==================== HiddenSchemaNetworks

Installation

To install the code and all dependencies first install Python 3.8. Then run in the project directory::

pip install -e .

Experiments

Synthetic Data

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Training

To reproduce any of the synthetic data experiments, run::

python scripts/train_synthetic_schema.py -c experiments/synthetic/<config_file>

where <config_file> is one of the .yaml files in experiments/synthetic.

In the config file, you can change a number of parameters and most importantly the path where results and trained models are saved.

Evaluation

To compute metrics for a whole directory of models, run::

python scripts/evaluation_toolbox_synth.py -p results -n synth/<experiment_name>

where <experiment_name> is the name of the .yaml file, e.g. erdos_schema

Real Data

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Training

To train GPT2 or Schema models on the PTB, Yahoo, or Yelp data sets, run::

python scripts/train_model.py -c experiments/<path>

with <path> path to a config file, e.g. ptb/gpt2.yaml

For different numbers of symbols, you can modify the option n_symbols in the .yaml file, and for different random walk lengths, change rw_length in the encoder section. You can also change the directory of the saved models here.

Evaluation

To compute metrics for a trained model saved in results/<path>, run::

python scripts/evaluation_toolbox.py -p results -n <path>

Functions for generating text, interpolations, and other graph statistics are all available in evaluation_toolbox.py

Note

This project has been set up using PyScaffold 3.2.1. For details and usage information on PyScaffold see https://pyscaffold.org/.