max_ratio_errors = pd.DataFrame()
min_ratio_errors = pd.DataFrame()
max_distances = pd.DataFrame()
min_distances = pd.DataFrame()
gini_coefficients = pd.DataFrame()
mean_idenratio_errors = pd.DataFrame()
max_idenratio_errors = pd.DataFrame()
min_idenratio_errors = pd.DataFrame()
iden_gini_coefficients = pd.DataFrame()
for i in range(t):
print(i)
c = np.random.choice(len(W_lst), size=k)
Ws = W_lst[c]
Wn = W_name[c]
As = A_lst[c]
res = error_calc(Ws, Ws, n, eps, modes, rep, As=As, Ar=As)
results.append(res)
names.append(Wn)
total_error = OrderedDict()
mean_ratio_error = OrderedDict()
max_ratio_error = OrderedDict()
min_ratio_error = OrderedDict()
max_distance = OrderedDict()
min_distance = OrderedDict()
gini_coefficient = OrderedDict()
mean_idenratio_error = OrderedDict()
max_idenratio_error = OrderedDict()
min_idenratio_error = OrderedDict()
iden_gini_coefficient = OrderedDict()
for mode in modes:
total_error[mode] = np.sum(res[mode])
np.random.seed(seed)
print(experiment_name)
print('n={}, k={}, eps={}, rep={}, seed={}'.format(n,k,eps,rep,seed))
conf = OrderedDict()
conf['n']=n
conf['eps'] = eps
conf['rep']=rep
conf['seed'] =seed
modes = ['ind', 'uni', 'fdiff', 'fmax', 'fsum']
W_name = ['adult', 'age1', 'age2', 'age3']
W_lst = [census.__adult(), census.__age1(), census.__age2(), census.__age3()]
c = np.random.choice(len(W_lst))
Ws = [W_lst[c]]
Wn = [W_name[c]]
res = error_calc(Ws, Ws, n, 1/k*eps, modes, rep)
analysis = pd.DataFrame()
result = pd.DataFrame.from_dict(res, orient='index')
result = result.set_index(result.index+'_1')
results = result
out_dict = dict()
out_dict['res_1'] = res
res_prev = res
for j in range(2,k+1):
outs = []
index =[]
c = np.random.choice(len(W_lst))
Ws.append(W_lst[c])
Wn.append(W_name[c])
res = error_calc(Ws, Ws, n, j/k*eps, modes, rep)
'buc_qsd'
]
W1 = np.zeros(n)
W1[1] = 1
W1 = matrix.EkteloMatrix(W1.reshape(1, n))
W2 = workload.Total(n)
Ws = [W1]
for i in range(1, k):
Ws.append(W2)
Wr = Ws[:2]
outs = []
index = []
res = error_calc(Ws, Wr, n, eps, modes, rep)
res_noW1 = error_calc(Ws[1:], Wr[1:], n, eps * (k - 1) / k, modes, rep)
for mode in modes[1:]:
print(mode)
outs.append(crossmode_analysis(res['ind'], res[mode]))
outs.append(interference_analysis(res_noW1[mode], res[mode][1:]))
index.extend([mode + '_ind', mode + '_inter'])
analysis = pd.DataFrame(outs, index=index)
results = pd.DataFrame.from_dict(res, orient='index')
results_noW1 = pd.DataFrame.from_dict(res_noW1, orient='index')
results_noW1 = results_noW1.set_index(results_noW1.index + '_noW1')
results_noW1.insert(len(results_noW1.columns), len(results_noW1.columns),
np.zeros(len(results_noW1)))
results_noW1 = results_noW1.shift(1, axis=1)
results = results.append(results_noW1)
print(results)
conf['seed'] =seed
outs =[]
modes = ['ind', 'uni', 'fdiff', 'fmax', 'fsum', 'buc_eq', 'buc_con', 'buc_qeq', 'buc_qsd']
W1 = workload.Identity(n)
W2 = workload.Total(n)
Ws = [W1]
for i in range(1,k):
Ws.append(W2)
Wr = Ws[:2]
As = strategy_comp(Ws, n, rep)
Ar = As[:2]
outs = []
index =[]
res = error_calc(Ws, Wr, n, eps, modes, rep, As=As, Ar=Ar)
res_noW1 = error_calc(Ws[1:], Wr[1:], n, eps*(k-1)/k, modes, rep, As=As[1:], Ar=Ar[1:])
for mode in modes[1:]:
print(mode)
outs.append(crossmode_analysis(res['ind'], res[mode]))
outs.append(interference_analysis(res_noW1[mode], res[mode][1:]))
index.extend([mode+'_ind', mode+'_inter'])
analysis = pd.DataFrame(outs, index=index)
results = pd.DataFrame.from_dict(res, orient='index')
results_noW1 = pd.DataFrame.from_dict(res_noW1, orient='index')
results_noW1 = results_noW1.set_index(results_noW1.index+'_noW1')
results_noW1.insert(len(results_noW1.columns), len(results_noW1.columns), np.zeros(len(results_noW1)))
results_noW1 = results_noW1.shift(1,axis=1)
results = results.append(results_noW1)
print(results)