mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
fc5eba4e2d
* feat(Models): added lightGBM, moved other models to separate files * feat(Models): added non-working statsmodel wrapper * fix(Models): added work-in-progress comment to StatsModels
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
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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from sklearn.base import clone
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class XGBoostModel(Model):
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data_scaling = 'scaled'
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only_column = None
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feature_selection = 'on'
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model_type = 'ml'
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predict_window_size = 'single_timestamp'
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def __init__(self, model: XGBClassifier):
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self.model = model
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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.model.fit(X, map_to_xgb(y))
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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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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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def clone(self) -> XGBoostModel:
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return XGBoostModel(clone(self.model))
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def get_name(self) -> str:
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return self.model.__class__.__name__
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def initialize_network(self, input_dim:int, output_dim:int):
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pass
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