def ci_umle_wald(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_wald(arr, strict = False, alpha_level = alpha_level)
return safe_ci(fit_model, 'x_0', 'wald_inverse')
def ci_umle_wald(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_wald(arr, alpha_level=alpha_level)
return safe_ci(fit_model, "x_0", "wald")
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']
def ci_brazzale(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.fit_brazzale(arr, 'x_0', alpha_level = alpha_level)
return safe_ci(fit_model, 'x_0', 'brazzale')
def ci_umle(X, v, theta_grid, alpha_level):
arr = array_from_data(X, [v])
arr.offset_extremes()
alpha_zero(arr)
fit_model = NonstationaryLogistic()
umle = np.empty_like(theta_grid)
for l, theta_l in enumerate(theta_grid):
fit_model.beta['x_0'] = theta_l
fit_model.fit(arr, fix_beta = True)
umle[l] = -fit_model.nll(arr)
crit = -0.5 * chi2.ppf(1 - alpha_level, 1)
ci = invert_test(theta_grid, umle - umle.max(), crit)
if params['plot']:
plot_statistics(ax_umle, theta_grid, umle - umle.max(), crit)
umle_coverage_data['cis'].append(ci)
umle_coverage_data['theta_grid'] = theta_grid
umle_coverage_data['crit'] = crit
return ci
theta_grid_max = 3.0
theta_grid_G = 121
def cond_a_nll(X, w):
return cond_a_nll_b(X, w, sort_by_wopt_var = True)
def cond_a_sample(r, c, w, T = 0):
return cond_a_sample_b(r, c, w, T, sort_by_wopt_var = True)
while True:
a = Array(M, N)
alpha_norm(a, 1.0)
a.new_edge_covariate('x')[:,:] = np.random.normal(0, 1, (M, N))
d = NonstationaryLogistic()
d.beta['x'] = theta
d.match_kappa(a, kappa_target)
a.generate(d)
f = NonstationaryLogistic()
f.beta['x'] = None
f.fit_conditional(a, T = T_fit, verbose = True)
abs_err = abs(f.beta['x'] - d.beta['x'])
if abs_err > min_error:
print f.beta['x']
break
theta_vec = np.linspace(theta_grid_min, theta_grid_max, theta_grid_G)
'cov_structure': 'multimodal_4_cycles',
'cov_mult': 2.0,
'num_reps': 10,
'coverage_increments': [0.01]*10 + [0.1]*10 + [0.2]*10 + [0.5]*10,
'arb_init': False }
# Set random seed for reproducible output
np.random.seed(137)
# Initialize full network
net = Network(params['N'])
alpha_zero(net)
# Generate covariates and associated coefficients
data_model = NonstationaryLogistic()
covariates = []
for b in range(params['B']):
name = 'x_%d' % b
covariates.append(name)
if name in params['theta_fixed']:
data_model.beta[name] = params['theta_fixed'][name]
else:
data_model.beta[name] = np.random.normal(0, params['theta_sd'])
if params['cov_structure'] == 'none':
def f_x(i_1, i_2):
return np.random.uniform(-np.sqrt(3), np.sqrt(3))
elif params['cov_structure'] == 'unimodal':
def f_x(i_1, i_2):
from Network import Network
from Models import NonstationaryLogistic, alpha_unif
from Experiment import RandomSubnetworks
from numpy.random import normal, seed
# Seed random number for reproducible results
seed(137)
# Initialize full network
N = 300
net = Network(N)
alpha_unif(net, 0.5)
# Initialize the data model; generate covariates and associated coefficients
data_model = NonstationaryLogistic()
data_model.kappa = -7.0
covariates = ['x_%d' % i for i in range(5)]
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]) < 0.3
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
from Network import Network
from Models import NonstationaryLogistic, alpha_unif
from Experiment import RandomSubnetworks
from numpy.random import normal, seed
# Seed random number for reproducible results
seed(137)
# Initialize full network
N = 100
net = Network(N)
alpha_unif(net, 0.5)
# Initialize the data model; generate covariates and associated coefficients
data_model = NonstationaryLogistic()
data_model.kappa = -1.0
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
def cond_a_nll(X, w):
return cond_a_nll_b(X, w, sort_by_wopt_var=True)
def cond_a_sample(r, c, w, T=0):
return cond_a_sample_b(r, c, w, T, sort_by_wopt_var=True)
while True:
a = Array(M, N)
alpha_norm(a, 1.0)
a.new_edge_covariate('x')[:, :] = np.random.normal(0, 1, (M, N))
d = NonstationaryLogistic()
d.beta['x'] = theta
d.match_kappa(a, kappa_target)
a.generate(d)
f = NonstationaryLogistic()
f.beta['x'] = None
f.fit_conditional(a, T=T_fit, verbose=True)
abs_err = abs(f.beta['x'] - d.beta['x'])
if abs_err > min_error:
print f.beta['x']
break
theta_vec = np.linspace(theta_grid_min, theta_grid_max, theta_grid_G)
init_latex_rendering()
# Parameters
N = 25
G = 20
alpha_sd = 2.0
theta_true = { 'x_1': 2.0, 'x_2': -1.0 }
target_degree = 2
# Setup network
net = Network(N)
alpha_norm(net, alpha_sd)
# Setup data model and network covariates
data_model = NonstationaryLogistic()
covariates = []
for name in theta_true:
covariates.append(name)
data_model.beta[name] = theta_true[name]
def f_x(i_1, i_2):
return np.random.normal(0, 1.0)
net.new_edge_covariate(name).from_binary_function_ind(f_x)
# Instantiate network according to data model
data_model.match_kappa(net, ('row_sum', target_degree))
net.generate(data_model)
#net.show_heatmap(order_by_row = 'alpha_out')
#net.show_heatmap(order_by_col = 'alpha_in')
