abstract_model.py

from dataclasses import dataclass, field
from typing import List, Any, Dict
import torch
from torch.nn.utils import clip_grad_norm_
import numpy as np
from scipy.sparse import csc_matrix
from abc import abstractmethod
from pytorch_tabnet import tab_network
from pytorch_tabnet.utils import (
    PredictDataset,
    create_explain_matrix,
    validate_eval_set,
    create_dataloaders,
    check_nans,
    define_device,
)
from pytorch_tabnet.callbacks import (
    CallbackContainer,
    History,
    EarlyStopping,
    LRSchedulerCallback,
)
from pytorch_tabnet.metrics import MetricContainer, check_metrics
from sklearn.base import BaseEstimator
from torch.utils.data import DataLoader
import io
import json
from pathlib import Path
import shutil
import zipfile


@dataclass
class TabModel(BaseEstimator):
    """ Class for TabNet model."""

    n_d: int = 8
    n_a: int = 8
    n_steps: int = 3
    gamma: float = 1.3
    cat_idxs: List[int] = field(default_factory=list)
    cat_dims: List[int] = field(default_factory=list)
    cat_emb_dim: int = 1
    n_independent: int = 2
    n_shared: int = 2
    epsilon: float = 1e-15
    momentum: float = 0.02
    lambda_sparse: float = 1e-3
    seed: int = 0
    clip_value: int = 1
    verbose: int = 1
    optimizer_fn: Any = torch.optim.Adam
    optimizer_params: Dict = field(default_factory=lambda: dict(lr=2e-2))
    scheduler_fn: Any = None
    scheduler_params: Dict = field(default_factory=dict)
    mask_type: str = "sparsemax"
    input_dim: int = None
    output_dim: int = None
    device_name: str = "auto"

    def __post_init__(self):
        self.batch_size = 1024
        self.virtual_batch_size = 1024
        torch.manual_seed(self.seed)
        # Defining device
        self.device = torch.device(define_device(self.device_name))
        print(f"Device used : {self.device}")

    def fit(
        self,
        X_train,
        y_train,
        eval_set=None,
        eval_name=None,
        eval_metric=None,
        loss_fn=None,
        weights=0,
        max_epochs=100,
        patience=10,
        batch_size=1024,
        virtual_batch_size=128,
        num_workers=0,
        drop_last=False,
        callbacks=None,
        pin_memory=True
    ):
        """Train a neural network stored in self.network
        Using train_dataloader for training data and
        valid_dataloader for validation.

        Parameters
        ----------
            X_train: np.ndarray
                Train set
            y_train : np.array
                Train targets
            eval_set: list of tuple
                List of eval tuple set (X, y).
                The last one is used for early stopping
            eval_name: list of str
                List of eval set names.
            eval_metric : list of str
                List of evaluation metrics.
                The last metric is used for early stopping.
            weights : bool or dictionnary
                0 for no balancing
                1 for automated balancing
                dict for custom weights per class
            max_epochs : int
                Maximum number of epochs during training
            patience : int
                Number of consecutive non improving epoch before early stopping
            batch_size : int
                Training batch size
            virtual_batch_size : int
                Batch size for Ghost Batch Normalization (virtual_batch_size < batch_size)
            num_workers : int
                Number of workers used in torch.utils.data.DataLoader
            drop_last : bool
                Whether to drop last batch during training
            callbacks : list of callback function
                List of custom callbacks
            pin_memory: bool
                Whether to set pin_memory to True or False during training

        """
        # update model name

        self.max_epochs = max_epochs
        self.patience = patience
        self.batch_size = batch_size
        self.virtual_batch_size = virtual_batch_size
        self.num_workers = num_workers
        self.drop_last = drop_last
        self.input_dim = X_train.shape[1]
        self._stop_training = False
        self.pin_memory = pin_memory

        eval_set = eval_set if eval_set else []

        if loss_fn is None:
            self.loss_fn = self._default_loss
        else:
            self.loss_fn = loss_fn

        check_nans(X_train)
        check_nans(y_train)
        self.update_fit_params(
            X_train, y_train, eval_set, weights,
        )

        # Validate and reformat eval set depending on training data
        eval_names, eval_set = validate_eval_set(eval_set, eval_name, X_train, y_train)

        train_dataloader, valid_dataloaders = self._construct_loaders(
            X_train, y_train, eval_set
        )

        self._set_network()
        self._set_metrics(eval_metric, eval_names)
        self._set_optimizer()
        self._set_callbacks(callbacks)

