def test_gradients():
K = 1
B = 3
T = 100
dt = 1.0
true_model = DiscreteTimeNetworkHawkesModelGammaMixture(K=K, B=B, dt=dt)
S, R = true_model.generate(T=T)
# Test with a standard Hawkes model
test_model = DiscreteTimeStandardHawkesModel(K=K, B=B, dt=dt)
test_model.add_data(S)
# Check gradients with the initial parameters
def objective(x):
test_model.weights[0, :] = np.exp(x)
return test_model.log_likelihood()
def gradient(x):
test_model.weights[0, :] = np.exp(x)
return test_model.compute_gradient(0)
print "Checking initial gradient: "
print gradient(np.log(test_model.weights[0, :]))
check_grad(objective, gradient, np.log(test_model.weights[0, :]))
print "Checking gradient at true model parameters: "
test_model.initialize_with_gibbs_model(true_model)
print gradient(np.log(test_model.weights[0, :]))
check_grad(objective, gradient, np.log(test_model.weights[0, :]))
def test_gradients():
K = 1
B = 3
T = 100
dt = 1.0
true_model = DiscreteTimeNetworkHawkesModelGammaMixture(K=K, B=B, dt=dt)
S,R = true_model.generate(T=T)
# Test with a standard Hawkes model
test_model = DiscreteTimeStandardHawkesModel(K=K, B=B, dt=dt)
test_model.add_data(S)
# Check gradients with the initial parameters
def objective(x):
test_model.weights[0,:] = np.exp(x)
return test_model.log_likelihood()
def gradient(x):
test_model.weights[0,:] = np.exp(x)
return test_model.compute_gradient(0)
print("Checking initial gradient: ")
print(gradient(np.log(test_model.weights[0,:])))
check_grad(objective, gradient,
np.log(test_model.weights[0,:]))
print("Checking gradient at true model parameters: ")
test_model.initialize_with_gibbs_model(true_model)
print(gradient(np.log(test_model.weights[0,:])))
check_grad(objective, gradient,
np.log(test_model.weights[0,:]))
def fit_network_hawkes_svi(S, K, C, B, dt, dt_max,
output_path,
standard_model=None):
samples_and_timestamps = load_partial_results(output_path, typ="svi2")
if samples_and_timestamps is not None:
samples, timestamps = samples_and_timestamps
else:
print "Fitting the data with a network Hawkes model using SVI"
# Make a new model for inference
network_hypers = {'C': C, 'alpha': 1.0, 'beta': 1.0/20.0}
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(K=K, dt=dt, dt_max=dt_max, B=B,
network_hypers=network_hypers)
# 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)
# TODO: Add the data in minibatches
minibatchsize = 500
import pdb; pdb.set_trace()
test_model.add_data(S)
# Stochastic variational inference
N_iters = 10000
samples = []
delay = 1.0
forgetting_rate = 0.5
stepsize = (np.arange(N_iters) + delay)**(-forgetting_rate)
start = time.clock()
timestamps = []
for itr in xrange(N_iters):
print "SVI Iter: ", itr, "\tStepsize: ", stepsize[itr]
test_model.sgd_step(minibatchsize=minibatchsize, stepsize=stepsize[itr])
test_model.resample_from_mf()
samples.append(test_model.copy_sample())
timestamps.append(time.clock())
if itr % 1 == 0:
im.set_data(test_model.weight_model.expected_W())
plt.pause(0.001)
# Save this sample
with open(output_path + ".svi.itr%04d.pkl" % itr, 'w') as f:
cPickle.dump((samples[-1], timestamps[-1] -start), f, protocol=-1)
# Save the Gibbs samples
timestamps = np.array(timestamps)
with gzip.open(output_path + ".svi.pkl.gz", 'w') as f:
print "Saving SVI samples to ", (output_path + ".svi.pkl.gz")
cPickle.dump((samples, timestamps - start), f, protocol=-1)
return samples, timestamps
def demo(seed=None):
"""
Fit a weakly sparse
: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 weak 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['v'] = None
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(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_hypers=network_hypers)
test_model.add_data(S)
# Initialize with the standard model parameters
if init_model is not None:
test_model.initialize_with_standard_model(init_model)
###########################################################
# Fit the test model with Gibbs sampling
###########################################################
N_samples = 500
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()
###########################################################
