def fit_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"
# Make a new model for inference
# test_model = DiscreteTimeNetworkHawkesModelGammaMixture(C=C, K=K, dt=dt, dt_max=dt_max, B=B,
# alpha=1.0, beta=1.0/20.0)
test_basis = IdentityBasis(dt, dt_max, allow_instantaneous=True)
network_hypers = {'C': C, 'alpha': 1.0, 'beta': 1.0/10.0,
'tau1': 1.0, 'tau0': 10.0,
'allow_self_connections': False}
test_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(K=K, dt=dt, dt_max=dt_max,
basis=test_basis,
network_hypers=network_hypers)
test_model.add_data(S)
# 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=0.5)
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())
# 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_network_hawkes_gibbs_ss(S, K, C, B, dt, dt_max,
output_path, p,
standard_model=None):
samples_and_timestamps = load_partial_results(output_path, typ="gibbs_ss")
if samples_and_timestamps is not None:
samples, timestamps = samples_and_timestamps
else:
print "Fitting the data with a spike and slab network Hawkes model using Gibbs sampling"
# Make a new model for inference
network_hypers = {'C': C, 'alpha': 1.0, 'beta': 1.0/20.0, 'p': p,
'v': 5.0,'c': np.arange(C).repeat((K // C))}
test_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(K=K, dt=dt, dt_max=dt_max, B=B,
network_hypers=network_hypers)
test_model.add_data(S)
# Initialize with the standard model parameters
if standard_model is not None:
test_model.initialize_with_standard_model(standard_model)
# Gibbs sample
N_samples = 1000
samples = []
lps = []
timestamps = [time.clock()]
for itr in xrange(N_samples):
lps.append(test_model.log_probability())
samples.append(test_model.resample_and_copy())
timestamps.append(time.clock())
print test_model.network.v
if itr % 1 == 0:
print "Iteration ", itr, "\t LP: ", lps[-1]
# Save this sample
with open(output_path + ".gibbs_ss.itr%04d.pkl" % itr, 'w') as f:
cPickle.dump((samples[-1], timestamps[-1]-timestamps[0]), f, protocol=-1)
# Save the Gibbs timestamps
timestamps = np.array(timestamps)
with open(output_path + ".gibbs_ss.timestamps.pkl", 'w') as f:
print "Saving spike and slab Gibbs samples to ", (output_path + ".gibbs_ss.timestamps.pkl")
cPickle.dump(timestamps, f, protocol=-1)
# Save the Gibbs samples
with open(output_path + ".gibbs_ss.pkl", 'w') as f:
print "Saving Gibbs samples to ", (output_path + ".gibbs_ss.pkl")
cPickle.dump((samples, timestamps[1:] - timestamps[0]), f, protocol=-1)
return samples, timestamps
def fit_spikeslab_network_hawkes_gibbs(S,
S_test,
dt,
dt_max,
output_path,
model_args={},
standard_model=None,
N_samples=100,
time_limit=8 * 60 * 60):
T, K = S.shape
# 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 network Hawkes model using Gibbs sampling"
)
test_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(K=K,
dt=dt,
dt_max=dt_max,
**model_args)
test_model.add_data(S)
# Initialize with the standard model parameters
if standard_model is not None:
test_model.initialize_with_standard_model(standard_model)
# TODO: Precompute F_test
F_test = test_model.basis.convolve_with_basis(S_test)
# Gibbs sample
samples = []
lps = [test_model.log_probability()]
hlls = [test_model.heldout_log_likelihood(S_test)]
times = [0]
for _ in progprint_xrange(N_samples, perline=10):
# Update the model
tic = time.time()
test_model.resample_model()
samples.append(copy.deepcopy(test_model.get_parameters()))
times.append(time.time() - tic)
# Compute log probability and heldout log likelihood
# lps.append(test_model.log_probability())
hlls.append(test_model.heldout_log_likelihood(S_test, F=F_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 demo(seed=None):
"""
Create a discrete time Hawkes model and generate from it.
:return:
"""
if seed is None:
seed = np.random.randint(2**32)
print("Setting seed to ", seed)
np.random.seed(seed)
###########################################################
# Load some example data.
# See data/synthetic/generate.py to create more.
###########################################################
data_path = os.path.join("data", "synthetic",
"synthetic_K20_C4_T10000.pkl.gz")
with gzip.open(data_path, 'r') as f:
S, true_model = pickle.load(f)
T = S.shape[0]
K = true_model.K
B = true_model.B
dt = true_model.dt
dt_max = true_model.dt_max
###########################################################
# Initialize with MAP estimation on a standard Hawkes model
###########################################################
init_with_map = True
if init_with_map:
init_len = T
print("Initializing with BFGS on first ", init_len, " time bins.")
