- Charles Du
- Michael Kyeyune
- Python 3.4.3+
To utilise this system you need to generate a collection from a testbed, index the collection then compare MAP/Avg NDCG results for the modified and unmodified engine.
- generate collection from testbed
python collect.py testbedx
- x being the number of the testbed. This will generate a filetestbedx_collection
- index testbed collection
python index.py testbedx_collection
- analyse modified and unmodified engine performance
python analyse.py testbedx
- finding optimal indicative terms and top k documents to utilise for blind relevance feedback (BRF) for a single testbed
python optimise.py -s testbedx 200
- 200 being the number of documents to consider in MAP/Avg NDCG calculations
- finding optimal indicative terms and top k documents to utilise for BRF for all testbeds
python optimise.py -a 200
- Documents that are not in UTF-8 format are ignored when generating the collection for a testbed
- All testbeds must be indexed before attempting to optimise across all testbeds