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https://github.com/OpenBB-finance/OpenBB.git
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7ba964d61e
* Basic integration of crypto pred * Add loading into crypto pred * Change all pred _views to allow for other datetime than daily * small mypy change * Add pred menu description and catch if no coin is loaded. Co-authored-by: James Maslek <james@jamess-mbp.myfiosgateway.com>
85 lines
2.1 KiB
Python
85 lines
2.1 KiB
Python
""" KNN Prediction View"""
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__docformat__ = "numpy"
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from typing import Union
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import pandas as pd
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from pandas.plotting import register_matplotlib_converters
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from gamestonk_terminal.common.prediction_techniques.pred_helper import (
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print_pretty_prediction,
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plot_data_predictions,
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)
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from gamestonk_terminal.common.prediction_techniques import knn_model
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register_matplotlib_converters()
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# pylint:disable=too-many-arguments
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def display_k_nearest_neighbors(
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ticker: str,
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data: Union[pd.DataFrame, pd.Series],
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n_neighbors: int,
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n_input_days: int,
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n_predict_days: int,
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test_size: float,
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end_date: str = "",
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no_shuffle: bool = True,
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time_res: str = "",
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):
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"""Display predictions using knn
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Parameters
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----------
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ticker : str
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Stock data
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data : Union[pd.DataFrame, pd.Series]
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Data to use for ML
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n_neighbors : int
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Number of neighborns for knn
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n_input_days : int
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Length of input sequences
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n_predict_days : int
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Number of days to predict
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test_size : float
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Fraction of data for testing
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end_date : str, optional
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End date for backtesting, by default ""
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no_shuffle : bool, optional
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Flag to shuffle data randomly, by default True
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time_res : str
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Resolution for data, allowing for predicting outside of standard market days
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"""
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(
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forecast_data_df,
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preds,
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y_valid,
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y_dates_valid,
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scaler,
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) = knn_model.get_knn_model_data(
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data, n_input_days, n_predict_days, n_neighbors, test_size, end_date, no_shuffle
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)
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if forecast_data_df.empty:
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print("Issue performing data prep and prediction")
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return
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if time_res:
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forecast_data_df.index = pd.date_range(
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data.index[-1], periods=n_predict_days + 1, freq=time_res
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)[1:]
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print_pretty_prediction(forecast_data_df[0], data.values[-1])
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plot_data_predictions(
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data,
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preds,
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y_valid,
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y_dates_valid,
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scaler,
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f"KNN Model with {n_neighbors} Neighbors on {ticker}",
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forecast_data_df,
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1,
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time_res,
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)
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print("")
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