Search Rank experimentation in python
pip install rankum
Rankum provides features to diversify a search rank (a simple list of documents) by a specified criteria. For example, assuming each document in the input list (original rank) is coupled to a topic like a category id, which is very common in e-commerces. Rankum can help you to diversify the input list to have a more "uniform" distribution of each document category.
Rankum implements the following diversifiers:
- Scores Diff
- MMR (coming soon)
from rankum import JsonDocReader, ScoresDiffDiversifier
doc_list = '[
{"id": 1, "category": 1},
{"id": 2, "category": 1},
{"id": 3, "category": 2},
{"id": 4, "category": 1},
{"id": 5, "category": 3},
{"id": 6, "category": 4}
]'
reader = rankum.JsonDocReader(doc_list)
diversifier = ScoresDiffDiversifier(reader)
In: list(diversifier.diversify(by='category'))
Out:
[Doc(id=1, {'id': '1', 'category': 1, 'score': 2.0}),
Doc(id=3, {'id': '3', 'category': 2, 'score': 1.333}),
Doc(id=5, {'id': '5', 'category': 3, 'score': 1.2}),
Doc(id=6, {'id': '6', 'category': 4, 'score': 1.167}),
Doc(id=2, {'id': '2', 'category': 1, 'score': 1.0}),
Doc(id=4, {'id': '4', 'category': 1, 'score': 0.583})]
It is possible to read ranked document list from different sources:
- Json
- Elasticsearch (to be implemented)
- Solr (to be implement)
- Text files (to be implemented)
- Pandas dataframes (to be implemented)
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