API

def __init__(
    self,
    config: Optional[DictConfig] = None,
    data_config: Optional[Union[DataConfig, str]] = None,
    model_config: Optional[Union[ModelConfig, str]] = None,
    optimizer_config: Optional[Union[OptimizerConfig, str]] = None,
    trainer_config: Optional[Union[TrainerConfig, str]] = None,
    experiment_config: Optional[Union[ExperimentConfig, str]] = None,
    model_callable: Optional[Callable] = None,
) -> None:
    """The core model which orchestrates everything from initializing the datamodule, the model, trainer, etc.

    Args:
        config (Optional[Union[DictConfig, str]], optional): Single OmegaConf DictConfig object or
            the path to the yaml file holding all the config parameters. Defaults to None.

        data_config (Optional[Union[DataConfig, str]], optional): DataConfig object or path to the yaml file. Defaults to None.

        model_config (Optional[Union[ModelConfig, str]], optional): A subclass of ModelConfig or path to the yaml file.
            Determines which model to run from the type of config. Defaults to None.

        optimizer_config (Optional[Union[OptimizerConfig, str]], optional): OptimizerConfig object or path to the yaml file.
            Defaults to None.

        trainer_config (Optional[Union[TrainerConfig, str]], optional): TrainerConfig object or path to the yaml file.
            Defaults to None.

        experiment_config (Optional[Union[ExperimentConfig, str]], optional): ExperimentConfig object or path to the yaml file.
            If Provided configures the experiment tracking. Defaults to None.

        model_callable (Optional[Callable], optional): If provided, will override the model callable that will be loaded from the config.
            Typically used when providing Custom Models
    """
    super().__init__()
    self.exp_manager = ExperimentRunManager()
    if config is None:
        assert (
            (data_config is not None)
            or (model_config is not None)
            or (optimizer_config is not None)
            or (trainer_config is not None)
        ), "If `config` is None, `data_config`, `model_config`, `trainer_config`, and `optimizer_config` cannot be None"
        data_config = self._read_parse_config(data_config, DataConfig)
        model_config = self._read_parse_config(model_config, ModelConfig)
        trainer_config = self._read_parse_config(trainer_config, TrainerConfig)
        optimizer_config = self._read_parse_config(
            optimizer_config, OptimizerConfig
        )
        if experiment_config is None:
            logger.info("Experiment Tracking is turned off")
            self.track_experiment = False
            self.config = OmegaConf.merge(
                OmegaConf.to_container(data_config),
                OmegaConf.to_container(model_config),
                OmegaConf.to_container(trainer_config),
                OmegaConf.to_container(optimizer_config),
            )
        else:
            experiment_config = self._read_parse_config(
                experiment_config, ExperimentConfig
            )
            self.track_experiment = True
            self.config = OmegaConf.merge(
                OmegaConf.to_container(data_config),
                OmegaConf.to_container(model_config),
                OmegaConf.to_container(trainer_config),
                OmegaConf.to_container(experiment_config),
                OmegaConf.to_container(optimizer_config),
            )
    else:
        self.config = config
        if hasattr(config, "log_target") and (config.log_target is not None):
            # experiment_config = OmegaConf.structured(experiment_config)
            self.track_experiment = True
        else:
            logger.info("Experiment Tracking is turned off")
            self.track_experiment = False

    self.name, self.uid = self._get_run_name_uid()
    if self.track_experiment:
        self._setup_experiment_tracking()
    else:
        self.logger = None

    self.exp_manager = ExperimentRunManager()
    if model_callable is None:
        self.model_callable = getattr(
            getattr(models, self.config._module_src), self.config._model_name
        )
        self.custom_model = False
    else:
        self.model_callable = model_callable
        self.custom_model = True
    self._run_validation()

def evaluate(self, test: Optional[pd.DataFrame]) -> Union[dict, list]:
    """Evaluates the dataframe using the loss and metrics already set in config

    Args:
        test (Optional[pd.DataFrame]): The dataframe to be evaluated. If not provided, will try to use the
            test provided during fit. If that was also not provided will return an empty dictionary

    Returns:
        Union[dict, list]: The final test result dictionary.
    """
    if test is not None:
        test_loader = self.datamodule.prepare_inference_dataloader(test)
    elif self.test is not None:
        test_loader = self.datamodule.test_dataloader()
    else:
        return {}
    result = self.trainer.test(
        test_dataloaders=test_loader,
        ckpt_path="best" if self.config.checkpoints else None,
    )
    return result

def find_learning_rate(
    self,
    train: pd.DataFrame,
    validation: Optional[pd.DataFrame] = None,
    test: Optional[pd.DataFrame] = None,
    loss: Optional[torch.nn.Module] = None,
    metrics: Optional[List[Callable]] = None,
    optimizer: Optional[torch.optim.Optimizer] = None,
    optimizer_params: Dict = {},
    min_lr: float = 1e-8,
    max_lr: float = 1,
    num_training: int = 100,
    mode: str = "exponential",
    early_stop_threshold: float = 4.0,
    plot=True,
) -> None:
    """Enables the user to do a range test of good initial learning rates, to reduce the amount of guesswork in picking a good starting learning rate.

