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homework_3.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CSCE 470 :: Information Storage and Retrieval :: Texas A&M University :: Fall 2017\n",
"\n",
"\n",
"# Homework 3 and 4 United Forever: Recommenders and Classification!\n",
"\n",
"### 200 points [10% of your final grade]\n",
"\n",
"### Due: November 16, 2017\n",
"\n",
"*Goals of this homework:* Put your knowledge of recommenders and classifiers to work. \n",
"\n",
"*Submission Instructions (ecampus):* To submit your homework, rename this notebook as `lastname_firstinitial_hw#.ipynb`. For example, my homework submission would be: `caverlee_j_hw3.ipynb`. Submit this notebook via **ecampus**. Your IPython notebook should be completely self-contained, with the results visible in the notebook. We should not have to run any code from the command line, nor should we have to run your code within the notebook (though we reserve the right to do so)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 1: Recommending Movies\n",
"\n",
"For this first part, we're going to use part of the Movielens 100k dataset. Prior to the Netflix Prize, the Movielens data was **the** most important collection of movie ratings.\n",
"\n",
"First off, we need to load the data (see the data files in the \"Resources\" tab, including u.user, u.item, and ua.base). Here, we provide you with some helper code to load the data using [Pandas](http://pandas.pydata.org/). Pandas is a nice package for Python data analytics."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MovieId</th>\n",
" <th>Title</th>\n",
" <th>UserId</th>\n",
" <th>Rating</th>\n",
" <th>Age</th>\n",
" <th>Gender</th>\n",
" <th>Occupation</th>\n",
" <th>ZipCode</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Toy Story (1995)</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>24</td>\n",
" <td>M</td>\n",
" <td>technician</td>\n",
" <td>85711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>GoldenEye (1995)</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>24</td>\n",
" <td>M</td>\n",
" <td>technician</td>\n",
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" <td>Four Rooms (1995)</td>\n",
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" <th>3</th>\n",
" <td>4</td>\n",
" <td>Get Shorty (1995)</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>24</td>\n",
" <td>M</td>\n",
" <td>technician</td>\n",
" <td>85711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Copycat (1995)</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>24</td>\n",
" <td>M</td>\n",
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],
"text/plain": [
" MovieId Title UserId Rating Age Gender Occupation ZipCode\n",
"0 1 Toy Story (1995) 1 5 24 M technician 85711\n",
"1 2 GoldenEye (1995) 1 3 24 M technician 85711\n",
"2 3 Four Rooms (1995) 1 4 24 M technician 85711\n",
"3 4 Get Shorty (1995) 1 3 24 M technician 85711\n",
"4 5 Copycat (1995) 1 3 24 M technician 85711"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Load the user data\n",
"users_df = pd.read_csv('u.user', sep='|', names=['UserId', 'Age', 'Gender', 'Occupation', 'ZipCode'])\n",
"\n",
"# Load the movies data: we will only use movie id and title for this homework\n",
"movies_df = pd.read_csv('u.item', sep='|', names=['MovieId', 'Title'], usecols=range(2))\n",
"\n",
"# Load the ratings data: ignore the timestamps\n",
"ratings_df = pd.read_csv('ua.base', sep='\\t', names=['UserId', 'MovieId', 'Rating'],usecols=range(3))\n",
"\n",
"# Working on three different data frames is a pain\n",
"# Let us create a single dataset by \"joining\" these three data frames\n",
"movie_ratings_df = pd.merge(movies_df, ratings_df)\n",
"movielens_df = pd.merge(movie_ratings_df, users_df)\n",
"\n",
"movielens_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 1a. Let's Explore the Data [20 points]\n",
"\n",
"Before we get to the actual task of building our recommender, let's familiarize ourselves with the Movielens data.\n",
"\n",
"Pandas is really nice, since it let's us do simple aggregates. For example, we can find the top-10 movies with the most ratings like so:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print movielens_df.groupby('Title').size().order(ascending=False)[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Top-10 movies\n",
"OK, can you find the top-10 highest-rated movies? "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Most polarizing movies\n",
"Some movies draw a mixed reaction from fans -- where some people love them and some people hate them. Let's look for such *polarizing* movies that have lots of high ratings and lots of low ratings. \n",
