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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
d047b7417e
* refactor(WalkForward): cleaned up training & evaluation code * refactor: added run_whole_pipeline(), moved all previous models to archive
56 lines
2.5 KiB
Python
56 lines
2.5 KiB
Python
from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score, r2_score, classification_report
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from sklearn.metrics import confusion_matrix
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import pandas as pd
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import numpy as np
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def format_data_for_backtest(aggregated_data: pd.DataFrame, returns_col: str, only_test_data: pd.DataFrame, preds) -> pd.DataFrame:
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backtest_data = aggregated_data.iloc[-only_test_data.shape[0]:].copy()[returns_col]
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assert backtest_data.shape[0] == only_test_data.shape[0]
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backtest_data = backtest_data.reset_index(drop=True)
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return pd.concat([backtest_data, pd.Series(preds)], axis='columns')
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def evaluate_predictions_regression(model_name: str, y_true, y_pred, sliding_window_size: int):
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evaluate_from = sliding_window_size+1
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y_true = pd.Series(y_true[evaluate_from:-1])
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y_pred = pd.Series(y_pred[evaluate_from:])
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def preprocess(y_true, y_pred):
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y_pred.name = 'y_pred'
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y_true.name = 'y_true'
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df = pd.concat([y_pred, y_true],axis=1).dropna()
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df['sign_pred'] = df.y_pred.apply(np.sign)
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df['sign_true'] = df.y_true.apply(np.sign)
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df['is_correct'] = 0
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df.loc[df.sign_pred * df.sign_true > 0 ,'is_correct'] = 1 # only registers 1 when prediction was made AND it was correct
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df['is_incorrect'] = 0
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df.loc[df.sign_pred * df.sign_true < 0,'is_incorrect'] = 1 # only registers 1 when prediction was made AND it was wrong
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df['is_predicted'] = df.is_correct + df.is_incorrect
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df['result'] = df.sign_pred * df.y_true
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return df
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df = preprocess(y_true, y_pred)
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scorecard = pd.Series()
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scorecard.loc['RSQ'] = r2_score(df.y_true,df.y_pred)
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scorecard.loc['MAE'] = mean_absolute_error(df.y_true,df.y_pred)
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scorecard.loc['directional_accuracy'] = df.is_correct.sum()*1. / (df.is_predicted.sum()*1.)*100
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scorecard.loc['edge'] = df.result.mean()
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scorecard.loc['noise'] = df.y_pred.diff().abs().mean()
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scorecard.loc['edge_to_noise'] = scorecard.loc['edge'] / scorecard.loc['noise']
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scorecard.loc['edge_to_mae'] = scorecard.loc['edge'] / scorecard.loc['MAE']
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print("Model name: ", model_name)
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print(scorecard)
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return scorecard
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def evaluate_predictions_classification(model_name: str, y, preds, sliding_window_size: int):
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print("Model: ", model_name)
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evaluate_from = sliding_window_size+1
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y = pd.Series(y[evaluate_from:-1])
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preds = pd.Series(preds[evaluate_from:])
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print(accuracy_score(y, preds))
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print(confusion_matrix(y, preds))
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print(classification_report(y, preds))
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