    Args:
        train (pd.DataFrame): Training Dataframe

        valid (Optional[pd.DataFrame], optional): If provided, will use this dataframe as the validation while training.
            Used in Early Stopping and Logging. If left empty, will use 20% of Train data as validation. Defaults to None.

        test (Optional[pd.DataFrame], optional): If provided, will use as the hold-out data,
            which you'll be able to check performance after the model is trained. Defaults to None.

        loss (Optional[torch.nn.Module], optional): Custom Loss functions which are not in standard pytorch library

        metrics (Optional[List[Callable]], optional): Custom metric functions(Callable) which has the signature metric_fn(y_hat, y)

        optimizer (Optional[torch.optim.Optimizer], optional): Custom optimizers which are a drop in replacements for standard PyToch optimizers.
            This should be the Class and not the initialized object

        optimizer_params (Optional[Dict], optional): The parmeters to initialize the custom optimizer.

        min_lr (Optional[float], optional): minimum learning rate to investigate

        max_lr (Optional[float], optional): maximum learning rate to investigate

        num_training (Optional[int], optional): number of learning rates to test

        mode (Optional[str], optional): search strategy, either 'linear' or 'exponential'. If set to
            'linear' the learning rate will be searched by linearly increasing
            after each batch. If set to 'exponential', will increase learning
            rate exponentially.

        early_stop_threshold(Optional[float], optional): threshold for stopping the search. If the
            loss at any point is larger than early_stop_threshold*best_loss
            then the search is stopped. To disable, set to None.

        plot(bool, optional): If true, will plot using matplotlib
    """

    train_loader, val_loader = self._pre_fit(
        train,
        validation,
        test,
        loss,
        metrics,
        optimizer,
        optimizer_params,
        target_transform=None,
        max_epochs=None,
        min_epochs=None,
        reset=True,
    )
    lr_finder = self.trainer.tuner.lr_find(
        self.model,
        train_loader,
        val_loader,
        min_lr,
        max_lr,
        num_training,
        mode,
        early_stop_threshold,
    )
    if plot:
        fig = lr_finder.plot(suggest=True)
        fig.show()
    new_lr = lr_finder.suggestion()
    # cancelling the model and trainer that was loaded
    self.model = None
    self.trainer = None
    self.datamodule = None
    return new_lr, pd.DataFrame(lr_finder.results)

def fit(
    self,
    train: pd.DataFrame,
    validation: Optional[pd.DataFrame] = None,
    test: Optional[pd.DataFrame] = None,
    loss: Optional[torch.nn.Module] = None,
    metrics: Optional[List[Callable]] = None,
    optimizer: Optional[torch.optim.Optimizer] = None,
    optimizer_params: Dict = {},
    train_sampler: Optional[torch.utils.data.Sampler] = None,
    target_transform: Optional[Union[TransformerMixin, Tuple]] = None,
    max_epochs: Optional[int] = None,
    min_epochs: Optional[int] = None,
    reset: bool = False,
    seed: Optional[int] = None,
) -> None:
    """The fit method which takes in the data and triggers the training

    Args:
        train (pd.DataFrame): Training Dataframe

        valid (Optional[pd.DataFrame], optional): If provided, will use this dataframe as the validation while training.
            Used in Early Stopping and Logging. If left empty, will use 20% of Train data as validation. Defaults to None.

        test (Optional[pd.DataFrame], optional): If provided, will use as the hold-out data,
            which you'll be able to check performance after the model is trained. Defaults to None.

        loss (Optional[torch.nn.Module], optional): Custom Loss functions which are not in standard pytorch library

        metrics (Optional[List[Callable]], optional): Custom metric functions(Callable) which has the
            signature metric_fn(y_hat, y) and works on torch tensor inputs

        optimizer (Optional[torch.optim.Optimizer], optional): Custom optimizers which are a drop in replacements for standard PyToch optimizers.
            This should be the Class and not the initialized object

        optimizer_params (Optional[Dict], optional): The parmeters to initialize the custom optimizer.

