Get results using the exponential mechanism.
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import sys, os, time
from collections import deque
import argparse
import numpy as np
import imp
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SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
Utility functions.
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def import_anywhere(module_name, paths):
"""import methods from any folder"""
try:
f, filename, desc = imp.find_module(module_name, paths)
return imp.load_module(module_name, f, filename, desc)
finally:
# Since we may exit via an exception, close fp explicitly.
if f:
f.close()
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utility_functions = import_anywhere('utility_functions', [SCRIPT_DIR])
from utility_functions import get_chisq_sensitivity
Parse command line arguments.
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parser = argparse.ArgumentParser(description="Get results based on the JS method.")
parser.add_argument("k", metavar="NUM_SNP", help="number of SNPs to output", type=int)
parser.add_argument("e", metavar="EPSILON", help="privacy budget epsilon", type=float)
parser.add_argument("n_case", help="number of cases", type=int)
parser.add_argument("n_control", help="number of controls", type=int)
parser.add_argument("infile", help="input file of chisquare statistics")
parser.add_argument("-s", help="sensitivity of the scoring function", default=1, type=int)
args = parser.parse_args()
if not os.path.isfile(args.infile):
sys.exit("The follwoing file does not exist: {}".format(args.infile))
Setup data.
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name_score_tuples = []
with open(args.infile, 'r') as infile:
# skip header line
garbage = infile.readline()
for line in infile:
name, score = line.strip().split()
name_score_tuples.append((name, float(score)))
indexed_snp_name_dict = dict(enumerate([name for name, ss in name_score_tuples]))
snp_scores = np.array([ss for name, ss in name_score_tuples])
Perform exponential algorithm.
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loc_sig = import_anywhere('loc_sig', [SCRIPT_DIR])
from loc_sig import loc_sig
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sensitivity = get_chisq_sensitivity(args.n_case, args.n_control)
results_indices = loc_sig(args.e, args.k, sensitivity, snp_scores)
results_names = map(indexed_snp_name_dict.get, results_indices)
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for nn in results_names:
print nn