# Analyze the samples
###########################################################
N_samples = len(samples)
A_samples = np.array([s.weight_model.A for s in samples])
W_samples = np.array([s.weight_model.W for s in samples])
g_samples = np.array([s.impulse_model.g for s in samples])
lambda0_samples = np.array([s.bias_model.lambda0 for s in samples])
lps = np.array(lps)
offset = N_samples // 2
A_mean = A_samples[offset:, ...].mean(axis=0)
W_mean = W_samples[offset:, ...].mean(axis=0)
g_mean = g_samples[offset:, ...].mean(axis=0)
lambda0_mean = lambda0_samples[offset:, ...].mean(axis=0)
plt.figure()
plt.plot(np.arange(N_samples), lps, 'k')
plt.xlabel("Iteration")
plt.ylabel("Log probability")
plt.show()
# Compute the link prediction accuracy curves
auc_init = roc_auc_score(true_model.weight_model.A.ravel(),
init_model.W.ravel())
auc_A_mean = roc_auc_score(true_model.weight_model.A.ravel(),
A_mean.ravel())
auc_W_mean = roc_auc_score(true_model.weight_model.A.ravel(),
W_mean.ravel())
aucs = []
for A in A_samples:
aucs.append(roc_auc_score(true_model.weight_model.A.ravel(), A.ravel()))
plt.figure()
plt.plot(aucs, '-r')
plt.plot(auc_A_mean * np.ones_like(aucs), '--r')
plt.plot(auc_W_mean * np.ones_like(aucs), '--b')
plt.plot(auc_init * np.ones_like(aucs), '--k')
plt.xlabel("Iteration")
plt.ylabel("Link prediction AUC")
plt.show()
plt.ioff()
plt.show()
:return:
"""
T = 50
dt = 1.0
dt_max = 3.0
network_hypers = {
'c': np.array([0], dtype=np.int),
'p': 0.5,
'kappa': 3.0,
'v': 15.0
}
weight_hypers = {"kappa_0": 3.0, "nu_0": 15.0}
model = DiscreteTimeNetworkHawkesModelGammaMixture(
K=1,
dt=dt,
dt_max=dt_max,
weight_hypers=weight_hypers,
network_hypers=network_hypers)
model.generate(T=T)
# Gibbs sample and then generate new data
N_samples = 10000
samples = []
lps = []
for itr in progprint_xrange(N_samples, perline=50):
# Resample the model
model.resample_model(resample_network=False)
samples.append(model.copy_sample())
lps.append(model.log_probability())
# Geweke step
def fit_network_hawkes_gibbs(S, K, C, B, dt, dt_max,
output_path,
standard_model=None):
samples_and_timestamps = load_partial_results(output_path, typ="gibbs")
if samples_and_timestamps is not None:
samples, timestamps = samples_and_timestamps
# # 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
network_hypers = {'C': C, 'alpha': 1.0, 'beta': 1.0/20.0}
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(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)
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 = 1000
samples = []
lps = []
timestamps = [time.clock()]
for itr in xrange(N_samples):
lps.append(test_model.log_probability())
# lps.append(test_model.log_likelihood())
samples.append(test_model.resample_and_copy())
timestamps.append(time.clock())
if itr % 1 == 0:
print "Iteration ", itr, "\t LL: ", lps[-1]
# im.set_data(test_model.weight_model.A * \
# test_model.weight_model.W)
# plt.pause(0.001)
# Save this sample
with open(output_path + ".gibbs.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.timestamps.pkl", 'w') as f:
print "Saving Gibbs samples to ", (output_path + ".gibbs.timestamps.pkl")
cPickle.dump(timestamps, 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[1:] - timestamps[0]), f, protocol=-1)
return samples, timestamps
else:
init_model = None
###########################################################
# Create a test weak 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['v'] = None
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(
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)
test_model.add_data(S)
# Initialize with the standard model parameters
if init_model is not None:
test_model.initialize_with_standard_model(init_model)
###########################################################
# Fit the test model with Gibbs sampling
###########################################################
N_samples = 500
samples = []
def fit_network_hawkes_vb(S,
K,
C,
B,
dt,
dt_max,
output_path,
standard_model=None):
samples_and_timestamps = load_partial_results(output_path, typ="vb")
if samples_and_timestamps is not None:
