display_cis(s_model)
print 'Fitting conditional model'
c_model = FixedMargins(StationaryLogistic())
net.new_row_covariate('r', np.int)[:] = r
net.new_col_covariate('c', np.int)[:] = c
c_model.fit = c_model.base_model.fit_conditional
for cov_name in cov_names:
c_model.base_model.beta[cov_name] = None
c_model.fit(net)
print 'NLL: %.2f' % c_model.nll(net)
for cov_name in cov_names:
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)
c_model.base_model.beta[cov_name] = None
c_model.base_model.fit_conditional(net, verbose = True)
print 'NLL: %.2f' % c_model.nll(net)
for cov_name in cov_names:
print '%s: %.2f' % (cov_name, c_model.base_model.beta[cov_name])
print
# Sample typical networks from fit models
reps = 100
s_samples = np.empty((reps, net.N, net.N))
ns_samples = np.empty((reps, net.N, net.N))
c_samples = np.empty((reps, net.N, net.N))
for rep in range(reps):
s_samples[rep,:,:] = s_model.generate(net)
ns_samples[rep,:,:] = ns_model.generate(net)
c_samples[rep,:,:] = c_model.generate(net, coverage = 0.2)
# 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.subplot(334)
plt.title('Stationary')
heatmap(s_samples_mean)
plt.subplot(337)
residuals(s_samples_mean, s_samples_sd)
arr = Array(M, N)
arr.new_edge_covariate('x_0')[:] = logit_P
arr.new_row_covariate('r', dtype = np.int)[:] = r
arr.new_col_covariate('c', dtype = np.int)[:] = c
base_model = StationaryLogistic()
base_model.beta['x_0'] = 1.0
data_model = FixedMargins(base_model)
while True:
# Advance random seed for data generation
seed.next()
# Generate data for this trial
if conditional_sample:
X = data_model.generate(arr, coverage = 100.0)
else:
P = 1.0 / (1.0 + np.exp(-logit_P))
X = np.random.random((M,N)) < P
yield X, v
def timing(func):
def inner(*args, **kwargs):
start_time = time()
val = func(*args, **kwargs)
elapsed = time() - start_time
return val, elapsed
return inner
def generate_data(case, theta, seed):
# Advance random seed for parameter and covariate construction
seed.next()
case = params['case']
alpha = beta = kappa = offset = 0
conditional_sample = False
if 'fixed_example' in case:
# Load parameters and covariates
with open(case['fixed_example'], 'r') as example_file:
example = json.load(example_file)
v = np.array(example['nu'])
M, N = v.shape
if 'alpha' in example:
alpha = np.array(example['alpha']).reshape((M,1))
if 'beta' in example:
beta = np.array(example['beta']).reshape((1,N))
if 'kappa' in example:
kappa = example['kappa']
if 'offset' in example:
offset = example['offset']
if ('r' in example) and ('c' in example):
conditional_sample = True
r = example['r']
c = example['c']
else:
# Generate parameters and covariates
M, N = case['M'], case['N']
if 'alpha_min' in case:
alpha = np.random.uniform(size = (M,1)) + case['alpha_min']
if 'beta_min' in case:
beta = np.random.uniform(size = (1,N)) + case['beta_min']
if 'kappa' in case:
kappa = case['kappa']
if case['v_discrete']:
v = np.sign(np.random.random(size = (M,N)) - 0.5)
elif case['v_uniform']:
v = np.random.uniform(size = (M,N))
elif case['v_normal']:
v = np.random.normal(size = (M,N))
if 'v_scale' in case:
v *= case['v_scale']
if 'v_loc' in case:
v += case['v_loc']
if ('r' in case) and ('c' in case):
conditional_sample = True
r = case['r']
c = case['c']
# Generate Bernoulli probabilities from logistic regression model
logit_P = np.zeros((M,N)) + kappa
logit_P += alpha
logit_P += beta
logit_P += theta * v
logit_P += offset
if conditional_sample:
arr = Array(M, N)
arr.new_edge_covariate('x_0')[:] = logit_P
arr.new_row_covariate('r', dtype = np.int)[:] = r
arr.new_col_covariate('c', dtype = np.int)[:] = c
base_model = StationaryLogistic()
base_model.beta['x_0'] = 1.0
data_model = FixedMargins(base_model)
while True:
# Advance random seed for data generation
seed.next()
# Generate data for this trial
if conditional_sample:
X = data_model.generate(arr, coverage = 100.0)
else:
P = 1.0 / (1.0 + np.exp(-logit_P))
X = np.random.random((M,N)) < P
yield X, v
def generate_data(case, theta, seed):
# Advance random seed for parameter and covariate construction
seed.next()
case = params["case"]
alpha = beta = kappa = offset = 0
conditional_sample = False
if "fixed_example" in case:
# Load parameters and covariates
with open(case["fixed_example"], "r") as example_file:
example = json.load(example_file)
v = np.array(example["nu"])
M, N = v.shape
if "alpha" in example:
alpha = np.array(example["alpha"]).reshape((M, 1))
if "beta" in example:
beta = np.array(example["beta"]).reshape((1, N))
if "kappa" in example:
kappa = example["kappa"]
if "offset" in example:
offset = example["offset"]
if ("r" in example) and ("c" in example):
conditional_sample = True
r = example["r"]
c = example["c"]
else:
# Generate parameters and covariates
M, N = case["M"], case["N"]
if "alpha_min" in case:
alpha = np.random.uniform(size=(M, 1)) + case["alpha_min"]
if "beta_min" in case:
beta = np.random.uniform(size=(1, N)) + case["beta_min"]
if "kappa" in case:
kappa = case["kappa"]
if case["v_discrete"]:
v = np.random.random(size=(M, N)) < 0.5
else:
v = np.random.uniform(size=(M, N))
if "v_min" in case:
v += case["v_min"]
if ("r" in case) and ("c" in case):
conditional_sample = True
r = case["r"]
c = case["c"]
# Generate Bernoulli probabilities from logistic regression model
logit_P = np.zeros((M, N)) + kappa
logit_P += alpha
logit_P += beta
logit_P += theta * v
logit_P += offset
if conditional_sample:
arr = Array(M, N)
arr.new_edge_covariate("x_0")[:] = logit_P
arr.new_row_covariate("r", dtype=np.int)[:] = r
arr.new_col_covariate("c", dtype=np.int)[:] = c
base_model = StationaryLogistic()
base_model.beta["x_0"] = 1.0
data_model = FixedMargins(base_model)
while True:
# Advance random seed for data generation
seed.next()
# Generate data for this trial
if conditional_sample:
X = data_model.generate(arr, coverage=2.0)
else:
P = 1.0 / (1.0 + np.exp(-logit_P))
X = np.random.random((M, N)) < P
yield X, v
