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##  Kickstarter Text Classification: README  ######
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Dependencies:
   scikit-learn
   spaCy


Hey there. There are three top-level pipeline scripts that I should probably describe here:

1. classify.py -

    This is the script you use to classify new pitches against my best trained classification model.

    USAGE:
        python3 classify.py /path/to/JSON/file/containing/data.json
        EG: python3 classify.py ./Data/mini_corpus.json

    That's it. One argument which points to the JSON data file containing pitches in the same format as the training
    data I used to develop my system. The output of the system is a list of newline-separated strings corresponding to
    the predicted classes. Output is printed to stdout.

    The last two scripts described below are just FYI, this^ is the only script necessary to use my classification model
    for prediction.

2. train_model.py -

    This pipeline trains a text classification model on all the available data. Used to produce my final model which
    will be scored against the Textio holdout data set. Model is stored automatically in Data/final_model

    Can be run as-is, without any command line arguments.

3. train_test_dev.py -

    This is the pipeline I used to simultaneously train and test my models in an iterative fashion as part of the
    development process. The data loading process divides the input data into and 80:20 training and test split.
    The model is trained and stored in the Data/dev_models folder, and is immediately reloaded again by the testing
    process which predicts labels for the test split and evaluates the model's performance.

    Performance metrics are calculated on a per-class basis, and printed to stdout. A directory structure is also generated
    under Data/eval_data which sorts predictions by label into true positive, false positive, and false negative buckets.
    This greatly facilitates the manual error analysis process.

    Can be run as-is, without any arguments.