def fit_ct_network_hawkes_gibbs(S, S_test, dt, dt_max, output_path, model_args={}, standard_model=None, N_samples=100, time_limit=8 * 60 * 60): K = S.shape[1] S_ct, C_ct, T = convert_discrete_to_continuous(S, dt) S_test_ct, C_test_ct, T_test = convert_discrete_to_continuous(S_test, dt) # Check for existing Gibbs results if os.path.exists(output_path): with gzip.open(output_path, 'r') as f: print("Loading Gibbs results from ", output_path) results = pickle.load(f) else: print( "Fitting the data with a continuous time network Hawkes model using Gibbs sampling" ) test_model = \ ContinuousTimeNetworkHawkesModel(K, dt_max=dt_max, **model_args) test_model.add_data(S_ct, C_ct, T) # Initialize with the standard model parameters if standard_model is not None: test_model.initialize_with_standard_model(standard_model) # Gibbs sample samples = [] lps = [test_model.log_probability()] hlls = [ test_model.heldout_log_likelihood(S_test_ct, C_test_ct, T_test) ] times = [0] for _ in progprint_xrange(N_samples, perline=25): # Update the model tic = time.time() test_model.resample_model() times.append(time.time() - tic) samples.append(copy.deepcopy(test_model.get_parameters())) # Compute log probability and heldout log likelihood # lps.append(test_model.log_probability()) hlls.append( test_model.heldout_log_likelihood(S_test_ct, C_test_ct, T_test)) # # Save this sample # with open(output_path + ".gibbs.itr%04d.pkl" % itr, 'w') as f: # cPickle.dump(samples[-1], f, protocol=-1) # Check if time limit has been exceeded if np.sum(times) > time_limit: break # Get cumulative timestamps timestamps = np.cumsum(times) lps = np.array(lps) hlls = np.array(hlls) # Make results object results = Results(samples, timestamps, lps, hlls) # Save the Gibbs samples with gzip.open(output_path, 'w') as f: print("Saving Gibbs samples to ", output_path) pickle.dump(results, f, protocol=-1) return results
def fit_ct_network_hawkes_gibbs(S, K, C, dt, dt_max, output_path, standard_model=None): # Check for existing Gibbs results if os.path.exists(output_path + ".gibbs.pkl"): with open(output_path + ".gibbs.pkl", "r") as f: print "Loading Gibbs results from ", (output_path + ".gibbs.pkl") (samples, timestamps) = cPickle.load(f) else: print "Fitting the data with a network Hawkes model using Gibbs sampling" S_ct, C_ct, T = convert_discrete_to_continuous(S, dt) # Set the network prior such that E[W] ~= 0.01 # W ~ Gamma(kappa, v) for kappa = 1.25 => v ~ 125 # v ~ Gamma(alpha, beta) for alpha = 10, beta = 10 / 125 E_W = 0.2 kappa = 10.0 E_v = kappa / E_W alpha = 5.0 beta = alpha / E_v network_hypers = { "C": 1, "c": np.zeros(K).astype(np.int), "p": 0.25, "v": E_v, # 'kappa': kappa, # 'alpha': alpha, 'beta': beta, # 'p': 0.1, "allow_self_connections": False, } test_model = ContinuousTimeNetworkHawkesModel(K, dt_max=dt_max, network_hypers=network_hypers) test_model.add_data(S_ct, C_ct, T) # Initialize with the standard model parameters if standard_model is not None: test_model.initialize_with_standard_model(standard_model) plt.ion() im = plot_network(test_model.weight_model.A, test_model.weight_model.W, vmax=0.025) plt.pause(0.001) # Gibbs sample N_samples = 100 samples = [] lps = [test_model.log_probability()] timestamps = [] for itr in xrange(N_samples): if itr % 1 == 0: print "Iteration ", itr, "\tLL: ", lps[-1] im.set_data(test_model.weight_model.W_effective) plt.pause(0.001) # lps.append(test_model.log_probability()) lps.append(test_model.log_probability()) samples.append(test_model.resample_and_copy()) timestamps.append(time.clock()) print test_model.network.p # Save this sample with open(output_path + ".gibbs.itr%04d.pkl" % itr, "w") as f: cPickle.dump(samples[-1], f, protocol=-1) # Save the Gibbs samples with open(output_path + ".gibbs.pkl", "w") as f: print "Saving Gibbs samples to ", (output_path + ".gibbs.pkl") cPickle.dump((samples, timestamps), f, protocol=-1) return samples, timestamps
