# pylint: disable=too-many-arguments
"""Regression Model"""
__docformat__ = "numpy"

import logging
import warnings
from typing import List, Optional, Tuple, Union

import pandas as pd
from darts import TimeSeries
from darts.models import RegressionModel

from openbb_terminal.decorators import log_start_end
from openbb_terminal.forecast import helpers

logger = logging.getLogger(__name__)


@log_start_end(log=logger)
def get_regression_data(
    data: Union[pd.Series, pd.DataFrame],
    target_column: str = "close",
    n_predict: int = 5,
    past_covariates: Optional[str] = None,
    train_split: float = 0.85,
    forecast_horizon: int = 5,
    output_chunk_length: int = 5,
    lags: Union[int, List[int]] = 14,
    metric: str = "mape",
) -> Tuple[
    List[TimeSeries], List[TimeSeries], List[TimeSeries], float, type[RegressionModel]
]:
    """Perform Regression Forecasting

    Parameters
    ----------
    data: Union[pd.Series, pd.DataFrame]
        Input Data
    n_predict: int
        Days to predict. Defaults to 5.
    target_column: str
        Target column to forecast. Defaults to "close".
    train_split: float
        Train/val split. Defaults to 0.85.
    past_covariates: str
        Multiple secondary columns to factor in when forecasting. Defaults to None.
    forecast_horizon: int
        Forecast horizon when performing historical forecasting. Defaults to 5.
    output_chunk_length: int
        The length of the forecast of the model. Defaults to 1.
    lags: Union[int, List[int]]
        lagged target values to predict the next time step
    metric: str
        Metric to use for evaluation. Defaults to "mape".

    Returns
    -------
    Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], float, type[RegressionModel]]
        Adjusted Data series,
        Historical forecast by best RNN model,
        list of Predictions,
        Mean average precision error,
        Best Regression Model.
    """

    use_scalers = False
    probabilistic = False

    scaler, ticker_series = helpers.get_series(
        data, target_column, is_scaler=use_scalers
    )

    past_covariate_whole, _, _ = helpers.past_covs(
        past_covariates, data, train_split, use_scalers
    )

    lags_past_covariates = lags if past_covariates is not None else None

    reg_model = RegressionModel(
        output_chunk_length=output_chunk_length,
        lags=lags,
        lags_past_covariates=lags_past_covariates,
    )

    with warnings.catch_warnings():
        warnings.simplefilter(action="ignore", category=FutureWarning)
        reg_model.fit(ticker_series, past_covariate_whole)

    # Showing historical backtesting without retraining model (too slow)
    return helpers.get_prediction(
        "Regression",
        probabilistic,
        use_scalers,
        scaler,
        past_covariates,
        reg_model,
        ticker_series,
        past_covariate_whole,
        train_split,
        forecast_horizon,
        n_predict,
        metric,
    )