appSHNE: The Application of Representation Learning for Semantic-Associated Heterogeneous Networks in Creating Android App Embedding Layers

3.8.2021 updates - Alex

wrote EDA notebook that is callable from command line
- Run EDA with the following command line parameter: -eda
- EDA can be run with the following parameters: time and limit
  - python run.py -eda -time will run the EDA and print the time to run it on completion
Cleaned old code and adding documentation
To do:
- Clean up parameters in config/params.json and delete unused parameters
- Remove unused methods
- update dockerfile with nbconvert and pandoc to run EDA.ipynb from command line
- Run EDA on 1000 apps

added argument -log for the <redirect_std_out> (save console output to log file) parameter
Moved SHNE_code to src directory

-t, -test, -Test: Run on test set
-node2vec, -n2v: Run with node2vec instead of word2vec
--skip-embeddings: Skip the word embeddings stage
--skip-shne: Skip SHNE model creation final step
-p, -parse: Only create node dictionaries dict_A.json, dict_B.json, dict_P.json, dict_I.json, api_calls.json, and naming_key.json
-o, -overwrite: Overwrite previous node dictionaries created when parsing
--save-out: Save console output to file
-time: time how long to run main.py

All outputs will be saved under the values for <out_path> and <test_out_path>
- Subdirectories to save configured in respective dictionary.
  - For instance word2vec embeddings will be saved under the path <save_dir> in the <word2vec-params> dictionary int config/params.json
All filenames parameterizable