Project 5: Classification: CS343H Spring 2015
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Acknowledgements: This tutorial is based closely on the one created by Dan Klein and John DeNero that was given as part of the programming assignments of Berkeley's CS188 course. We thank Dan and John for sharing it with us and for their permission to use it as a part of our course.

We also thank Daniel Urieli for the initial adaptation of this assignment for the CS343 Artificial Intelligence course at The University of Texas at Austin.

Project 5: Classification


Which Digit?

Which Digit?

Introduction

In this project, you will design three classifiers: a perceptron classifier, a large-margin (MIRA) classifier, and a slightly modified perceptron classifier for behavioral cloning. You will test the first two classifiers on a set of scanned handwritten digit images, and the last on sets of recorded pacman games from various agents. Even with simple features, your classifiers will be able to do quite well on these tasks when given enough training data.

Optical character recognition (OCR) is the task of extracting text from image sources. The data set on which you will run your classifiers is a collection of handwritten numerical digits (0-9). This is a very commercially useful technology, similar to the technique used by the US post office to route mail by zip codes. There are systems that can perform with over 99% classification accuracy (see LeNet-5 for an example system in action).

Behavioral cloning is the task of learning to copy a behavior simply by observing examples of that behavior. In this project, you will be using this idea to mimic various pacman agents by using recorded games as training examples. Your agent will then run the classifier at each action in order to try and determine which action would be taken by the observed agent.

The code for this project contains the following files, available as a zip archive.

Files you will edit
Data file
data.zip Data file, including the digit and face data.
Files you will edit
naiveBayes.py The location where you will write your naive Bayes classifier.
perceptron.py The location where you will write your perceptron classifier.
mira.py The location where you will write your MIRA classifier.
dataClassifier.py The wrapper code that will call your classifiers. You will also write your enhanced feature extractor here. You will also use this code to analyze the behavior of your classifier.
Files you should read but NOT edit
classificationMethod.py Abstract super class for the classifiers you will write.
(You should read this file carefully to see how the infrastructure is set up.)
samples.py I/O code to read in the classification data.
util.py Code defining some useful tools. You may be familiar with some of these by now, and they will save you a lot of time.
mostFrequent.py A simple baseline classifier that just labels every instance as the most frequent class.

What to submit: You will fill in portions of, perceptron.py, mira.py, answers.py, perceptron_pacman.py and dataClassifier.py (only) during the assignment, and submit them.

This assignment should be submitted via Canvas with the assignment name cs343-5-classification using these submission instructions.

Evaluation: Your code will be autograded for technical correctness. Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder.

Academic Dishonesty: We will be checking your code against other submissions in the class for logical redundancy. If you copy someone else's code and submit it with minor changes, we will know. These cheat detectors are quite hard to fool, so please don't try. We trust you all to submit your own work only; please don't let us down. Instead, contact the course staff if you are having trouble.

Getting Help: You are not alone! If you find yourself stuck on something, contact the course staff for help. Office hours, and Piazza are there for your support; please use them. If you can't make our office hours, let us know and we will schedule more. We want these projects to be rewarding and instructional, not frustrating and demoralizing. But, we don't know when or how to help unless you ask. One more piece of advice: if you don't know what a variable does or what kind of values it takes, print it out.

Getting Started

To try out the classification pipeline, run dataClassifier.py from the command line. This will classify the digit data using the default classifier (mostFrequent) which blindly classifies every example with the most frequent label. 

python dataClassifier.py

As usual, you can learn more about the possible command line options by running: 

python dataClassifier.py -h

We have defined some simple features for you. Later you will design some better features. Our simple feature set includes one feature for each pixel location, which can take values 0 or 1 (off or on). The features are encoded as a Counter where keys are feature locations (represented as (column,row)) and values are 0 or 1. The face recognition data set has value 1 only for those pixels identified by a Canny edge detector.

Question 1 (4 points): Perceptron

A skeleton implementation of a perceptron classifier is provided for you in perceptron.py. You will fill in the train function, and the findHighOddsFeatures function.