        # Call method on_train_begin for all callbacks
        self._callback_container.on_train_begin()

        # Training loop over epochs
        for epoch_idx in range(self.max_epochs):

            # Call method on_epoch_begin for all callbacks
            self._callback_container.on_epoch_begin(epoch_idx)

            self._train_epoch(train_dataloader)

            # Apply predict epoch to all eval sets
            for eval_name, valid_dataloader in zip(eval_names, valid_dataloaders):
                self._predict_epoch(eval_name, valid_dataloader)

            # Call method on_epoch_end for all callbacks
            self._callback_container.on_epoch_end(epoch_idx,
                                                  logs=self.history.epoch_metrics)

            if self._stop_training:
                break

        # Call method on_train_end for all callbacks
        self._callback_container.on_train_end()
        self.network.eval()

        # compute feature importance once the best model is defined
        self._compute_feature_importances(train_dataloader)

    def predict(self, X):
        """
        Make predictions on a batch (valid)

        Parameters
        ----------
            X: a :tensor: `torch.Tensor`
                Input data

        Returns
        -------
            predictions: np.array
                Predictions of the regression problem
        """
        self.network.eval()
        dataloader = DataLoader(
            PredictDataset(X),
            batch_size=self.batch_size,
            shuffle=False,
        )

        results = []
        for batch_nb, data in enumerate(dataloader):
            data = data.to(self.device).float()
            output, M_loss = self.network(data)
            predictions = output.cpu().detach().numpy()
            results.append(predictions)
        res = np.vstack(results)
        return self.predict_func(res)

    def explain(self, X):
        """
        Return local explanation

        Parameters
        ----------
            X: tensor: `torch.Tensor`
                Input data

        Returns
        -------
            M_explain: matrix
                Importance per sample, per columns.
            masks: matrix
                Sparse matrix showing attention masks used by network.
        """
        self.network.eval()

        dataloader = DataLoader(
            PredictDataset(X),
            batch_size=self.batch_size,
            shuffle=False,
        )

        res_explain = []

        for batch_nb, data in enumerate(dataloader):
            data = data.to(self.device).float()

            M_explain, masks = self.network.forward_masks(data)
            for key, value in masks.items():
                masks[key] = csc_matrix.dot(
                    value.cpu().detach().numpy(), self.reducing_matrix
                )

            res_explain.append(
                csc_matrix.dot(M_explain.cpu().detach().numpy(), self.reducing_matrix)
            )

            if batch_nb == 0:
                res_masks = masks
            else:
                for key, value in masks.items():
                    res_masks[key] = np.vstack([res_masks[key], value])

        res_explain = np.vstack(res_explain)

        return res_explain, res_masks

    def save_model(self, path):
        """Saving TabNet model in two distinct files.

        Parameters
        ----------
            filepath : str
                Path of the model.
        """
        saved_params = {}
        for key, val in self.get_params().items():
            if isinstance(val, type):
                # Don't save torch specific params
                continue
            else:
                saved_params[key] = val

        # Create folder
        Path(path).mkdir(parents=True, exist_ok=True)

        # Save models params
        with open(Path(path).joinpath("model_params.json"), "w", encoding="utf8") as f:
            json.dump(saved_params, f)

        # Save state_dict
        torch.save(self.network.state_dict(), Path(path).joinpath("network.pt"))
        shutil.make_archive(path, "zip", path)
        shutil.rmtree(path)
        print(f"Successfully saved model at {path}.zip")
        return f"{path}.zip"

    def load_model(self, filepath):
        """Load TabNet model.

        Parameters
        ----------
            filepath : str
                Path of the model.
        """
        try:
            with zipfile.ZipFile(filepath) as z:
                with z.open("model_params.json") as f:
                    loaded_params = json.load(f)
                with z.open("network.pt") as f:
                    try:
                        saved_state_dict = torch.load(f, map_location=self.device)
                    except io.UnsupportedOperation:
                        # In Python <3.7, the returned file object is not seekable (which at least
                        # some versions of PyTorch require) - so we'll try buffering it in to a
                        # BytesIO instead:
                        saved_state_dict = torch.load(
                            io.BytesIO(f.read()),
                            map_location=self.device,
                        )
        except KeyError:
            raise KeyError("Your zip file is missing at least one component")

        self.__init__(**loaded_params)

        self._set_network()
        self.network.load_state_dict(saved_state_dict)
        self.network.eval()
        return

    def _train_epoch(self, train_loader):
        """
        Trains one epoch of the network in self.network

