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Tools to analyze KA logs and other data
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branch: master

Fix some papercuts when dealing with very small data sets

When testing D16217, I ran some training jobs over very small datasets, just to make sure things were working end-to-end. This generated some performance data with anomolous characteristics that were not handled by the previous version of the script. Basically, the default value of `MINIMUM_SAMPLES` was too low, so I added better handling in that situation with an appropriate error message.

Ideally, `MINIMUM_SAMPLES` would be a command-line parameter, but I don't have time for that at the moment, so I added a `TODO`.

Test Plan:
1. Download performance data with `gsutil cp gs://ka_prediction_data/classic-2015_02_18-27151de6b7c211e4bee60242ac110007/performance_data/*  classic-2015_02_18-27151de6b7c211e4bee60242ac110007`
2. Plot performance data with `./src/graph_auto_trained_models.py --data misc/performance_data/classic-2015_02_18-096053c4b7c211e4bee60242ac110007`

Auditors:
tom
latest commit 638fc56551
@MattFaus MattFaus authored

README.md

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