def ci_umle_boot(X, v, alpha_level): arr = array_from_data(X, [v]) arr.offset_extremes() alpha_zero(arr) fit_model = NonstationaryLogistic() fit_model.beta['x_0'] = None fit_model.confidence_boot(arr, alpha_level = alpha_level) return fit_model.conf['x_0']['pivotal']
covariates = ['x_%d' % i for i in range(1)] for covariate in covariates: data_model.beta[covariate] = normal(0, 1.0) x_node = normal(0, 1.0, N) def f_x(i_1, i_2): return abs(x_node[i_1] - x_node[i_2]) < 0.6 net.new_edge_covariate(covariate).from_binary_function_ind(f_x) net.generate(data_model) net.offset_extremes() net.show() print 'True theta_0: %.2f' % data_model.beta['x_0'] # Initialize the fit model; specify which covariates it should have terms for fit_model = NonstationaryLogistic() for covariate in covariates: fit_model.beta[covariate] = None # Set up random subnetwork generator, and run fitting experiments gen = RandomSubnetworks(net, (40, 40)) for rep in range(5): subnet = gen.sample() fit_model.fit_brazzale(subnet, 'x_0') print 'Estimated theta_0: %.2f' % fit_model.beta['x_0'] fit_model.confidence_boot(subnet, n_bootstrap = 10) cis = fit_model.conf['x_0'] print 'Brazzale CI for theta_0: (%.2f, %.2f)' % cis['brazzale'] print 'Pivotal CI for theta_0: (%.2f, %.2f)' % cis['pivotal']
# Offset extreme substructure only for Nonstationary model net.offset_extremes() print 'Fitting nonstationary model' ns_model = NonstationaryLogistic() for cov_name in cov_names: ns_model.beta[cov_name] = None ns_model.fit(net) print 'NLL: %.2f' % ns_model.nll(net) print 'kappa: %.2f' % ns_model.kappa for cov_name in cov_names: print '%s: %.2f' % (cov_name, ns_model.beta[cov_name]) print for rep in range(params['n_samples']): ns_samples[rep,:,:] = ns_model.generate(net) ns_model.confidence_boot(net, n_bootstrap = params['n_bootstrap']) ns_model.confidence_wald(net) display_cis(ns_model) # Calculate sample means and variances s_samples_mean = np.mean(s_samples, axis = 0) s_samples_sd = np.sqrt(np.var(s_samples, axis = 0)) ns_samples_mean = np.mean(ns_samples, axis = 0) ns_samples_sd = np.sqrt(np.var(ns_samples, axis = 0)) c_samples_mean = np.mean(c_samples, axis = 0) c_samples_sd = np.sqrt(np.var(c_samples, axis = 0)) # Finish plotting plt.figure() ax = plt.subplot(231) ax.set_title('Stationary')
data_model.beta[covariate] = normal(0, 1.0) x_node = normal(0, 1.0, N) def f_x(i_1, i_2): return abs(x_node[i_1] - x_node[i_2]) < 0.6 net.new_edge_covariate(covariate).from_binary_function_ind(f_x) net.generate(data_model) net.offset_extremes() net.show() print 'True theta_0: %.2f' % data_model.beta['x_0'] # Initialize the fit model; specify which covariates it should have terms for fit_model = NonstationaryLogistic() for covariate in covariates: fit_model.beta[covariate] = None # Set up random subnetwork generator, and run fitting experiments gen = RandomSubnetworks(net, (40, 40)) for rep in range(5): subnet = gen.sample() fit_model.fit_brazzale(subnet, 'x_0') print 'Estimated theta_0: %.2f' % fit_model.beta['x_0'] fit_model.confidence_boot(subnet, n_bootstrap=10) cis = fit_model.conf['x_0'] print 'Brazzale CI for theta_0: (%.2f, %.2f)' % cis['brazzale'] print 'Pivotal CI for theta_0: (%.2f, %.2f)' % cis['pivotal']
# Offset extreme substructure only for Nonstationary model net.offset_extremes() print 'Fitting nonstationary model' ns_model = NonstationaryLogistic() for cov_name in cov_names: ns_model.beta[cov_name] = None ns_model.fit(net) print 'NLL: %.2f' % ns_model.nll(net) print 'kappa: %.2f' % ns_model.kappa for cov_name in cov_names: print '%s: %.2f' % (cov_name, ns_model.beta[cov_name]) print for rep in range(params['n_samples']): ns_samples[rep, :, :] = ns_model.generate(net) ns_model.confidence_boot(net, n_bootstrap=params['n_bootstrap']) ns_model.confidence_wald(net) display_cis(ns_model) # Calculate sample means and variances s_samples_mean = np.mean(s_samples, axis=0) s_samples_sd = np.sqrt(np.var(s_samples, axis=0)) ns_samples_mean = np.mean(ns_samples, axis=0) ns_samples_sd = np.sqrt(np.var(ns_samples, axis=0)) c_samples_mean = np.mean(c_samples, axis=0) c_samples_sd = np.sqrt(np.var(c_samples, axis=0)) # Finish plotting plt.figure() ax = plt.subplot(231) ax.set_title('Stationary')
s_fit.reset_confidence() s_fit.confidence_wald(new) ws_ci_l, ws_ci_u = safe_ci(s_fit, 'x_0', 'wald') if ws_ci_l < theta < ws_ci_u: ws_covered += 1 new.offset_extremes() ns_fit.reset_confidence() ns_fit.confidence_wald(new) wn_ci_l, wn_ci_u = safe_ci(ns_fit, 'x_0', 'wald') if wn_ci_l < theta < wn_ci_u: wn_covered += 1 ns_fit.confidence_boot(new, n_bootstrap = n_boot, alpha_level = alpha_level) bn_ci_l, bn_ci_u = ns_fit.conf['x_0']['pivotal'] if bn_ci_l < theta < bn_ci_u: bn_covered += 1 A = new.as_dense() r = A.sum(1) c = A.sum(0) c_fit = FixedMargins(s_fit) new.new_row_covariate('r', np.int)[:] = r new.new_col_covariate('c', np.int)[:] = c c_fit.fit = c_fit.base_model.fit_conditional c_fit.reset_confidence() c_fit.confidence_wald(new)