category_predictor: Given some text, predict likely categories. For example:
python category_predictor/predict.py category_predictor.json "bacon donut" Category: "Food" - 82.66% chance Category: "Restaurants" - 16.99% chance Category: "Donuts" - 0.12% chance Category: "Basque" - 0.02% chance Category: "Spanish" - 0.02% chance
review_autopilot: Use a markov chain to finish a review. For
python review_autopilot/generate.py Food 'They have the best' They have the best coffee is good food was delicious cookies and a few friends i think they make this
positive_category_words: See the Yelp engineering blog for
details about this example. In short, it generates positivity
scores for words either globally or per-category.
You can use any of mjrob's runner with these examples, but we'll focus on the local and emr runner (if you have access to your own hadoop cluster, check out the mrjob docs for instructions on how to set this up).
Local mode couldn't be easier:
# this step will take a VERY long time python review_autopilot/autopilot.py yelp_academic_dataset.json > autopilot.json # this should be instant python review_autopilot/generate.py Food 'They have the best' > hot dogs ever
Waiting a long time is kind of lame, no? Let's try the same thing using EMR.
First off, you'll need an
aws_access_key and an
aws_secret_access_key. You can get these from the AWS console
(you'll need to sign up for an AWS developer account and enable s3 /
emr usage, if you haven't already).
Create a simple mrjob.conf file, like this:
runners: emr: aws_access_key_id: YOUR_ACCESS_KEY aws_secret_access_key: YOUR_SECRET_KEY
Now that that's done, you can run the autopilot script on EMR.
# WARNING: this will cost you roughly $2 and take 10-20 minutes python review_autopilot/autopilot.py --num-ec2-instances 10 --ec2-instance-type c1.medium -v --runner emr yelp_academic_dataset.json
You can save money (and time) by re-using jobflows and uploading the dataset to a person, private s3 bucket - check out the mrjob docs for instructions on doing this.