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ee34a1f278
* Fixed isort * Fixed issues * FIxed linters --------- Co-authored-by: James Maslek <jmaslek11@gmail.com>
103 lines
2.8 KiB
Python
103 lines
2.8 KiB
Python
# pylint: disable=too-many-arguments
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"""Regression Model"""
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__docformat__ = "numpy"
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import logging
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import warnings
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from typing import List, Tuple, Union
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import pandas as pd
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from darts import TimeSeries
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from darts.models import RegressionModel
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from openbb_terminal.decorators import log_start_end
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from openbb_terminal.forecast import helpers
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logger = logging.getLogger(__name__)
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@log_start_end(log=logger)
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def get_regression_data(
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data: Union[pd.Series, pd.DataFrame],
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target_column: str = "close",
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n_predict: int = 5,
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past_covariates: str = None,
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train_split: float = 0.85,
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forecast_horizon: int = 5,
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output_chunk_length: int = 5,
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lags: Union[int, List[int]] = 14,
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) -> Tuple[
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List[TimeSeries], List[TimeSeries], List[TimeSeries], float, type[RegressionModel]
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]:
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"""Perform Regression Forecasting
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Parameters
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----------
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data: Union[pd.Series, pd.DataFrame]
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Input Data
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n_predict: int
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Days to predict. Defaults to 5.
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target_column: str
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Target column to forecast. Defaults to "close".
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train_split: float
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Train/val split. Defaults to 0.85.
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past_covariates: str
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Multiple secondary columns to factor in when forecasting. Defaults to None.
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forecast_horizon: int
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Forecast horizon when performing historical forecasting. Defaults to 5.
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output_chunk_length: int
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The length of the forecast of the model. Defaults to 1.
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lags: Union[int, List[int]]
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lagged target values to predict the next time step
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Returns
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-------
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Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], float, type[RegressionModel]]
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Adjusted Data series,
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Historical forecast by best RNN model,
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list of Predictions,
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Mean average precision error,
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Best Regression Model.
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"""
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use_scalers = False
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probabilistic = False
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scaler, ticker_series = helpers.get_series(
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data, target_column, is_scaler=use_scalers
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)
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past_covariate_whole, _, _ = helpers.past_covs(
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past_covariates, data, train_split, use_scalers
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)
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if past_covariates is not None:
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lags_past_covariates = lags
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else:
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lags_past_covariates = None
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reg_model = RegressionModel(
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output_chunk_length=output_chunk_length,
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lags=lags,
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lags_past_covariates=lags_past_covariates,
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)
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with warnings.catch_warnings():
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warnings.simplefilter(action="ignore", category=FutureWarning)
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reg_model.fit(ticker_series, past_covariate_whole)
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# Showing historical backtesting without retraining model (too slow)
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return helpers.get_prediction(
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"Regression",
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probabilistic,
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use_scalers,
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scaler,
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past_covariates,
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reg_model,
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ticker_series,
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past_covariate_whole,
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train_split,
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forecast_horizon,
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n_predict,
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)
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