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metrics.py
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metrics.py
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import numpy as np
import torch
import torch.nn.functional as F
from sklearn import metrics
def calc_metrics(loader, label, label_onehot, model, criterion):
acc, softmax, correct, logit = get_metric_values(loader, model, criterion)
# aurc, eaurc
aurc, eaurc = calc_aurc_eaurc(softmax, correct)
# fpr, aupr
aupr, fpr = calc_fpr_aupr(softmax, correct)
# calibration measure ece , mce, rmsce
ece = calc_ece(softmax, label, bins=15)
# brier, nll
nll, brier = calc_nll_brier(softmax, logit, label, label_onehot)
return acc, aurc, eaurc, aupr, fpr, ece, nll, brier
# AURC, EAURC
def calc_aurc_eaurc(softmax, correct):
softmax = np.array(softmax)
correctness = np.array(correct)
softmax_max = np.max(softmax, 1)
sort_values = sorted(zip(softmax_max[:], correctness[:]), key=lambda x:x[0], reverse=True)
sort_softmax_max, sort_correctness = zip(*sort_values)
risk_li, coverage_li = coverage_risk(sort_softmax_max, sort_correctness)
aurc, eaurc = aurc_eaurc(risk_li)
return aurc, eaurc
# AUPR ERROR
def calc_fpr_aupr(softmax, correct):
softmax = np.array(softmax)
correctness = np.array(correct)
softmax_max = np.max(softmax, 1)
fpr, tpr, thresholds = metrics.roc_curve(correctness, softmax_max)
idx_tpr_95 = np.argmin(np.abs(tpr - 0.95))
fpr_in_tpr_95 = fpr[idx_tpr_95]
aupr_err = metrics.average_precision_score(-1 * correctness + 1, -1 * softmax_max)
print("AUPR {0:.2f}".format(aupr_err*100))
print('FPR {0:.2f}'.format(fpr_in_tpr_95*100))
return aupr_err, fpr_in_tpr_95
# ECE
def calc_ece(softmax, label, bins=15):
bin_boundaries = torch.linspace(0, 1, bins + 1)
bin_lowers = bin_boundaries[:-1]
bin_uppers = bin_boundaries[1:]
softmax = torch.tensor(softmax)
labels = torch.tensor(label)
softmax_max, predictions = torch.max(softmax, 1)
correctness = predictions.eq(labels)
ece = torch.zeros(1)
for bin_lower, bin_upper in zip(bin_lowers, bin_uppers):
in_bin = softmax_max.gt(bin_lower.item()) * softmax_max.le(bin_upper.item())
prop_in_bin = in_bin.float().mean()
if prop_in_bin.item() > 0.0:
accuracy_in_bin = correctness[in_bin].float().mean()
avg_confidence_in_bin = softmax_max[in_bin].mean()
ece += torch.abs(avg_confidence_in_bin - accuracy_in_bin) * prop_in_bin
print("ECE {0:.2f} ".format(ece.item()*100))
return ece.item()
# NLL & Brier Score
def calc_nll_brier(softmax, logit, label, label_onehot):
brier_score = np.mean(np.sum((softmax - label_onehot) ** 2, axis=1))
logit = torch.tensor(logit, dtype=torch.float)
label = torch.tensor(label, dtype=torch.int)
logsoftmax = torch.nn.LogSoftmax(dim=1)
log_softmax = logsoftmax(logit)
nll = calc_nll(log_softmax, label)
print("NLL {0:.2f} ".format(nll.item()*10))
print('Brier {0:.2f}'.format(brier_score*100))
return nll.item(), brier_score
# Calc NLL
def calc_nll(log_softmax, label):
out = torch.zeros_like(label, dtype=torch.float)
for i in range(len(label)):
out[i] = log_softmax[i][label[i]]
return -out.sum()/len(out)
# Calc coverage, risk
def coverage_risk(confidence, correctness):
risk_list = []
coverage_list = []
risk = 0
for i in range(len(confidence)):
coverage = (i + 1) / len(confidence)
coverage_list.append(coverage)
if correctness[i] == 0:
risk += 1
risk_list.append(risk / (i + 1))
return risk_list, coverage_list
# Calc aurc, eaurc
def aurc_eaurc(risk_list):
r = risk_list[-1]
risk_coverage_curve_area = 0
optimal_risk_area = r + (1 - r) * np.log(1 - r)
for risk_value in risk_list:
risk_coverage_curve_area += risk_value * (1 / len(risk_list))
aurc = risk_coverage_curve_area
eaurc = risk_coverage_curve_area - optimal_risk_area
print("AURC {0:.2f}".format(aurc*1000))
print("EAURC {0:.2f}".format(eaurc*1000))
return aurc, eaurc
# Get softmax, logit
def get_metric_values(loader, model, criterion):
model.eval()
with torch.no_grad():
total_loss = 0
total_acc = 0
accuracy = 0
list_softmax = []
list_correct = []
list_logit = []
for input, target, idx in loader:
input = input.cuda()
target = target.cuda()
output = model(input)
loss = criterion(output, target).cuda()
total_loss += loss.mean().item()
pred = output.data.max(1, keepdim=True)[1]
total_acc += pred.eq(target.data.view_as(pred)).sum()
for i in output:
list_logit.append(i.cpu().data.numpy())
list_softmax.extend(F.softmax(output).cpu().data.numpy())
for j in range(len(pred)):
if pred[j] == target[j]:
accuracy += 1
cor = 1
else:
cor = 0
list_correct.append(cor)
total_loss /= len(loader)
total_acc = 100. * total_acc / len(loader.dataset)
print('Accuracy {:.2f}'.format(total_acc))
return total_acc.item(), list_softmax, list_correct, list_logit