/appSHNE

The application of representation learning for semantic associated heterogeneous networks in creating android app embedding layers.

Primary LanguageHTML

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

3.5.2021 updates - Alex

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

3.2.2021 updates - Alex

run.py has been updated to include more command line arguments

  • -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

Updated params config file. All parameters used are now found in config/params.json.

  • 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