accuracy = num_correct * 100.0 / len(test_images_filenames)
# Show results and timing
print('\nACCURACY: {:.2f}'.format(accuracy))
print('\nTOTAL TIME: {:.2f} s'.format(time.time() - start))
classes = lin_svm.classes_
# Create confusion matrix
conf = confusion_matrix(test_labels, pred_class, labels=classes)
# Create ROC curve and AUC score
test_labels_bin = label_binarize(test_labels, classes=classes)
fpr = []
tpr = []
roc_auc = []
for i in range(len(classes)):
c_fpr, c_tpr, _ = roc_curve(test_labels_bin[:, i], np.array(pred_prob)[:, i])
c_roc_auc = auc(c_fpr, c_tpr)
fpr.append(c_fpr)
tpr.append(c_tpr)
roc_auc.append(c_roc_auc)
# Plot
plotting.plot_confusion_matrix(conf, classes=classes, normalize=True)
plotting.plot_roc_curve(fpr, tpr, roc_auc, classes=classes,
title='ROC curve for linear SVM with codebook of {} words'.format(settings.codebook_size)
)
print('Done.')
svm = io.load_object(SESSION1['model'])
std_scaler = io.load_object(SESSION1['scaler'])
pca = io.load_object(SESSION1['pca'])
# Feature extraction with sift, prediction with SVM and aggregation to obtain final class
print('Predicting test data...')
result = joblib.Parallel(n_jobs=SESSION1['n_jobs'], backend='threading')(
joblib.delayed(parallel_testing)(test_image, test_label, svm,
std_scaler, pca)
for test_image, test_label in zip(test_images_filenames, test_labels))
correct_class = [i[0] for i in result]
predicted = [i[1] for i in result]
expected = [i[2] for i in result]
num_correct = np.count_nonzero(correct_class)
print('Time spend: {:.2f} s'.format(time.time() - start))
temp = time.time()
# Compute accuracy
accuracy = num_correct * 100.0 / len(test_images_filenames)
# Plot and save normalized confusion matrix
conf = metrics.confusion_matrix(expected, predicted, labels=svm.classes_)
plot_confusion_matrix(conf, classes=svm.classes_, normalize=True)
io.save_object(conf, SESSION1['conf_matrix'])
# Show results and timing
print('\nACCURACY: {:.2f}'.format(accuracy))
print('\nTOTAL TIME: {:.2f} s'.format(time.time() - start))
