ADAPTIVE LEARNED BLOOM FILTER (ADA-BF): EFFICIENT UTILIZATION OF THE CLASSIFIER

The python files include the implementation of the Bloom filter, learned Bloom filter, Ada-BF and disjoint Ada-BF, and print the size of False Positives of the corresponding algorithm.

Input arguments:

• --data_path: a csv file includes the items, scores and labels; --size_of_Ada_BF: size of Bloom filter;
• (for learned Bloom filter) --threshold_min and --threshold_max provide the range of the score threshold (between threshold_min and threshold_max). Items with score larger than the threshold are identified as keys;
• (for Ada-BF and disjoint Ada-BF) --num_group_min and --num_group_max give the range of number of groups to divide (range of g ); --c_min and --c_max provide the range of c where c=m_j/m_{j+1}

Commands:

• Run Bloom filter: python Bloom_filter.py --data_path ./Datasets/URL_data.csv --size_of_Ada_BF 200000
• Run learned Bloom filter: python learned_Bloom_filter.py --data_path ./Datasets/URL_data.csv --size_of_Ada_BF 200000 --threshold_min 0.5 --threshold_max 0.95
• Run Ada-BF: python Ada-BF.py --data_path ./Datasets/URL_data.csv --size_of_Ada_BF 200000 --num_group_min 8 --num_group_max 12 --c_min 1.6 --c_max 2.5
• Run disjoint Ada-BF: python disjoint_Ada-BF.py --data_path ./Datasets/URL_data.csv --size_of_Ada_BF 200000 --num_group_min 8 --num_group_max 12 --c_min 1.6 --c_max 2.5
• Run PLBF: python PLBF.py --data_path ./Datasets/URL_data.csv --size_of_PLBF 400000 --model_path ./models/URL_NN_hidden_dim$i.pickle --model_type NN --num_group_min 6 --num_group_max 20