samples, timestamps = samples_and_timestamps
# # Check for existing Gibbs results
# if os.path.exists(output_path + ".vb.pkl.gz"):
# with gzip.open(output_path + ".vb.pkl.gz", 'r') as f:
# print "Loading vb results from ", (output_path + ".vb.pkl.gz")
# (samples, timestamps) = cPickle.load(f)
#
# if isinstance(timestamps, list):
# timestamps = np.array(timestamps)
else:
print("Fitting the data with a network Hawkes model using Batch VB")
# Make a new model for inference
network_hypers = {'C': C, 'alpha': 1.0, 'beta': 1.0 / 20.0}
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(
K=K, dt=dt, dt_max=dt_max, B=B, network_hypers=network_hypers)
# 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)
# TODO: Add the data in minibatches
minibatchsize = 500
test_model.add_data(S)
# Stochastic variational inference
N_iters = 1000
vlbs = []
samples = []
start = time.clock()
timestamps = []
for itr in range(N_iters):
vlbs.append(test_model.meanfield_coordinate_descent_step())
print("Batch VB Iter: ", itr, "\tVLB: ", vlbs[-1])
samples.append(test_model.copy_sample())
timestamps.append(time.clock())
if itr % 1 == 0:
im.set_data(test_model.weight_model.expected_W())
plt.pause(0.001)
# Save this sample
with open(output_path + ".vb.itr%04d.pkl" % itr, 'w') as f:
pickle.dump((samples[-1], timestamps[-1] - start),
f,
protocol=-1)
# Save the Gibbs samples
timestamps = np.array(timestamps)
with gzip.open(output_path + ".vb.pkl.gz", 'w') as f:
print("Saving VB samples to ", (output_path + ".vb.pkl.gz"))
pickle.dump((samples, timestamps - start), f, protocol=-1)
return samples, timestamps
def demo(seed=None):
"""
Fit a weakly sparse
: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 weak 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['v'] = None
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(
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_hypers=network_hypers)
test_model.add_data(S)
# Initialize with the standard model parameters
if init_model is not None:
test_model.initialize_with_standard_model(init_model)
###########################################################
# Fit the test model with Gibbs sampling
###########################################################
N_samples = 500
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()
###########################################################
# Analyze the samples
###########################################################
N_samples = len(samples)
A_samples = np.array([s.weight_model.A for s in samples])
W_samples = np.array([s.weight_model.W for s in samples])
g_samples = np.array([s.impulse_model.g for s in samples])
lambda0_samples = np.array([s.bias_model.lambda0 for s in samples])
lps = np.array(lps)
offset = N_samples // 2
A_mean = A_samples[offset:, ...].mean(axis=0)
W_mean = W_samples[offset:, ...].mean(axis=0)
g_mean = g_samples[offset:, ...].mean(axis=0)
lambda0_mean = lambda0_samples[offset:, ...].mean(axis=0)
plt.figure()
plt.plot(np.arange(N_samples), lps, 'k')
plt.xlabel("Iteration")
plt.ylabel("Log probability")
plt.show()
# Compute the link prediction accuracy curves
auc_init = roc_auc_score(true_model.weight_model.A.ravel(),
init_model.W.ravel())
auc_A_mean = roc_auc_score(true_model.weight_model.A.ravel(),
A_mean.ravel())
auc_W_mean = roc_auc_score(true_model.weight_model.A.ravel(),
W_mean.ravel())
aucs = []
for A in A_samples:
aucs.append(roc_auc_score(true_model.weight_model.A.ravel(),
A.ravel()))
plt.figure()
plt.plot(aucs, '-r')
plt.plot(auc_A_mean * np.ones_like(aucs), '--r')
plt.plot(auc_W_mean * np.ones_like(aucs), '--b')
plt.plot(auc_init * np.ones_like(aucs), '--k')
plt.xlabel("Iteration")
plt.ylabel("Link prediction AUC")
plt.show()
plt.ioff()
plt.show()
def demo(seed=None):
"""
Fit a weakly sparse
:return:
"""
import warnings
warnings.warn("This test runs but the parameters need to be tuned. "
"Right now, the SVI algorithm seems to walk away from "
"the MAP estimate and yield suboptimal results. "
"I'm not convinced the variational inference with the "
"gamma mixture provides the best estimates of the sparsity.")