print '%s: %.2f' % (cov_name, c_model.base_model.beta[cov_name])
print
for rep in range(params['n_samples']):
c_samples[rep,:,:] = c_model.generate(net, coverage = 0.1)
c_model.confidence_boot(net, n_bootstrap = params['n_bootstrap'])
c_model.confidence_wald(net)
for cov_name in cov_names:
c_model.confidence_cons(net, cov_name, L = 121, test = 'score')
c_model.confidence_cons(net, cov_name, L = 121, test = 'lr')
display_cis(c_model)
# 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
from Network import Network
from Models import NonstationaryLogistic, alpha_unif
from Experiment import RandomSubnetworks
from numpy.random import normal, seed
# Seed random number for reproducible results
seed(137)
# Initialize full network
N = 300
net = Network(N)
alpha_unif(net, 0.5)
# Initialize the data model; generate covariates and associated coefficients
data_model = NonstationaryLogistic()
data_model.kappa = -7.0
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]) < 0.3
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
N = 50
D = 1
theta = 2.0
kappa_target = ('row_sum', 2)
alpha_sd = 2.0
n_rep = 100
n_boot = 10
alpha_level = 0.05
net = Network(N)
alpha_norm(net, alpha_sd)
for d in range(D):
net.new_edge_covariate('x_%d' % d)[:,:] = np.random.normal(0, 1, (N, N))
data_model = NonstationaryLogistic()
for d in range(D):
data_model.beta['x_%d' % d] = np.random.normal(0, 1)
data_model.beta['x_0'] = theta
data_model.match_kappa(net, kappa_target)
s_fit = StationaryLogistic()
ns_fit = NonstationaryLogistic()
for d in range(D):
s_fit.beta['x_%d' % d] = None
ns_fit.beta['x_%d' % d] = None
def safe_ci(model, name, method):
if name in model.conf:
if method in model.conf[name]:
return model.conf[name][method]
import numpy as np
import matplotlib.pyplot as plt
from Network import Network
from Models import StationaryLogistic, NonstationaryLogistic, alpha_unif
from Experiment import RandomSubnetworks
from Utility import draw_confidence
# Initialize full network
N = 300
sub_N = 100
net = Network(N)
alpha_unif(net, 0.5)
# Initialize the data model; generate covariates and associated coefficients
data_model = NonstationaryLogistic()
data_model.kappa = -7.0
covariates = ['x_1', 'x_2', 'x_3', 'x_4', 'x_5']
for covariate in covariates:
data_model.beta[covariate] = np.random.normal(0, 1.0)
x_node = np.random.normal(0, 1.0, N)
def f_x(i_1, i_2):
return abs(x_node[i_1] - x_node[i_2]) < 0.3
net.new_edge_covariate(covariate).from_binary_function_ind(f_x)
net.generate(data_model)
print 'True beta_1: %.2f' % data_model.beta['x_1']
# Initialize the fit model; specify which covariates it should have terms for
fit_model = StationaryLogistic()
for covariate in covariates:
for cov_name in cov_names:
fit_model.beta[cov_name] = None
fit_model.fit(net, verbose=params['verbose'])
print 'NLL: %.2f' % fit_model.nll(net)
print 'kappa: %.2f' % fit_model.kappa
if use_covs:
for cov_name in cov_names:
print '%s: %.2f' % (cov_name, fit_model.beta[cov_name])
print '\n'
fit_and_summarize('Stationary', Stationary(), False)
fit_and_summarize('Stationary', StationaryLogistic(), True)
if params['offset_extremes']:
print 'Detecting subnetworks associated with infinite parameter estimates.\n'
net.offset_extremes()
if params['plot']: net.show_offset('pub_date')
fit_and_summarize('Nonstationary', NonstationaryLogistic(), False)
fit_and_summarize('Nonstationary', NonstationaryLogistic(), True)
# Redisplay heatmap, ordered by estimated alphas from last fit, i.e.,
# NonstationaryLogistic with publication date difference covariates
# XX: Following plots are broken
#if params['plot']:
# net.show_heatmap('alpha_out')
# net.show_heatmap('alpha_in')
outfile = open('scratch.json', 'w')
outfile.write(dump_to_json(net))
outfile.close()
net.new_node_covariate('low_degree').from_pairs(net.names,
degree < med_degree)
for v_1, v_2, name in [(0, 0, 'high_to_high'),
(1, 1, 'low_to_low'),
(0, 1, 'high_to_low')]:
covariates.append(name)
def f_x(i_1, i_2):
return ((net.node_covariates['low_degree'][i_1] == v_1) and
(net.node_covariates['low_degree'][i_2] == v_2))
net.new_edge_covariate(name).from_binary_function_ind(f_x)
# Initialize fitting model
fit_model = StationaryLogistic()
n_fit_model = NonstationaryLogistic()
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']:
data_model = StationaryLogistic()
covariates = []
data_model.beta = {}
for b in range(params['B']):
name = 'x_%d' % b
covariates.append(name)
data_model.beta[name] = np.random.normal(0, params['beta_sd'])
x_node = np.random.normal(0, 1, params['N'])
def f_x(i_1, i_2):
return abs(x_node[i_1] - x_node[i_2]) < params['x_diff_cutoff']
net.new_edge_covariate(name).from_binary_function_ind(f_x)
if params['fit_nonstationary']:
fit_model = NonstationaryLogistic()
else:
fit_model = StationaryLogistic()
for c in covariates:
fit_model.beta[c] = None
# Set up recording of results from experiment
results = Results(params['sub_sizes'], params['sub_sizes'],
params['num_reps'])
add_array_stats(results)
def f_c(c):
return (lambda d, f: d.beta[c]), (lambda d, f: f.beta[c])
for c in covariates:
