knn.fit(X_train, Y_train) print("Training time:", (datetime.now() - t0)) t0 = datetime.now() train_score = knn.score(X_train, Y_train) train_scores.append(train_score) print("Train accuracy:", train_score) print("Time to compute train accuracy:", (datetime.now() - t0), "Train size:", len(Y_train)) t0 = datetime.now() test_score = knn.score(X_test, Y_test) print("Test accuracy:", test_score) test_scores.append(test_score) print("Time to compute test accuracy:", (datetime.now() - t0), "Test size:", len(Y_test)) exec_times.append((datetime.now() - t0).total_seconds()) # update best accuracy (for comparison purpose later) if test_score > best_test_score: best_test_score = test_score stored_accuracy = [test_score, k] fig = plt.figure() plt.plot(ks, train_scores, label='train scores') plt.plot(ks, test_scores, label='test scores') plt.legend() plt.show() fig.savefig('hw5/results/result-knn-scratch.png', dpi=fig.dpi) save_to_csv('exec-knn-scratch.csv', exec_times) # X is an array save_to_csv('knn-test-scores-scratch.csv', test_scores) save_to_csv('knn-best-acc-scratch.csv', stored_accuracy)
train_prob = kde.predict(X_train) train_score = kde.eval_acc(train_prob, Y_train) train_scores.append(train_score) print("Train accuracy:", train_score) print("Time to compute train accuracy:", (datetime.now() - t0), "Train size:", len(Y_train)) t0 = datetime.now() test_prob = kde.predict(X_test) test_score = kde.eval_acc(test_prob, Y_test) print("Test accuracy:", test_score) test_scores.append(test_score) print("Time to compute test accuracy:", (datetime.now() - t0), "Test size:", len(Y_test)) exec_times.append((datetime.now() - t0).total_seconds()) # update best accuracy (for comparison purpose later) if test_score > best_test_score: best_test_score = test_score stored_accuracy = [test_score, bandwidth] fig = plt.figure() plt.plot(ks, train_scores, label='train scores') plt.plot(ks, test_scores, label='test scores') plt.legend() plt.show() fig.savefig('hw5/results/result-kde.png', dpi=fig.dpi) save_to_csv('exec-kde.csv', exec_times) save_to_csv('test-scores-kde.csv', test_scores) save_to_csv('best-acc-kde.csv', stored_accuracy)
train_score = knn.score(X_train, Y_train) / 100 train_scores.append(train_score) print("Train accuracy:", train_score) print("Time to compute train accuracy:", (datetime.now() - t0), "Train size:", len(Y_train)) t0 = datetime.now() test_score = knn.score(X_test, Y_test) / 100 print("Test accuracy:", test_score) test_scores.append(test_score) print("Time to compute test accuracy:", (datetime.now() - t0), "Test size:", len(Y_test)) exec_times.append((datetime.now() - t0).total_seconds()) # update best accuracy (for comparison purpose later) if test_score > best_test_score: best_test_score = test_score stored_accuracy = [test_score, k] # np.savetxt(fname=csv_path + 'exec-knn-theano.csv', X=str("dsd"), delimiter=',', fmt='%d') fig = plt.figure() plt.plot(ks, train_scores, label='train scores') plt.plot(ks, test_scores, label='test scores') plt.legend() plt.show() fig.savefig('hw5/results/result-knn-theano.png', dpi=fig.dpi) save_to_csv('exec-knn-theano.csv', exec_times) save_to_csv('knn-test-scores-theano.csv', test_scores) save_to_csv('knn-best-acc-theano.csv', stored_accuracy)
t0 = datetime.now() train_prob = kmeans.predict(X_train) train_score = kmeans.eval_acc(train_prob, Y_train) train_scores.append(train_score) print("Train accuracy:", train_score) print("Time to compute train accuracy:", (datetime.now() - t0), "Train size:", len(Y_train)) t0 = datetime.now() test_prob = kmeans.predict(X_test) test_score = kmeans.eval_acc(test_prob, Y_test) print("Test accuracy:", test_score) test_scores.append(test_score) print("Time to compute test accuracy:", (datetime.now() - t0), "Test size:", len(Y_test)) exec_times.append((datetime.now() - t0).total_seconds()) # update best accuracy (for comparison purpose later) if test_score > best_test_score: best_test_score = test_score stored_accuracy = [test_score, ds[i]] fig = plt.figure() plt.plot(ds, train_scores, label='train scores') plt.plot(ds, test_scores, label='test scores') plt.legend() plt.show() fig.savefig('hw6/results/result-kmeans.png', dpi=fig.dpi) save_to_csv('exec-kmeans.csv', exec_times, rot_path="..") save_to_csv('test-scores-kmeans.csv', test_scores, rot_path="..") save_to_csv('best-acc-kmeans.csv', stored_accuracy, rot_path="..")
t0 = datetime.now() # Start GMM: Sklearn highest_acc = 0.0 for i in range(K): gmm.fit(X_train) y_gmm = gmm.predict(X_train) accuracy = gmm.eval_acc(y_gmm, Y_train) acc_scores.append(accuracy) highest_acc = accuracy if accuracy > highest_acc else highest_acc elapsed_time = datetime.now() - t0 fig = plt.figure() mean_acc = str(round(np.mean(np.array(acc_scores)), 2)) title = "Highest (Red) = " + str(round(highest_acc, 2)) + "; AVG (Black) = " + mean_acc plt.title(title) plt.plot(ks, acc_scores, label='accuracy') plt.axhline(highest_acc, color='red', linestyle='dashed', linewidth=2) plt.axhline(np.mean(np.array(acc_scores)), color='k', linestyle='dashed', linewidth=2) plt.legend() plt.show() fig.savefig('hw6/results/result-gmm-accuracy.png', dpi=fig.dpi) save_to_csv('gmm-acc.csv', acc_scores, "hw6") save_to_csv('gmm-exec-time.csv', [elapsed_time.total_seconds()], "hw6")