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
###########################################################
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 weak 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_network = StochasticBlockModel(K=K, C=1)
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(
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)
# Initialize with the standard model parameters
if init_model is not None:
test_model.initialize_with_standard_model(init_model)
###########################################################
# Fit the test model with variational Bayesian inference
###########################################################
# VB coordinate descent
N_iters = 100
vlbs = []
samples = []
for itr in range(N_iters):
vlbs.append(test_model.meanfield_coordinate_descent_step())
print("VB Iter: ", itr, "\tVLB: ", vlbs[-1])
if itr > 0:
if (vlbs[-2] - vlbs[-1]) > 1e-1:
print("WARNING: VLB is not increasing!")
# Resample from variational distribution and plot
test_model.resample_from_mf()
samples.append(test_model.copy_sample())
###########################################################
# Analyze the samples
###########################################################
N_samples = len(samples)
# Compute sample statistics for second half of samples
A_samples = np.array([s.weight_model.A for s in samples])
W_samples = np.array([s.weight_model.W for s in samples])
g_samples = np.array([s.impulse_model.g for s in samples])
lambda0_samples = np.array([s.bias_model.lambda0 for s in samples])
vlbs = np.array(vlbs)
offset = N_samples // 2
A_mean = A_samples[offset:, ...].mean(axis=0)
W_mean = W_samples[offset:, ...].mean(axis=0)
g_mean = g_samples[offset:, ...].mean(axis=0)
lambda0_mean = lambda0_samples[offset:, ...].mean(axis=0)
# Plot the VLBs
plt.figure()
plt.plot(np.arange(N_samples), vlbs, 'k')
plt.xlabel("Iteration")
plt.ylabel("VLB")
plt.show()
# Compute the link prediction accuracy curves
auc_init = roc_auc_score(true_model.weight_model.A.ravel(),
init_model.W.ravel())
auc_A_mean = roc_auc_score(true_model.weight_model.A.ravel(),
A_mean.ravel())
auc_W_mean = roc_auc_score(true_model.weight_model.A.ravel(),
W_mean.ravel())
aucs = []
for A in A_samples:
aucs.append(roc_auc_score(true_model.weight_model.A.ravel(),
A.ravel()))
plt.figure()
plt.plot(aucs, '-r')
plt.plot(auc_A_mean * np.ones_like(aucs), '--r')
plt.plot(auc_W_mean * np.ones_like(aucs), '--b')
plt.plot(auc_init * np.ones_like(aucs), '--k')
plt.xlabel("Iteration")
plt.ylabel("Link prediction AUC")
plt.show()
plt.ioff()
plt.show()
def geweke_test():
"""
Create a discrete time Hawkes model and generate from it.
