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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
6440ced32c
* feat(Tests): added basic unit tests for walk_forward_train_test() * fix(Tests): inherit from BaseEstimator, fix index problems in walk_forward_train_test * fix(WalkForward): predictions were mistakenly removed, oops * fix(WalkForward): mistakenly re-assiging model
44 lines
1.4 KiB
Python
44 lines
1.4 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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predictions[window_end+1] = current_model.predict(next_timestep).item()
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return models, predictions
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