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2014-02-13-mrjob.html
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2014-02-13-mrjob.html
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<!DOCTYPE html>
<html>
<head>
<title>Data Mining</title>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"/>
<style type="text/css">
@import url(http://fonts.googleapis.com/css?family=Droid+Serif);
@import url(http://fonts.googleapis.com/css?family=Yanone+Kaffeesatz);
body {
font-family: 'Droid Serif';
font-size: 25px;
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</head>
<body>
<textarea id="source">
name: inverse
layout: true
class: left, top, inverse
---
# Lab: mrjob
+ Understand ```review_word_count.py```
+ Find review with most unique words
+ Fill in ```unique_review.py```
+ Find similar users
+ Write ```user_similarity.py```
???
## mrjob
+ Using the Yelp Academic Dataset
+ In lecture, we covered the steps for most unique words
+ Use Jaccard similarity for user_similarity
---
## Data
+ May copy or use in place on ischool machine
+ ischool:
```bash
~jretz/
yelp_phoenix_academic_dataset/
yelp_academic_dataset_review.json
```
## Agreement
+ Dataset can only be used for academic purposes
+ You can download it yourself from the [Yelp Dataset Challenge](http://www.yelp.com/dataset_challenge/)
---
## Understand review_word_count.py
+ Note, this will take ~8 minutes on the ischool machine
+ Don't run it just yet!
.tight-code[
```bash
$ python review_word_count.py yelp_academic_dataset_review.json
no configs found; falling back on auto-configuration
creating tmp directory /tmp/review_word_count.jretz.20130215.071901.095847
reading from file
> /home/jretz/src/datamining290/code/venv/bin/python review_word_count.py --step-num=0 --mapper /tmp/review_word_count.jretz.20130215.071901.095847/input_part-00000
writing to /tmp/review_word_count.jretz.20130215.071901.095847/step-0-mapper_part-00000
Counters from step 1:
(no counters found)
...
Streaming final output from /tmp/review_word_count.jretz.20130215.071901.095847/output
"4" 2
"5" 1
"50" 1
"6" 2
"7" 2
"70s" 1
"9" 2
"a" 46
"abbey" 4
"able" 1
"about" 4
```
]
---
## A trick for running quickly while developing...
.tight-code[
```bash
$ head -n 1000 yelp_academic_dataset_review.json | \
python review_word_count.py
```
]
+ That runs over the first 1,000 lines
+ When things start looking good, try 10,000, then the entire file
---
## Fill in unique_review.py
+ Mutli-step map reduce
+ Steps are explained in lecture
+ Skeleton in code
---
## Write user_similarity.py
+ Find users >= 0.5 similarity
+ User Similarity: Jaccard similarity of businesses reviewed
+ {BizA, BizB, BizC} ~ {BizF, BizB, BizG}
</textarea>
<script src="production/remark-0.5.9.min.js" type="text/javascript">
</script>
<script type="text/javascript">
var slideshow = remark.create();
</script>
</body>
</html>