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 construct_loaders(self, X_train, y_train, X_valid, y_valid, weights, batch_size): """ Returns ------- train_dataloader, valid_dataloader : torch.DataLoader, torch.DataLoader Training and validation dataloaders ------- """ train_dataloader, valid_dataloader = create_dataloaders( X_train, y_train, X_valid, y_valid, 0, batch_size) return train_dataloader, valid_dataloader
def construct_loaders(self, X_train, y_train, X_valid, y_valid, weights, batch_size, num_workers, drop_last): """ Returns ------- train_dataloader, valid_dataloader : torch.DataLoader, torch.DataLoader Training and validation dataloaders ------- """ y_train_mapped = np.vectorize(self.target_mapper.get)(y_train) y_valid_mapped = np.vectorize(self.target_mapper.get)(y_valid) train_dataloader, valid_dataloader = create_dataloaders( X_train, y_train_mapped, X_valid, y_valid_mapped, weights, batch_size, num_workers, drop_last) return train_dataloader, valid_dataloader
def construct_loaders(self, X_train, y_train, X_valid, y_valid, weights, batch_size, num_workers, drop_last): """ Returns ------- train_dataloader, valid_dataloader : torch.DataLoader, torch.DataLoader Training and validation dataloaders ------- """ if isinstance(weights, int): if weights == 1: raise ValueError( "Please provide a list of weights for regression.") if isinstance(weights, dict): raise ValueError( "Please provide a list of weights for regression.") train_dataloader, valid_dataloader = create_dataloaders( X_train, y_train, X_valid, y_valid, weights, batch_size, num_workers, drop_last) return train_dataloader, valid_dataloader