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e1bba247e2
* init commit for doc string fixes * quick fix * Forecast Guide Draft 1 * grammar * sentence edit * missing word * target column is a string, not a list. ooops * clarification on default target column * minor mods * more fixes * Fixed dashboard * val instead of tickerseries * Added fixes * $ --> <> * Fixed pylint * one quick change Co-authored-by: Danglewood <85772166+deeleeramone@users.noreply.github.com> Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com> Co-authored-by: colin99d <colin99delahunty@gmail.com>
130 lines
4.2 KiB
Python
130 lines
4.2 KiB
Python
"""Linear Regression View"""
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__docformat__ = "numpy"
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import logging
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from typing import Union, List, Optional
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from datetime import datetime
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import pandas as pd
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import matplotlib.pyplot as plt
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from openbb_terminal.forecast import linregr_model
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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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# pylint: disable=too-many-arguments
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@log_start_end(log=logger)
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def display_linear_regression(
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data: Union[pd.Series, pd.DataFrame],
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target_column: str = "close",
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dataset_name: str = "",
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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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export: str = "",
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residuals: bool = False,
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forecast_only: bool = False,
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start_date: Optional[datetime] = None,
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end_date: Optional[datetime] = None,
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naive: bool = False,
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explainability_raw: bool = False,
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export_pred_raw: bool = False,
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external_axes: Optional[List[plt.axes]] = None,
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):
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"""Display 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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dataset_name: str
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The name of the ticker to be predicted
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n_predict: int
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Days to predict. Defaults to 5.
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target_col: str
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Target column to forecast. Defaults to "close".
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train_split: (float, optional)
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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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export: str
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Format to export data
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residuals: bool
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Whether to show residuals for the model. Defaults to False.
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forecast_only: bool
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Whether to only show dates in the forecasting range. Defaults to False.
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start_date: Optional[datetime]
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The starting date to perform analysis, data before this is trimmed. Defaults to None.
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end_date: Optional[datetime]
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The ending date to perform analysis, data after this is trimmed. Defaults to None.
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naive: bool
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Whether to show the naive baseline. This just assumes the closing price will be the
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same as the previous day's closing price. Defaults to False.
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external_axes: Optional[List[plt.axes]]
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External axes to plot on
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"""
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data = helpers.clean_data(
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data, start_date, end_date, target_column, past_covariates
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)
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if not helpers.check_data(data, target_column, past_covariates):
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return
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output_chunk_length = helpers.check_output(
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output_chunk_length, n_predict, bool(past_covariates)
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)
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(
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ticker_series,
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historical_fcast,
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predicted_values,
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precision,
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_model,
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) = linregr_model.get_linear_regression_data(
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data=data,
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n_predict=n_predict,
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target_column=target_column,
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past_covariates=past_covariates,
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train_split=train_split,
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forecast_horizon=forecast_horizon,
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output_chunk_length=output_chunk_length,
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lags=lags,
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)
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probabilistic = True
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helpers.plot_forecast(
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name="LINREGR",
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target_col=target_column,
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historical_fcast=historical_fcast,
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predicted_values=predicted_values,
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ticker_series=ticker_series,
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ticker_name=dataset_name,
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data=data,
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n_predict=n_predict,
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forecast_horizon=forecast_horizon,
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past_covariates=past_covariates,
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precision=precision,
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probabilistic=probabilistic,
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export=export,
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forecast_only=forecast_only,
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naive=naive,
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export_pred_raw=export_pred_raw,
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external_axes=external_axes,
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)
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if residuals:
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helpers.plot_residuals(
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_model, past_covariates, ticker_series, forecast_horizon=forecast_horizon
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)
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# SHAP
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helpers.plot_explainability(_model, explainability_raw)
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