        train_sampler (Optional[torch.utils.data.Sampler], optional): Custom PyTorch batch samplers which will be passed to the DataLoaders. Useful for dealing with imbalanced data and other custom batching strategies

        target_transform (Optional[Union[TransformerMixin, Tuple(Callable)]], optional): If provided, applies the transform to the target before modelling
            and inverse the transform during prediction. The parameter can either be a sklearn Transformer which has an inverse_transform method, or
            a tuple of callables (transform_func, inverse_transform_func)

        max_epochs (Optional[int]): Overwrite maximum number of epochs to be run

        min_epochs (Optional[int]): Overwrite minimum number of epochs to be run

        reset: (bool): Flag to reset the model and train again from scratch

        seed: (int): If you have to override the default seed set as part of of ModelConfig
    """
    seed_everything(seed if seed is not None else self.config.seed)
    train_loader, val_loader = self._pre_fit(
        train,
        validation,
        test,
        loss,
        metrics,
        optimizer,
        optimizer_params,
        train_sampler,
        target_transform,
        max_epochs,
        min_epochs,
        reset,
    )
    self.model.train()
    if self.config.auto_lr_find and (not self.config.fast_dev_run):
        self.trainer.tune(self.model, train_loader, val_loader)
        # Parameters in models needs to be initialized again after LR find
        self.model.data_aware_initialization(self.datamodule)
    self.model.train()
    self.trainer.fit(self.model, train_loader, val_loader)
    logger.info("Training the model completed...")
    if self.config.load_best:
        self.load_best_model()

def load_best_model(self):
    """Loads the best model after training is done"""
    if self.trainer.checkpoint_callback is not None:
        logger.info("Loading the best model...")
        ckpt_path = self.trainer.checkpoint_callback.best_model_path
        if ckpt_path != "":
            logger.debug(f"Model Checkpoint: {ckpt_path}")
            ckpt = pl_load(ckpt_path, map_location=lambda storage, loc: storage)
            self.model.load_state_dict(ckpt["state_dict"])
        else:
            logger.info(
                "No best model available to load. Did you run it more than 1 epoch?..."
            )
    else:
        logger.info(
            "No best model available to load. Did you run it more than 1 epoch?..."
        )

@classmethod
def load_from_checkpoint(cls, dir: str, map_location = None, strict=True):
    """Loads a saved model from the directory

    Args:
        dir (str): The directory where the model wa saved, along with the checkpoints
        map_location (Union[Dict[str, str], str, device, int, Callable, None]) – If your checkpoint 
            saved a GPU model and you now load on CPUs or a different number of GPUs, use this to map 
            to the new setup. The behaviour is the same as in torch.load()
        strict (bool) – Whether to strictly enforce that the keys in checkpoint_path match the keys 
            returned by this module’s state dict. Default: True.

    Returns:
        TabularModel: The saved TabularModel
    """
    config = OmegaConf.load(os.path.join(dir, "config.yml"))
    datamodule = joblib.load(os.path.join(dir, "datamodule.sav"))
    if (
        hasattr(config, "log_target")
        and (config.log_target is not None)
        and os.path.exists(os.path.join(dir, "exp_logger.sav"))
    ):
        logger = joblib.load(os.path.join(dir, "exp_logger.sav"))
    else:
        logger = None
    if os.path.exists(os.path.join(dir, "callbacks.sav")):
        callbacks = joblib.load(os.path.join(dir, "callbacks.sav"))
    else:
        callbacks = []
    if os.path.exists(os.path.join(dir, "custom_model_callable.sav")):
        model_callable = joblib.load(os.path.join(dir, "custom_model_callable.sav"))
        custom_model = True
    else:
        model_callable = getattr(
            getattr(models, config._module_src), config._model_name
        )
        custom_model = False
    custom_params = joblib.load(os.path.join(dir, "custom_params.sav"))
    model_args = {}
    if custom_params.get("custom_loss") is not None:
        model_args["loss"] = "MSELoss"
    if custom_params.get("custom_metrics") is not None:
        model_args["metrics"] = ["mean_squared_error"]
        model_args["metric_params"] = [{}]
    if custom_params.get("custom_optimizer") is not None:
        model_args["optimizer"] = "Adam"
    if custom_params.get("custom_optimizer_params") is not None:
        model_args["optimizer_params"] = {}

    # Initializing with default metrics, losses, and optimizers. Will revert once initialized
    model = model_callable.load_from_checkpoint(
        checkpoint_path=os.path.join(dir, "model.ckpt"),map_location=map_location, strict=strict, **model_args
    )
    # else:
    #     # Initializing with default values
    #     model = model_callable.load_from_checkpoint(
    #         checkpoint_path=os.path.join(dir, "model.ckpt"),
    #     )
    # Updating config with custom parameters for experiment tracking
    if custom_params.get("custom_loss") is not None:
        model.custom_loss = custom_params["custom_loss"]
    if custom_params.get("custom_metrics") is not None:
        model.custom_metrics = custom_params["custom_metrics"]
    if custom_params.get("custom_optimizer") is not None:
        model.custom_optimizer = custom_params["custom_optimizer"]
    if custom_params.get("custom_optimizer_params") is not None:
        model.custom_optimizer_params = custom_params["custom_optimizer_params"]
    model._setup_loss()
    model._setup_metrics()
    tabular_model = cls(config=config, model_callable=model_callable)
    tabular_model.model = model
    tabular_model.custom_model = custom_model
    tabular_model.datamodule = datamodule
    tabular_model.callbacks = callbacks
    tabular_model._prepare_trainer()
    tabular_model.trainer.model = model
    tabular_model.logger = logger
    return tabular_model

def predict(
    self,
    test: pd.DataFrame,
    quantiles: Optional[List] = [0.25, 0.5, 0.75],
    n_samples: Optional[int] = 100,
    ret_logits=False,
) -> pd.DataFrame:
    """Uses the trained model to predict on new data and return as a dataframe

