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machine_learning/course1/mlclass-ex8-008/mlclass-ex8/ex8_cofi.m
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machine_learning/course1/mlclass-ex8-008/mlclass-ex8/ex8_cofi.m
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%% Machine Learning Online Class
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% Exercise 8 | Anomaly Detection and Collaborative Filtering
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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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% estimateGaussian.m
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% selectThreshold.m
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% cofiCostFunc.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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%% =============== Part 1: Loading movie ratings dataset ================
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% You will start by loading the movie ratings dataset to understand the
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% structure of the data.
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%
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fprintf('Loading movie ratings dataset.\n\n');
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% Load data
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load ('ex8_movies.mat');
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% Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies on
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% 943 users
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%
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% R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a
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% rating to movie i
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% From the matrix, we can compute statistics like average rating.
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fprintf('Average rating for movie 1 (Toy Story): %f / 5\n\n', ...
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mean(Y(1, R(1, :))));
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% We can "visualize" the ratings matrix by plotting it with imagesc
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imagesc(Y);
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ylabel('Movies');
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xlabel('Users');
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ============ Part 2: Collaborative Filtering Cost Function ===========
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% You will now implement the cost function for collaborative filtering.
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% To help you debug your cost function, we have included set of weights
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% that we trained on that. Specifically, you should complete the code in
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% cofiCostFunc.m to return J.
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% Load pre-trained weights (X, Theta, num_users, num_movies, num_features)
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load ('ex8_movieParams.mat');
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% Reduce the data set size so that this runs faster
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num_users = 4; num_movies = 5; num_features = 3;
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X = X(1:num_movies, 1:num_features);
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Theta = Theta(1:num_users, 1:num_features);
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Y = Y(1:num_movies, 1:num_users);
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R = R(1:num_movies, 1:num_users);
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% Evaluate cost function
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J = cofiCostFunc([X(:) ; Theta(:)], Y, R, num_users, num_movies, ...
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num_features, 0);
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fprintf(['Cost at loaded parameters: %f '...
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'\n(this value should be about 22.22)\n'], J);
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ============== Part 3: Collaborative Filtering Gradient ==============
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% Once your cost function matches up with ours, you should now implement
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% the collaborative filtering gradient function. Specifically, you should
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% complete the code in cofiCostFunc.m to return the grad argument.
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%
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fprintf('\nChecking Gradients (without regularization) ... \n');
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% Check gradients by running checkNNGradients
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checkCostFunction;
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ========= Part 4: Collaborative Filtering Cost Regularization ========
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% Now, you should implement regularization for the cost function for
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% collaborative filtering. You can implement it by adding the cost of
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% regularization to the original cost computation.
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%
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% Evaluate cost function
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J = cofiCostFunc([X(:) ; Theta(:)], Y, R, num_users, num_movies, ...
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num_features, 1.5);
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fprintf(['Cost at loaded parameters (lambda = 1.5): %f '...
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'\n(this value should be about 31.34)\n'], J);
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ======= Part 5: Collaborative Filtering Gradient Regularization ======
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% Once your cost matches up with ours, you should proceed to implement
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% regularization for the gradient.
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%
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%
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fprintf('\nChecking Gradients (with regularization) ... \n');
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% Check gradients by running checkNNGradients
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checkCostFunction(1.5);
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ============== Part 6: Entering ratings for a new user ===============
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% Before we will train the collaborative filtering model, we will first
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% add ratings that correspond to a new user that we just observed. This
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% part of the code will also allow you to put in your own ratings for the
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% movies in our dataset!
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%
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movieList = loadMovieList();
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% Initialize my ratings
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my_ratings = zeros(1682, 1);
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% Check the file movie_idx.txt for id of each movie in our dataset
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% For example, Toy Story (1995) has ID 1, so to rate it "4", you can set
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my_ratings(1) = 4;
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% Or suppose did not enjoy Silence of the Lambs (1991), you can set
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my_ratings(98) = 2;
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% We have selected a few movies we liked / did not like and the ratings we
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% gave are as follows:
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my_ratings(7) = 3;
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my_ratings(12)= 5;
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my_ratings(54) = 4;
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my_ratings(64)= 5;
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my_ratings(66)= 3;
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my_ratings(69) = 5;
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my_ratings(183) = 4;
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my_ratings(226) = 5;
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my_ratings(355)= 5;
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fprintf('\n\nNew user ratings:\n');
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for i = 1:length(my_ratings)
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if my_ratings(i) > 0
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fprintf('Rated %d for %s\n', my_ratings(i), ...
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movieList{i});
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end
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end
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ================== Part 7: Learning Movie Ratings ====================
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% Now, you will train the collaborative filtering model on a movie rating
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% dataset of 1682 movies and 943 users
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%
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fprintf('\nTraining collaborative filtering...\n');
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% Load data
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load('ex8_movies.mat');
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% Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies by
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% 943 users
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%
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% R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a
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% rating to movie i
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% Add our own ratings to the data matrix
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Y = [my_ratings Y];
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R = [(my_ratings ~= 0) R];
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% Normalize Ratings
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[Ynorm, Ymean] = normalizeRatings(Y, R);
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% Useful Values
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num_users = size(Y, 2);
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num_movies = size(Y, 1);
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num_features = 10;
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% Set Initial Parameters (Theta, X)
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X = randn(num_movies, num_features);
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Theta = randn(num_users, num_features);
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initial_parameters = [X(:); Theta(:)];
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% Set options for fmincg
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options = optimset('GradObj', 'on', 'MaxIter', 100);
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% Set Regularization
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lambda = 10;
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theta = fmincg (@(t)(cofiCostFunc(t, Y, R, num_users, num_movies, ...
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num_features, lambda)), ...
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initial_parameters, options);
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% Unfold the returned theta back into U and W
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X = reshape(theta(1:num_movies*num_features), num_movies, num_features);
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Theta = reshape(theta(num_movies*num_features+1:end), ...
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num_users, num_features);
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fprintf('Recommender system learning completed.\n');
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ================== Part 8: Recommendation for you ====================
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% After training the model, you can now make recommendations by computing
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% the predictions matrix.
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%
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p = X * Theta';
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my_predictions = p(:,1) + Ymean;
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movieList = loadMovieList();
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[r, ix] = sort(my_predictions, 'descend');
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fprintf('\nTop recommendations for you:\n');
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for i=1:10
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j = ix(i);
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fprintf('Predicting rating %.1f for movie %s\n', my_predictions(j), ...
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movieList{j});
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end
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fprintf('\n\nOriginal ratings provided:\n');
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for i = 1:length(my_ratings)
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if my_ratings(i) > 0
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fprintf('Rated %d for %s\n', my_ratings(i), ...
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movieList{i});
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end
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end
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