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)