drift/models/sklearn.py

from __future__ import annotations
from typing import Literal
from .base import Model
import numpy as np



def SKLearnModel(instance) -> Model:

    instance.data_transformation = 'transformed'
    instance.only_column = None
    instance.predict_window_size = 'single_timestamp'
    instance.name = instance.__class__.__name__

    return instance
    

    # def predict(self, X) -> tuple[float, np.ndarray]:
    #     pred = self.model.predict(X).item()
    #     probability = self.model.predict_proba(X).squeeze()
    #     return (pred, probability)