def calculateAuc():
high_mcc_diff = h5py.File('/ddnB/work/jaydy/dat/output/linr_out/08ff_all_decoy.h5')['/gaussian_nb/high/high_mcc_diff'][()]
low_mcc_diff = h5py.File('/ddnB/work/jaydy/dat/output/linr_out/08ff_all_decoy.h5')['/gaussian_nb/low/low_mcc_diff'][()]
import NB_classifier as nb
TPRs, FPRs = nb.constructRoc(low_mcc_diff[:, 1], high_mcc_diff[:, 1])
import numpy as np
auc = np.trapz(TPRs, x=FPRs)
print "AUC\t: ", auc
def weightDiff(likelihood_diff_high, likelihood_diff_low, weights):
likelihood_diff_low = np.dot(likelihood_diff_low, weights)
likelihood_diff_high = np.dot(likelihood_diff_high, weights)
return likelihood_diff_high, likelihood_diff_low
if __name__ == '__main__':
condi_dist_fn = 'low.dist'
sub_path = 'noncentralized_path/low_decoy'
buildPdf(condi_dist_fn, sub_path)
condi_dist_fn = 'high.dist'
sub_path = 'noncentralized_path/high_decoy'
buildPdf(condi_dist_fn, sub_path)
likelihood_diff_high, likelihood_diff_low = calculateLikelihoodDiff()
weights_fn = '/work/jaydy/working/nb_ff_running/even_weight.txt'
weights = np.loadtxt(weights_fn)
likelihood_diff_high, likelihood_diff_low = weightDiff(likelihood_diff_high, likelihood_diff_low, weights)
TPRs, FPRs = nb.constructRoc(likelihood_diff_high, likelihood_diff_low)
auc = np.trapz(TPRs, x=FPRs)
print "AUC\t: ", auc
