import numpy as np
from scipy.stats import expon
from caliber.binary_classification.base import AbstractBinaryClassificationModel
[docs]
class DistanceAwareHistogramBinningBinaryClassificationModel(
AbstractBinaryClassificationModel
):
def __init__(
self,
n_prob_bins: int = 10,
n_dist_bins: int = 10,
conf_distance: float = 0.95,
min_prob_bin: float = 0.0,
):
super().__init__()
self.n_prob_bins = n_prob_bins
self.n_dist_bins = n_dist_bins
self.conf_distance = conf_distance
self._min_prob_bin = min_prob_bin
self._prob_bin_edges = None
self._dist_bin_edges = None
self._cdf = None
self._max_distance = None
self._quantile_distance = None
[docs]
def fit(self, probs: np.ndarray, distances: np.ndarray, targets: np.ndarray):
self._quantile_distance = np.quantile(distances, self.conf_distance)
self._max_distance = np.max(distances)
self._cdf = expon(0, self._max_distance).cdf
self._prob_bin_edges = self._get_prob_bin_edges()
self._dist_bin_edges = self._get_dist_bin_edges()
prob_bin_indices = np.digitize(probs, self._prob_bin_edges)
dist_bin_indices = np.digitize(distances, self._dist_bin_edges)
self._params = np.empty((self.n_prob_bins + 1, self.n_dist_bins + 1))
for i in range(1, self.n_prob_bins + 2):
for j in range(1, self.n_dist_bins + 2):
mask = self._get_mask(i, j, prob_bin_indices, dist_bin_indices)
self._fit_bin(i, j, mask, targets)
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def predict_proba(self, probs: np.ndarray, distances: np.ndarray) -> np.ndarray:
if self._prob_bin_edges is None:
raise ValueError("Run `fit` first.")
prob_bin_indices = np.digitize(probs, self._prob_bin_edges)
dist_bin_indices = np.digitize(distances, self._dist_bin_edges)
probs = np.copy(probs)
for i in range(1, self.n_prob_bins + 2):
for j in range(1, self.n_dist_bins + 2):
mask = self._get_mask(i, j, prob_bin_indices, dist_bin_indices)
if not np.isnan(self._params[i - 1, j - 1]):
probs[mask] = self._params[i - 1, j - 1]
else:
cdf = self._get_cdf(distances[mask])
probs[mask] = (1 - cdf) * probs[mask] + 0.5 * cdf
return probs
[docs]
def predict(self, probs: np.ndarray, distances: np.ndarray) -> np.ndarray:
return (self.predict_proba(probs, distances) >= 0.5).astype(int)
def _get_prob_bin_edges(self) -> np.ndarray:
return np.linspace(0, 1, self.n_prob_bins + 1)
def _get_dist_bin_edges(self) -> np.ndarray:
return np.linspace(0, self._max_distance, self.n_dist_bins + 1)
def _fit_bin(self, i: int, j: int, mask: np.ndarray, targets: np.ndarray):
prob_bin = np.mean(mask)
if prob_bin >= self._min_prob_bin:
cdf = self._get_cdf(self._dist_bin_edges[j - 1])
self._params[i - 1, j - 1] = (1 - cdf) * np.mean(targets[mask]) + 0.5 * cdf
else:
self._params[i - 1, j - 1] = np.nan
@staticmethod
def _get_mask(
prob_bin_idx: int,
dist_bin_idx: int,
prob_bin_indices: np.ndarray,
dist_bin_indices: np.ndarray,
) -> np.ndarray:
return (prob_bin_indices == prob_bin_idx) * (dist_bin_indices == dist_bin_idx)
def _get_cdf(self, d: float) -> np.ndarray:
d -= self._quantile_distance
return self._cdf(d) * (d > 0)