def get_stats(modelh5, lmbda, mu, x_test, y_test):
print(modelh5[14:20], end=",")
model = load_model(modelh5,
custom_objects={
'comb_mse':
closs.get_diff_comb_mae_loss(1., lmbda, mu),
'comb_mae':
closs.get_diff_comb_mae_loss(1., lmbda, mu),
'pod': closs.get_pod_loss(1.),
'pom': closs.get_pom_loss(1.),
'far': closs.get_far_loss(1.),
'pofd': closs.get_pofd_loss(1.)
})
y_pred = model.predict(x_test)
for v in [.2, .5, 1., 2., 5., 10.]:
pod = verif_pod(y_test, y_pred, v)
print(pod, end=",")
for v in [.2, .5, 1., 2., 5., 10.]:
pofd = verif_pofd(y_test, y_pred, v)
print(pofd, end=",")
print(verif_mae(y_test, y_pred), end=",")
print(verif_mse(y_test, y_pred))
def get_plot(modelh5, lmbda, mu):
model = load_model(modelh5,
custom_objects={
'comb_mse':
closs.get_diff_comb_mse_loss(1., lmbda, mu),
'comb_mae':
closs.get_diff_comb_mae_loss(1., lmbda, mu),
'pod': closs.get_pod_loss(1.),
'pom': closs.get_pom_loss(1.),
'far': closs.get_far_loss(1.),
'pofd': closs.get_pofd_loss(1.)
})
y_pred = model.predict(x_test)
np.save(modelh5[:-3], y_pred)
tpr = []
tpr.append(1)
fpr = []
fpr.append(1)
for v in [.2, .5, 1., 2., 5., 10.]:
tpr.append(verif_pod(y_test, y_pred, v))
fpr.append(verif_pofd(y_test, y_pred, v))
roc_auc = auc(fpr, tpr)
print(tpr)
print(fpr)
print(roc_auc)
plt.figure()
plt.plot(fpr,
tpr,
color='darkorange',
lw=2,
label='ROC curve (area = %0.4f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0., 1.])
plt.ylim([0., 1.])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.savefig("{}.png".format(modelh5[3:-3]))
print("done")
def plot_prec(modelh5, coef, lmbda, mu):
i = 1
model = load_model(modelh5,
custom_objects={
'comb_mse':
closs.get_diff_comb_mse_loss(1., lmbda, mu),
'comb_mae':
closs.get_diff_comb_mae_loss(1., lmbda, mu),
'pod': closs.get_pod_loss(1.),
'pom': closs.get_pom_loss(1.),
'far': closs.get_far_loss(1.),
'pofd': closs.get_pofd_loss(1.)
})
out = model.predict(x_test[i:i + 1, :])
plt.imsave('test_mse{}_{}{}_pred_{}.png'.format(coef, lmbda, mu, i),
out[0, :, :, 0],
vmin=0,
vmax=20,
cmap=rain)