Source code for caliber.multiclass_classification.ood.da_kolmogorov_interpolant
from typing import Any, Optional
import numpy as np
from scipy import stats
from scipy.special import kolmogorov
from caliber.multiclass_classification.base import AbstractMulticlassClassificationModel
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class DistanceAwareKolmogorovInterpolantMulticlassClassificationModel(
AbstractMulticlassClassificationModel
):
def __init__(self, model: Optional[Any] = None):
super().__init__()
self.model = model
self._train_ecdf = None
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def fit(self, probs: np.ndarray, distances: np.ndarray, targets: np.ndarray):
if self.model is not None:
self.model.fit(probs, targets)
self._train_ecdf = stats.ecdf(distances).cdf
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def predict_proba(self, probs: np.ndarray, distances: np.ndarray) -> np.ndarray:
probs = np.copy(probs)
if self.model is not None:
probs = self.model.predict_proba(probs)
ecdf = stats.ecdf(distances).cdf
w = kolmogorov(
np.sqrt(len(distances))
* np.abs(ecdf.evaluate(distances) - self._train_ecdf.evaluate(distances))
)[:, None]
return w * probs + (1 - w) / probs.shape[1]
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def predict(self, probs: np.ndarray, distances: np.ndarray) -> np.ndarray:
return np.argmax(self.predict_proba(probs, distances), axis=1)