# Need to do this hackily to avoid for-loop/lambda-binding weirdness.
f_true, f_estimated = f_c(c)
results.new('True beta_{%s}' % c, 'm', f_true)
# Initialize full network
net = Network(params['N'])
# Generate node-level propensities to extend and receive edges
if params['alpha_norm_sd'] > 0.0:
alpha_norm(net, params['alpha_norm_sd'])
elif params['alpha_unif'] > 0.0:
alpha_unif(net, params['alpha_unif'])
elif params['alpha_gamma_sd'] > 0.0:
# Choosing location somewhat arbitrarily to give unit skewness
alpha_gamma(net, 4.0, params['alpha_gamma_sd'])
else:
alpha_zero(net)
# Generate covariates and associated coefficients
data_base_model = NonstationaryLogistic()
covariates = []
for b in range(params['B']):
name = 'x_%d' % b
covariates.append(name)
data_base_model.beta[name] = np.random.normal(0, params['beta_sd'])
def f_x(i_1, i_2):
return np.random.uniform(-np.sqrt(3), np.sqrt(3))
net.new_edge_covariate(name).from_binary_function_ind(f_x)
# Initialize data (block)model from base model
class_probs = np.random.dirichlet(np.repeat(params['class_conc'], params['K']))
z = np.where(np.random.multinomial(1, class_probs, params['N']) == 1)[1]
print 'kappa: %.2f' % s_model.kappa
for cov_name in cov_names:
print '%s: %.2f' % (cov_name, s_model.beta[cov_name])
print
for rep in range(params['n_samples']):
s_samples[rep, :, :] = s_model.generate(net)
s_model.confidence(net, n_bootstrap=params['n_bootstrap'])
print 'Pivotal:'
for cov_name in cov_names:
ci = s_model.conf[cov_name]['pivotal']
print ' %s: (%.2f, %.2f)' % (cov_name, ci[0], ci[1])
print
print 'Fitting nonstationary model'
alpha_zero(net)
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(net, n_bootstrap=params['n_bootstrap'])
print 'Pivotal:'
for cov_name in cov_names:
ci = ns_model.conf[cov_name]['pivotal']
print ' %s: (%.2f, %.2f)' % (cov_name, ci[0], ci[1])
data_model.beta = {}
for b in range(params['B']):
name = 'x_%d' % b
covariates.append(name)
data_model.beta[name] = np.random.normal(0, params['beta_sd'])
x_node = np.random.normal(0, 1, params['N'])
def f_x(i_1, i_2):
return abs(x_node[i_1] - x_node[i_2]) < params['x_diff_cutoff']
net.new_edge_covariate(name).from_binary_function_ind(f_x)
if params['fit_nonstationary']:
fit_model = NonstationaryLogistic()
else:
fit_model = StationaryLogistic()
for c in covariates:
fit_model.beta[c] = None
# Set up recording of results from experiment
results = Results(params['sub_sizes'], params['sub_sizes'], params['num_reps'])
add_array_stats(results)
def f_c(c):
return (lambda d, f: d.beta[c]), (lambda d, f: f.beta[c])
for c in covariates:
# Initialize array
arr = Array(params['M'], params['N'])
# Generate node-level propensities to extend and receive edges
if params['alpha_norm_sd'] > 0.0:
alpha_norm(arr, params['alpha_norm_sd'])
elif params['alpha_unif_sd'] > 0.0:
alpha_unif(arr, params['alpha_unif_sd'])
elif params['alpha_gamma_sd'] > 0.0:
# Choosing location somewhat arbitrarily to give unit skewness
alpha_gamma(arr, 4.0, params['alpha_gamma_sd'])
else:
alpha_zero(arr)
# Generate covariates and associated coefficients
data_model = NonstationaryLogistic()
covariates = []
for b in range(params['B']):
name = 'x_%d' % b
covariates.append(name)
if name in params['beta_fixed']:
data_model.beta[name] = params['beta_fixed'][name]
else:
data_model.beta[name] = np.random.normal(0, params['beta_sd'])
def f_x(i_1, i_2):
return np.random.uniform(-np.sqrt(3), np.sqrt(3))
arr.new_edge_covariate(name).from_binary_function_ind(f_x)
data_model.match_kappa(arr, params['kappa_target'])
import numpy as np
import matplotlib.pyplot as plt
from Network import Network
from Models import NonstationaryLogistic
from Models import alpha_zero, alpha_norm, alpha_gamma, alpha_unif
from Experiment import RandomSubnetworks
# Parameters
N = 300
reps = 10
sub_sizes = range(10, 110, 10)
kappa_target = ('row_sum', 2)
net = Network(N)
model = NonstationaryLogistic()
num_sizes = len(sub_sizes)
data_none = np.empty((num_sizes, reps))
data_het = np.empty((3, 3, num_sizes, reps))
for i, degree_het in enumerate(['Normal', 'Gamma', 'Uniform', 'None']):
if degree_het == 'None':
alpha_zero(net)
for j, het_sd in enumerate([1.0, 2.0, 3.0, 0.0]):
if degree_het == 'None' and het_sd != 0.0: continue
if degree_het != 'None' and het_sd == 0.0: continue
if degree_het == 'Normal':
alpha_norm(net, het_sd)
if degree_het == 'Gamma':
alpha_gamma(net, 4.0, het_sd)
nx.draw(graph, pos, node_size = 10, with_labels = False)
print 'Fitting stationary model'
s_model = StationaryLogistic()
for cov_name in cov_names:
s_model.beta[cov_name] = None
s_model.fit(net, verbose = True)
print 'NLL: %.2f' % s_model.nll(net)
print 'kappa: %.2f' % s_model.kappa
for cov_name in cov_names:
print '%s: %.2f' % (cov_name, s_model.beta[cov_name])
print
print 'Fitting nonstationary model'
alpha_zero(net)
ns_model = NonstationaryLogistic()
for cov_name in cov_names:
ns_model.beta[cov_name] = None
ns_model.fit(net, verbose = True)
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
print 'Fitting conditional model'
c_model = FixedMargins(StationaryLogistic())
for cov_name in cov_names:
c_model.base_model.beta[cov_name] = None
c_model.base_model.fit_conditional(net, verbose = True)
print 'NLL: %.2f' % c_model.nll(net)
from Network import Network
from Models import NonstationaryLogistic, alpha_unif
from Experiment import RandomSubnetworks
from numpy.random import normal, seed
# Seed random number for reproducible results
seed(137)
# Initialize full network
N = 300
net = Network(N)
alpha_unif(net, 0.5)
# Initialize the data model; generate covariates and associated coefficients
data_model = NonstationaryLogistic()
data_model.kappa = -7.0
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]) < 0.3
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']
'sub_sizes': np.floor(np.logspace(1.0, 2.1, 20)),
'verbose': True,
'plot_mse': True,
'plot_network': False,
'plot_fit_info': True }
# Set random seed for reproducible output
np.random.seed(137)
# Initialize full network
net = Network(params['N'])
alpha_zero(net)
# Generate covariates and associated coefficients
data_model = NonstationaryLogistic()
for b in range(params['B']):
name = 'x_%d' % b
if name in params['theta_fixed']:
data_model.beta[name] = params['theta_fixed'][name]
else:
data_model.beta[name] = np.random.normal(0, params['theta_sd'])
if params['cov_unif_sd'] > 0.0:
c = np.sqrt(12) / 2
def f_x(i_1, i_2):
return np.random.uniform(-c * params['cov_unif_sd'],
c * params['cov_unif_sd'])
elif params['cov_norm_sd'] > 0.0:
def f_x(i_1, i_2):
init_latex_rendering()
# Parameters
N = 20
G = 30
alpha_sd = 2.0
theta_true = {'x_1': 2.0, 'x_2': -1.0}
target_degree = 2
# Setup network
net = Network(N)
alpha_norm(net, alpha_sd)
# Setup data model and network covariates
data_model = NonstationaryLogistic()
covariates = []
for name in theta_true:
covariates.append(name)
data_model.beta[name] = theta_true[name]
def f_x(i_1, i_2):
return np.random.normal(0, 1.0)
net.new_edge_covariate(name).from_binary_function_ind(f_x)
# Instantiate network according to data model
data_model.match_kappa(net, ('row_sum', target_degree))
net.generate(data_model)
net.show_heatmap(order_by_row='alpha_out')
# Initialize array
arr = Array(params['M'], params['N'])
# Generate node-level propensities to extend and receive edges
if params['alpha_norm_sd'] > 0.0:
alpha_norm(arr, params['alpha_norm_sd'])
elif params['alpha_unif_sd'] > 0.0:
alpha_unif(arr, params['alpha_unif_sd'])
elif params['alpha_gamma_sd'] > 0.0:
# Choosing location somewhat arbitrarily to give unit skewness
alpha_gamma(arr, 4.0, params['alpha_gamma_sd'])
else:
alpha_zero(arr)
# Generate covariates and associated coefficients
data_model = NonstationaryLogistic()
covariates = []
for b in range(params['B']):
name = 'x_%d' % b
covariates.append(name)
if name in params['beta_fixed']:
data_model.beta[name] = params['beta_fixed'][name]
else:
data_model.beta[name] = np.random.normal(0, params['beta_sd'])
def f_x(i_1, i_2):
return np.random.uniform(-np.sqrt(3), np.sqrt(3))
arr.new_edge_covariate(name).from_binary_function_ind(f_x)
data_model.match_kappa(arr, params['kappa_target'])
def do_experiment(params):
if params['dump_fits'] and params['load_fits']:
print 'Warning: simultaneously dumping and loading is a bad idea.'