        Parameters
        ----------
            train_loader: a :class: `torch.utils.data.Dataloader`
                DataLoader with train set
        """
        self.network.train()

        for batch_idx, (X, y) in enumerate(train_loader):
            self._callback_container.on_batch_begin(batch_idx)

            batch_logs = self._train_batch(X, y)

            self._callback_container.on_batch_end(batch_idx, batch_logs)

        epoch_logs = {"lr": self._optimizer.param_groups[-1]["lr"]}
        self.history.epoch_metrics.update(epoch_logs)

        return

    def _train_batch(self, X, y):
        """
        Trains one batch of data

        Parameters
        ----------
            X: torch.tensor
                Train matrix
            y: torch.tensor
                Target matrix

        Returns
        -------
            batch_outs : dict
                Dictionnary with "y": target and "score": prediction scores.
            batch_logs : dict
                Dictionnary with "batch_size" and "loss".
        """
        batch_logs = {"batch_size": X.shape[0]}

        X = X.to(self.device).float()
        y = y.to(self.device).float()

        for param in self.network.parameters():
            param.grad = None

        output, M_loss = self.network(X)

        loss = self.compute_loss(output, y)
        # Add the overall sparsity loss
        loss -= self.lambda_sparse * M_loss

        # Perform backward pass and optimization
        loss.backward()
        if self.clip_value:
            clip_grad_norm_(self.network.parameters(), self.clip_value)
        self._optimizer.step()

        batch_logs["loss"] = loss.cpu().detach().numpy().item()

        return batch_logs

    def _predict_epoch(self, name, loader):
        """
        Predict an epoch and update metrics.

        Parameters
        ----------
            name: str
                Name of the validation set
            loader: torch.utils.data.Dataloader
                    DataLoader with validation set
        """
        # Setting network on evaluation mode (no dropout etc...)
        self.network.eval()

        list_y_true = []
        list_y_score = []

        # Main loop
        for batch_idx, (X, y) in enumerate(loader):
            scores = self._predict_batch(X)
            list_y_true.append(y)
            list_y_score.append(scores)

        y_true, scores = self.stack_batches(list_y_true, list_y_score)

        metrics_logs = self._metric_container_dict[name](y_true, scores)
        self.network.train()
        self.history.epoch_metrics.update(metrics_logs)
        return

    def _predict_batch(self, X):
        """
        Predict one batch of data.

        Parameters
        ----------
            x: torch.tensor
                Owned products

        Returns
        -------
            np.array
                model scores
        """
        X = X.to(self.device).float()

        # compute model output
        scores, _ = self.network(X)

        if isinstance(scores, list):
            scores = [x.cpu().detach().numpy() for x in scores]
        else:
            scores = scores.cpu().detach().numpy()

        return scores

    def _set_network(self):
        """Setup the network and explain matrix."""
        self.network = tab_network.TabNet(
            self.input_dim,
            self.output_dim,
            n_d=self.n_d,
            n_a=self.n_a,
            n_steps=self.n_steps,
            gamma=self.gamma,
            cat_idxs=self.cat_idxs,
            cat_dims=self.cat_dims,
            cat_emb_dim=self.cat_emb_dim,
            n_independent=self.n_independent,
            n_shared=self.n_shared,
            epsilon=self.epsilon,
            virtual_batch_size=self.virtual_batch_size,
            momentum=self.momentum,
            device_name=self.device_name,
            mask_type=self.mask_type,
        ).to(self.device)

        self.reducing_matrix = create_explain_matrix(
            self.network.input_dim,
            self.network.cat_emb_dim,
            self.network.cat_idxs,
            self.network.post_embed_dim,
        )

    def _set_metrics(self, metrics, eval_names):
        """Set attributes relative to the metrics.

        Parameters
        ----------
        metrics : list of str
            List of eval metric names.
        eval_names : list of str
            List of eval set names.

        """
        metrics = metrics or [self._default_metric]

        metrics = check_metrics(metrics)
        # Set metric container for each sets
        self._metric_container_dict = {}
        for name in eval_names:
            self._metric_container_dict.update(
                {name: MetricContainer(metrics, prefix=f"{name}_")}
            )

        self._metrics = []
        self._metrics_names = []
        for _, metric_container in self._metric_container_dict.items():
            self._metrics.extend(metric_container.metrics)
            self._metrics_names.extend(metric_container.names)

        # Early stopping metric is the last eval metric
        self.early_stopping_metric = (
            self._metrics_names[-1] if len(self._metrics_names) > 0 else None
        )

    def _set_callbacks(self, custom_callbacks):
        """Setup the callbacks functions.