init_model = DiscreteTimeStandardHawkesModel(K=K,
dt=dt,
dt_max=dt_max,
B=B,
alpha=1.0,
beta=1.0)
init_model.add_data(S[:init_len, :])
init_model.initialize_to_background_rate()
init_model.fit_with_bfgs()
else:
init_model = None
###########################################################
# Create a test spike and slab model
###########################################################
# Copy the network hypers.
# Give the test model p, but not c, v, or m
network_hypers = true_model.network_hypers.copy()
network_hypers['c'] = None
network_hypers['v'] = None
network_hypers['m'] = None
test_network = StochasticBlockModel(K=K, **network_hypers)
test_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(
K=K,
dt=dt,
dt_max=dt_max,
B=B,
basis_hypers=true_model.basis_hypers,
bkgd_hypers=true_model.bkgd_hypers,
impulse_hypers=true_model.impulse_hypers,
weight_hypers=true_model.weight_hypers,
network=test_network)
test_model.add_data(S)
# F_test = test_model.basis.convolve_with_basis(S_test)
# Initialize with the standard model parameters
if init_model is not None:
test_model.initialize_with_standard_model(init_model)
# Initialize plots
ln, im_net, im_clus = initialize_plots(true_model, test_model, S)
###########################################################
# Fit the test model with Gibbs sampling
###########################################################
N_samples = 50
samples = []
lps = []
# plls = []
for itr in range(N_samples):
lps.append(test_model.log_probability())
# plls.append(test_model.heldout_log_likelihood(S_test, F=F_test))
samples.append(test_model.copy_sample())
print("")
print("Gibbs iteration ", itr)
print("LP: ", lps[-1])
test_model.resample_model()
# Update plot
if itr % 1 == 0:
update_plots(itr, test_model, S, ln, im_clus, im_net)
###########################################################
# Analyze the samples
###########################################################
analyze_samples(true_model, init_model, samples, lps)
def execute_toy(self,mode="discrete",dt_max=3,N_samples=1000,network_priors={"p": 1.0, "allow_self_connections": False}):
#np.random.seed(0)
if mode == 'discrete':
test_model1 = DiscreteTimeNetworkHawkesModelSpikeAndSlab(K=self.K, dt_max=dt_max,
network_hypers=network_priors)
test_model1.add_data(self.data)
test_model1.initialize_with_standard_model(None)
elif mode == 'continuous':
test_model = ContinuousTimeNetworkHawkesModel(self.K, dt_max=dt_max,
network_hypers=network_hypers)
test_model.add_data(self.data,self.labels)
###########################################################
# Fit the test model with Gibbs sampling
###########################################################
samples = []
lps = []
#for itr in xrange(N_samples):
# test_model1.resample_model()
# lps.append(test_model1.log_probability())
# samples.append(test_model1.copy_sample())
test_model = DiscreteTimeStandardHawkesModel(K=self.K, dt_max=dt_max, allow_self_connections= False)
#test_model.initialize_with_gibbs_model(test_model1)
test_model.add_data(self.data)
test_model.fit_with_bfgs()
impulse = test_model1.impulse_model.impulses
responses = {}
#for i in range(3):
# responses[str(i)] = []
# for j in range(3):
# responses[str(i)].append({"key":"response: process "+str(i)+" to "+str(j),"values":[{"x":idx,"y":k} for idx,k in enumerate(impulse[:,i,j])]})
# with open('/Users/PauKung/hawkes_demo/webapp/static/data/response'+str(i)+'.json','w') as outfile:
# json.dump({"out":responses[str(i)]},outfile)
# calculate convolved basis
rr = test_model.basis.convolve_with_basis(np.ones((dt_max*2,self.K)))
impulse = np.sum(rr, axis=2)
impulse[dt_max:,:] = 0
for i in range(3):
responses[str(i)] = {"key":"response: process "+str(i),"values":[{"x":idx,"y":k} for idx,k in enumerate(impulse[:,i])]}
with open('/Users/PauKung/hawkes_demo/webapp/static/data/response'+str(i)+'.json','w') as outfile:
json.dump({"out":responses[str(i)]},outfile)
rates = test_model.compute_rate()#self.compute_rate(test_model,mode,dt_max)
inferred_rate = {}
S,F = test_model.data_list[0]
print F
for i in range(3):
inferred_rate[str(i)] = []
inferred_rate[str(i)].append({"key":"background",
"values":[[j,test_model.bias[i]] for j in range(self.T)]})
#"values":[[j,test_model1.bias_model.lambda0[i]] for j in range(self.T)]})
for i in range(3):
inferred_rate[str(i)].append({"key":"influence: process"+str(i),
"values":[[idx,j-test_model.bias[i]] for idx,j in enumerate(rates[:,i])]})
with open('/Users/PauKung/hawkes_demo/webapp/static/data/infer'+str(i)+'.json','w') as outfile:
json.dump({"out":inferred_rate[str(i)]},outfile)
# output response function diagram (K x K timeseries)
#plt.subplot(3,3,1)
#for i in range(3):
# for j in range(3):
# plt.subplot(3,3,3*i+(j+1))
# plt.plot(np.arange(4),impulse[:,i,j],color="#377eb8", lw=2)
#plt.savefig(fpath+"response_fun.png",transparent=True)
# output background bias diagram (K x 1 timeseries)
#plt.subplot(3,1,1)
#for i in range(3):
# plt.subplot(3,1,i+1)
# plt.plot(np.arange(4),[test_model.bias_model.lambda0[i] for j in range(4)],color="#333333",lw=2)
#plt.savefig(fpath+"bias.png",transparent=True)