Unlike the naive Bayes classifier, a perceptron does not use probabilities to make its decisions. Instead, it keeps a weight vector $w^y$ of each class $y$ ($y$ is an identifier, not an exponent). Given a feature list $f$, the perceptron compute the class $y$ whose weight vector is most similar to the input vector $f$. Formally, given a feature vector $f$ (in our case, a map from pixel locations to indicators of whether they are on), we score each class with:

\begin{displaymath} \mbox{score}(f,y) = \sum_i f_i w^y_i \end{displaymath}
Then we choose the class with highest score as the predicted label for that data instance. In the code, we will represent $w^y$ as a Counter.

Learning weights

In the basic multi-class perceptron, we scan over the data, one instance at a time. When we come to an instance $(f, y)$, we find the label with highest score:
\begin{displaymath} y' = \textmd{arg max}_{y''} score(f,y'') \end{displaymath}

We compare $y'$ to the true label $y$. If $y' = y$, we've gotten the instance correct, and we do nothing. Otherwise, we guessed $y'$ but we should have guessed $y$. That means that $w^y$ should have scored $f$ higher, and $w^{y'}$ should have scored $f$ lower, in order to prevent this error in the future. We update these two weight vectors accordingly:

\begin{displaymath} w^y += f \end{displaymath}
\begin{displaymath} w^{y'} -= f \end{displaymath}

Using the addition, subtraction, and multiplication functionality of the Counter class in util.py, the perceptron updates should be relatively easy to code. Certain implementation issues have been taken care of for you in perceptron.py, such as handling iterations over the training data and ordering the update trials. Furthermore, the code sets up the weights data structure for you. Each legal label needs its own Counter full of weights.

Question

Fill in the train method in perceptron.py. Run your code with: 

python dataClassifier.py -c perceptron

Hints and observations:

Question 2 (1 point): Perceptron Analysis

Visualizing weights

Perceptron classifiers, and other discriminative methods, are often criticized because the parameters they learn are hard to interpret. To see a demonstration of this issue, we can repeat the visualization exercise from the naive Bayes classifier.

Question
Fill in findHighWeightFeatures(self, label) in perceptron.py. It should return a list of the 100 features with highest feature weights for that label. You can display the 100 pixels with the largest weights using the command: 

python dataClassifier.py -c perceptron -w
Use this command to look at the weights, and answer the following true/false question. Which of the following sequence of weights is most representative of the perceptron?

$y_2$.

Answer the question answers.py in the method q2, returning either 'a' or 'b'.

Question 3 (6 points): MIRA

A skeleton implementation of the MIRA classifier is provided for you in mira.py. MIRA is an online learner which is closely related to both the support vector machine and perceptron classifiers. You will fill in the trainAndTune function.

Theory

Similar to a multi-class perceptron classifier, multi-class MIRA classifier also keeps a weight vector $w^y$ of each label $y$. We also scan over the data, one instance at a time. When we come to an instance $(f, y)$, we find the label with highest score:

We compare $y'$ to the true label $y$. If $y' = y$, we've gotten the instance correct, and we do nothing. Otherwise, we guessed $y'$ but we should have guessed $y$. Unlike perceptron, we update the weight vectors of these labels with variable step size:

where is chosen such that it minimizes
subject to the condition that

which is equivalent to
subject to and

Note that, , so the condition is always true given Solving this simple problem, we then have
However, we would like to cap the maximum possible value of by a positive constant C, which leads us to

Question
Implement trainAndTune in mira.py. This method should train a MIRA classifier using each value of C in Cgrid. Evaluate accuracy on the held-out validation set for each C and choose the C with the highest validation accuracy. In case of ties, prefer the lowest value of C. Test your MIRA implementation with: 
python dataClassifier.py -c mira --autotune

Hints and observations:

Question 4 (6 points): Digit Feature Design

Building classifiers is only a small part of getting a good system working for a task. Indeed, the main difference between a good classification system and a bad one is usually not the classifier itself (e.g. perceptron vs. naive Bayes), but rather the quality of the features used. So far, we have used the simplest possible features: the identity of each pixel (being on/off).