    Args:
        test (pd.DataFrame): The new dataframe with the features defined during training
        quantiles (Optional[List]): For probabilistic models like Mixture Density Networks, this specifies
            the different quantiles to be extracted apart from the `central_tendency` and added to the dataframe.
            For other models it is ignored. Defaults to [0.25, 0.5, 0.75]
        n_samples (Optional[int]): Number of samples to draw from the posterior to estimate the quantiles.
            Ignored for non-probabilistic models. Defaults to 100
        ret_logits (bool): Flag to return raw model outputs/logits except the backbone features along
            with the dataframe. Defaults to False

    Returns:
        pd.DataFrame: Returns a dataframe with predictions and features.
            If classification, it returns probabilities and final prediction
    """
    assert all(
        [q <= 1 and q >= 0 for q in quantiles]
    ), "Quantiles should be a decimal between 0 and 1"
    self.model.eval()
    inference_dataloader = self.datamodule.prepare_inference_dataloader(test)
    point_predictions = []
    quantile_predictions = []
    logits_predictions = defaultdict(list)
    is_probabilistic = (
        hasattr(self.model.hparams, "_probabilistic")
        and self.model.hparams._probabilistic
    )
    for batch in tqdm(inference_dataloader, desc="Generating Predictions..."):
        for k, v in batch.items():
            if isinstance(v, list) and (len(v) == 0):
                # Skipping empty list
                continue
            batch[k] = v.to(self.model.device)
        if is_probabilistic:
            samples, ret_value = self.model.sample(
                batch, n_samples, ret_model_output=True
            )
            y_hat = torch.mean(samples, dim=-1)
            quantile_preds = []
            for q in quantiles:
                quantile_preds.append(
                    torch.quantile(samples, q=q, dim=-1).unsqueeze(1)
                )
        else:
            y_hat, ret_value = self.model.predict(batch, ret_model_output=True)
        if ret_logits:
            for k, v in ret_value.items():
                # if k == "backbone_features":
                #     continue
                logits_predictions[k].append(v.detach().cpu())
        point_predictions.append(y_hat.detach().cpu())
        if is_probabilistic:
            quantile_predictions.append(
                torch.cat(quantile_preds, dim=-1).detach().cpu()
            )
    point_predictions = torch.cat(point_predictions, dim=0)
    if point_predictions.ndim == 1:
        point_predictions = point_predictions.unsqueeze(-1)
    if is_probabilistic:
        quantile_predictions = torch.cat(quantile_predictions, dim=0).unsqueeze(-1)
        if quantile_predictions.ndim == 2:
            quantile_predictions = quantile_predictions.unsqueeze(-1)
    pred_df = test.copy()
    if self.config.task == "regression":
        point_predictions = point_predictions.numpy()
        # Probabilistic Models are only implemented for Regression
        if is_probabilistic:
            quantile_predictions = quantile_predictions.numpy()
        for i, target_col in enumerate(self.config.target):
            if self.datamodule.do_target_transform:
                if self.config.target[i] in pred_df.columns:
                    pred_df[
                        self.config.target[i]
                    ] = self.datamodule.target_transforms[i].inverse_transform(
                        pred_df[self.config.target[i]].values.reshape(-1, 1)
                    )
                pred_df[
                    f"{target_col}_prediction"
                ] = self.datamodule.target_transforms[i].inverse_transform(
                    point_predictions[:, i].reshape(-1, 1)
                )
                if is_probabilistic:
                    for j, q in enumerate(quantiles):
                        pred_df[
                            f"{target_col}_q{int(q*100)}"
                        ] = self.datamodule.target_transforms[i].inverse_transform(
                            quantile_predictions[:, j, i].reshape(-1, 1)
                        )
            else:
                pred_df[f"{target_col}_prediction"] = point_predictions[:, i]
                if is_probabilistic:
                    for j, q in enumerate(quantiles):
                        pred_df[
                            f"{target_col}_q{int(q*100)}"
                        ] = quantile_predictions[:, j, i].reshape(-1, 1)

    elif self.config.task == "classification":
        point_predictions = nn.Softmax(dim=-1)(point_predictions).numpy()
        for i, class_ in enumerate(self.datamodule.label_encoder.classes_):
            pred_df[f"{class_}_probability"] = point_predictions[:, i]
        pred_df[f"prediction"] = self.datamodule.label_encoder.inverse_transform(
            np.argmax(point_predictions, axis=1)
        )
    if ret_logits:
        for k, v in logits_predictions.items():
            v = torch.cat(v, dim=0).numpy()
            if v.ndim == 1:
                v = v.reshape(-1, 1)
            for i in range(v.shape[-1]):
                if v.shape[-1] > 1:
                    pred_df[f"{k}_{i}"] = v[:, i]
                else:
                    pred_df[f"{k}"] = v[:, i]
    return pred_df

def save_model(self, dir: str):
    """Saves the model and checkpoints in the specified directory