        Parameters
        ----------
        callbacks : list of func
            List of callback functions.

        """
        # Setup default callbacks history, early stopping and scheduler
        callbacks = []
        self.history = History(self, verbose=self.verbose)
        callbacks.append(self.history)
        if (self.early_stopping_metric is not None) and (self.patience > 0):
            early_stopping = EarlyStopping(
                early_stopping_metric=self.early_stopping_metric,
                is_maximize=(
                    self._metrics[-1]._maximize if len(self._metrics) > 0 else None
                ),
                patience=self.patience,
            )
            callbacks.append(early_stopping)
        else:
            print("No early stopping will be performed, last training weights will be used.")
        if self.scheduler_fn is not None:
            # Add LR Scheduler call_back
            is_batch_level = self.scheduler_params.pop("is_batch_level", False)
            scheduler = LRSchedulerCallback(
                scheduler_fn=self.scheduler_fn,
                scheduler_params=self.scheduler_params,
                optimizer=self._optimizer,
                early_stopping_metric=self.early_stopping_metric,
                is_batch_level=is_batch_level,
            )
            callbacks.append(scheduler)

        if custom_callbacks:
            callbacks.extend(custom_callbacks)
        self._callback_container = CallbackContainer(callbacks)
        self._callback_container.set_trainer(self)

    def _set_optimizer(self):
        """Setup optimizer."""
        self._optimizer = self.optimizer_fn(
            self.network.parameters(), **self.optimizer_params
        )

    def _construct_loaders(self, X_train, y_train, eval_set):
        """Generate dataloaders for train and eval set.

        Parameters
        ----------
        X_train : np.array
            Train set.
        y_train : np.array
            Train targets.
        eval_set: list of tuple
            List of eval tuple set (X, y).

        Returns
        -------
        train_dataloader : `torch.utils.data.Dataloader`
            Training dataloader.
        valid_dataloaders : list of `torch.utils.data.Dataloader`
            List of validation dataloaders.

        """
        # all weights are not allowed for this type of model
        y_train_mapped = self.prepare_target(y_train)
        for i, (X, y) in enumerate(eval_set):
            y_mapped = self.prepare_target(y)
            eval_set[i] = (X, y_mapped)

        train_dataloader, valid_dataloaders = create_dataloaders(
            X_train,
            y_train_mapped,
            eval_set,
            self.updated_weights,
            self.batch_size,
            self.num_workers,
            self.drop_last,
            self.pin_memory,
        )
        return train_dataloader, valid_dataloaders

    def _compute_feature_importances(self, loader):
        """Compute global feature importance.

        Parameters
        ----------
        loader : `torch.utils.data.Dataloader`
            Pytorch dataloader.

        """
        self.network.eval()
        feature_importances_ = np.zeros((self.network.post_embed_dim))
        for data, targets in loader:
            data = data.to(self.device).float()
            M_explain, masks = self.network.forward_masks(data)
            feature_importances_ += M_explain.sum(dim=0).cpu().detach().numpy()

        feature_importances_ = csc_matrix.dot(
            feature_importances_, self.reducing_matrix
        )
        self.feature_importances_ = feature_importances_ / np.sum(feature_importances_)

    @abstractmethod
    def update_fit_params(self, X_train, y_train, eval_set, weights):
        """
        Set attributes relative to fit function.

        Parameters
        ----------
            X_train: np.ndarray
                Train set
            y_train : np.array
                Train targets
            eval_set: list of tuple
                List of eval tuple set (X, y).
            weights : bool or dictionnary
                0 for no balancing
                1 for automated balancing
        """
        raise NotImplementedError(
            "users must define update_fit_params to use this base class"
        )

    @abstractmethod
    def compute_loss(self, y_score, y_true):
        """
        Compute the loss.

        Parameters
        ----------
            y_score: a :tensor: `torch.Tensor`
                Score matrix
            y_true: a :tensor: `torch.Tensor`
                Target matrix

        Returns
        -------
            float
                Loss value
        """
        raise NotImplementedError(
            "users must define compute_loss to use this base class"
        )

    @abstractmethod
    def prepare_target(self, y):
        """
        Prepare target before training.

        Parameters
        ----------
            y: a :tensor: `torch.Tensor`
                Target matrix.

        Returns
        -------
            `torch.Tensor`
                Converted target matrix.
        """
        raise NotImplementedError(
            "users must define prepare_target to use this base class"
        )