if params['dump_fits']:
fits = []
if params['load_fits']:
with open(params['load_fits'], 'r') as fits_file:
loaded_params_pick, loaded_fits = json.load(fits_file)
loaded_params = dict([(k,unpick(v)) for (k,v) in loaded_params_pick])
# Compare on parameters that control data generation and inference
run_params = ['N', 'B', 'theta_sd', 'theta_fixed',
'alpha_unif_sd', 'alpha_norm_sd', 'alpha_gamma_sd',
'cov_unif_sd', 'cov_norm_sd', 'cov_disc_sd',
'kappa_target', 'pre_offset', 'post_fit',
'fit_nonstationary', 'fit_method', 'num_reps',
'is_T', 'sampling', 'sub_sizes_r', 'sub_sizes_c',
'random_seed']
for p in run_params:
if not np.all(loaded_params[p] == params[p]):
print 'Warning: load mismatch on', p
# Set random seed for reproducible output
seed = Seed(params['random_seed'])
# Initialize full network
arr = Network(params['N'])
# Generate node-level propensities to extend and receive edges
if params['alpha_norm_sd'] > 0.0:
alpha_norm(arr, params['alpha_norm_sd'])
elif params['alpha_unif_sd'] > 0.0:
alpha_unif(arr, params['alpha_unif_sd'])
elif params['alpha_gamma_sd'] > 0.0:
# Choosing location somewhat arbitrarily to give unit skewness
alpha_gamma(arr, 4.0, params['alpha_gamma_sd'])
else:
alpha_zero(arr)
# Generate covariates and associated coefficients
data_model = NonstationaryLogistic()
covariates = []
for b in range(params['B']):
name = 'x_%d' % b
covariates.append(name)
if name in params['theta_fixed']:
data_model.beta[name] = params['theta_fixed'][name]
else:
data_model.beta[name] = np.random.normal(0, params['theta_sd'])
if params['cov_unif_sd'] > 0.0:
c = np.sqrt(12) / 2
def f_x(i_1, i_2):
return np.random.uniform(-c * params['cov_unif_sd'],
c * params['cov_unif_sd'])
elif params['cov_norm_sd'] > 0.0:
def f_x(i_1, i_2):
return np.random.normal(0, params['cov_norm_sd'])
elif params['cov_disc_sd'] > 0.0:
def f_x(i_1, i_2):
return (params['cov_disc_sd'] *
(np.sign(np.random.random() - 0.5)))
else:
print 'Error: no covariate distribution specified.'
sys.exit()
arr.new_edge_covariate(name).from_binary_function_ind(f_x)
# Generate large network, if necessary
if not params['sampling'] == 'new':
data_model.match_kappa(arr, params['kappa_target'])
arr.generate(data_model)
if params['fit_nonstationary']:
fit_model = NonstationaryLogistic()
else:
fit_model = StationaryLogistic()
for c in covariates:
fit_model.beta[c] = None
# Set up recording of results from experiment
results = Results(params['sub_sizes_r'], params['sub_sizes_c'],
params['num_reps'], interactive = params['interactive'])
add_array_stats(results)
if params['plot_sig']:
from scipy.stats import chi2
crit = lambda dof: -0.5 * chi2.ppf(0.95, dof)
umle_f = lambda n, f: f.nll(n, ignore_offset = True)
umle_d = lambda n, d: d.nll(n, ignore_offset = True)
umle_n = lambda n: NonstationaryLogistic().nll(n, ignore_offset = True)
results.new('UMLE F-N', 'nm',
lambda n, d, f: umle_f(n, f) - umle_n(n))
results.new('UMLE F-D', 'nm',
lambda n, d, f: umle_f(n, f) - umle_d(n, d))
cmle_a_f = lambda n, f: acnll(n.as_dense(), np.exp(f.edge_probabilities(n)))
cmle_a_d = lambda n, d: acnll(n.as_dense(), np.exp(d.edge_probabilities(n)))
cmle_a_n = lambda n: acnll(n.as_dense(), np.ones_like(n.as_dense()))
results.new('CMLE-A F-N', 'nm',
lambda n, d, f: cmle_a_f(n, f) - cmle_a_n(n))
results.new('CMLE-A F-D', 'nm',
lambda n, d, f: cmle_a_f(n, f) - cmle_a_d(n, d))
cmle_is_f = lambda n, f: f.fit_conditional(n, evaluate = True, T = 50)
cmle_is_d = lambda n, d: d.fit_conditional(n, evaluate = True, T = 50)
cmle_is_n = lambda n: NonstationaryLogistic().fit_conditional(n, evaluate = True, T = 50)
results.new('CMLE-IS F-N', 'nm',
lambda n, d, f: cmle_is_f(n, f) - cmle_is_n(n))
results.new('CMLE-IS F-D', 'nm',
lambda n, d, f: cmle_is_f(n, f) - cmle_is_d(n, d))
c_cmle_f = lambda n, f: f.fit_c_conditional(n, evaluate = True)
c_cmle_d = lambda n, d: d.fit_c_conditional(n, evaluate = True)
c_cmle_n = lambda n: NonstationaryLogistic().fit_c_conditional(n, evaluate = True)
results.new('C-CMLE F-N', 'nm',
lambda n, d, f: c_cmle_f(n, f) - c_cmle_n(n))
results.new('C-CMLE F-D', 'nm',
lambda n, d, f: c_cmle_f(n, f) - c_cmle_d(n, d))
results.new('UMLE sig.', 'dof',
lambda M, N, B: crit((M - 1) + (N - 1) + 1 + B))
results.new('CMLE sig.', 'dof', lambda M, N, B: crit(B))
results.new('C-CMLE sig.', 'dof', lambda M, N, B: crit((M - 1) + B))
if params['sampling'] == 'new':
results.new('Subnetwork kappa', 'm', lambda d, f: d.kappa)
def true_est_theta_c(c):
return (lambda d, f: d.beta[c]), (lambda d, f: f.beta[c])
for c in covariates:
# Need to do this hackily to avoid for-loop/lambda-binding weirdness.
f_true, f_est = true_est_theta_c(c)
results.new('True theta_{%s}' % c, 'm', f_true)
results.new('Est. theta_{%s}' % c, 'm', f_est)
if params['pre_offset'] or params['post_fit']:
results.new('# Active', 'n',
lambda n: np.isfinite(n.offset.matrix()).sum())
else:
results.new('# Active', 'n', lambda n: n.M * n.N)
if params['fisher_information']:
def info_theta_c(c):
def f_info_theta_c(d, f):
return d.I_inv['theta_{%s}' % c]
return f_info_theta_c
for c in covariates:
results.new('Info theta_{%s}' % c, 'm', info_theta_c(c))
if params['baseline']:
def rel_mse_p_ij(n, d, f):
P = d.edge_probabilities(n)
return rel_mse(f.edge_probabilities(n), f.baseline(n), P)
results.new('Rel. MSE(P_ij)', 'nm', rel_mse_p_ij)
if not (params['pre_offset'] or params['post_fit']):
def rel_mse_logit_p_ij(n, d, f):
logit_P = d.edge_probabilities(n, logit = True)
logit_Q = f.baseline_logit(n)
return rel_mse(f.edge_probabilities(n, logit = True),
logit_Q, logit_P)
results.new('Rel. MSE(logit P_ij)', 'nm', rel_mse_logit_p_ij)
if params['fit_method'] in ['convex_opt', 'conditional', 'c_conditional',
'irls', 'conditional_is']:
results.new('Wall time (sec.)', 'm',
lambda d, f: f.fit_info['wall_time'])
if params['fit_method'] in ['convex_opt',
'conditional', 'conditional_is']:
def work(f):
w = 0
for work_type in ['nll_evals', 'grad_nll_evals', 'cnll_evals']:
if work_type in f.fit_info:
w += f.fit_info[work_type]
return w
results.new('Work', 'm', lambda d, f: work(f))
results.new('||ET_final - T||_2', 'm',
lambda d, f: l2(f.fit_info['grad_nll_final']))
for sub_size in zip(results.M_sizes, results.N_sizes):
print 'subnetwork size =', sub_size
if params['sampling'] == 'new':
gen = RandomSubnetworks(arr, sub_size)
else:
gen = RandomSubnetworks(arr, sub_size,
method = params['sampling'])
for rep in range(params['num_reps']):
seed.next()
sub = gen.sample()
if params['fisher_information']:
data_model.fisher_information(sub)
if params['sampling'] == 'new':
data_model.match_kappa(sub, params['kappa_target'])
sub.generate(data_model)
if params['load_fits']:
fit, loaded_fits = loaded_fits[0], loaded_fits[1:]
fit_model.beta = unpick(fit['theta'])
if params['fix_broken_cmle_is']:
for b_n in fit_model.beta:
fit_model.beta[b_n] += 0.1474
if 'alpha' in fit:
sub.row_covariates['alpha_out'] = unpick(fit['alpha'])
if 'beta' in fit:
sub.col_covariates['alpha_in'] = unpick(fit['beta'])
if 'kappa' in fit:
fit_model.kappa = fit['kappa']
if 'offset' in fit:
sub.offset = unpick(fit['offset'])
if 'fit_info' in fit:
fit_model.fit_info = unpick(fit['fit_info'])
else:
if params['pre_offset']:
sub.offset_extremes()
if params['fit_method'] == 'convex_opt':
fit_model.fit_convex_opt(sub,
verbose = params['verbose'])
elif params['fit_method'] == 'irls':
fit_model.fit_irls(sub, verbose = params['verbose'])
elif params['fit_method'] == 'logistic':
fit_model.fit_logistic(sub)
elif params['fit_method'] == 'logistic_l2':
fit_model.fit_logistic_l2(sub, prior_precision = 1.0)
elif params['fit_method'] == 'conditional':
fit_model.fit_conditional(sub,
verbose = params['verbose'])
elif params['fit_method'] == 'conditional_is':
fit_model.fit_conditional(sub, T = params['is_T'],
verbose = params['verbose'])
elif params['fit_method'] == 'c_conditional':
fit_model.fit_c_conditional(sub,
verbose = params['verbose'])
elif params['fit_method'] == 'composite':
fit_model.fit_composite(sub, T = 100,
verbose = params['verbose'])
elif params['fit_method'] == 'brazzale':
fit_model.fit_brazzale(sub)
elif params['fit_method'] == 'saddlepoint':
fit_model.fit_saddlepoint(sub)
elif params['fit_method'] == 'none':
pass
if params['post_fit']:
sub.offset_extremes()
fit_model.fit_convex_opt(sub, fix_beta = True)
if params['dump_fits']:
fit = {}
fit['theta'] = pick(fit_model.beta)
if 'alpha_out' in sub.row_covariates:
fit['alpha'] = pick(sub.row_covariates['alpha_out'])
if 'alpha_in' in sub.row_covariates:
fit['beta'] = pick(sub.col_covariates['alpha_in'])
if not fit_model.kappa is None:
fit['kappa'] = fit_model.kappa
if not sub.offset is None:
sub.offset.dirty()
fit['offset'] = pick(sub.offset)
if not fit_model.fit_info is None:
fit['fit_info'] = pick(fit_model.fit_info)
fits.append(fit)
if params['find_good'] > 0:
abs_err = abs(fit_model.beta['x_0'] - data_model.beta['x_0'])
if abs_err < params['find_good']:
print abs_err
sub.offset = None
fit_model.fit_conditional(sub, T = 1000,
verbose = True)
print fit_model.beta['x_0']
print fit_model.fit_info
f = file('goodmat.mat', 'wb')
import scipy.io
Y = np.array(sub.as_dense(), dtype=np.float)
X = sub.edge_covariates['x_0'].matrix()
scipy.io.savemat(f, { 'Y': Y, 'X': X })
sys.exit()
if params['find_bad'] > 0:
abs_err = abs(fit_model.beta['x_0'] - data_model.beta['x_0'])
if abs_err > params['find_bad']:
print abs_err
sub.offset = None
fit_model.fit_conditional(sub, T = 1000,
verbose = True)
print fit_model.beta['x_0']
print fit_model.fit_info
f = file('badmat.mat', 'wb')
import scipy.io
Y = np.array(sub.as_dense(), dtype=np.float)
X = sub.edge_covariates['x_0'].matrix()
scipy.io.savemat(f, { 'Y': Y, 'X': X })
sys.exit()
results.record(sub_size, rep, sub, data_model, fit_model)
if params['verbose']:
print
if params['dump_fits']:
with open(params['dump_fits'], 'w') as outfile:
json.dump(([(p, pick(params[p])) for p in params], fits), outfile)
# Compute beta MSEs
covariate_naming = []
for c in covariates:
mse_name = 'MSE(theta_{%s})' % c
true_name = 'True theta_{%s}' % c
est_name = 'Est. theta_{%s}' % c
results.estimate_mse(mse_name, true_name, est_name)
covariate_naming.append((c, mse_name, true_name, est_name))
# Report parameters for the run
print 'Parameters:'
for field in params:
print '%s: %s' % (field, str(params[field]))
# Should not vary between runs with the same seed and same number
# of arrays tested
seed.final()
results.summary()
return results, covariate_naming
nx.draw(graph, pos, node_size=10, with_labels=False)
print 'Fitting stationary model'
s_model = StationaryLogistic()
for cov_name in cov_names:
s_model.beta[cov_name] = None
s_model.fit(net, verbose=True)
print 'NLL: %.2f' % s_model.nll(net)
print 'kappa: %.2f' % s_model.kappa
for cov_name in cov_names:
print '%s: %.2f' % (cov_name, s_model.beta[cov_name])
print
print 'Fitting nonstationary model'
alpha_zero(net)
ns_model = NonstationaryLogistic()
for cov_name in cov_names:
ns_model.beta[cov_name] = None
ns_model.fit(net, verbose=True)
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
print 'Fitting conditional model'
c_model = StationaryLogistic()
for cov_name in cov_names:
c_model.beta[cov_name] = None
c_model.fit_conditional(net, T=0, verbose=True)
print 'NLL: %.2f' % c_model.nll(net)
def do_experiment(params):
if params['dump_fits'] and params['load_fits']:
print 'Warning: simultaneously dumping and loading is a bad idea.'