:return:
"""
T = 50
dt = 1.0
dt_max = 3.0
network_hypers = {
'c': np.array([0], dtype=np.int),
'p': 0.5,
'kappa': 3.0,
'v': 15.0
}
model = DiscreteTimeNetworkHawkesModelGammaMixture(
K=1, dt=dt, dt_max=dt_max, network_hypers=network_hypers)
model.generate(T=T)
# Gibbs sample and then generate new data
N_samples = 10000
samples = []
lps = []
for itr in xrange(N_samples):
if itr % 10 == 0:
print "Iteration: ", itr
# Resample the model
model.resample_model(resample_network=False)
samples.append(model.copy_sample())
lps.append(model.log_probability())
# Geweke step
model.data_list.pop()
model.generate(T=T)
# Compute sample statistics for second half of samples
A_samples = np.array([s.weight_model.A for s in samples])
W_samples = np.array([s.weight_model.W for s in samples])
g_samples = np.array([s.impulse_model.g for s in samples])
lambda0_samples = np.array([s.bias_model.lambda0 for s in samples])
c_samples = np.array([s.network.c for s in samples])
p_samples = np.array([s.network.p for s in samples])
v_samples = np.array([s.network.v for s in samples])
lps = np.array(lps)
offset = 0
A_mean = A_samples[offset:, ...].mean(axis=0)
W_mean = W_samples[offset:, ...].mean(axis=0)
g_mean = g_samples[offset:, ...].mean(axis=0)
lambda0_mean = lambda0_samples[offset:, ...].mean(axis=0)
print "A mean: ", A_mean
print "W mean: ", W_mean
print "g mean: ", g_mean
print "lambda0 mean: ", lambda0_mean
# Plot the log probability over iterations
plt.figure()
plt.plot(np.arange(N_samples), lps)
plt.xlabel("Iteration")
plt.ylabel("Log probability")
# Plot the histogram of bias samples
plt.figure()
p_lmbda0 = gamma(model.bias_model.alpha, scale=1. / model.bias_model.beta)
_, bins, _ = plt.hist(lambda0_samples[:, 0],
bins=20,
alpha=0.5,
normed=True)
bincenters = 0.5 * (bins[1:] + bins[:-1])
plt.plot(bincenters, p_lmbda0.pdf(bincenters), 'r--', linewidth=1)
plt.xlabel('lam0')
plt.ylabel('p(lam0)')
print "Expected p(A): ", model.network.P
print "Empirical p(A): ", A_samples.mean(axis=0)
# Plot the histogram of weight samples
plt.figure()
Aeq1 = A_samples[:, 0, 0] == 1
p_W1 = gamma(model.network.kappa, scale=1. / model.network.v[0, 0])
_, bins, _ = plt.hist(W_samples[Aeq1, 0, 0],
bins=20,
alpha=0.5,
normed=True)
bincenters = 0.5 * (bins[1:] + bins[:-1])
plt.plot(bincenters, p_W1.pdf(bincenters), 'r--', linewidth=1)
plt.xlabel('W')
plt.ylabel('p(W | A=1)')
plt.figure()
Aeq0 = A_samples[:, 0, 0] == 0
p_W1 = gamma(model.weight_model.kappa_0,
scale=1. / model.weight_model.nu_0)
_, bins, _ = plt.hist(W_samples[Aeq0, 0, 0],
bins=20,
alpha=0.5,
normed=True)
bincenters = 0.5 * (bins[1:] + bins[:-1])
plt.plot(bincenters, p_W1.pdf(bincenters), 'r--', linewidth=1)
plt.xlabel('W')
plt.ylabel('p(W | A=0)')
# Plot the histogram of impulse samples
plt.figure()
for b in range(model.B):
plt.subplot(1, model.B, b + 1)
a = model.impulse_model.gamma[b]
b = model.impulse_model.gamma.sum() - a
p_beta11b = beta(a, b)
_, bins, _ = plt.hist(g_samples[:, 0, 0, b],
bins=20,
alpha=0.5,
normed=True)
bincenters = 0.5 * (bins[1:] + bins[:-1])
plt.plot(bincenters, p_beta11b.pdf(bincenters), 'r--', linewidth=1)
plt.xlabel('g_%d' % b)
plt.ylabel('p(g_%d)' % b)
plt.show()
init_model.initialize_to_background_rate()
init_model.fit_with_bfgs()
else:
init_model = None
###########################################################
# Create a test weak 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['v'] = None
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(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)
test_model.add_data(S)
# Initialize with the standard model parameters
if init_model is not None:
test_model.initialize_with_standard_model(init_model)
###########################################################
