mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-04 06:37:45 +00:00
Mark Aron Szulyovszky
1eaba0c221
* fix(Evaluate): ignore empty data at evaluation time, so we don't inflate the model's performance * refactor(Pipeline): pass in data_loader arguments to the pipeline * feat(Evaluation): added sharpe, sortino, etc * fix: Took out the method to fill NaN numbers with 0s. This way in evaluation we can ignore NaN values. * fix: Fix of the fix added fillna back. Either we root out NaN lines in the very beginning or we stick with the method you created. Co-authored-by: Daniel Szemerey <szemy2@gmail.com>
49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
import pandas as pd
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from sklearn.base import clone
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from utils.typing import SKLearnModel
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import numpy as np
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def walk_forward_train_test(
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model_name: str,
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model: SKLearnModel,
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X: pd.DataFrame,
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y: pd.Series,
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window_size: int,
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retrain_every: int
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) -> tuple[pd.Series, pd.Series]:
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predictions = pd.Series(index=y.index).rename(model_name)
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models = pd.Series(index=y.index).rename(model_name)
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train_from = window_size
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train_till = y.index[-1]
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iterations_since_retrain = 0
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for i in range(train_from, train_till):
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iterations_since_retrain += 1
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window_start = i - window_size
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window_end = i
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X_slice = X[window_start:window_end]
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y_slice = y[window_start:window_end]
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if iterations_since_retrain >= retrain_every or pd.isna(models[i-1]):
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current_model = clone(model)
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current_model.fit(X_slice.to_numpy(), y_slice.to_numpy())
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iterations_since_retrain = 0
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else:
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current_model = models[i-1]
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models[window_end] = current_model
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next_timestep = X.iloc[window_end+1].to_numpy().reshape(1, -1)
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prediction = current_model.predict(next_timestep).item()
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if prediction == 0.:
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# TODO: we shouldn't feed in zeros to the model, and skip training / predicting when everything is 0
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# print("Warning: model predicted 0., overriding it with 0.0001")
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prediction = 0.0001
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predictions[window_end+1] = prediction
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return models, predictions
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