How to use gensim BM25 rating in python

Question

I found that gensim has a BM25 ranking function. However, I cannot find a tutorial on how to use it.

In my case, I had one request. several documents that were obtained from a search engine. How to use the gensim BM 25 rating to compare a query and documents to find the most similar?

I am new to gensim. Thanks.

request:

"experimental studies of creep buckling ." 

document 1:

 " the 7 x 7 in . hypersonic wind tunnel at rae farnborough, part 1, design, instrumentation and flow visualization techniques . this is the first of three parts of the calibration report on the rae some details of the design and lay-out of the plant are given, together with the calculated performance figures, and the major components of the facility are briefly described . the instrumentation provided for the wind-tunnel is described in some detail, including the optical and other methods of flow visualization used in the tunnel . later parts will describe the calibration of the flow in the working-section, including temperature measurements . a discussion of the heater performance will also be included as well as the results of tests to determine starting and running pressure ratios, blockage effects, model starting loads, and humidity of the air flow ." 

document 2:

 " the 7 in. x 7 in. hypersonic wind tunnel at rae farnborough part ii. heater performance . tests on the storage heater, which is cylindrical in form and mounted horizontally, show that its performance is adequate for operation at m=6.8 and probably adequate for flows at m=8.2 with the existing nozzles . in its present state, the maximum design temperature of 680 degrees centigrade for operation at m=9 cannot be realised in the tunnel because of heat loss to the outlet attachments of the heater and quick-acting valve which form, in effect, a large heat sink . because of this heat loss there is rather poor response of stagnation temperature in the working section at the start of a run . it is hoped to cure this by preheating the heater outlet cone and the quick-acting valve . at pressures greater than about 100 psig free convection through the fibrous thermal insulation surrounding the heated core causes the top of the heater shell to become somewhat hotter than the bottom, which results in /hogging/ distortion of the shell . this free convection cools the heater core and a vertical temperature gradient is set up across it after only a few minutes at high pressure . modifications to be incorporated in the heater to improve its performance are described ." 

document 3:

 " supersonic flow at the surface of a circular cone at angle of attack . formulas for the inviscid flow properties on the surface of a cone at angle of attack are derived for use in conjunction with the mit cone tables . these formulas are based upon an entropy distribution on the cone surface which is uniform and equal to that of the shocked fluid in the windward meridian plane . they predict values for the flow variables which may differ significantly from the corresponding values obtained directly from the cone tables . the differences in the magnitudes of the flow variables computed by the two methods tend to increase with increasing free-stream mach number, cone angle and angle of attack ." 

document 4:

 " theory of aircraft structural models subjected to aerodynamic heating and external loads . the problem of investigating the simultaneous effects of transient aerodynamic heating and external loads on aircraft structures for the purpose of determining the ability of the structure to withstand flight to supersonic speeds is studied . by dimensional analyses it is shown that .. constructed of the same materials as the aircraft will be thermally similar to the aircraft with respect to the flow of heat through the structure will be similar to those of the aircraft when the structural model is constructed at the same temperature as the aircraft . external loads will be similar to those of the aircraft . subjected to heating and cooling that correctly simulate the aerodynamic heating of the aircraft, except with respect to angular velocities and angular accelerations, without requiring determination of the heat flux at each point on the surface and its variation with time . acting on the aerodynamically heated structural model to those acting on the aircraft is determined for the case of zero angular velocity and zero angular acceleration, so that the structural model may be subjected to the external loads required for simultaneous simulation of stresses and deformations due to external loads ." 

Answer 1

Full disclosure I have no experience using the BM25 rating, however I have a lot of experience with the distributed TF-IDF and LSI models with gensim, as well as the gensim affinity index.

The author has a really good job of preserving a readable code base, so if you ever have problems with something like that, I recommend just moving to the source code.

Looking at the source code: https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/summarization/bm25.py

So, I initialized the BM25() object with the documents you pasted above.

It seems that our good old friend Radim did not average_idf on the function for calculating average_idf for us, which does not really matter, we can just schedule line 65 for our reason:

average_idf = sum(map(lambda k: float(bm25.idf[k]), bm25.idf.keys())) / len(bm25.idf.keys())

Then, if I understood correctly the initial intention of get_scores , you should get every BM25 score in relation to your original request by simply completing

scores = bm_25_object.get_scores(query_doc, average_idf)

which returns all scores for each document, and then, if I understand the BM25 rating, based on what I read on this page on Wikipedia: https://en.wikipedia.org/wiki/Okapi_BM25

You can simply select the document with the highest score as follows:

best_result = docs[scores.index(max(scores))]

So, should the first document be the most relevant for your request? I hope you were expecting anyway, and I hope this helped to some extent. Good luck

Answer 2

I confirm that the answer above is correct. However, I will add my 2 bits to the other community members who land here. :)

The following 4 links are very helpful and cover the whole question.

1. https://github.com/nhirakawa/BM25 Python implementation of the BM25 ranking function. Extremely easy to use, I used it for my project. Works great! I think this is a system that will work for your problem.

2. https://sajalsharma.com/portfolio/cross_language_information_retrieval Shows a very detailed and step-by-step use of Okapi BM25 in a system that can be used to draw links for current system design tasks.

3. http://lixinzhang.imtqy.com/implementation-of-okapi-bm25-on-python.html Additional code for Okapi BM25 only.

4. https://github.com/thunlp/EntityDuetNeuralRanking Entity-Duet Neural Ranking Model. Great for research and academic work.

World!

--- addition: https://github.com/airalcorn2/RankNet RankNet and LambdaRank.