# Fit the test model with Gibbs sampling
###########################################################
N_samples = 500
samples = []
lps = []
def fit_network_hawkes_svi(S, K, C, dt, dt_max,
output_path,
standard_model=None,
N_iters=500):
# Check for existing Gibbs results
# if os.path.exists(output_path + ".svi.pkl.gz"):
# with gzip.open(output_path + ".svi.pkl.gz", 'r') as f:
# print "Loading SVI results from ", (output_path + ".svi.pkl.gz")
# (samples, timestamps) = cPickle.load(f)
if os.path.exists(output_path + ".svi.itr%04d.pkl" % (N_iters-1)):
with open(output_path + ".svi.itr%04d.pkl" % (N_iters-1), 'r') as f:
print "Loading SVI results from ", (output_path + ".svi.itr%04d.pkl" % (N_iters-1))
sample = cPickle.load(f)
samples = [sample]
timestamps = None
# (samples, timestamps) = cPickle.load(f)
else:
print "Fitting the data with a network Hawkes model using SVI"
# Make a new model for inference
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 = DiscreteTimeNetworkHawkesModelGammaMixture(K=K, dt=dt, dt_max=dt_max,
basis=test_basis,
network_hypers=network_hypers)
# 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)
# Plot the block affiliations
plt.figure(2)
KC = np.zeros((K,C))
KC[np.arange(K), test_model.network.c] = 1.0
im_clus = plt.imshow(KC,
interpolation="none", cmap="Greys",
aspect=float(C)/K)
# TODO: Add the data in minibatches
minibatchsize = 1000
test_model.add_data(S)
# Stochastic variational inference
samples = []
delay = 1.0
forgetting_rate = 0.5
stepsize = (np.arange(N_iters) + delay)**(-forgetting_rate)
timestamps = []
for itr in xrange(N_iters):
print "SVI Iter: ", itr, "\tStepsize: ", stepsize[itr]
test_model.sgd_step(minibatchsize=minibatchsize, stepsize=stepsize[itr])
test_model.resample_from_mf()
samples.append(test_model.copy_sample())
timestamps.append(time.clock())
if itr % 1 == 0:
plt.figure(1)
im.set_data(test_model.weight_model.expected_W())
plt.pause(0.001)
plt.figure(2)
im_clus.set_data(test_model.network.mf_m)
plt.title("Iteration %d" % itr)
plt.pause(0.001)
# Save this sample
with open(output_path + ".svi.itr%04d.pkl" % itr, 'w') as f:
cPickle.dump(samples[-1], f, protocol=-1)
# Save the Gibbs samples
# with gzip.open(output_path + ".svi.pkl.gz", 'w') as f:
# print "Saving SVI samples to ", (output_path + ".svi.pkl.gz")
# cPickle.dump((samples, timestamps), f, protocol=-1)
return samples, timestamps
def fit_network_hawkes_vb(S, K, C, B, dt, dt_max,
output_path,
standard_model=None):
samples_and_timestamps = load_partial_results(output_path, typ="vb")
if samples_and_timestamps is not None:
samples, timestamps = samples_and_timestamps
# # Check for existing Gibbs results
# if os.path.exists(output_path + ".vb.pkl.gz"):
# with gzip.open(output_path + ".vb.pkl.gz", 'r') as f:
# print "Loading vb results from ", (output_path + ".vb.pkl.gz")
# (samples, timestamps) = cPickle.load(f)
#
# if isinstance(timestamps, list):
# timestamps = np.array(timestamps)
else:
print "Fitting the data with a network Hawkes model using Batch VB"
# Make a new model for inference
network_hypers = {'C': C, 'alpha': 1.0, 'beta': 1.0/20.0}
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(K=K, dt=dt, dt_max=dt_max, B=B,
network_hypers=network_hypers)
# 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)
# TODO: Add the data in minibatches
minibatchsize = 500
test_model.add_data(S)
# Stochastic variational inference
N_iters = 1000
vlbs = []
samples = []
start = time.clock()
timestamps = []
for itr in xrange(N_iters):
vlbs.append(test_model.meanfield_coordinate_descent_step())
print "Batch VB Iter: ", itr, "\tVLB: ", vlbs[-1]
samples.append(test_model.copy_sample())
timestamps.append(time.clock())
if itr % 1 == 0:
im.set_data(test_model.weight_model.expected_W())
