mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Mark Aron Szulyovszky
7aedb91069
* feat(Eval): added format_data_for_backtest() * feat(Data): added many configurable parameters to load_files to reduce boilerplate and prepare for HPO * feat(Core): added walk forward method of training/testing * fix(Model): remove the unnecessary softmax activation from the keras models * feat(Core): added walk_forward_train_test()
24 lines
990 B
Python
24 lines
990 B
Python
from math import sqrt
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from sklearn.metrics import mean_squared_error, mean_absolute_error, accuracy_score
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from sklearn.metrics import confusion_matrix
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import pandas as pd
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def print_regression_metrics(y_true, y_pred):
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rmse = sqrt(mean_squared_error(y_true, y_pred))
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print("RMSE: %.2f" % rmse)
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mae = mean_absolute_error(y_true, y_pred)
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print("MAE: %.2f" % mae)
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def print_classification_metrics(y_true, y_pred):
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print("Accuracy: %.2f" % accuracy_score(y_true, y_pred))
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print("Confusion Matrix: \n", confusion_matrix(y_true, y_pred))
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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 backtest() |