Entering Kaggle Competitions with Google Predict

BigML had a great series of posts over the summer pitting some prediction-as-a-service products against each other. One of those was the Google Predict API. I thought it might be fun to enter a Kaggle competition using the API and see how it did against some of the world's top data scientists.

It turns out this was a terrible, terrible waste of time.

If you are expecting a rigorous analysis of the Google Predict API, this post will be a disappointment. In fact, I'll go so far as to ruin the surprise right now: on the Biological Response competition, the Predict API turned in a 0.67245 on the private leaderboard, just edging out the optimized constant value benchmark (in a nutshell - it did badly). It fared a bit better on the Titanic competition, scoring a somewhat-reasonable 0.79426 (tied with 55 other users for 112th place as of this writing).

So instead of focusing on the actual performance of the algorithm, I will instead share some tips and tricks for using the Google Predict API:

Chris' Tips for Using the Google Predict API:

• Turn off two factor authentication, unless your idea of a good time is spending a furious evening at home, battling OAuth. It is a formidable enemy.
• If you are predicting a binary response, be sure to replace those 0s and 1s in your response column with strings like TRUE and FALSE, or else Google will attempt to perform Magic (stand back!) and decide that your model is actually a regression model and will cheerfully return values like -0.8 and 1.2.

• Nice work - now your response column is now filled with TRUE and FALSE strings and you have persuaded Google that you want a categorical response. You also remembered to remove that header row, right? Oh...you didn't? Well then get ready for the excitement of responses like this:

  "outputMulti": [
     {
       "label": "TRUE",
       "score": 0.2001
     },
     {
       "label": "FALSE",
       "score": 0.7829
     },
     {
       "label": "HEADER_TEXT",
       "score": 0.017
     }
   ],
  

• So yeah, remove those headers. And on a related note, have fun writing your own custom parsing code to extract the most likely label from this unsorted list. Because we certainly wouldn't want to pollute the integrity of the response schema with something as handy as providing the predicted label as a first class property.

• The thing you are creating is a TrainedModel, not a HostedModel. Just trust me on this - they may have identical predict methods, and be used interchangeably in example code, but you are making TrainedModels and will get errors if you ever accidentally refer to HostedModels.
• If you are using Google's Python API wrappers, be sure you are constructing your feature array like this: ["foo","bar",1.23\] and not like this: ["foo","bar","1.23"\]. I made the mistake of reading in my training CSV file as an array of strings, which caused my requests to look like the latter. The result?  The Predict API merrily chirps back with a valid, but completely terrible, prediction.
• Get a cup of coffee. Google gets angry if you make too many prediction requests in a row and starts throwing errors. So you have to resort to weird throttling tricks if you need more than a few hundred predictions at once. And bulk prediction is not available.

My terrible, horrible code that does this is on github. It's not a particularly usable form and does some...questionable things because I didn't learn some of these lessons until it was too late. But if you are absolutely determined to use the Google Predict API (god help you), it might get you started.

Also, kudos to the BigML engineers who are either way smarter than me and got this working right away, or have a remarkable amount of self-restraint and did not complain one jot.

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