multi class metrics, including nice confusion matrix plot
more classifier metrics, and flickr style data interface
searchable_collection working again
top-k accuracy metric, and per-class AP plot from multiclass_metrics
pascal now includes metaclasses and some plots in notebook
giant refactor of a bunch of stuff. lord give me strength
fixes to get featurization and prediction to work on pascal dataset
lots of small fixes and improvements; pascal results
updated pascal style results
respecting existing split for ava_style to see if gets better performance
write_data_in_vw_format function, helper, and test
tests for vw caching
tests for forming binary and regression dataset dicts in predict.py
additional test for get_binary_or_regression_dataset
successfully training with cross validation
messed around with using tee to split feat file reading into filters;
ended up just doing it in parallel
simple binary data tests: confirm that features give different results
updated simple dataset test: now unbalanced classes
lots of work on how VW and predict work resulting in small updates, and
new ava_style results
get_multiclass_dataset working (not multi label yet)