mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
9d47ee942d
* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba * fix(WalkForward): inference mini-batch parallelization * fix(WalkForward): don't use the parallel version of any of the functions * feat(CI): download the data required * fix(Project): 5min_crypto folder added * fix(Evaluate): make sure we have numerical stability in returns * feat(Models): use SKLearn models directly to enable composability * feat(Inference): batched inference now working, added forecasting_horizon * fix(Inference): works again * fix(Inference) * chore(Models): remove unused Ensemble model * fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then * Update test.yml
23 lines
562 B
Python
23 lines
562 B
Python
from __future__ import annotations
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from typing import Literal
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from .base import Model
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import numpy as np
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def SKLearnModel(instance) -> Model:
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instance.data_transformation = 'transformed'
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instance.only_column = None
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instance.predict_window_size = 'single_timestamp'
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instance.name = instance.__class__.__name__
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return instance
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# def predict(self, X) -> tuple[float, np.ndarray]:
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# pred = self.model.predict(X).item()
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# probability = self.model.predict_proba(X).squeeze()
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# return (pred, probability)
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