Machine Learning Online Class
Exercise 8 | Anomaly Detection and Collaborative Filtering
Instructions
------------
This file contains code that helps you get started on the
exercise. You will need to complete the following functions:
estimateGaussian.m
selectThreshold.m
cofiCostFunc.m
For this exercise, you will not need to change any code in this file,
or any other files other than those mentioned above.
=============== Part 1: Loading movie ratings dataset ================
You will start by loading the movie ratings dataset to understand the
structure of the data.
fprintf('Loading movie ratings dataset.\n\n');
% Load data
load ('ex8_movies.mat');
% Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies on
% 943 users
%
% R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a
% rating to movie i
% From the matrix, we can compute statistics like average rating.
fprintf('Average rating for movie 1 (Toy Story): %f / 5\n\n', ...
mean(Y(1, R(1, :))));
% We can "visualize" the ratings matrix by plotting it with imagesc
imagesc(Y);
ylabel('Movies');
xlabel('Users');
============ Part 2: Collaborative Filtering Cost Function ===========
You will now implement the cost function for collaborative filtering.
To help you debug your cost function, we have included set of weights
that we trained on that. Specifically, you should complete the code in
cofiCostFunc.m to return J.
% Load pre-trained weights (X, Theta, num_users, num_movies, num_features)
load ('ex8_movieParams.mat');
% Reduce the data set size so that this runs faster
num_users = 4;
num_movies = 5;
num_features = 3;
% X是电影的特征矩阵，一行一个样本特征向量
% Theta是用户的参数矩阵，一行一个用户参数向量
X = X(1 : num_movies, 1 : num_features);
Theta = Theta(1:num_users, 1:num_features);
Y = Y(1 : num_movies, 1 : num_users);
R = R(1 : num_movies, 1 : num_users);
% Evaluate cost function
J = cofiCostFunc([X(:) ; Theta(:)], Y, R, num_users, num_movies, num_features, 0);
fprintf(['Cost at loaded parameters: %f '...
'\n(this value should be about 22.22)\n'], J);
============== Part 3: Collaborative Filtering Gradient ==============
Once your cost function matches up with ours, you should now implement
the collaborative filtering gradient function. Specifically, you should
complete the code in cofiCostFunc.m to return the grad argument.
fprintf('\nChecking Gradients (without regularization) ... \n');
% Check gradients by running checkNNGradients
checkCostFunction;
========= Part 4: Collaborative Filtering Cost Regularization ========
Now, you should implement regularization for the cost function for
collaborative filtering. You can implement it by adding the cost of
regularization to the original cost computation.
% Evaluate cost function
J = cofiCostFunc([X(:) ; Theta(:)], Y, R, num_users, num_movies, ...
num_features, 1.5);
fprintf(['Cost at loaded parameters (lambda = 1.5): %f '...
'\n(this value should be about 31.34)\n'], J);
======= Part 5: Collaborative Filtering Gradient Regularization ======
Once your cost matches up with ours, you should proceed to implement
regularization for the gradient.
fprintf('\nChecking Gradients (with regularization) ... \n');
% Check gradients by running checkNNGradients
checkCostFunction(1.5);
============== Part 6: Entering ratings for a new user ===============
Before we will train the collaborative filtering model, we will first
add ratings that correspond to a new user that we just observed. This
part of the code will also allow you to put in your own ratings for the
movies in our dataset!
movieList = loadMovieList();
% Initialize my ratings
my_ratings = zeros(1682, 1);
% Check the file movie_idx.txt for id of each movie in our dataset
% For example, Toy Story (1995) has ID 1, so to rate it "4", you can set
my_ratings(1) = 4;
% Or suppose did not enjoy Silence of the Lambs (1991), you can set
my_ratings(98) = 2;
% We have selected a few movies we liked / did not like and the ratings we
% gave are as follows:
my_ratings(7) = 3;
my_ratings(12)= 5;
my_ratings(54) = 4;
my_ratings(64)= 5;
my_ratings(66)= 3;
my_ratings(69) = 5;
my_ratings(183) = 4;
my_ratings(226) = 5;
my_ratings(355)= 5;
fprintf('\n\nNew user ratings:\n');
for i = 1 : length(my_ratings)
if my_ratings(i) > 0
fprintf('Rated %d for %s\n', my_ratings(i), ...
movieList{i});
end
end
================== Part 7: Learning Movie Ratings ====================
Now, you will train the collaborative filtering model on a movie rating
dataset of 1682 movies and 943 users
fprintf('\nTraining collaborative filtering...\n');
% Load data
load('ex8_movies.mat');
% Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies by
% 943 users
%
% R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a
% rating to movie i
% Add our own ratings to the data matrix
% 相当于新增了一个个用户的数据
Y = [my_ratings, Y];
R = [(my_ratings ~= 0), R];
% Normalize Ratings
[Ynorm, Ymean] = normalizeRatings(Y, R);
% Useful Values
num_users = size(Y, 2);
num_movies = size(Y, 1);
num_features = 10;
% Set Initial Parameters (Theta, X)
% 正态分布的随机数
X = randn(num_movies, num_features);
Theta = randn(num_users, num_features);
initial_parameters = [X(:); Theta(:)];
% Set options for fmincg
options = optimset('GradObj', 'on', 'MaxIter', 100);
% Set Regularization
lambda = 10;
% 开始训练X Theta
theta = fmincg (@(t)(cofiCostFunc(t, Ynorm, R, num_users, num_movies, ...
num_features, lambda)), ...
initial_parameters, options);
% Unfold the returned theta back into U and W
% 将X和Theta恢复正常形式
X = reshape(theta(1 : num_movies * num_features), num_movies, num_features);
Theta = reshape(theta(num_movies * num_features + 1 : end), num_users, num_features);
fprintf('Recommender system learning completed.\n');
================== Part 8: Recommendation for you ====================
After training the model, you can now make recommendations by computing
the predictions matrix.
% 新用户的预测也是先用整体求出后，加上均值
p = X * Theta';
% 对训练出来的预估结果加上均值
my_predictions = p(:, 1) + Ymean;
movieList = loadMovieList();
[r, ix] = sort(my_predictions, 'descend');
fprintf('\nTop recommendations for you:\n');
for i = 1 : 10
j = ix(i);
fprintf('Predicting rating %.1f for movie %s\n', my_predictions(j), movieList{j});
end
fprintf('\n\nOriginal ratings provided:\n');
for i = 1 : length(my_ratings)
if my_ratings(i) > 0
fprintf('Rated %d for %s\n', my_ratings(i), movieList{i});
end
end