To increase your classifier's accuracy further, you will need to extract more useful features from the data. The EnhancedFeatureExtractorDigit in dataClassifier.py is your new playground. When analyzing your classifiers' results, you should look at some of your errors and look for characteristics of the input that would give the classifier useful information about the label. You can add code to the analysis function in dataClassifier.py to inspect what your classifier is doing. For instance in the digit data, consider the number of separate, connected regions of white pixels, which varies by digit type. 1, 2, 3, 5, 7 tend to have one contiguous region of white space while the loops in 6, 8, 9 create more. The number of white regions in a 4 depends on the writer. This is an example of a feature that is not directly available to the classifier from the per-pixel information. If your feature extractor adds new features that encode these properties, the classifier will be able exploit them. Note that some features may require non-trivial computation to extract, so write efficient and correct code.

Note: You will be working with digits, so make sure you are using DIGIT_DATUM_WIDTH and DIGIT_DATUM_HEIGHT, instead of FACE_DATUM_WIDTH and FACE_DATUM_HEIGHT.

Question
Add new features for the digit dataset in the EnhancedFeatureExtractorDigit function. Note that you can encode a feature which takes 3 values [1,2,3] by using 3 binary features, of which only one is on at the time, to indicate which of the three possibilities you have. In theory, features aren't conditionally independent as naive Bayes requires, but your classifier can still work well in practice. We will test your classifier with the following command: 

python dataClassifier.py -d digits -c naiveBayes -f -a -t 1000
With the basic features (without the -f option), your optimal choice of smoothing parameter should yield 82% on the validation set with a test performance of 78%. You will receive 3 points for implementing new feature(s) which yield any improvement at all. You will receive 3 additional points if your new feature(s) give you a test performance greater than or equal to 84% with the above command.

Question 5 (4 points): Behavioral Cloning

You have built two different types of classifiers, a perceptron classifier and mira. You will now use a modified version of perceptron in order to learn from pacman agents. In this question, you will fill in the classify and train methods in perceptron_pacman.py. This code should be similar to the methods you've written in perceptron.py.

For this application of classifiers, the data will be states, and the labels for a state will be all legal actions possible from that state. Unlike perceptron for digits, all of the labels share a single weight vector w, and the features extracted are a function of both the state and possible label.

For each action, calculate the score as follows: $$score(s, a) = w*f(s,a)$$

Then the classifier assigns whichever label receives the highest score:

$$a' = arg \max\limits_{a''} score (s,a'')$$

Training updates occur in much the same way that they do for the standard classifiers. Instead of modifying two separate weight vectors on each update, the weights for the actual and predicted labels, both updates occur on the shared weights as follows:

$$w= w+ f(s,a)$$
# Correct action

$$w= w- f(s,a')$$
# Guessed action
Question

Fill in the train method in perceptron_pacman.py. Run your code with:

python dataClassifier.py -c perceptron -d pacman

This command should yield validation and test accuracy of over 70%.

Question 6 (4 points): Pacman Feature Design

In this part you will write your own features in order to allow the classifier agent to clone the behavior of observed agents. We have provided several agents for you to try to copy behavior from:

We've placed files containing multiple recorded games for each agent in the data/pacmandata directory. Each agent has 15 games recorded and saved for training data, and 10 games for both validation and testing.

Question

Add new features for behavioral cloning in the EnhancedPacmanFeatures function in dataClassifier.py.

Upon completing your features, you should get at least 90% accuracy on the ContestAgent, and 80% on each of the other 3 provided agents. You can directly test this using the --agentToClone <Agent name>, -g <Agent name> option for dataClassifier.py:

python dataClassifier.py -c perceptron -d pacman -f -g ContestAgent -t 1000 -s 1000

Other helpful options:

We have also provided a new ClassifierAgent, in pacmanAgents.py, for you that uses your implementation of perceptron_pacman. This agent takes in training, and optionally validation, data and performs the training step of the classifier upon initialization. Then each time it makes an action it runs the trained classifier on the state and performs the returned action. You can run this agent with the following command:

python pacman.py -p ClassifierAgent --agentArgs trainingData=<path to training data>

You can also use the --agentToClone <Agent Name> option to use one of the four agents specified above to train on:

python pacman.py -p ClassifierAgent --agentArgs agentToClone=<Agent Name>

Congratulations! You're finished with the CS 343H projects, don't forget about the contest.