def fit_ct_network_hawkes_gibbs(S, K, C, dt, dt_max, output_path, standard_model=None): # Check for existing Gibbs results if os.path.exists(output_path + ".gibbs.pkl"): with open(output_path + ".gibbs.pkl", 'r') as f: print "Loading Gibbs results from ", (output_path + ".gibbs.pkl") (samples, timestamps) = cPickle.load(f) else: print "Fitting the data with a network Hawkes model using Gibbs sampling" S_ct, C_ct, T = convert_discrete_to_continuous(S, dt) # Set the network prior such that E[W] ~= 0.01 # W ~ Gamma(kappa, v) for kappa = 1.25 => v ~ 125 # v ~ Gamma(alpha, beta) for alpha = 10, beta = 10 / 125 E_W = 0.2 kappa = 10. E_v = kappa / E_W alpha = 5. beta = alpha / E_v network_hypers = { 'C': 1, "c": np.zeros(K).astype(np.int), "p": 0.25, "v": E_v, # 'kappa': kappa, # 'alpha': alpha, 'beta': beta, # 'p': 0.1, 'allow_self_connections': False } test_model = \ ContinuousTimeNetworkHawkesModel(K, dt_max=dt_max, network_hypers=network_hypers) test_model.add_data(S_ct, C_ct, T) # Initialize with the standard model parameters if standard_model is not None: test_model.initialize_with_standard_model(standard_model) plt.ion() im = plot_network(test_model.weight_model.A, test_model.weight_model.W, vmax=0.025) plt.pause(0.001) # Gibbs sample N_samples = 100 samples = [] lps = [test_model.log_probability()] timestamps = [] for itr in xrange(N_samples): if itr % 1 == 0: print "Iteration ", itr, "\tLL: ", lps[-1] im.set_data(test_model.weight_model.W_effective) plt.pause(0.001) # lps.append(test_model.log_probability()) lps.append(test_model.log_probability()) samples.append(test_model.resample_and_copy()) timestamps.append(time.clock()) print test_model.network.p # Save this sample with open(output_path + ".gibbs.itr%04d.pkl" % itr, 'w') as f: cPickle.dump(samples[-1], f, protocol=-1) # Save the Gibbs samples with open(output_path + ".gibbs.pkl", 'w') as f: print "Saving Gibbs samples to ", (output_path + ".gibbs.pkl") cPickle.dump((samples, timestamps), f, protocol=-1) return samples, timestamps
def fit_ct_network_hawkes_gibbs(S, S_test, dt, dt_max, output_path, model_args={}, standard_model=None, N_samples=100, time_limit=8*60*60): K = S.shape[1] S_ct, C_ct, T = convert_discrete_to_continuous(S, dt) S_test_ct, C_test_ct, T_test = convert_discrete_to_continuous(S_test, dt) # Check for existing Gibbs results if os.path.exists(output_path): with gzip.open(output_path, 'r') as f: print "Loading Gibbs results from ", output_path results = cPickle.load(f) else: print "Fitting the data with a continuous time network Hawkes model using Gibbs sampling" test_model = \ ContinuousTimeNetworkHawkesModel(K, dt_max=dt_max, **model_args) test_model.add_data(S_ct, C_ct, T) # Initialize with the standard model parameters if standard_model is not None: test_model.initialize_with_standard_model(standard_model) # Gibbs sample samples = [] lps = [test_model.log_probability()] hlls = [test_model.heldout_log_likelihood(S_test_ct, C_test_ct, T_test)] times = [0] for _ in progprint_xrange(N_samples, perline=25): # Update the model tic = time.time() test_model.resample_model() times.append(time.time() - tic) samples.append(copy.deepcopy(test_model.get_parameters())) # Compute log probability and heldout log likelihood # lps.append(test_model.log_probability()) hlls.append(test_model.heldout_log_likelihood(S_test_ct, C_test_ct, T_test)) # # Save this sample # with open(output_path + ".gibbs.itr%04d.pkl" % itr, 'w') as f: # cPickle.dump(samples[-1], f, protocol=-1) # Check if time limit has been exceeded if np.sum(times) > time_limit: break # Get cumulative timestamps timestamps = np.cumsum(times) lps = np.array(lps) hlls = np.array(hlls) # Make results object results = Results(samples, timestamps, lps, hlls) # Save the Gibbs samples with gzip.open(output_path, 'w') as f: print "Saving Gibbs samples to ", output_path cPickle.dump(results, f, protocol=-1) return results