Remove "suspicious data" functionality from model exploration #178
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Using a single select() should let us take better advantage of Spark's parallel/distributed computing. My initial results profiling this are pretty promising.
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The tests were failing because of an update to scikit-learn 1.6.0, which came out yesterday. It's only a problem with xgboost, so I've added an additional requirement to the xgboost extra, and that seems to have fixed things. |
ccdavis
approved these changes
Dec 11, 2024
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This is for #176.
I've removed all of the code for keeping track of and saving the suspicious data, and I've removed
output_suspicious_TDfrom the docs. I took this opportunity to rewrite_get_confusion_matrix()to use a singleselect()instead of 4filter()+count()s. I did a little bit of initial profiling, and I think this should be a pretty significant speedup.I renamed some variables which had capital letters like
TPandFN, replacing those with their lowercase counterparts, or spelling out "true_positives" and "false_negatives", etc.