print('n={}, k={}, eps={}, rep={}, seed={}, t={}'.format(
n, k, eps, rep, seed, t))
conf = OrderedDict()
conf['n'] = n
conf['k'] = k
conf['eps'] = eps
conf['rep'] = rep
conf['seed'] = seed
conf['t'] = t
modes = [
'ind', 'iden', 'uni', 'fdiff', 'fmax', 'fsum', 'buc_eq', 'buc_con',
'buc_qeq', 'buc_qsd'
]
W_name = np.array(['race1', 'race2', 'white'])
W_lst = np.array([census.__race1(), census.__race2(), census.__white()])
A_lst = strategy_comp(W_lst, n, rep)
results = []
names = []
total_errors = pd.DataFrame()
mean_ratio_errors = pd.DataFrame()
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)
conf = OrderedDict()
conf['n']=n
conf['k']=k
conf['eps'] = eps
conf['rep']=rep
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')
