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')