Source code for caliber.multiclass_classification.binning.histogram_binning

import numpy as np

from caliber.multiclass_classification.base import AbstractMulticlassClassificationModel
from caliber.multiclass_classification.pred_from_probs_mixin import (
    PredFromProbsMulticlassClassificationMixin,
)


[docs] class HistogramBinningMulticlassClassificationModel( PredFromProbsMulticlassClassificationMixin, AbstractMulticlassClassificationModel ): def __init__(self, n_prob_bins: int = 10, min_prob_bin: float = 0.0): super().__init__() self.n_prob_bins = n_prob_bins self._min_prob_bin = min_prob_bin self._prob_bin_edges = None
[docs] def fit(self, probs: np.ndarray, targets: np.ndarray): self._n_classes = probs.shape[1] self._prob_bin_edges = self._get_prob_bin_edges() prob_bin_indices = np.digitize(probs, self._prob_bin_edges) top_class_indices = np.argmax(probs, axis=1) self._params = np.empty((self.n_prob_bins + 1, self._n_classes)) for i in range(1, self.n_prob_bins + 2): for c in range(self._n_classes): mask = self._get_mask(i, c, prob_bin_indices, top_class_indices) self._fit_bin(i, c, mask, targets)
[docs] def predict_proba(self, probs: np.ndarray) -> np.ndarray: if self._prob_bin_edges is None: raise ValueError("Run `fit` first.") if probs.shape[1] != self._n_classes: raise ValueError( "The number of classes when fitting and predicting must be the same." ) prob_bin_indices = np.digitize(probs, self._prob_bin_edges) top_class_indices = np.argmax(probs, axis=1) probs = np.copy(probs) for i in range(1, self.n_prob_bins + 2): for c in range(0, self._n_classes): mask = self._get_mask(i, c, prob_bin_indices, top_class_indices) if not np.isnan(self._params[i - 1, c]): probs[mask, c] = self._params[i - 1, c] return probs
def _get_prob_bin_edges(self) -> np.ndarray: return np.linspace(0, 1, self.n_prob_bins + 1) def _fit_bin(self, i: int, c: int, mask: np.ndarray, targets: np.ndarray): prob_bin = np.mean(mask) if prob_bin >= self._min_prob_bin: self._params[i - 1, c] = np.mean(targets[mask] == c) else: self._params[i - 1, c] = np.nan @staticmethod def _get_mask( prob_bin_idx: int, class_idx: int, prob_bin_indices: np.ndarray, top_class_indices: np.ndarray, ) -> np.ndarray: return (prob_bin_indices[:, class_idx] == prob_bin_idx) * ( top_class_indices == class_idx )