if params['dump_fits']:
fits = []
if params['load_fits']:
with open(params['load_fits'], 'r') as fits_file:
loaded_params_pick, loaded_fits = json.load(fits_file)
loaded_params = dict([(k, unpick(v)) for (k, v) in loaded_params_pick])
# Compare on parameters that control data generation and inference
run_params = [
'N', 'B', 'theta_sd', 'theta_fixed', 'alpha_unif_sd',
'alpha_norm_sd', 'alpha_gamma_sd', 'cov_unif_sd', 'cov_norm_sd',
'cov_disc_sd', 'kappa_target', 'pre_offset', 'post_fit',
'fit_nonstationary', 'fit_method', 'num_reps', 'is_T', 'sampling',
'sub_sizes_r', 'sub_sizes_c', 'random_seed'
]
for p in run_params:
if not np.all(loaded_params[p] == params[p]):
print 'Warning: load mismatch on', p
# Set random seed for reproducible output
seed = Seed(params['random_seed'])
# Initialize full network
arr = Network(params['N'])
# Generate node-level propensities to extend and receive edges
if params['alpha_norm_sd'] > 0.0:
alpha_norm(arr, params['alpha_norm_sd'])
elif params['alpha_unif_sd'] > 0.0:
alpha_unif(arr, params['alpha_unif_sd'])
elif params['alpha_gamma_sd'] > 0.0:
# Choosing location somewhat arbitrarily to give unit skewness
alpha_gamma(arr, 4.0, params['alpha_gamma_sd'])
else:
alpha_zero(arr)
# Generate covariates and associated coefficients
data_model = NonstationaryLogistic()
covariates = []
for b in range(params['B']):
name = 'x_%d' % b
covariates.append(name)
if name in params['theta_fixed']:
data_model.beta[name] = params['theta_fixed'][name]
else:
data_model.beta[name] = np.random.normal(0, params['theta_sd'])
if params['cov_unif_sd'] > 0.0:
c = np.sqrt(12) / 2
def f_x(i_1, i_2):
return np.random.uniform(-c * params['cov_unif_sd'],
c * params['cov_unif_sd'])
elif params['cov_norm_sd'] > 0.0:
def f_x(i_1, i_2):
return np.random.normal(0, params['cov_norm_sd'])
elif params['cov_disc_sd'] > 0.0:
def f_x(i_1, i_2):
return (params['cov_disc_sd'] *
(np.sign(np.random.random() - 0.5)))
else:
print 'Error: no covariate distribution specified.'
sys.exit()
arr.new_edge_covariate(name).from_binary_function_ind(f_x)
# Generate large network, if necessary
if not params['sampling'] == 'new':
data_model.match_kappa(arr, params['kappa_target'])
arr.generate(data_model)
if params['fit_nonstationary']:
fit_model = NonstationaryLogistic()
else:
fit_model = StationaryLogistic()
for c in covariates:
fit_model.beta[c] = None
# Set up recording of results from experiment
results = Results(params['sub_sizes_r'],
params['sub_sizes_c'],
params['num_reps'],
interactive=params['interactive'])
add_array_stats(results)
if params['plot_sig']:
from scipy.stats import chi2
crit = lambda dof: -0.5 * chi2.ppf(0.95, dof)
umle_f = lambda n, f: f.nll(n, ignore_offset=True)
umle_d = lambda n, d: d.nll(n, ignore_offset=True)
umle_n = lambda n: NonstationaryLogistic().nll(n, ignore_offset=True)
results.new('UMLE F-N', 'nm', lambda n, d, f: umle_f(n, f) - umle_n(n))
results.new('UMLE F-D', 'nm',
lambda n, d, f: umle_f(n, f) - umle_d(n, d))
cmle_a_f = lambda n, f: acnll(n.as_dense(),
np.exp(f.edge_probabilities(n)))
cmle_a_d = lambda n, d: acnll(n.as_dense(),
np.exp(d.edge_probabilities(n)))
cmle_a_n = lambda n: acnll(n.as_dense(), np.ones_like(n.as_dense()))
results.new('CMLE-A F-N', 'nm',
lambda n, d, f: cmle_a_f(n, f) - cmle_a_n(n))
results.new('CMLE-A F-D', 'nm',
lambda n, d, f: cmle_a_f(n, f) - cmle_a_d(n, d))
cmle_is_f = lambda n, f: f.fit_conditional(n, evaluate=True, T=50)
cmle_is_d = lambda n, d: d.fit_conditional(n, evaluate=True, T=50)
cmle_is_n = lambda n: NonstationaryLogistic().fit_conditional(
n, evaluate=True, T=50)
results.new('CMLE-IS F-N', 'nm',
lambda n, d, f: cmle_is_f(n, f) - cmle_is_n(n))
results.new('CMLE-IS F-D', 'nm',
lambda n, d, f: cmle_is_f(n, f) - cmle_is_d(n, d))
c_cmle_f = lambda n, f: f.fit_c_conditional(n, evaluate=True)
c_cmle_d = lambda n, d: d.fit_c_conditional(n, evaluate=True)
c_cmle_n = lambda n: NonstationaryLogistic().fit_c_conditional(
n, evaluate=True)
results.new('C-CMLE F-N', 'nm',
lambda n, d, f: c_cmle_f(n, f) - c_cmle_n(n))
results.new('C-CMLE F-D', 'nm',
lambda n, d, f: c_cmle_f(n, f) - c_cmle_d(n, d))
results.new('UMLE sig.', 'dof', lambda M, N, B: crit((M - 1) +
(N - 1) + 1 + B))
results.new('CMLE sig.', 'dof', lambda M, N, B: crit(B))
results.new('C-CMLE sig.', 'dof', lambda M, N, B: crit((M - 1) + B))
if params['sampling'] == 'new':
results.new('Subnetwork kappa', 'm', lambda d, f: d.kappa)
def true_est_theta_c(c):
return (lambda d, f: d.beta[c]), (lambda d, f: f.beta[c])
for c in covariates:
# Need to do this hackily to avoid for-loop/lambda-binding weirdness.
f_true, f_est = true_est_theta_c(c)
results.new('True theta_{%s}' % c, 'm', f_true)
results.new('Est. theta_{%s}' % c, 'm', f_est)
if params['pre_offset'] or params['post_fit']:
results.new('# Active', 'n',
lambda n: np.isfinite(n.offset.matrix()).sum())
else:
results.new('# Active', 'n', lambda n: n.M * n.N)
if params['fisher_information']:
def info_theta_c(c):
def f_info_theta_c(d, f):
return d.I_inv['theta_{%s}' % c]
return f_info_theta_c
for c in covariates:
results.new('Info theta_{%s}' % c, 'm', info_theta_c(c))
if params['baseline']:
def rel_mse_p_ij(n, d, f):
P = d.edge_probabilities(n)
return rel_mse(f.edge_probabilities(n), f.baseline(n), P)
results.new('Rel. MSE(P_ij)', 'nm', rel_mse_p_ij)
if not (params['pre_offset'] or params['post_fit']):
def rel_mse_logit_p_ij(n, d, f):
logit_P = d.edge_probabilities(n, logit=True)
logit_Q = f.baseline_logit(n)
return rel_mse(f.edge_probabilities(n, logit=True), logit_Q,
logit_P)
results.new('Rel. MSE(logit P_ij)', 'nm', rel_mse_logit_p_ij)
if params['fit_method'] in [
'convex_opt', 'conditional', 'c_conditional', 'irls',
'conditional_is'
]:
results.new('Wall time (sec.)', 'm',
lambda d, f: f.fit_info['wall_time'])
if params['fit_method'] in ['convex_opt', 'conditional', 'conditional_is']:
def work(f):
w = 0
for work_type in ['nll_evals', 'grad_nll_evals', 'cnll_evals']:
if work_type in f.fit_info:
w += f.fit_info[work_type]
return w
results.new('Work', 'm', lambda d, f: work(f))
results.new('||ET_final - T||_2', 'm',
lambda d, f: l2(f.fit_info['grad_nll_final']))
for sub_size in zip(results.M_sizes, results.N_sizes):
print 'subnetwork size =', sub_size
if params['sampling'] == 'new':
gen = RandomSubnetworks(arr, sub_size)
else:
gen = RandomSubnetworks(arr, sub_size, method=params['sampling'])
for rep in range(params['num_reps']):
seed.next()
sub = gen.sample()
if params['fisher_information']:
data_model.fisher_information(sub)
if params['sampling'] == 'new':
data_model.match_kappa(sub, params['kappa_target'])
sub.generate(data_model)
if params['load_fits']:
fit, loaded_fits = loaded_fits[0], loaded_fits[1:]
fit_model.beta = unpick(fit['theta'])
if params['fix_broken_cmle_is']:
for b_n in fit_model.beta:
fit_model.beta[b_n] += 0.1474
if 'alpha' in fit:
sub.row_covariates['alpha_out'] = unpick(fit['alpha'])
if 'beta' in fit:
sub.col_covariates['alpha_in'] = unpick(fit['beta'])
if 'kappa' in fit:
fit_model.kappa = fit['kappa']
if 'offset' in fit:
sub.offset = unpick(fit['offset'])
if 'fit_info' in fit:
fit_model.fit_info = unpick(fit['fit_info'])
else:
if params['pre_offset']:
sub.offset_extremes()
if params['fit_method'] == 'convex_opt':
fit_model.fit_convex_opt(sub, verbose=params['verbose'])
elif params['fit_method'] == 'irls':
fit_model.fit_irls(sub, verbose=params['verbose'])
elif params['fit_method'] == 'logistic':
fit_model.fit_logistic(sub)
elif params['fit_method'] == 'logistic_l2':
fit_model.fit_logistic_l2(sub, prior_precision=1.0)
elif params['fit_method'] == 'conditional':
fit_model.fit_conditional(sub, verbose=params['verbose'])
elif params['fit_method'] == 'conditional_is':
fit_model.fit_conditional(sub,
T=params['is_T'],
verbose=params['verbose'])
elif params['fit_method'] == 'c_conditional':
fit_model.fit_c_conditional(sub, verbose=params['verbose'])
elif params['fit_method'] == 'composite':
fit_model.fit_composite(sub,
T=100,
verbose=params['verbose'])
elif params['fit_method'] == 'brazzale':
fit_model.fit_brazzale(sub)
elif params['fit_method'] == 'saddlepoint':
fit_model.fit_saddlepoint(sub)
elif params['fit_method'] == 'none':
pass
if params['post_fit']:
sub.offset_extremes()
fit_model.fit_convex_opt(sub, fix_beta=True)
if params['dump_fits']:
fit = {}
fit['theta'] = pick(fit_model.beta)
if 'alpha_out' in sub.row_covariates:
fit['alpha'] = pick(sub.row_covariates['alpha_out'])
if 'alpha_in' in sub.row_covariates:
fit['beta'] = pick(sub.col_covariates['alpha_in'])
if not fit_model.kappa is None:
fit['kappa'] = fit_model.kappa
if not sub.offset is None:
sub.offset.dirty()
fit['offset'] = pick(sub.offset)
if not fit_model.fit_info is None:
fit['fit_info'] = pick(fit_model.fit_info)
fits.append(fit)
if params['find_good'] > 0:
abs_err = abs(fit_model.beta['x_0'] - data_model.beta['x_0'])
if abs_err < params['find_good']:
print abs_err
sub.offset = None
fit_model.fit_conditional(sub, T=1000, verbose=True)
print fit_model.beta['x_0']
print fit_model.fit_info
f = file('goodmat.mat', 'wb')
import scipy.io
Y = np.array(sub.as_dense(), dtype=np.float)
X = sub.edge_covariates['x_0'].matrix()
scipy.io.savemat(f, {'Y': Y, 'X': X})
sys.exit()
if params['find_bad'] > 0:
abs_err = abs(fit_model.beta['x_0'] - data_model.beta['x_0'])
if abs_err > params['find_bad']:
print abs_err
sub.offset = None
fit_model.fit_conditional(sub, T=1000, verbose=True)
print fit_model.beta['x_0']
print fit_model.fit_info
f = file('badmat.mat', 'wb')
import scipy.io
Y = np.array(sub.as_dense(), dtype=np.float)
X = sub.edge_covariates['x_0'].matrix()
scipy.io.savemat(f, {'Y': Y, 'X': X})
sys.exit()
results.record(sub_size, rep, sub, data_model, fit_model)
if params['verbose']:
