General Instructions

1. Run pip3 install -e .. If you expect dependency conflicts to arise, it may be wise to activate a Python virtual environment first.
2. The NLP tasks require some external datasets to be downloaded on the local machine.

• Run the following command in your terminal: python -m textblob.download_corpora. If you encounter issues, consult: https://stackoverflow.com/questions/41310885/error-downloading-textblob-certificate-verify-failed
• Then, open a python instance and run: import nltk, then nltk.download('stopwords'), then nltk.download('omw-1.4'). If you have problems, consult here: https://stackoverflow.com/questions/38916452/nltk-download-ssl-certificate-verify-failed
• If more problems persist, you may need to download nltk corpora "al la carte". Follow the python shell commands outputted in red by nltk.

Setup config file

cp config.py.tmpl config.py

And then edit it, filling in the various parts that you are using. The TRAINING sections should be used for GlobalView, while the SCORING sections are for the specific competition.

Competition Scoring Pipeline

Example command: python scoringPipeline.py /path/to/model.joblib

The scoring pipeline will apply the trained model to predict judge scores for a given set of scoring data.

The first argument is the name of the model file to run score the input proposals with.

Model Building (Training) Pipeline

Example command: python trainingPipeline.py /path/to/model.joblib

You may want to re-train the model for a number of reasons - adding new fields, changing parameters, tweaking the NLP approach, or simply because new proposals/cleaned/refactored data have been entered into Torque. This pipeline will build the pipeline needed for predicting intelligent scores for LFC competitions.

Using from other python code

Look at ./trainingPipeline.py and ./scoringPipeline.py to see how they build up a torque instance and call into the module.