    Args:
        dir (str): The path to the directory to save the model
    """
    if os.path.exists(dir) and (os.listdir(dir)):
        logger.warning("Directory is not empty. Overwriting the contents.")
        for f in os.listdir(dir):
            os.remove(os.path.join(dir, f))
    os.makedirs(dir, exist_ok=True)
    with open(os.path.join(dir, "config.yml"), "w") as fp:
        OmegaConf.save(self.config, fp, resolve=True)
    joblib.dump(self.datamodule, os.path.join(dir, "datamodule.sav"))
    if hasattr(self.config, "log_target") and self.config.log_target is not None:
        joblib.dump(self.logger, os.path.join(dir, "exp_logger.sav"))
    if hasattr(self, "callbacks"):
        joblib.dump(self.callbacks, os.path.join(dir, "callbacks.sav"))
    self.trainer.save_checkpoint(os.path.join(dir, "model.ckpt"))
    custom_params = {}
    custom_params["custom_loss"] = self.model.custom_loss
    custom_params["custom_metrics"] = self.model.custom_metrics
    custom_params["custom_optimizer"] = self.model.custom_optimizer
    custom_params["custom_optimizer_params"] = self.model.custom_optimizer_params
    joblib.dump(custom_params, os.path.join(dir, "custom_params.sav"))
    if self.custom_model:
        joblib.dump(
            self.model_callable, os.path.join(dir, "custom_model_callable.sav")
        )

def __init__(
    self,
    train: pd.DataFrame,
    config: DictConfig,
    validation: pd.DataFrame = None,
    test: pd.DataFrame = None,
    target_transform: Optional[Union[TransformerMixin, Tuple]] = None,
    train_sampler: Optional[torch.utils.data.Sampler] = None,
):
    """The Pytorch Lightning Datamodule for Tabular Data

    Args:
        train (pd.DataFrame): The Training Dataframe
        config (DictConfig): Merged configuration object from ModelConfig, DataConfig,
        TrainerConfig, OptimizerConfig & ExperimentConfig
        validation (pd.DataFrame, optional): Validation Dataframe.
        If left empty, we use the validation split from DataConfig to split a random sample as validation.
        Defaults to None.
        test (pd.DataFrame, optional): Holdout DataFrame to check final performance on.
        Defaults to None.
        target_transform (Optional[Union[TransformerMixin, Tuple(Callable)]], optional): If provided, applies the transform to the target before modelling
        and inverse the transform during prediction. The parameter can either be a sklearn Transformer which has an inverse_transform method, or
        a tuple of callables (transform_func, inverse_transform_func)
    """
    super().__init__()
    self.train = train.copy()
    self.validation = validation
    if target_transform is not None:
        if isinstance(target_transform, Iterable):
            target_transform = FunctionTransformer(
                func=target_transform[0], inverse_func=target_transform[1]
            )
        self.do_target_transform = True
    else:
        self.do_target_transform = False
    self.target_transform_template = target_transform
    self.test = test if test is None else test.copy()
    self.target = config.target
    self.batch_size = config.batch_size
    self.train_sampler = train_sampler
    self.config = config
    self._fitted = False

@classmethod
def add_datepart(
    cls,
    df: pd.DataFrame,
    field_name: str,
    frequency: str,
    prefix: str = None,
    drop: bool = True,
):
    "Helper function that adds columns relevant to a date in the column `field_name` of `df`."
    field = df[field_name]
    prefix = (
        re.sub("[Dd]ate$", "", field_name) if prefix is None else prefix
    ) + "_"
    attr = cls.time_features_from_frequency_str(frequency)
    added_features = []
    for n in attr:
        if n == "Week":
            continue
        df[prefix + n] = getattr(field.dt, n.lower())
        added_features.append(prefix + n)
    # Pandas removed `dt.week` in v1.1.10
    if "Week" in attr:
        week = (
            field.dt.isocalendar().week
            if hasattr(field.dt, "isocalendar")
            else field.dt.week
        )
        df.insert(3, prefix + "Week", week)
        added_features.append(prefix + "Week")
    #TODO Not adding Elapsed by default. Need to route it through config
    # mask = ~field.isna()
    # df[prefix + "Elapsed"] = np.where(
    #     mask, field.values.astype(np.int64) // 10 ** 9, None
    # )
    # added_features.append(prefix + "Elapsed")
    if drop:
        df.drop(field_name, axis=1, inplace=True)

    # Removing features woth zero variations
    # for col in added_features:
    #     if len(df[col].unique()) == 1:
    #         df.drop(columns=col, inplace=True)
    #         added_features.remove(col)
    return df, added_features

def do_leave_one_out_encoder(self) -> bool:
    """Checks the special condition for NODE where we use a LeaveOneOutEncoder to encode categorical columns

