Spaces:
Sleeping
Sleeping
File size: 2,214 Bytes
e75a247 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
import numpy as np
import traceback
import sklearn.metrics as _m
from functools import partial
from src.logger.logger import _logger
# def _bkg_rejection(y_true, y_score, sig_eff):
# fpr, tpr, _ = _m.roc_curve(y_true, y_score)
# idx = next(idx for idx, v in enumerate(tpr) if v > sig_eff)
# rej = 1. / fpr[idx]
# return rej
#
#
# def bkg_rejection(y_true, y_score, sig_eff):
# if y_score.ndim == 1:
# return _bkg_rejection(y_true, y_score, sig_eff)
# else:
# num_classes = y_score.shape[1]
# for i in range(num_classes):
# for j in range(i + 1, num_classes):
# weights = np.logical_or(y_true == i, y_true == j)
# truth =
def roc_auc_score_ovo(y_true, y_score):
if y_score.ndim == 1:
return _m.roc_auc_score(y_true, y_score)
else:
num_classes = y_score.shape[1]
result = np.zeros((num_classes, num_classes), dtype='float32')
for i in range(num_classes):
for j in range(i + 1, num_classes):
weights = np.logical_or(y_true == i, y_true == j)
truth = y_true == j
score = y_score[:, j] / np.maximum(y_score[:, i] + y_score[:, j], 1e-6)
result[i, j] = _m.roc_auc_score(truth, score, sample_weight=weights)
return result
def confusion_matrix(y_true, y_score):
if y_score.ndim == 1:
y_pred = y_score > 0.5
else:
y_pred = y_score.argmax(1)
return _m.confusion_matrix(y_true, y_pred, normalize='true')
_metric_dict = {
'roc_auc_score': partial(_m.roc_auc_score, multi_class='ovo'),
'roc_auc_score_matrix': roc_auc_score_ovo,
'confusion_matrix': confusion_matrix,
}
def _get_metric(metric):
try:
return _metric_dict[metric]
except KeyError:
return getattr(_m, metric)
def evaluate_metrics(y_true, y_score, eval_metrics=[]):
results = {}
for metric in eval_metrics:
func = _get_metric(metric)
try:
results[metric] = func(y_true, y_score)
except Exception as e:
results[metric] = None
_logger.error(str(e))
_logger.debug(traceback.format_exc())
return |