A Python library for disparate exposure in ranking (a learning to rank approach)
Fair search DELTR for Python
This is the Python library that implements the DELTR model for fair ranking.
fairsearchdeltr, simply use
pip install fairsearchdeltr
And, that's it!
Using it in your code
You need to import the class from the package first:
from fairsearchdeltr import Deltr
Train a model
You need to train the model before it can rank documents.
# import other helper libraries import pandas as pd from io import StringIO # load some train data (this is just a sample - more is better) train_data_raw = """q_id,doc_id,gender,score,judgment 1,1,1,0.962650646167003,1 1,2,0,0.940172822166108,0.98 1,3,0,0.925288002880488,0.96 1,4,1,0.896143226020877,0.94 1,5,0,0.89180775633204,0.92 1,6,0,0.838704766545679,0.9 """ train_data = pd.read_csv(StringIO(train_data_raw)) # setup the DELTR object protected_feature = "gender" # column name of the protected attribute (index after query and document id) gamma = 1 # value of the gamma parameter number_of_iterations = 10000 # number of iterations the training should run standardize = True # let's apply standardization to the features # create the Deltr object dtr = Deltr(protected_feature, gamma, number_of_iterations, standardize=standardize) # train the model dtr.train(train_data) >> array([0.02527054, 0.07692437]) # your run should have approximately same results
Use the model to rank
Now, you can use the obtained model to rank some data.
# load some test/prediction data prediction_data_raw = """q_id,doc_id,gender,score 1,7,0,0.9645 1,8,0,0.9524 1,9,0,0.9285 1,10,0,0.8961 1,11,1,0.8911 1,12,1,0.8312 """ prediction_data = pd.read_csv(StringIO(prediction_data_raw)) # use the model to rank the data dtr.rank(prediction_data) >> doc_id gender judgement 4 11 1 0.074849 5 12 1 0.063770 0 7 0 0.063486 1 8 0 0.061248 2 9 0 0.056828 3 10 0 0.050836 # the result will be a re-ranked dataframe
The library contains sufficient code documentation for each of the functions.
Checking the model a bit deeper
You can check how the training of the model progressed using a special property called
dtr.log >> [<TrainStep [1553844278383,[0.01926469 0.00976336],[[-0.00125304 -0.0014605 ] [-0.00125304 -0.0014605 ] [-0.00125304 -0.0014605 ] [-0.00125304 -0.0014605 ] [-0.00125304 -0.0014605 ] [-0.00125304 -0.0014605 ]],5.999620187652397,0.0]>, ...]
log returns a list of objects from the
fairsearchdeltr.models.TrainStep class. The class is a representation of the parameters in each step of the training.
- Clone this repository
git clone https://github.com/fair-search/fairsearchdeltr-python
- Change directory to the directory where you cloned the repository
- Use any IDE to work with the code
python setup.py test
The DELTR algorithm is described in this paper:
- Meike Zehlike, Gina-Theresa Diehn, Carlos Castillo. "Reducing Disparate Exposure in Ranking: A Learning to Rank Approach." preprint arXiv:1805.08716 (2018).
For any questions contact Mieke Zehlike
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