def print_eval_metrics(self, nmes, show=False):
def ced_curve(nmes):
Y = []
X = np.linspace(0, 10, 50)
for th in X:
recall = 1.0 - lmutils.calc_landmark_failure_rate(nmes, th)
recall *= 1/len(X)
Y.append(recall)
return X,Y
def auc(recalls):
return np.sum(recalls)
# for err_scale in np.linspace(0.1, 1, 10):
for err_scale in [1.0]:
# print('\nerr_scale', err_scale)
# print(np.clip(lm_errs_max_all, a_min=0, a_max=10).mean())
fr = lmutils.calc_landmark_failure_rate(nmes*err_scale)
X, Y = ced_curve(nmes)
log.info('NME: {:>6.3f}'.format(nmes.mean()*err_scale))
log.info('FR@10: {:>6.3f} ({})'.format(fr*100, np.sum(nmes.mean(axis=1) > 10)))
log.info('AUC: {:>6.4f}'.format(auc(Y)))
if show:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(1,2)
axes[0].plot(X, Y)
print(nmes.mean(axis=1).shape)
print(nmes.mean(axis=1).max())
axes[1].hist(nmes.mean(axis=1), bins=20)
plt.show()
def ced_curve(_nmes):
y = []
x = np.linspace(0, 10, 50)
for th in x:
recall = 1.0 - lmutils.calc_landmark_failure_rate(_nmes, th)
recall *= 1 / len(x)
y.append(recall)
return x, y
def ced_curve(nmes):
Y = []
X = np.linspace(0, 10, 50)
for th in X:
recall = 1.0 - lmutils.calc_landmark_failure_rate(nmes, th)
recall *= 1/len(X)
Y.append(recall)
return X,Y
def print_eval_metrics(nmes, show=False):
def ced_curve(_nmes):
y = []
x = np.linspace(0, 10, 50)
for th in x:
recall = 1.0 - lmutils.calc_landmark_failure_rate(_nmes, th)
recall *= 1 / len(x)
y.append(recall)
return x, y
def auc(recalls):
return np.sum(recalls)
# for err_scale in np.linspace(0.1, 1, 10):
for err_scale in [1.0]:
# print('\nerr_scale', err_scale)
# print(np.clip(lm_errs_max_all, a_min=0, a_max=10).mean())
fr = lmutils.calc_landmark_failure_rate(nmes * err_scale)
X, Y = ced_curve(nmes)
log.info("NME: {:>6.3f}".format(nmes.mean() * err_scale))
log.info("FR@10: {:>6.3f} ({})".format(
fr * 100, np.sum(nmes.mean(axis=1) > 10)))
log.info("AUC: {:>6.4f}".format(auc(Y)))
# log.info('NME: {nme:>6.3f}, FR@10: {fr:>6.3f} ({fc}), AUC: {auc:>6.4f}'.format(
# nme=nmes.mean()*err_scale,
# fr=fr*100,
# fc=np.sum(nmes.mean(axis=1) > 10),
# auc=auc(Y)))
if show:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(1, 2)
axes[0].plot(X, Y)
axes[1].hist(nmes.mean(axis=1), bins=20)
plt.show()