Question on rank aggregation usage about ranx HOT 4 CLOSED

Thanks a lot. I got it.

from ranx.

Hi, you can find an example on Colab and other information here.

As for your example, you could to something like this:

from ranx import Run, fuse

run_1 = Run({"q1": {"item1": 0.4, "item2": 0.8, "item3": 0.7, "item4": 0.5}})
run_2 = Run({"q1": {"item1": 0.8, "item2": 0.7, "item3": 0.7, "item4": 0.6}})
run_3 = Run({"q1": {"item1": 0.2, "item2": 0.7, "item3": 1.0, "item4": 0.7}})

combined_run = fuse(runs=[run_1, run_2, run_3], method="max")

print(combined_run.to_dict()["q1"])

Output:

{
    "item2": 1.0,
    "item3": 1.0,
    "item1": 1.0,
    "item4": 0.625,
}

EDIT: copy-pasted wrong output dict.

from ranx.

Thank you for your reply! it is very helpful and I will try it soon.
By the way, any suggestions for speeding up when the number of items and the subranks (e.g., tens of thousands of items and dozens of subranks) are both large?

from ranx.

Neither the implemented methods nor the implementation itself are meant to be used in the context you describe.
Usually, we aggregate two or three rankings for 1k documents each (the top 1k in each ranking).
Give ranx a try for your use case. Maybe use it to find the method that works best with a sample of your data and then build your custom implementation that can run on the GPU.
I do not think you can do much better than my implementation performance-wise, at least not in Python.

from ranx.