"\n",
"For this part, let's define a **polarizing movie** as one meeting both of the following conditions:\n",
"\n",
"- The count of ratings that are 2, 3, or 4 < the count of ratings that are 1 or 5\n",
"- |The count of 1 ratings - the count of 5 ratings| < 0.3 * Max(count of 1 ratings, count of 5 ratings)\n",
"\n",
"For example, a movie with ratings like:\n",
"- 1 star = 100 ratings\n",
"- 2 stars = 10 ratings \n",
"- 3 stars = 10 ratings\n",
"- 4 stars = 10 ratings\n",
"- 5 stars = 80\n",
"\n",
"meets both of our conditions, since 10 + 10 + 10 < 100 + 80 (condition 1) and |100-80| < 0.3 * Max(100,80)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 1b: Find the Baseline ratings [30 points]\n",
"\n",
"Now let's find some estimated baseline ratings. Recall that the baseline rating for a user x on item i = the overall average rating + item bias for i + user bias for x. \n",
"\n",
"For the part, you should find the baseline ratings for several of our user/movie pairs.\n",
"\n",
"Baseline rating for user 1 for movie 155:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Baseline rating for user 6 for movie 492:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Baseline rating for user 21 for movie 164:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part 1c. Please help me make a recommendation decision! [50 points]\n",
"Suppose you're trying to recommend a movie to my friend Ellen (User 24). You are trying to decide between two movies:\n",
"\n",
"- Clueless (367); or\n",
"- To Kill a Mockingbird (427) \n",
"\n",
"To build your recommender, you have many possibilities, including:\n",
"\n",
"1. Baseline estimate rating b_xi \n",
"2. User-user collaborative filtering \n",
"3. Item-item collaborative filtering\n",
"4. Latent factor model\n",
"5. Some other awesome methods ...\n",
"\n",
"First off, please make your best guess using the baseline rating estimate approach. Your output should like like:\n",
"\n",
"movie 367, rating: 2\n",
"\n",
"movie 427, rating: 3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# your code for your baseline recommendation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, update your baseline approach by incorporating item-item collaborative filtering. You have many design choices here (e.g., number of neighbors k, etc.). Do your best to make a good recommendation:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# your code here for augmenting baseline with item-item CF"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### BONUS: \n",
"Can you use a latent factor model to create a new recommendation method? You can try using something like numpy.linalg.svd(...). [here's an example](http://www.frankcleary.com/svd/) and [here's another one](https://alyssaq.github.io/2015/20150426-simple-movie-recommender-using-svd/)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 2: Classification with Yelp review data\n",
"\n",
"For this part, given a Yelp review, your task is to implement a classifier to predict if the business category of this review is \"food-relevant\" or not, **only based on the review text**. The data is from the [Yelp Dataset Challenge](https://www.yelp.com/dataset_challenge).\n",
"\n",
"## Build the training data\n",
"\n",
"First, you will need to download this data file as your training data: [training_data.json](https://drive.google.com/open?id=0B_13wIEAmbQMdzBVTndwenoxQlk) \n",
"\n",
"The training data file includes 40,000 Yelp reviews. Each line is a json-encoded review, and **you should only focus on the \"text\" field**. You should tokenize the review text by using the regular expression \"\\W+\". So something like wordlist = re.split('\\W+', text). Do NOT remove stop words. **Do casefolding but no stemming**.\n",
"\n",
"The label (class) information of each review is in the \"label\" field. It is **either \"Food-relevant\" or \"Food-irrelevant\"**.\n",
"\n",
"## Testing data\n",
"\n",
"We provide 100 yelp reviews here: [testing_data.json](https://drive.google.com/open?id=0B_13wIEAmbQMbXdyTkhrZDN4Wms). The testing data file has the same format as the training data file. Again, you can get the label informaiton in the \"label\" field. Only use it when you evalute your classifiers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build your Rocchio classifier [60 points]\n",
"\n",
"In this part, your job is to implement a Rocchio classifier for \"food-relevant vs. food-irrelevant\". You need to aggregate all the reviews of each class, and find the center. **Use the normalized raw term frequency**.\n",
