def run_synth_test():
""" Run a test with synthetic data and MCMC inference
"""
options, popn, data, client, popn_true, x_true = initialize_parallel_test_harness(
)
# If x0 specified, load x0 from file
x0 = None
if options.x0_file is not None:
with open(options.x0_file, 'r') as f:
print "Initializing with state from: %s" % options.x0_file
prev_x0 = cPickle.load(f)
if isinstance(prev_x0, list):
x0 = prev_x0[-1]
else:
mle_x0 = prev_x0
# HACK: We're assuming x0 came from a standard GLM
mle_model = make_model('standard_glm', N=data['N'])
mle_popn = Population(mle_model)
mle_popn.set_data(data)
x0 = popn.sample(None)
x0 = convert_model(mle_popn, mle_model, mle_x0, popn,
popn.model, x0)
use_existing = False
fname = os.path.join(options.resultsDir,
'%s_marginal_lkhd.pkl' % options.model)
if use_existing and \
os.path.exists(fname):
print "Found existing results"
with open(fname) as f:
marg_lkhd = cPickle.load(f)
else:
N_samples = 10
popn_true.set_data(data)
# Estimate the marginal log likelihood
print "Performing parallel inference"
marg_lkhd, log_weights = parallel_ais(client,
data,
x0=x0,
N_samples=N_samples,
steps_per_B=50,
resdir=options.resultsDir)
# Save results
print "Saving results to %s" % fname
with open(fname, 'w') as f:
cPickle.dump((marg_lkhd, log_weights), f, protocol=-1)
def run_synth_test():
""" Run a test with synthetic data and MCMC inference
"""
options, popn, data, client, popn_true, x_true = initialize_parallel_test_harness()
# If x0 specified, load x0 from file
x0 = None
if options.x0_file is not None:
with open(options.x0_file, "r") as f:
print "Initializing with state from: %s" % options.x0_file
prev_x0 = cPickle.load(f)
if isinstance(prev_x0, list):
x0 = prev_x0[-1]
else:
mle_x0 = prev_x0
# HACK: We're assuming x0 came from a standard GLM
mle_model = make_model("standard_glm", N=data["N"])
mle_popn = Population(mle_model)
mle_popn.set_data(data)
x0 = popn.sample(None)
x0 = convert_model(mle_popn, mle_model, mle_x0, popn, popn.model, x0)
use_existing = False
fname = os.path.join(options.resultsDir, "%s_marginal_lkhd.pkl" % options.model)
if use_existing and os.path.exists(fname):
print "Found existing results"
with open(fname) as f:
marg_lkhd = cPickle.load(f)
else:
N_samples = 10
popn_true.set_data(data)
# Estimate the marginal log likelihood
print "Performing parallel inference"
marg_lkhd, log_weights = parallel_ais(
client, data, x0=x0, N_samples=N_samples, steps_per_B=50, resdir=options.resultsDir
)
# Save results
print "Saving results to %s" % fname
with open(fname, "w") as f:
cPickle.dump((marg_lkhd, log_weights), f, protocol=-1)
def fit_latent_network_to_mle():
""" Run a test with synthetic data and MCMC inference
"""
options, popn, data, popn_true, x_true = initialize_test_harness()
import pdb; pdb.set_trace()
# Load MLE parameters from command line
mle_x = None
if options.x0_file is not None:
with open(options.x0_file, 'r') as f:
print "Initializing with state from: %s" % options.x0_file
mle_x = cPickle.load(f)
mle_model = make_model('standard_glm', N=data['N'])
mle_popn = Population(mle_model)
mle_popn.set_data(data)
# Create a location sampler
print "Initializing latent location sampler"
loc_sampler = LatentLocationUpdate()
loc_sampler.preprocess(popn)
# Convert the mle results into a weighted adjacency matrix
x_aw = popn.sample(None)
x_aw = convert_model(mle_popn, mle_model, mle_x, popn, popn.model, x_aw)
# Get rid of unnecessary keys
del x_aw['glms']
# Fit the latent distance network to a thresholded adjacency matrix
ws = np.sort(np.abs(x_aw['net']['weights']['W']))
wperm = np.argsort(np.abs(x_aw['net']['weights']['W']))
nthrsh = 20
threshs = np.arange(ws.size, step=ws.size/nthrsh)
res = []
N = popn.N
for th in threshs:
print "Fitting network for threshold: %.3f" % th
A = np.zeros_like(ws, dtype=np.int8)
A[wperm[th:]] = 1
A = A.reshape((N,N))
# A = (np.abs(x_aw['net']['weights']['W']) >= th).astype(np.int8).reshape((N,N))
# Make sure the diag is still all 1s
A[np.diag_indices(N)] = 1
x = copy.deepcopy(x_aw)
x['net']['graph']['A'] = A
smpls = fit_latent_network_given_A(x, loc_sampler)
# Index the results by the overall sparsity of A
key = (np.sum(A)-N) / (np.float(np.size(A))-N)
res.append((key, smpls))
# Save results
results_file = os.path.join(options.resultsDir, 'fit_latent_network_results.pkl')
print "Saving results to %s" % results_file
with open(results_file, 'w') as f:
cPickle.dump(res, f)
