/
metrics.py
169 lines (137 loc) · 5.51 KB
/
metrics.py
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from __future__ import print_function
import argparse
import os
import sys
import numpy as np
def cal_metric(known, novel, ratio=0.95, method=None):
tp, fp, fpr_at_tpr95 = get_curve(known, novel, method, ratio=ratio)
results = dict()
mtypes = ['FPR', 'AUROC', 'DTERR', 'AUIN', 'AUOUT']
results = dict()
# FPR
mtype = 'FPR'
results[mtype] = fpr_at_tpr95
# AUROC
mtype = 'AUROC'
tpr = np.concatenate([[1.], tp/tp[0], [0.]])
fpr = np.concatenate([[1.], fp/fp[0], [0.]])
results[mtype] = -np.trapz(1.-fpr, tpr)
# DTERR
mtype = 'DTERR'
results[mtype] = ((tp[0] - tp + fp) / (tp[0] + fp[0])).min()
# AUIN
mtype = 'AUIN'
denom = tp+fp
denom[denom == 0.] = -1.
pin_ind = np.concatenate([[True], denom > 0., [True]])
pin = np.concatenate([[.5], tp/denom, [0.]])
results[mtype] = -np.trapz(pin[pin_ind], tpr[pin_ind])
# AUOUT
mtype = 'AUOUT'
denom = tp[0]-tp+fp[0]-fp
denom[denom == 0.] = -1.
pout_ind = np.concatenate([[True], denom > 0., [True]])
pout = np.concatenate([[0.], (fp[0]-fp)/denom, [.5]])
results[mtype] = np.trapz(pout[pout_ind], 1.-fpr[pout_ind])
return results
def get_curve(known, novel, method=None, ratio=0.95):
tp, fp = dict(), dict()
fpr_at_tpr95 = dict()
known.sort()
novel.sort()
end = np.max([np.max(known), np.max(novel)])
start = np.min([np.min(known),np.min(novel)])
all = np.concatenate((known, novel))
all.sort()
num_k = known.shape[0]
num_n = novel.shape[0]
if method == 'row':
threshold = -0.5
else:
threshold = known[round(ratio * num_k)]
tp = -np.ones([num_k+num_n+1], dtype=int)
fp = -np.ones([num_k+num_n+1], dtype=int)
tp[0], fp[0] = num_k, num_n
k, n = 0, 0
for l in range(num_k+num_n):
if k == num_k:
tp[l+1:] = tp[l]
fp[l+1:] = np.arange(fp[l]-1, -1, -1)
break
elif n == num_n:
tp[l+1:] = np.arange(tp[l]-1, -1, -1)
fp[l+1:] = fp[l]
break
else:
if novel[n] < known[k]:
n += 1
tp[l+1] = tp[l]
fp[l+1] = fp[l] - 1
else:
k += 1
tp[l+1] = tp[l] - 1
fp[l+1] = fp[l]
j = num_k+num_n-1
for l in range(num_k+num_n-1):
if all[j] == all[j-1]:
tp[j] = tp[j+1]
fp[j] = fp[j+1]
j -= 1
fpr_at_tpr95 = np.sum(novel < threshold) / float(num_n)
return tp, fp, fpr_at_tpr95
def print_results(results, in_dataset, out_dataset, name, method):
mtypes = ['FPR', 'DTERR', 'AUROC', 'AUIN', 'AUOUT']
print('in_distribution: ' + in_dataset)
print('out_distribution: '+ out_dataset)
print('Model Name: ' + name)
print('')
print(' OOD detection method: ' + method)
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
print('\n{val:6.2f}'.format(val=100.*results['FPR']), end='')
print(' {val:6.2f}'.format(val=100.*results['DTERR']), end='')
print(' {val:6.2f}'.format(val=100.*results['AUROC']), end='')
print(' {val:6.2f}'.format(val=100.*results['AUIN']), end='')
print(' {val:6.2f}\n'.format(val=100.*results['AUOUT']), end='')
print('')
# print(' {val:6.2f}'.format(val=100.*results['FPR']), end='')
# print(' {val:6.2f}'.format(val=100.*results['DTERR']), end='')
# print(' {val:6.2f}'.format(val=100.*results['AUROC']), end='')
# print(' {val:6.2f}'.format(val=100.*results['AUIN']), end='')
# print(' {val:6.2f}'.format(val=100.*results['AUOUT']), end='')
def print_all_results_tab(results, datasets, method):
[print('{:6.2f}\t{:6.2f}\t{:6.2f}\t'.format(100.*result['FPR'], 100.*result['AUROC'], 100.*result['AUIN']), end='') for result in results]
def print_all_results(results, datasets, method):
mtypes = ['FPR', 'AUROC', 'AUIN']#['FPR', 'DTERR', 'AUROC', 'AUIN', 'AUOUT']
avg_results = compute_average_results(results)
print(' OOD detection method: ' + method)
print(' ', end='')
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
for result, dataset in zip(results,datasets):
print('\n{dataset:12s}'.format(dataset=dataset), end='')
print(' {val:6.2f}'.format(val=100.*result['FPR']), end='')
# print(' {val:6.2f}'.format(val=100.*result['DTERR']), end='')
print(' {val:6.2f}'.format(val=100.*result['AUROC']), end='')
print(' {val:6.2f}'.format(val=100.*result['AUIN']), end='')
# print(' {val:6.2f}'.format(val=100.*result['AUOUT']), end='')
print('\nAVG ', end='')
print(' {val:6.2f}'.format(val=100.*avg_results['FPR']), end='')
# print(' {val:6.2f}'.format(val=100.*avg_results['DTERR']), end='')
print(' {val:6.2f}'.format(val=100.*avg_results['AUROC']), end='')
print(' {val:6.2f}'.format(val=100.*avg_results['AUIN']), end='')
print()
# [print('{:6.2f}\t{:6.2f}\t{:6.2f}\t'.format(100.*result['FPR'], 100.*result['AUROC'], 100.*result['AUIN']), end='') for result in results]
# print()
# print(' {val:6.2f}\n'.format(val=100.*avg_results['AUOUT']), end='')
def compute_average_results(all_results):
mtypes = ['FPR', 'DTERR', 'AUROC', 'AUIN', 'AUOUT']
avg_results = dict()
for mtype in mtypes:
avg_results[mtype] = 0.0
for results in all_results:
for mtype in mtypes:
avg_results[mtype] += results[mtype]
for mtype in mtypes:
avg_results[mtype] /= float(len(all_results))
return avg_results