Generates cliques using MDS-Clique from topics extracted by LDA from a corpus.

Uses Python 3 and various Python libraries (gensim, networkx, scikit-learn, etc.)

1. Use virtualenv to create a virtual Python 3 environment within the git repository (virtualenv -p python3 venv)
2. Activate your Python virtualenv source venv/bin/activate (you should now see something like (venv) in your console)
3. Install the required Python libraries by running pip install -r requirements.txt

1. Clone https://github.com/hateno/scikit-learn.git and go to branch v0.19.0
2. Ensure you are still within the same virtualenv from the previous section and run python setup.py build and then python setup.py install (this may take a while)

1. Configure config.ini
2. python corpus.py
3. python gen_topic.py
4. python sim.py -dim 2 -data corpus

Run corpus.py with config.ini filled out (see config.ini section) which reads a corpus (a directory of text documents) for pre-processing (e.g. stemming and tokenization). Then execute gen_topic.py which uses the output artifacts of corpus.py in order to perform LDA topic modeling over the pre-processed corpus. Then execute sim.py which will either perform a specific experiment or simply execute MDS-Clique (see sections below).

Execute python sim.py --help (make sure you are in your virtualenv) and go through the commands

Generate a random pre-computed dissimilarity matrix, run MDS-Clique using the standard deviation (named stress) measure, it will write cliques to out/cliques_<num> python sim.py -dim 2 -data random --matrix -clique stress

Use extracted LDA topics and run MDS-Clique using the distance measure, write cliques to out/cliques_<num>, note that there -clusters <num> needs to be higher than the number of topics extracted or an error will be thrown python sim.py -dim 2 -data corpus -clusters 3 -clique distance

Run the RMDS experiment, set -data none since each sample will generate its own random data set python sim.py -dim 2 -data none --matrix -clique stress --rmds

Each experiment is denoted with a flag --<experiment_codename>, by default an experiment will run 8 samples, you can manually specify number of samples with -e <num_samples>, and utilize 1/4 of the max cores available on the system, you can manually specify number of cores with -c <num_cores>

Relative MDS experiment (k-values are hard-coded) python sim.py -dim 2 -data random --matrix --relative

MDS-Clique RMDS experiment python sim.py -dim 2 -data none --matrix --rmds

MDS-Clique experiment python sim.py -dim 2 -data none --matrix --rclique

Relative Online experiment python sim.py -dim 2 -data none --matrix --relativeonline

Online Clique experiment python sim.py -dim 2 -data none --matrix -clique stress --onlineclique

Online experiment python sim.py -dim 2 -data none --matrix --online

• corpus: directory to the corpus (text documents)
  • A directory of plain text document(s) that will be pre-processed by corpus.py
• mds_seed: set MDS random_state
  • Optional, if blank the MDS algorithm (SMACOF) will start with a random configuration therefore most likely a different final result, set the seed value if you want a deterministic solution (useful for debugging), used by sim.py

[Global]
corpus = /path/to/sample-corpus/
mds_seed = 7

Run in interactive debug mode ipython -i -c "%run -dim 2 -data corpus" --pdb

You may need to manually create store, out/final, out/experiment, and out/ident directories