    Returns:
        bool
    """
    return (self.config._model_name == "NODEModel") and (
        not self.config.embed_categorical
    )

@classmethod
def make_date(cls, df: pd.DataFrame, date_field: str):
    "Make sure `df[date_field]` is of the right date type."
    field_dtype = df[date_field].dtype
    if isinstance(field_dtype, pd.core.dtypes.dtypes.DatetimeTZDtype):
        field_dtype = np.datetime64
    if not np.issubdtype(field_dtype, np.datetime64):
        df[date_field] = pd.to_datetime(df[date_field], infer_datetime_format=True)
    return df

def prepare_inference_dataloader(self, df: pd.DataFrame) -> DataLoader:
    """Function that prepares and loads the new data.

    Args:
        df (pd.DataFrame): Dataframe with the features and target

    Returns:
        DataLoader: The dataloader for the passed in dataframe
    """
    df = df.copy()
    if len(set(self.target) - set(df.columns)) > 0:
        if self.config.task == "classification":
            df.loc[:, self.target] = np.array(
                [self.label_encoder.classes_[0]] * len(df)
            )
        else:
            df.loc[:, self.target] = np.zeros((len(df), len(self.target)))
    df, _ = self.preprocess_data(df, stage="inference")

    dataset = TabularDataset(
        task=self.config.task,
        data=df,
        categorical_cols=self.config.categorical_cols,
        continuous_cols=self.config.continuous_cols,
        embed_categorical=(not self.do_leave_one_out_encoder()),
        target=self.target
        if all([col in df.columns for col in self.target])
        else None,
    )
    return DataLoader(
        dataset,
        self.batch_size,
        shuffle=False,
        num_workers=self.config.num_workers,
    )

def preprocess_data(
    self, data: pd.DataFrame, stage: str = "inference"
) -> Tuple[pd.DataFrame, list]:
    """The preprocessing, like Categorical Encoding, Normalization, etc. which any dataframe should undergo before feeding into the dataloder

    Args:
        data (pd.DataFrame): A dataframe with the features and target
        stage (str, optional): Internal parameter. Used to distinguisj between fit and inference. Defaults to "inference".

    Returns:
        tuple[pd.DataFrame, list]: Returns the processed dataframe and the added features(list) as a tuple
    """
    logger.info(f"Preprocessing data: Stage: {stage}...")
    added_features = None
    if self.config.encode_date_columns:
        for field_name, freq in self.config.date_columns:
            data = self.make_date(data, field_name)
            data, added_features = self.add_datepart(
                data, field_name, frequency=freq, prefix=None, drop=True
            )
    # The only features that are added are the date features extracted
    # from the date which are categorical in nature
    if (added_features is not None) and (stage == "fit"):
        logger.debug(
            f"Added {added_features} features after encoding the date_columns"
        )
        self.config.categorical_cols += added_features
        self.config.categorical_dim = (
            len(self.config.categorical_cols)
            if self.config.categorical_cols is not None
            else 0
        )
    # Encoding Categorical Columns
    if len(self.config.categorical_cols) > 0:
        if stage == "fit":
            if self.do_leave_one_out_encoder():
                logger.debug("Encoding Categorical Columns using LeavOneOutEncoder")
                self.categorical_encoder = ce.LeaveOneOutEncoder(
                    cols=self.config.categorical_cols, random_state=42
                )
                # Multi-Target Regression uses the first target to encode the categorical columns
                if len(self.config.target) > 1:
                    logger.warning(
                        f"Multi-Target Regression: using the first target({self.config.target[0]}) to encode the categorical columns"
                    )
                data = self.categorical_encoder.fit_transform(
                    data, data[self.config.target[0]]
                )
            else:
                logger.debug("Encoding Categorical Columns using OrdinalEncoder")
                self.categorical_encoder = OrdinalEncoder(
                    cols=self.config.categorical_cols
                )
                data = self.categorical_encoder.fit_transform(data)
        else:
            data = self.categorical_encoder.transform(data)

    # Transforming Continuous Columns
    if (self.config.continuous_feature_transform is not None) and (
        len(self.config.continuous_cols) > 0
    ):
        if stage == "fit":
            transform = self.CONTINUOUS_TRANSFORMS[
                self.config.continuous_feature_transform
            ]
            self.continuous_transform = transform["callable"](**transform["params"])
            # TODO implement quantile noise
            data.loc[
                :, self.config.continuous_cols
            ] = self.continuous_transform.fit_transform(
                data.loc[:, self.config.continuous_cols]
            )
        else:
            data.loc[
                :, self.config.continuous_cols
            ] = self.continuous_transform.transform(
                data.loc[:, self.config.continuous_cols]
            )

