s_results.new('Rel. MSE(P)', 'nm', rel_mse_p_ij)
def rel_mse_logit_p_ij(n, d, f):
logit_P = logit(d.edge_probabilities(n))
logit_Q = f.baseline_logit(n)
return rel_mse(logit(f.edge_probabilities(n)), logit_Q, logit_P)
s_results.new('Rel. MSE(logit_P)', 'nm', rel_mse_logit_p_ij)
all_results = {'s': s_results}
if params['fit_nonstationary']:
n_results = s_results.copy()
n_results.title = 'Nonstationary fit'
all_results['n'] = n_results
if params['fit_conditional']:
c_results = s_results.copy()
c_results.title = 'Conditional fit'
all_results['c'] = c_results
def initialize(s, f):
if params['initialize_true_z']:
s.node_covariates['z'][:] = s.node_covariates['z_true'][:]
else:
s.node_covariates['z'][:] = np.random.randint(0, params['fit_K'], s.N)
for c in covariates:
fit_model.beta[c] = None
n_fit_model.beta[c] = None
# Set up recording of results from experiment
results = Results(params['sub_sizes'], params['num_reps'], 'Stationary fit')
add_network_stats(results)
def est_theta_c(c):
return lambda d, f: f.beta[c]
for c in covariates:
f_est = est_theta_c(c)
results.new('%s' % c, 'm', f_est)
all_results = {}
if params['fit_stationary']:
s_results = results.copy()
s_results.title = 'Stationary fit'
all_results['s'] = s_results
if params['fit_nonstationary']:
n_results = results.copy()
n_results.title = 'Nonstationary fit'
all_results['n'] = n_results
if params['fit_conditional']:
c_results = results.copy()
c_results.title = 'Conditional fit'
all_results['c'] = c_results
if params['fit_conditional_is']:
i_results = results.copy()
i_results.title = 'Conditional (importance sampled) fit'
all_results['i'] = i_results
fit_model.fit = fit_model.fit_kl
n_fit_model.fit = n_fit_model.fit_kl
# Set up recording of results from experiment
s_results = Results(params['sub_sizes'], params['sub_sizes'],
params['num_reps'], 'Stationary fit')
add_array_stats(s_results)
def class_mismatch(n):
truth = n.node_covariates['truth'][:]
estimated = n.node_covariates['z'][:]
return minimum_disagreement(truth, estimated, normalized = False)
s_results.new('Class mismatch', 'n', class_mismatch)
all_results = { 's': s_results }
if params['fit_nonstationary']:
n_results = s_results.copy()
n_results.title = 'Nonstationary fit'
all_results['n'] = n_results
if params['fit_conditional']:
c_results = s_results.copy()
c_results.title = 'Conditional fit'
all_results['c'] = c_results
if params['fit_conditional_is']:
i_results = s_results.copy()
i_results.title = 'Conditional (importance sampled) fit'
all_results['i'] = i_results
def initialize(s, f, offset_extremes):
if params['initialize_true_z']:
s.node_covariates['z'][:] = s.node_covariates['value'][:]
else:
# Set up recording of results from experiment
results = Results(params['sub_sizes'], params['num_reps'], 'Stationary fit')
add_network_stats(results)
def est_theta_c(c):
return lambda d, f: f.beta[c]
for c in covariates:
f_est = est_theta_c(c)
results.new('%s' % c, 'm', f_est)
all_results = {}
if params['fit_stationary']:
s_results = results.copy()
s_results.title = 'Stationary fit'
all_results['s'] = s_results
if params['fit_nonstationary']:
n_results = results.copy()
n_results.title = 'Nonstationary fit'
all_results['n'] = n_results
if params['fit_conditional']:
c_results = results.copy()
c_results.title = 'Conditional fit'
all_results['c'] = c_results
if params['fit_conditional_is']:
i_results = results.copy()
i_results.title = 'Conditional (importance sampled) fit'
all_results['i'] = i_results
