[Solved] EE5934 Assignment 1: K- Nearest Neighbours

Name: [Solved] EE5934 Assignment 1: K- Nearest Neighbours
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In this assignment, you are required to implement a k-NN classifier to perform image classification on the USPS dataset. First, set up the environment and load the dataset as follows. In [1]: # Run this cell to set up the environment and data import osimport syssys.path.append(../../homework/) import matplotlib.pyplot as pltimport numpy as np %load_ext autoreload%autoreload 2 # Load the USPS datausps_dir = ../data/usps.h5import h5pywith h5py.File(usps_dir, r) as hf: train = hf.get(train) trainX = train.get(data)[:] trainy = train.get(target)[:] test = hf.get(test) testX = test.get(data)[:] testy = test.get(target)[:] # A small subset of USPS for you to run kNN on# as kNN is a little bit slowsub_trainX, sub_trainy = trainX[:5000], trainy[:5000]sub_testX, sub_testy = testX[:1000], testy[:1000]

kNN

Next, implement the k-NN classifier which consists of the following two stages:

Training stage: k-NN classifier loads the training data and remembers it;
Testing stage: k-NN classifies every test image by comparing with all training images and transferring the labels of the k most similar training examples.

Training Stage:

In [2]: from knn import KNN classifier = KNN()classifier.train(sub_trainX, sub_trainy)

Testing Stage:

Implement the k-NN classifier by completing the class method KNN.predict in homework/Assignment1/knn.py and evaluate the k-NN classification error. In [3]: y_pred = classifier.predict(sub_testX, k=1) acc = np.sum(y_pred == sub_testy) / len(sub_testy)print(Accuracy: , acc) Accuracy: 0.942

Cross Validation:

