diff options
| -rwxr-xr-x | train.py | 69 |
1 files changed, 23 insertions, 46 deletions
@@ -63,18 +63,17 @@ def test_split(n_faces, raw_faces, split, seed): faces_test = faces_test.reshape(n_faces*n_test_faces, n_pixels) return faces_train, faces_test, target_train, target_test -def draw_conf_mat(args, target_test, target_pred): +def draw_results(args, target_test, target_pred): acc_sc = accuracy_score(target_test, target_pred) - if not args.classifyalt: - cm = confusion_matrix(target_test, target_pred) - print('Accuracy: ', acc_sc) - if (args.conf_mat): - plt.matshow(cm, cmap='Blues') - plt.colorbar() - plt.ylabel('Actual') - plt.xlabel('Predicted') - plt.show() - return acc_sc + cm = confusion_matrix(target_test, target_pred) + print('Accuracy: ', acc_sc) + if (args.conf_mat): + plt.matshow(cm, cmap='Blues') + plt.colorbar() + plt.ylabel('Actual') + plt.xlabel('Predicted') + plt.show() + return def test_model(M, faces_train, faces_test, target_train, target_test, args): raw_faces_train = faces_train @@ -185,8 +184,7 @@ def test_model(M, faces_train, faces_test, target_train, target_test, args): plt.show() #Better n_neighbors = 2 - accuracy = draw_conf_mat(args, target_test, target_pred) - return target_pred, accuracy, distances + return target_pred, distances def main(): parser = argparse.ArgumentParser() @@ -233,32 +231,23 @@ def main(): faces_train = faces_train.reshape(n_faces, int(faces_train.shape[0]/n_faces), n_pixels) target_train = target_train.reshape(n_faces, int(target_train.shape[0]/n_faces)) - accuracy = np.zeros(n_faces) distances = np.zeros((n_faces, faces_test.shape[0])) for i in range(n_faces): - target_pred, accuracy[i], distances[i] = test_model(args.eigen, faces_train[i], faces_test, target_train[i], target_test, args) + target_pred, distances[i] = test_model(args.eigen, faces_train[i], faces_test, target_train[i], target_test, args) target_pred = np.argmin(distances, axis=0) - acc_sc = accuracy_score(target_test, target_pred) - cm = confusion_matrix(target_test, target_pred) - print('Total Accuracy: ', acc_sc) - if (args.conf_mat): - plt.matshow(cm, cmap='Blues') - plt.colorbar() - plt.ylabel('Actual') - plt.xlabel('Predicted') - plt.show() - return + elif args.reigen: + target_pred = np.zeros((args.reigen-args.eigen, 2*n_faces)) + accuracy = np.zeros((args.reigen-args.eigen, 2*n_faces)) + rec_error = np.zeros((args.reigen-args.eigen, 2*n_faces)) - if args.reigen: - accuracy = np.zeros(args.reigen-args.eigen) - rec_error = np.zeros((args.reigen-args.eigen, 2*n_faces)) for M in range(args.eigen, args.reigen): start = timer() - target_pred, accuracy[M - args.eigen], rec_error[M - args.eigen] = test_model(M, faces_train, faces_test, target_train, target_test, args) + target_pred[i], rec_error[M - args.eigen] = test_model(M, faces_train, faces_test, target_train, target_test, args) end = timer() print("Run with", M, "eigenvalues completed in ", end-start, "seconds") print("Memory Used:", psutil.Process(os.getpid()).memory_info().rss) - #plot + accuracy[i] = accuracy_score(target_test, target_pred[i]) + # Plot print('Max efficiency of ', max(accuracy), '% for M =', np.argmax(accuracy)) plt.plot(range(args.eigen, args.reigen), 100*accuracy) plt.xlabel('Number of Eigenvectors used (M)') @@ -266,34 +255,22 @@ def main(): plt.grid(True) plt.show() elif args.ensemble: - accuracy = np.zeros(args.ensemble) rec_error = np.zeros((args.ensemble, n_faces, faces_test.shape[0])) target_pred = np.zeros((args.ensemble, target_test.shape[0])) for i in range(args.ensemble): - target_pred[i], accuracy[i], rec_error[i] = test_model(args.eigen, faces_train_bagged[i], faces_test, target_train, target_test, args) + target_pred[i], rec_error[i] = test_model(args.eigen, faces_train_bagged[i], faces_test, target_train, target_test, args) target_pred_comb = np.zeros(target_pred.shape[1]) target_pred = target_pred.astype(int).T for i in range(target_pred.shape[0]): target_pred_comb[i] = np.bincount(target_pred[i]).argmax() - print(target_pred_comb) - acc_sc = accuracy_score(target_test, target_pred_comb) - cm = confusion_matrix(target_test, target_pred_comb) - print('Total Accuracy: ', acc_sc) - if (args.conf_mat): - plt.matshow(cm, cmap='Blues') - plt.colorbar() - plt.ylabel('Actual') - plt.xlabel('Predicted') - plt.show() - return - - + target_pred = target_pred_comb else: M = args.eigen start = timer() - test_model(M, faces_train, faces_test, target_train, target_test, args) + target_pred, distances = test_model(M, faces_train, faces_test, target_train, target_test, args) end = timer() + draw_results(args, target_test, target_pred) if __name__ == "__main__": main() |