plt.pause(0.001)
# Save this sample
with open(output_path + ".vb.itr%04d.pkl" % itr, 'w') as f:
cPickle.dump((samples[-1], timestamps[-1] - start), f, protocol=-1)
# Save the Gibbs samples
timestamps = np.array(timestamps)
with gzip.open(output_path + ".vb.pkl.gz", 'w') as f:
print "Saving VB samples to ", (output_path + ".vb.pkl.gz")
cPickle.dump((samples, timestamps - start), f, protocol=-1)
return samples, timestamps
def demo(seed=None):
"""
Fit a weakly sparse
: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_K4_C1_T1000.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
###########################################################
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 weak 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_model = DiscreteTimeNetworkHawkesModelGammaMixture(
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_hypers=network_hypers,
)
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
if do_plot:
ln, im_net, im_clus = initialize_plots(true_model, test_model, S)
###########################################################
# Fit the test model with stochastic variational inference
###########################################################
N_iters = 500
minibatchsize = 100
delay = 1.0
forgetting_rate = 0.5
stepsize = (np.arange(N_iters) + delay) ** (-forgetting_rate)
samples = []
for itr in xrange(N_iters):
print "SVI Iter: ", itr, "\tStepsize: ", stepsize[itr]
test_model.sgd_step(minibatchsize=minibatchsize, stepsize=stepsize[itr])
test_model.resample_from_mf()
samples.append(test_model.copy_sample())
# Update plot
if itr % 1 == 0 and do_plot:
update_plots(itr, test_model, S, ln, im_clus, im_net)
###########################################################
# Analyze the samples
###########################################################
analyze_samples(true_model, init_model, samples)
def demo(seed=None):
"""
Fit a weakly sparse
: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 weak 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_model = DiscreteTimeNetworkHawkesModelGammaMixture(
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_hypers=network_hypers)
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 stochastic variational inference
###########################################################
N_iters = 500
minibatchsize = 500
delay = 1.0
forgetting_rate = 0.5
stepsize = (np.arange(N_iters) + delay)**(-forgetting_rate)
samples = []
for itr in xrange(N_iters):
print "SVI Iter: ", itr, "\tStepsize: ", stepsize[itr]
test_model.sgd_step(minibatchsize=minibatchsize,
stepsize=stepsize[itr])
test_model.resample_from_mf()
samples.append(test_model.copy_sample())
# 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)
def demo(seed=None):
"""
Fit a weakly sparse
: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_K4_C1_T1000.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
###########################################################
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 weak 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()
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(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_hypers=network_hypers)
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)
###########################################################
# Fit the test model with variational Bayesian inference
###########################################################
# VB coordinate descent
N_iters = 100
vlbs = []
samples = []
for itr in xrange(N_iters):
vlbs.append(test_model.meanfield_coordinate_descent_step())
print "VB Iter: ", itr, "\tVLB: ", vlbs[-1]
if itr > 0:
if (vlbs[-2] - vlbs[-1]) > 1e-1:
print "WARNING: VLB is not increasing!"
