280354 ], dtype=float32),
        ...
        }

        where:

        >>> deep_preprocessor.encoding_dict['education']
        {'11th': 0, 'HS-grad': 1, 'Assoc-acdm': 2, 'Some-college': 3, '10th': 4, 'Prof-school': 5,
        '7th-8th': 6, 'Bachelors': 7, 'Masters': 8, 'Doctorate': 9, '5th-6th': 10, 'Assoc-voc': 11,
        '9th': 12, '12th': 13, '1st-4th': 14, 'Preschool': 15}
        """
        for n,p in self.named_parameters():
            if 'embed_layers' in n and col_name in n:
                embed_mtx = p.cpu().data.numpy()
        encoding_dict = cat_encoding_dict[col_name]
        inv_encoding_dict = {v:k for k,v in encoding_dict.items()}
        cat_embed_dict = {}
        for idx,value in inv_encoding_dict.items():
            cat_embed_dict[value] = embed_mtx[idx]
        return cat_embed_dict