# output inferred rate diagram (K x 1 timeseries)
#test_figure, test_handles = test_model.plot(color="#e41a1c", T_slice=(0,self.T))
#plt.savefig(fpath+"inferred_rate.png",transparent=True)
print test_model.W
return test_model.W, inferred_rate, responses
def fit_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"
# Make a new model for inference
# test_model = DiscreteTimeNetworkHawkesModelGammaMixture(C=C, K=K, dt=dt, dt_max=dt_max, B=B,
# alpha=1.0, beta=1.0/20.0)
test_basis = IdentityBasis(dt, dt_max, allow_instantaneous=True)
# 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 = 0.01
kappa = 10.0
E_v = kappa / E_W
alpha = 10.0
beta = alpha / E_v
network_hypers = {
"C": 2,
"kappa": kappa,
"alpha": alpha,
"beta": beta,
"p": 0.8,
"allow_self_connections": False,
}
test_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(
K=K, dt=dt, dt_max=dt_max, basis=test_basis, network_hypers=network_hypers
)
test_model.add_data(S)
# 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=0.5)
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())
# 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_spikeslab_network_hawkes_gibbs(S, S_test, dt, dt_max, output_path,
model_args={}, standard_model=None,
N_samples=100, time_limit=8*60*60):
T,K = S.shape
# 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 network Hawkes model using Gibbs sampling"
test_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(K=K, dt=dt, dt_max=dt_max, **model_args)
test_model.add_data(S)
# Initialize with the standard model parameters
if standard_model is not None:
test_model.initialize_with_standard_model(standard_model)
# TODO: Precompute F_test
F_test = test_model.basis.convolve_with_basis(S_test)
# Gibbs sample
samples = []
lps = [test_model.log_probability()]
hlls = [test_model.heldout_log_likelihood(S_test)]
times = [0]
for _ in progprint_xrange(N_samples, perline=10):
# Update the model
tic = time.time()
test_model.resample_model()
samples.append(copy.deepcopy(test_model.get_parameters()))
times.append(time.time() - tic)
# Compute log probability and heldout log likelihood
# lps.append(test_model.log_probability())
hlls.append(test_model.heldout_log_likelihood(S_test, F=F_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
def demo(seed=None):
"""
Create a discrete time Hawkes model and generate from it.
:return:
"""
if seed is None:
seed = np.random.randint(2**32)
print "Setting seed to ", seed
np.random.seed(seed)
###########################################################
# Load some example data.
# See data/synthetic/generate.py to create more.
###########################################################
data_path = os.path.join("data", "synthetic", "synthetic_K20_C4_T10000.pkl.gz")
with gzip.open(data_path, 'r') as f:
S, true_model = cPickle.load(f)
T = S.shape[0]
K = true_model.K
B = true_model.B
dt = true_model.dt
dt_max = true_model.dt_max
###########################################################
# Initialize with MAP estimation on a standard Hawkes model
###########################################################
init_with_map = True
if init_with_map:
init_len = T
print "Initializing with BFGS on first ", init_len, " time bins."
init_model = DiscreteTimeStandardHawkesModel(K=K, dt=dt, dt_max=dt_max, B=B,
alpha=1.0, beta=1.0)
init_model.add_data(S[:init_len, :])
init_model.initialize_to_background_rate()
init_model.fit_with_bfgs()
else:
init_model = None
###########################################################
# Create a test spike and slab model
###########################################################
# Copy the network hypers.
# Give the test model p, but not c, v, or m
network_hypers = true_model.network_hypers.copy()
network_hypers['c'] = None
network_hypers['v'] = None
network_hypers['m'] = None
test_network = StochasticBlockModel(K=K, **network_hypers)
test_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(K=K, dt=dt, dt_max=dt_max, B=B,
basis_hypers=true_model.basis_hypers,
bkgd_hypers=true_model.bkgd_hypers,
impulse_hypers=true_model.impulse_hypers,
weight_hypers=true_model.weight_hypers,
network=test_network)
test_model.add_data(S)
# F_test = test_model.basis.convolve_with_basis(S_test)
# Initialize with the standard model parameters
if init_model is not None:
test_model.initialize_with_standard_model(init_model)
# Initialize plots
ln, im_net, im_clus = initialize_plots(true_model, test_model, S)
###########################################################
# Fit the test model with Gibbs sampling
###########################################################
N_samples = 50
samples = []
lps = []
# plls = []
for itr in xrange(N_samples):
lps.append(test_model.log_probability())
# plls.append(test_model.heldout_log_likelihood(S_test, F=F_test))
samples.append(test_model.copy_sample())
print ""
print "Gibbs iteration ", itr
print "LP: ", lps[-1]
test_model.resample_model()
# Update plot
if itr % 1 == 0:
update_plots(itr, test_model, S, ln, im_clus, im_net)
###########################################################
# Analyze the samples
###########################################################
analyze_samples(true_model, init_model, samples, lps)
