def fit_gp_multinomial_model(model, test, pi_train=None, N_samples=100, run=1):
if pi_train is not None:
if isinstance(model, pgmult.gp.LogisticNormalGP):
model.data_list[0]["psi"] = ln_pi_to_psi(pi_train) - model.mu
elif isinstance(model, pgmult.gp.MultinomialGP):
model.data_list[0]["psi"] = pi_to_psi(pi_train) - model.mu
model.resample_omega()
else:
model.initialize_from_data()
### Inference
results_base = os.path.join("results", "names", "run%03d" % run, "results")
results_file = results_base + ".pkl.gz"
if os.path.exists(results_file):
with gzip.open(results_file, "r") as f:
samples, lls, pred_lls, timestamps = pickle.load(f)
else:
Z_test = get_inputs(test)
lls = [model.log_likelihood()]
samples = [model.copy_sample()]
pred_ll, pred_pi = model.predictive_log_likelihood(Z_test, test.data)
pred_lls = [pred_ll]
pred_pis = [pred_pi]
times = [0]
# Print initial values
print("Initial LL: ", lls[0])
print("Initial Pred LL: ", pred_lls[0])
for itr in xrange(N_samples):
print("Iteration ", itr)
tic = time.time()
model.resample_model(verbose=True)
times.append(time.time()-tic)
samples.append(model.copy_sample())
lls.append(model.log_likelihood())
pred_ll, pred_pi = model.predictive_log_likelihood(get_inputs(test), test.data)
pred_lls.append(pred_ll)
pred_pis.append(pred_pi)
print("Log likelihood: ", lls[-1])
print("Pred Log likelihood: ", pred_ll)
# Save this sample
# with gzip.open(results_file + ".itr%03d.pkl.gz" % itr, "w") as f:
# pickle.dump(model, f, protocol=-1)
lls = np.array(lls)
pred_lls = np.array(pred_lls)
timestamps = np.cumsum(times)
return samples, lls, pred_lls, pred_pis, timestamps
def theta(self, theta):
self.psi = ln_pi_to_psi(theta)