To find the best k, it is tempting to test different k on the test data. However, this leads to overfitting the test data. Read Section 5.3.1 of the Deep Learning book for information about cross validation which is a technique for testing a model on unseen data. Complete the cell below to carry out cross-validation as follows: split the training data and the corresponding labels into 5 subsets(folds), and then do a 5-fold cross validation to test different values of k. In [4]: # k_to_accuracies is a dictionary to hold the classification accuracies obtained when# running cross-validation for different values of k. After running cross-validation,# k_to_accuracies[k] would comprise 5 classification accuracy values found for the # particular value of k.k_to_accuracies = {} ################################################################################# TODO: WRITE CODE FOR THE FOLLOWING ## Perform 5-fold cross validation to find the best value of k as follows: for ## each value of k being considered, run the k-NN algorithm 5 times where in ## each run, one fold is used as validation data while the other folds are ## used as training data. Store the accuracies for all folds for each value ## of k in the k_to_accuracies dictionary. #################################################################################k_cands = [1,2,3,4,5,7,9,12,15,18,22,26,30]trainX_folds = np.split(sub_trainX,5)trainy_folds = np.split(sub_trainy,5) for k in k_cands: accs = [] for i in range(5): trainX_fold = trainX_folds.copy() trainy_fold = trainy_folds.copy() valX_fold = trainX_fold.pop(i) valy_fold = trainy_fold.pop(i) trainX_fold = np.concatenate(trainX_fold) trainy_fold = np.concatenate(trainy_fold) classifier.train(trainX_fold, trainy_fold) y_pred = classifier.predict(valX_fold, k) acc = np.sum(y_pred == valy_fold) / len(valy_fold) accs.append(acc) k_to_accuracies[k] = accs################################################################################# END OF YOUR CODE ################################################################################# # Print out the computed accuraciesfor k in sorted(k_to_accuracies): for accuracy in k_to_accuracies[k]: print(k = %d, accuracy = %f % (k, accuracy)) k = 1, accuracy = 0.960000 k = 1, accuracy = 0.975000 k = 1, accuracy = 0.950000 k = 1, accuracy = 0.953000 k = 1, accuracy = 0.961000 k = 2, accuracy = 0.957000 k = 2, accuracy = 0.975000 k = 2, accuracy = 0.947000 k = 2, accuracy = 0.937000 k = 2, accuracy = 0.948000 k = 3, accuracy = 0.957000 k = 3, accuracy = 0.973000 k = 3, accuracy = 0.950000 k = 3, accuracy = 0.954000 k = 3, accuracy = 0.958000 k = 4, accuracy = 0.951000 k = 4, accuracy = 0.972000 k = 4, accuracy = 0.946000 k = 4, accuracy = 0.948000 k = 4, accuracy = 0.952000 k = 5, accuracy = 0.955000 k = 5, accuracy = 0.969000 k = 5, accuracy = 0.946000 k = 5, accuracy = 0.950000 k = 5, accuracy = 0.954000 k = 7, accuracy = 0.950000 k = 7, accuracy = 0.967000 k = 7, accuracy = 0.943000 k = 7, accuracy = 0.938000 k = 7, accuracy = 0.953000 k = 9, accuracy = 0.947000 k = 9, accuracy = 0.964000 k = 9, accuracy = 0.938000 k = 9, accuracy = 0.936000 k = 9, accuracy = 0.943000 k = 12, accuracy = 0.940000 k = 12, accuracy = 0.960000 k = 12, accuracy = 0.931000 k = 12, accuracy = 0.932000 k = 12, accuracy = 0.936000 k = 15, accuracy = 0.931000 k = 15, accuracy = 0.953000 k = 15, accuracy = 0.920000 k = 15, accuracy = 0.926000 k = 15, accuracy = 0.935000 k = 18, accuracy = 0.933000 k = 18, accuracy = 0.953000 k = 18, accuracy = 0.919000 k = 18, accuracy = 0.926000 k = 18, accuracy = 0.933000 k = 22, accuracy = 0.926000 k = 22, accuracy = 0.946000 k = 22, accuracy = 0.915000 k = 22, accuracy = 0.917000 k = 22, accuracy = 0.928000 k = 26, accuracy = 0.923000 k = 26, accuracy = 0.942000 k = 26, accuracy = 0.908000 k = 26, accuracy = 0.910000 k = 26, accuracy = 0.927000 k = 30, accuracy = 0.918000 k = 30, accuracy = 0.938000 k = 30, accuracy = 0.904000 k = 30, accuracy = 0.906000 k = 30, accuracy = 0.920000 In [5]: # visualize the results above. ################################################################################# TODO: WRITE CODE FOR THE FOLLOWING ## To better understand the influence of different k values, ## show the above printed results in the scatter plot and then plot the trend ## with error bars that correspond to standard deviation. #################################################################################import matplotlib.pyplot as plt# scatter plotfor k in k_cands: plt.scatter([k]*5,k_to_accuracies[k]) acc_means = []acc_stds = []for k in k_cands: acc_means.append(np.mean(k_to_accuracies[k])) acc_stds.append(np.std(k_to_accuracies[k])) plt.errorbar(k_cands,acc_means,yerr=acc_stds)################################################################################# END OF YOUR CODE #################################################################################plt.title(Cross-validation on k)plt.xlabel(k)plt.ylabel(Cross-validation accuracy)plt.show() In [6]: ################################################################################# TODO: ## Based on the cross-validation results above, identify the best value for k ## and apply it to best_k below. Then, retrain the classifier using all the ## training data, and test it on the test data. #################################################################################best_k = 1################################################################################# END OF YOUR CODE ################################################################################# classifier = KNN()classifier.train(sub_trainX, sub_trainy)y_test_pred = classifier.predict(sub_testX, k=best_k) # Compute and display the accuracyaccuracy = np.sum(y_test_pred == sub_testy) / len(sub_testy)print(accuracy: %f % accuracy) accuracy: 0.942000 Inline question: In practice, why do we often choose an odd number for k? Give your answer below. As we can see from figure-the Cross-validation on k, it has obvious vallys as k = 2,4,12. The ambiguity will happend when the test point has equal number voters of different classes. If we choose odd number, that case will not happen.