print
if params['dump_fits']:
with open(params['dump_fits'], 'w') as outfile:
json.dump(([(p, pick(params[p])) for p in params], fits), outfile)
# Compute beta MSEs
covariate_naming = []
for c in covariates:
mse_name = 'MSE(theta_{%s})' % c
true_name = 'True theta_{%s}' % c
est_name = 'Est. theta_{%s}' % c
results.estimate_mse(mse_name, true_name, est_name)
covariate_naming.append((c, mse_name, true_name, est_name))
# Report parameters for the run
print 'Parameters:'
for field in params:
print '%s: %s' % (field, str(params[field]))
# Should not vary between runs with the same seed and same number
# of arrays tested
seed.final()
results.summary()
return results, covariate_naming
from Network import Network
from Models import NonstationaryLogistic, alpha_unif
from Experiment import RandomSubnetworks
from numpy.random import normal, seed
# Seed random number for reproducible results
seed(137)
# Initialize full network
N = 300
net = Network(N)
alpha_unif(net, 0.5)
# Initialize the data model; generate covariates and associated coefficients
data_model = NonstationaryLogistic()
data_model.kappa = -7.0
covariates = ['x_%d' % i for i in range(5)]
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]) < 0.3
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']
from Network import Network
from Models import NonstationaryLogistic, alpha_unif
from Experiment import RandomSubnetworks
from numpy.random import normal, seed
# Seed random number for reproducible results
seed(137)
# Initialize full network
N = 100
net = Network(N)
alpha_unif(net, 0.5)
# Initialize the data model; generate covariates and associated coefficients
data_model = NonstationaryLogistic()
data_model.kappa = -1.0
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']
for v_1, v_2, name in [(0, 0, 'll'), (1, 1, 'rr'), (0, 1, 'lr')]:
def f_x(i_1, i_2):
return ((net.node_covariates['value'][i_1] == v_1)
and (net.node_covariates['value'][i_2] == v_2))
net.new_edge_covariate(name).from_binary_function_ind(f_x)
def f_x(i_1, i_2):
return np.random.uniform(-np.sqrt(3), np.sqrt(3))
net.new_edge_covariate('x').from_binary_function_ind(f_x)
data_model = NonstationaryLogistic()
data_model.beta['x'] = theta
for name, block_theta in [('ll', 4.0), ('rr', 3.0), ('lr', -2.0)]:
data_model.beta[name] = block_theta
alpha_norm(net, alpha_sd)
data_model.match_kappa(net, ('row_sum', 2))
net.generate(data_model)
net.show_heatmap()
net.offset_extremes()
fit_base_model = NonstationaryLogistic()
fit_base_model.beta['x'] = None
fit_model = Blockmodel(fit_base_model, 2)
#fit_model.base_model.fit = fit_model.base_model.fit_conditional
# Initialize block assignments
net.new_node_covariate('value').from_pairs(net.names, [0]*(N/2) + [1]*(N/2))
for v_1, v_2, name in [(0, 0, 'll'),
(1, 1, 'rr'),
(0, 1, 'lr')]:
def f_x(i_1, i_2):
return ((net.node_covariates['value'][i_1] == v_1) and
(net.node_covariates['value'][i_2] == v_2))
net.new_edge_covariate(name).from_binary_function_ind(f_x)
def f_x(i_1, i_2):
return np.random.uniform(-np.sqrt(3), np.sqrt(3))
net.new_edge_covariate('x').from_binary_function_ind(f_x)
data_model = NonstationaryLogistic()
data_model.beta['x'] = theta
for name, block_theta in [('ll', 4.0),
('rr', 3.0),
('lr', -2.0)]:
data_model.beta[name] = block_theta
alpha_norm(net, alpha_sd)
data_model.match_kappa(net, ('row_sum', 2))
net.generate(data_model)
net.show_heatmap()
net.offset_extremes()
fit_base_model = NonstationaryLogistic()
fit_base_model.beta['x'] = None
fit_model = Blockmodel(fit_base_model, 2)
#fit_model.base_model.fit = fit_model.base_model.fit_conditional
# Initialize full network
net = Network(params['N'])
# Generate node-level propensities to extend and receive edges
if params['alpha_norm_sd'] > 0.0:
alpha_norm(net, params['alpha_norm_sd'])
elif params['alpha_unif'] > 0.0:
alpha_unif(net, params['alpha_unif'])
elif params['alpha_gamma_sd'] > 0.0:
# Choosing location somewhat arbitrarily to give unit skewness
alpha_gamma(net, 4.0, params['alpha_gamma_sd'])
else:
alpha_zero(net)
# Generate covariates and associated coefficients
data_base_model = NonstationaryLogistic()
covariates = []
for b in range(params['B']):
name = 'x_%d' % b
covariates.append(name)
data_base_model.beta[name] = np.random.normal(0, params['beta_sd'])
def f_x(i_1, i_2):
return np.random.uniform(-np.sqrt(3), np.sqrt(3))
net.new_edge_covariate(name).from_binary_function_ind(f_x)
# Initialize data (block)model from base model
class_probs = np.random.dirichlet(np.repeat(params['class_conc'], params['K']))
z = np.where(np.random.multinomial(1, class_probs, params['N']) == 1)[1]
med_degree = np.median(degree)
net.new_node_covariate('low_degree').from_pairs(net.names,
degree < med_degree)
for v_1, v_2, name in [(0, 0, 'high_to_high'), (1, 1, 'low_to_low'),
(0, 1, 'high_to_low')]:
covariates.append(name)
def f_x(i_1, i_2):
return ((net.node_covariates['low_degree'][i_1] == v_1)
and (net.node_covariates['low_degree'][i_2] == v_2))
net.new_edge_covariate(name).from_binary_function_ind(f_x)
# Initialize fitting model
fit_model = StationaryLogistic()
n_fit_model = NonstationaryLogistic()
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)