    # Normalizing Continuous Columns
    if (self.config.normalize_continuous_features) and (
        len(self.config.continuous_cols) > 0
    ):
        if stage == "fit":
            self.scaler = StandardScaler()
            data.loc[:, self.config.continuous_cols] = self.scaler.fit_transform(
                data.loc[:, self.config.continuous_cols]
            )
        else:
            data.loc[:, self.config.continuous_cols] = self.scaler.transform(
                data.loc[:, self.config.continuous_cols]
            )

    # Converting target labels to a 0 indexed label
    if self.config.task == "classification":
        if stage == "fit":
            self.label_encoder = LabelEncoder()
            data[self.config.target[0]] = self.label_encoder.fit_transform(
                data[self.config.target[0]]
            )
        else:
            if self.config.target[0] in data.columns:
                data[self.config.target[0]] = self.label_encoder.transform(
                    data[self.config.target[0]]
                )
    # Target Transforms
    if all([col in data.columns for col in self.config.target]):
        if self.do_target_transform:
            if stage == "fit":
                target_transforms = []
                for col in self.config.target:
                    _target_transform = copy.deepcopy(self.target_transform_template)
                    data[col] = _target_transform.fit_transform(
                        data[col].values.reshape(-1, 1)
                    )
                    target_transforms.append(_target_transform)
                self.target_transforms = target_transforms
            else:
                for col, _target_transform in zip(self.config.target, self.target_transforms):
                    data[col] = _target_transform.transform(
                        data[col].values.reshape(-1, 1)
                    )
    return data, added_features

def setup(self, stage: Optional[str] = None) -> None:
    """Data Operations you want to perform on all GPUs, like train-test split, transformations, etc.
    This is called before accessing the dataloaders

    Args:
        stage (Optional[str], optional): Internal parameter to distinguish between fit and inference. Defaults to None.
    """
    if stage == "fit" or stage is None:
        if self.validation is None:
            logger.debug(
                f"No validation data provided. Using {self.config.validation_split*100}% of train data as validation"
            )
            val_idx = self.train.sample(
                int(self.config.validation_split * len(self.train)), random_state=42
            ).index
            self.validation = self.train[self.train.index.isin(val_idx)]
            self.train = self.train[~self.train.index.isin(val_idx)]
        else:
            self.validation = self.validation.copy()
        # Preprocessing Train, Validation
        self.train, _ = self.preprocess_data(self.train, stage="fit")
        self.validation, _ = self.preprocess_data(
            self.validation, stage="inference"
        )
        if self.test is not None:
            self.test, _ = self.preprocess_data(self.test, stage="inference")
        # Calculating the categorical dims and embedding dims etc and updating the config
        self.update_config()
        self._fitted = True

def test_dataloader(self) -> DataLoader:
    """ Function that loads the validation set. """
    if self.test is not None:
        dataset = TabularDataset(
            task=self.config.task,
            data=self.test,
            categorical_cols=self.config.categorical_cols,
            continuous_cols=self.config.continuous_cols,
            embed_categorical=(not self.do_leave_one_out_encoder()),
            target=self.target,
        )
        return DataLoader(
            dataset,
            self.batch_size,
            shuffle=False,
            num_workers=self.config.num_workers,
        )

@classmethod
def time_features_from_frequency_str(cls, freq_str: str) -> List[str]:
    """
    Returns a list of time features that will be appropriate for the given frequency string.

    Parameters
    ----------

    freq_str
        Frequency string of the form [multiple][granularity] such as "12H", "5min", "1D" etc.

    """

    features_by_offsets = {
        offsets.YearBegin: [],
        offsets.YearEnd: [],
        offsets.MonthBegin: [
            "Month",
            "Quarter",
            "Is_quarter_end",
            "Is_quarter_start",
            "Is_year_end",
            "Is_year_start",
        ],
        offsets.MonthEnd: [
            "Month",
            "Quarter",
            "Is_quarter_end",
            "Is_quarter_start",
            "Is_year_end",
            "Is_year_start",
        ],
        offsets.Week: [
            "Month",
            "Quarter",
            "Is_quarter_end",
            "Is_quarter_start",
            "Is_year_end",
            "Is_year_start",
            "Is_month_start",
            "Week",
        ],
        offsets.Day: [
            "Month",
            "Quarter",
            "Is_quarter_end",
            "Is_quarter_start",
            "Is_year_end",
            "Is_year_start",
            "Is_month_start",
            "Week" "Day",
            "Dayofweek",
            "Dayofyear",
        ],
        offsets.BusinessDay: [
            "Month",
            "Quarter",
            "Is_quarter_end",
            "Is_quarter_start",
            "Is_year_end",
            "Is_year_start",
            "Is_month_start",
            "Week" "Day",
            "Dayofweek",
            "Dayofyear",
        ],
        offsets.Hour: [
            "Month",
            "Quarter",
            "Is_quarter_end",
            "Is_quarter_start",
            "Is_year_end",
            "Is_year_start",
            "Is_month_start",
            "Week" "Day",
            "Dayofweek",
            "Dayofyear",
            "Hour",
        ],
        offsets.Minute: [
            "Month",
            "Quarter",
            "Is_quarter_end",
            "Is_quarter_start",
            "Is_year_end",
            "Is_year_start",
            "Is_month_start",
            "Week" "Day",
            "Dayofweek",
            "Dayofyear",
            "Hour",
            "Minute",
        ],
    }

    offset = to_offset(freq_str)

    for offset_type, feature in features_by_offsets.items():
        if isinstance(offset, offset_type):
            return feature

    supported_freq_msg = f"""
    Unsupported frequency {freq_str}

    The following frequencies are supported:

