mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +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
753 B
Python
23 lines
753 B
Python
# from __future__ import annotations
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# from models.base import Model
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# import numpy as np
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# from xgboost import XGBClassifier
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# class XGBoostModel(XGBClassifier):
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# method = 'classification'
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# data_transformation = 'transformed'
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# only_column = None
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# predict_window_size = 'single_timestamp'
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# def fit(self, X: np.ndarray, y: np.ndarray) -> None:
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# def map_to_xgb(y): return np.array([1 if i == 1 else 0 for i in y])
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# self.fit(X, map_to_xgb(y))
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# def predict(self, X) -> tuple[float, np.ndarray]:
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# pred = self.predict(X).item()
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# probability = self.predict_proba(X).squeeze()
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# def map_from_xgb(y): return 1 if y == 1 else -1
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# return (map_from_xgb(pred), probability)
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