def probability_values(datasize,epsilon,delta,prior,observation, mech):
Bayesian_Model = BayesInferwithDirPrior(prior, datasize, epsilon, delta)
Bayesian_Model._set_observation(observation)
Bayesian_Model._set_candidate_scores()
Bayesian_Model._set_local_sensitivities()
if(mech == r'Alg 3 - $\mathsf{EHD}$'):
Bayesian_Model._set_up_exp_mech_with_GS()
elif(mech == r'Alg 4 - $\mathsf{EHDL}$'):
Bayesian_Model._set_up_exp_mech_with_LS()
elif(mech == r'Alg 5 - $\mathsf{EHDS}$'):
Bayesian_Model._set_up_exp_mech_with_gamma_SS()
probabilities_exp = {}
for i in range(len(Bayesian_Model._candidates)):
z = Bayesian_Model._candidates[i]
if(mech == r'Alg 3 - $\mathsf{EHD}$'):
probabilities_exp[str(z._alphas)] = Bayesian_Model._GS_probabilities[i]
elif(mech == r'Alg 4 - $\mathsf{EHDL}$'):
probabilities_exp[str(z._alphas)] = Bayesian_Model._LS_probabilities[i]
elif(mech == r'Alg 5 - $\mathsf{EHDS}$'):
probabilities_exp[str(z._alphas)] = Bayesian_Model._gamma_SS_probabilities[i]
return probabilities_exp
def exp_distribution_over_candidates(dataobs, prior, epsilon, mech):
n = sum(dataobs)
Bayesian_Model = BayesInferwithDirPrior(prior, n, epsilon)
Bayesian_Model._set_observation(dataobs)
Bayesian_Model._set_candidate_scores()
Bayesian_Model._set_local_sensitivities()
if mech == "exp":
Bayesian_Model._set_up_exp_mech_with_GS()
elif mech == "gamma":
Bayesian_Model._set_up_exp_mech_with_gamma_SS()
exp_prob = {}
for i in range(len(Bayesian_Model._candidates)):
z = Bayesian_Model._candidates[i]._pointwise_sub(prior)
if mech == "exp":
exp_prob[list2key(z._alphas)] = Bayesian_Model._GS_probabilities[i]
elif mech == "gamma":
exp_prob[list2key(
z._alphas)] = Bayesian_Model._gamma_SS_probabilities[i]
# print exp_prob
return exp_prob
def row_discrete_probabilities(sample_size, epsilon, delta, prior,
observation):
Bayesian_Model = BayesInferwithDirPrior(prior, sample_size, epsilon, delta)
Bayesian_Model._set_observation(observation)
print Bayesian_Model._observation_counts
Bayesian_Model._set_candidate_scores()
Bayesian_Model._set_local_sensitivities()
Bayesian_Model._set_up_exp_mech_with_gamma_SS()
Bayesian_Model._set_up_exp_mech_with_SS()
Bayesian_Model._set_up_exp_mech_with_GS()
Bayesian_Model._set_up_exp_mech_with_LS()
#############################################################################
#SPLIT THE BINS
#############################################################################
Candidate_bins_by_step = {}
probability_distance_pairs_in_exp = []
nomalizer = 0.0
sorted_scores = sorted(Bayesian_Model._candidate_scores.items(),
key=operator.itemgetter(1))
counter = 0
while counter < len(sorted_scores):
flage = counter
key = str(sorted_scores[flage][1])
Candidate_bins_by_step[key] = []
# parameters_of_bin = []
while counter < len(sorted_scores) and sorted_scores[flage][
1] == sorted_scores[counter][1]:
Candidate_bins_by_step[key].append(sorted_scores[counter][0])
# parameters_of_bin.append(sorted_scores[counter][0]._alphas)
counter += 1
# print parameters_of_bin
print key
#############################################################################
#SPLIT THE BINS
#############################################################################
# Candidate_bins_by_step = {}
# for r in Bayesian_Model._candidates:
# if str(sorted(r._alphas)) not in Candidate_bins_by_step.keys():
# Candidate_bins_by_step[str(sorted(r._alphas))] = []
# for c in Bayesian_Model._candidates:
# if set(c._alphas) == set(r._alphas):
# Candidate_bins_by_step[str(sorted(r._alphas))].append(c)
#############################################################################
#SUM UP the prob within the same bin
#############################################################################
# exp = calculate_prob_exp(Candidate_bins_by_step, Bayesian_Model, mechanism_parameter = 4,
# sensitivity = Bayesian_Model._SS, savename = "_exp.txt")
#############################################################################
#SUM UP the prob within the same bin
#############################################################################
exp_gamma = calculate_prob_exp(Candidate_bins_by_step, Bayesian_Model,
"gamma")
#############################################################################
#SUM UP the prob within the same bin
#############################################################################
exp_LS = calculate_prob_exp(Candidate_bins_by_step, Bayesian_Model,
"local")
#############################################################################
#SUM UP the prob within the same bin
#############################################################################
exp_GS = calculate_prob_exp(Candidate_bins_by_step, Bayesian_Model, "exp")
#############################################################################
#SETTING THE SENSITIVITY
#############################################################################
if (len(prior._alphas) == 2):
sensitivity1 = 1.0
sensitivity2 = 2.0
else:
sensitivity1 = 2.0
sensitivity2 = len(prior._alphas) * 1.0
#############################################################################
#CALCULATE the Laplace prob within the same bin
#############################################################################
step, lap_1 = calculate_prob_lap(Candidate_bins_by_step,
Bayesian_Model,
sensitivity=sensitivity1,
savename="_lap_1.txt")
step, lap_2 = calculate_prob_lap(Candidate_bins_by_step,
Bayesian_Model,
sensitivity=sensitivity2,
savename="_lap_2.txt")
print step, exp_gamma, lap_1, lap_2
# return
#############################################################################
#PLOT the prob within the same bin
#############################################################################
#############################################################################
#LABELS SETTING
#############################################################################
labels = [
r'Alg 5 - $\mathsf{EHDS}$ ', r"Alg 4 - $\mathsf{EHDL}$",
r"Alg 3 - $\mathsf{EHD}$",
r'Alg 1 - $\mathsf{LSDim}$ (sensitivity = ' + str(sensitivity2) + ')',
r'Alg 2 - $\mathsf{LSHist}$ (sensitivity = ' + str(sensitivity1) + ')'
]
#############################################################################
#PLOTTING
#############################################################################
plt.figure()
plt.plot(step, lap_2, '-', color='lightblue', label=(labels[3]))
plt.plot(step, lap_1, '-', color='navy', label=(labels[4]))
plt.plot(step, exp_GS, '-', label=(labels[2]))
plt.plot(step, exp_LS, '-', label=(labels[1]))
plt.plot(step, exp_gamma, '-', label=(labels[0]))
# plt.plot(step, exp, label=(labels[0]))
# plt.plot(step, exp_new, label=(labels[1]))
# plt.plot(step, exp_LS, label=(labels[2]))
# plt.plot(step, exp_GS, label=(labels[3]))
# plt.plot(step, lap_1, color = 'navy', label=(labels[4]))
# plt.plot(step, lap_2, color = 'lightblue', label=(labels[5]))
#############################################################################
#PLOT FEATURE SETTING
#############################################################################
plt.xlabel("c / Hellinger distance from true posterior")
plt.ylabel("Pr[H(BI(x),r) = c]")
plt.title("Accuracy with Data size " + str(sample_size), fontsize=15)
plt.legend()
plt.grid()
plt.show()