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139 lines
4.5 KiB
Matlab
139 lines
4.5 KiB
Matlab
%% Machine Learning Online Class
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% Exercise 6 | Spam Classification with SVMs
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%
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% Instructions
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% ------------
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%
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% This file contains code that helps you get started on the
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% exercise. You will need to complete the following functions:
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%
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% gaussianKernel.m
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% dataset3Params.m
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% processEmail.m
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% emailFeatures.m
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%
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% For this exercise, you will not need to change any code in this file,
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% or any other files other than those mentioned above.
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%
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%% Initialization
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clear ; close all; clc
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%% ==================== Part 1: Email Preprocessing ====================
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% To use an SVM to classify emails into Spam v.s. Non-Spam, you first need
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% to convert each email into a vector of features. In this part, you will
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% implement the preprocessing steps for each email. You should
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% complete the code in processEmail.m to produce a word indices vector
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% for a given email.
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fprintf('\nPreprocessing sample email (emailSample1.txt)\n');
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% Extract Features
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file_contents = readFile('emailSample1.txt');
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word_indices = processEmail(file_contents);
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% Print Stats
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fprintf('Word Indices: \n');
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fprintf(' %d', word_indices);
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fprintf('\n\n');
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ==================== Part 2: Feature Extraction ====================
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% Now, you will convert each email into a vector of features in R^n.
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% You should complete the code in emailFeatures.m to produce a feature
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% vector for a given email.
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fprintf('\nExtracting features from sample email (emailSample1.txt)\n');
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% Extract Features
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file_contents = readFile('emailSample1.txt');
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word_indices = processEmail(file_contents);
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features = emailFeatures(word_indices);
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% Print Stats
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fprintf('Length of feature vector: %d\n', length(features));
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fprintf('Number of non-zero entries: %d\n', sum(features > 0));
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% =========== Part 3: Train Linear SVM for Spam Classification ========
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% In this section, you will train a linear classifier to determine if an
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% email is Spam or Not-Spam.
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% Load the Spam Email dataset
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% You will have X, y in your environment
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load('spamTrain.mat');
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fprintf('\nTraining Linear SVM (Spam Classification)\n')
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fprintf('(this may take 1 to 2 minutes) ...\n')
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C = 0.1;
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model = svmTrain(X, y, C, @linearKernel);
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p = svmPredict(model, X);
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fprintf('Training Accuracy: %f\n', mean(double(p == y)) * 100);
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%% =================== Part 4: Test Spam Classification ================
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% After training the classifier, we can evaluate it on a test set. We have
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% included a test set in spamTest.mat
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% Load the test dataset
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% You will have Xtest, ytest in your environment
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load('spamTest.mat');
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fprintf('\nEvaluating the trained Linear SVM on a test set ...\n')
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p = svmPredict(model, Xtest);
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fprintf('Test Accuracy: %f\n', mean(double(p == ytest)) * 100);
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pause;
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%% ================= Part 5: Top Predictors of Spam ====================
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% Since the model we are training is a linear SVM, we can inspect the
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% weights learned by the model to understand better how it is determining
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% whether an email is spam or not. The following code finds the words with
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% the highest weights in the classifier. Informally, the classifier
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% 'thinks' that these words are the most likely indicators of spam.
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%
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% Sort the weights and obtin the vocabulary list
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[weight, idx] = sort(model.w, 'descend');
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vocabList = getVocabList();
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fprintf('\nTop predictors of spam: \n');
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for i = 1:15
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fprintf(' %-15s (%f) \n', vocabList{idx(i)}, weight(i));
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end
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fprintf('\n\n');
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% =================== Part 6: Try Your Own Emails =====================
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% Now that you've trained the spam classifier, you can use it on your own
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% emails! In the starter code, we have included spamSample1.txt,
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% spamSample2.txt, emailSample1.txt and emailSample2.txt as examples.
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% The following code reads in one of these emails and then uses your
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% learned SVM classifier to determine whether the email is Spam or
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% Not Spam
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% Set the file to be read in (change this to spamSample2.txt,
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% emailSample1.txt or emailSample2.txt to see different predictions on
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% different emails types). Try your own emails as well!
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filename = 'spamSample1.txt';
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% Read and predict
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file_contents = readFile(filename);
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word_indices = processEmail(file_contents);
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x = emailFeatures(word_indices);
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p = svmPredict(model, x);
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fprintf('\nProcessed %s\n\nSpam Classification: %d\n', filename, p);
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fprintf('(1 indicates spam, 0 indicates not spam)\n\n');
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