PLBF

Code for ICLR 2020 paper Partitioned Learned Bloom Filter(PLBF).

Example Usage

plbf_algo.py: Contains the algorithm to find the optimal parameter values.

Input:

• Score samples for keys and non-keys
• Number of partitions in PLBF (5-10 recommended)
• Target False Positive Rate

Output:

• Score Thresholds
• False positive rate for each partition

Eg, Lets suppose we get the following output from the algorithm for K=5.

Thresholds:[0.2, 0.4, 0.6, 0.8, 1.0] FPR: [0.01, 0.2, 0.5, 1, 1]

This means that inputs(s) with score s(x)<= 0.2 go to the 1st parition, 0.2< s(x) <= 0.4 to the 2nd parition and so on. The FPR for 1st partition is 0.01, 2nd parition is 0.2 and so on