        Y, YS   - yearly
            alias: A
        M, MS   - monthly
        W   - weekly
        D   - daily
        B   - business days
        H   - hourly
        T   - minutely
            alias: min
    """
    raise RuntimeError(supported_freq_msg)

def train_dataloader(self, batch_size: Optional[int] = None) -> DataLoader:
    """ Function that loads the train set. """
    dataset = TabularDataset(
        task=self.config.task,
        data=self.train,
        categorical_cols=self.config.categorical_cols,
        continuous_cols=self.config.continuous_cols,
        embed_categorical=(not self.do_leave_one_out_encoder()),
        target=self.target,
    )
    return DataLoader(
        dataset,
        batch_size if batch_size is not None else self.batch_size,
        shuffle=True if self.train_sampler is None else False,
        num_workers=self.config.num_workers,
        sampler=self.train_sampler,
    )

def update_config(self) -> None:
    """Calculates and updates a few key information to the config object

    Raises:
        NotImplementedError: [description]
    """
    if self.config.task == "regression":
        self.config.output_dim = len(self.config.target)
    elif self.config.task == "classification":
        self.config.output_dim = len(self.train[self.config.target[0]].unique())
    if not self.do_leave_one_out_encoder():
        self.config.categorical_cardinality = [
            int(self.train[col].fillna("NA").nunique()) + 1
            for col in self.config.categorical_cols
        ]
        if hasattr(self.config, "embedding_dims") and self.config.embedding_dims is None:
            self.config.embedding_dims = [
                (x, min(50, (x + 1) // 2))
                for x in self.config.categorical_cardinality
            ]

def val_dataloader(self) -> DataLoader:
    """ Function that loads the validation set. """
    dataset = TabularDataset(
        task=self.config.task,
        data=self.validation,
        categorical_cols=self.config.categorical_cols,
        continuous_cols=self.config.continuous_cols,
        embed_categorical=(not self.do_leave_one_out_encoder()),
        target=self.target,
    )
    return DataLoader(
        dataset, self.batch_size, shuffle=False, num_workers=self.config.num_workers
    )

def forward(self, x: Dict):
    x = self.unpack_input(x)
    x = self.backbone(x)
    y_hat = self.output_layer(x)
    if (self.hparams.task == "regression") and (
        self.hparams.target_range is not None
    ):
        for i in range(self.hparams.output_dim):
            y_min, y_max = self.hparams.target_range[i]
            y_hat[:, i] = y_min + nn.Sigmoid()(y_hat[:, i]) * (y_max - y_min)
    return {"logits": y_hat, "backbone_features": x}

def data_aware_initialization(self, datamodule):
    """Performs data-aware initialization for NODE"""
    logger.info("Data Aware Initialization....")
    # Need a big batch to initialize properly
    alt_loader = datamodule.train_dataloader(batch_size=2000)
    batch = next(iter(alt_loader))
    for k, v in batch.items():
        if isinstance(v, list) and (len(v) == 0):
            # Skipping empty list
            continue
        # batch[k] = v.to("cpu" if self.config.gpu == 0 else "cuda")
        batch[k] = v.to(self.device)

    # single forward pass to initialize the ODST
    with torch.no_grad():
        self(batch)

def forward(self, x: Dict):
    x = self.unpack_input(x)
    if self.hparams.embed_categorical:
        if self.hparams.embedding_dropout != 0 and self.embedding_cat_dim != 0:
            x = self.embedding_dropout(x)
    x = self.backbone(x)
    y_hat = self.output_response(x)
    if (self.hparams.task == "regression") and (
        self.hparams.target_range is not None
    ):
        for i in range(self.hparams.output_dim):
            y_min, y_max = self.hparams.target_range[i]
            y_hat[:, i] = y_min + nn.Sigmoid()(y_hat[:, i]) * (y_max - y_min)
    return {"logits": y_hat, "backbone_features": x}

def forward(self, x: Dict):
    # unpacking into a tuple
    x = self.unpack_input(x)
    # Returns output
    x = self.backbone(x)
    if (self.hparams.task == "regression") and (
        self.hparams.target_range is not None
    ):
        for i in range(self.hparams.output_dim):
            y_min, y_max = self.hparams.target_range[i]
            x[:, i] = y_min + nn.Sigmoid()(x[:, i]) * (y_max - y_min)
    return {"logits": x}  # No Easy way to access the raw features in TabNet