helpers on top of sklearn featurization pipelines basically two files, features_pipeline.py has all the logic for constructing complex, config-driven featurization pipelines, and demo.py which gives a small demo how these pipelines might be used.
- first build the virtual environment, tested under anaconda py 3.5:
source scripts/setup.sh .virtualenv/bin/python -m feats.demo