"\n",
"\n",
"### What to report\n",
"\n",
"* For the entire testing dataset, report the overall accuracy.\n",
"* For the class \"Food-relevant\", report the precision and recall.\n",
"* For the class \"Food-irrelevant\", report the precision and recall.\n",
"\n",
"We will also grade on the quality of your code. So make sure that your code is clear and readable."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Build the Rocchio classifier\n",
"# Insert as many cells as you want"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Apply your classifier on the test data. Report the results.\n",
"# Insert as many cells as you want"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Improve your Rocchio classifier [40 points]\n",
"\n",
"OK, can you improve the quality of your classifier? Your goal here is to experiment with alternative weighting schemes, stopwords, etc. Whatever you like. See if you can improve the quality of your classifier."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Do whatever magic you need to improve your rocchio classifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Apply your classifier on the test data. Report the results."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Explain your strategies.** What did you do? Did it work? Why? Give us your best analysis of the results."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<explanation goes here>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### BONUS:\n",
"\n",
"Instead of Rocchio, implement any other classifier you like. How did it work out for you?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# your code here"
]
}
],
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
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"nbformat_minor": 2
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833|34|M|writer|90019
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835|44|F|executive|11577
836|44|M|artist|10018
837|36|F|artist|55409
838|23|M|student|01375
839|38|F|entertainment|90814
840|39|M|artist|55406
841|45|M|doctor|47401
842|40|M|writer|93055
843|35|M|librarian|44212
844|22|M|engineer|95662
845|64|M|doctor|97405
846|27|M|lawyer|47130
847|29|M|student|55417
848|46|M|engineer|02146
849|15|F|student|25652
850|34|M|technician|78390
851|18|M|other|29646
852|46|M|administrator|94086
853|49|M|writer|40515
854|29|F|student|55408
855|53|M|librarian|04988
856|43|F|marketing|97215
857|35|F|administrator|V1G4L
858|63|M|educator|09645
859|18|F|other|06492
860|70|F|retired|48322
861|38|F|student|14085
862|25|M|executive|13820
863|17|M|student|60089
864|27|M|programmer|63021
865|25|M|artist|11231
866|45|M|other|60302
867|24|M|scientist|92507
868|21|M|programmer|55303
869|30|M|student|10025
870|22|M|student|65203
871|31|M|executive|44648
872|19|F|student|74078
873|48|F|administrator|33763
874|36|M|scientist|37076
875|24|F|student|35802
876|41|M|other|20902
877|30|M|other|77504
878|50|F|educator|98027
879|33|F|administrator|55337
880|13|M|student|83702
881|39|M|marketing|43017
882|35|M|engineer|40503
883|49|M|librarian|50266
884|44|M|engineer|55337
885|30|F|other|95316
886|20|M|student|61820
887|14|F|student|27249
888|41|M|scientist|17036
889|24|M|technician|78704
890|32|M|student|97301
891|51|F|administrator|03062
892|36|M|other|45243
893|25|M|student|95823
894|47|M|educator|74075
895|31|F|librarian|32301
896|28|M|writer|91505
897|30|M|other|33484
898|23|M|homemaker|61755
899|32|M|other|55116
900|60|M|retired|18505
901|38|M|executive|L1V3W
902|45|F|artist|97203
903|28|M|educator|20850
904|17|F|student|61073
905|27|M|other|30350
906|45|M|librarian|70124
907|25|F|other|80526
908|44|F|librarian|68504
909|50|F|educator|53171
910|28|M|healthcare|29301
911|37|F|writer|53210
912|51|M|other|06512
913|27|M|student|76201
914|44|F|other|08105
915|50|M|entertainment|60614
916|27|M|engineer|N2L5N
917|22|F|student|20006
918|40|M|scientist|70116
919|25|M|other|14216
920|30|F|artist|90008
921|20|F|student|98801
922|29|F|administrator|21114
923|21|M|student|E2E3R
924|29|M|other|11753
925|18|F|salesman|49036
926|49|M|entertainment|01701
927|23|M|programmer|55428
928|21|M|student|55408
929|44|M|scientist|53711
930|28|F|scientist|07310
931|60|M|educator|33556
932|58|M|educator|06437
933|28|M|student|48105
934|61|M|engineer|22902
935|42|M|doctor|66221
936|24|M|other|32789
937|48|M|educator|98072
938|38|F|technician|55038
939|26|F|student|33319
940|32|M|administrator|02215
941|20|M|student|97229
942|48|F|librarian|78209
943|22|M|student|77841

90570
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