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* Added flake8-simplify * Update linting.yml * update. left 1 for you colin * Fix that one * Missed * Fix * mypy --------- Co-authored-by: james <jmaslek11@gmail.com>
117 lines
3.3 KiB
Python
117 lines
3.3 KiB
Python
# pylint: disable=too-many-arguments
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"""Linear 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, Optional, 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 LinearRegressionModel
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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_linear_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: Optional[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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random_state: Optional[int] = None,
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metric: str = "mape",
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) -> Tuple[
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List[TimeSeries],
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List[TimeSeries],
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List[TimeSeries],
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float,
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LinearRegressionModel,
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]:
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"""Perform Linear 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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target_column: str
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Target column to forecast. Defaults to "close".
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n_predict: int
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Days to predict. Defaults to 5.
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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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random_state: Optional[int]
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The state for the model
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metric: str
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The metric to use for the model. Defaults to "mape".
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Returns
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-------
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Tuple[List[TimeSeries], List[TimeSeries], List[TimeSeries], float, LinearRegressionModel]
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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 Linear Regression Model.
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"""
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use_scalers = False
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probabilistic = True
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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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lags_past_covariates = lags if past_covariates is not None else None
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lin_reg_model = LinearRegressionModel(
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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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likelihood="quantile",
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quantiles=[0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95],
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random_state=random_state,
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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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if past_covariates is not None:
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lin_reg_model.fit(
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series=ticker_series, past_covariates=past_covariate_whole
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)
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else:
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lin_reg_model.fit(series=ticker_series)
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return helpers.get_prediction(
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"Logistic 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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lin_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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metric,
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)
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