# Resample from variational distribution and plot
test_model.resample_from_mf()
samples.append(test_model.copy_sample())
###########################################################
# Analyze the samples
###########################################################
N_samples = len(samples)
# Compute sample statistics for second half of samples
A_samples = np.array([s.weight_model.A for s in samples])
W_samples = np.array([s.weight_model.W for s in samples])
g_samples = np.array([s.impulse_model.g for s in samples])
lambda0_samples = np.array([s.bias_model.lambda0 for s in samples])
vlbs = np.array(vlbs)
offset = N_samples // 2
A_mean = A_samples[offset:, ...].mean(axis=0)
W_mean = W_samples[offset:, ...].mean(axis=0)
g_mean = g_samples[offset:, ...].mean(axis=0)
lambda0_mean = lambda0_samples[offset:, ...].mean(axis=0)
# Plot the VLBs
plt.figure()
plt.plot(np.arange(N_samples), vlbs, 'k')
plt.xlabel("Iteration")
plt.ylabel("VLB")
plt.show()
# Compute the link prediction accuracy curves
auc_init = roc_auc_score(true_model.weight_model.A.ravel(),
init_model.W.ravel())
auc_A_mean = roc_auc_score(true_model.weight_model.A.ravel(),
A_mean.ravel())
auc_W_mean = roc_auc_score(true_model.weight_model.A.ravel(),
W_mean.ravel())
aucs = []
for A in A_samples:
aucs.append(roc_auc_score(true_model.weight_model.A.ravel(), A.ravel()))
plt.figure()
plt.plot(aucs, '-r')
plt.plot(auc_A_mean * np.ones_like(aucs), '--r')
plt.plot(auc_W_mean * np.ones_like(aucs), '--b')
plt.plot(auc_init * np.ones_like(aucs), '--k')
plt.xlabel("Iteration")
plt.ylabel("Link prediction AUC")
plt.show()
plt.ioff()
plt.show()
def fit_network_hawkes_gibbs(S,
K,
C,
B,
dt,
dt_max,
output_path,
standard_model=None):
samples_and_timestamps = load_partial_results(output_path, typ="gibbs")
if samples_and_timestamps is not None:
samples, timestamps = samples_and_timestamps
# # 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
network_hypers = {'C': C, 'alpha': 1.0, 'beta': 1.0 / 20.0}
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(
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)
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 = 1000
samples = []
lps = []
timestamps = [time.clock()]
for itr in range(N_samples):
lps.append(test_model.log_probability())
# lps.append(test_model.log_likelihood())
samples.append(test_model.resample_and_copy())
timestamps.append(time.clock())
if itr % 1 == 0:
print("Iteration ", itr, "\t LL: ", lps[-1])
# im.set_data(test_model.weight_model.A * \
# test_model.weight_model.W)
# plt.pause(0.001)
# Save this sample
with open(output_path + ".gibbs.itr%04d.pkl" % itr, 'w') as f:
pickle.dump((samples[-1], timestamps[-1] - timestamps[0]),
f,
protocol=-1)
# Save the Gibbs timestamps
timestamps = np.array(timestamps)
with open(output_path + ".gibbs.timestamps.pkl", 'w') as f:
print("Saving Gibbs samples to ",
(output_path + ".gibbs.timestamps.pkl"))
pickle.dump(timestamps, 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"))
pickle.dump((samples, timestamps[1:] - timestamps[0]),
f,
protocol=-1)
return samples, timestamps
def test_sbm_mf(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)
C = 5
K = 50
T = 1000
dt = 1.0
B = 3
p = 0.4 * np.eye(C) + (0.05) * (1-np.eye(C))
# Generate from a true model
network_hypers = {'C': C, 'beta': 1.0/K, 'p': p}
true_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(K, dt=dt, B=B,
network_hypers=network_hypers)
c = true_model.network.c
perm = np.argsort(c)
#
# Plot the true network
plt.ion()
plot_network(true_model.weight_model.A[np.ix_(perm, perm)],
true_model.weight_model.W[np.ix_(perm, perm)])
plt.pause(0.001)
# Make a new model for inference
test_network_hypers = {'C': C, 'beta': 1.0/K, 'tau0': 0.5, 'tau1': 0.5}
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(K=K, dt=dt, B=B,
network_hypers=test_network_hypers)
test_model.weight_model.initialize_from_gibbs(true_model.weight_model.A,
true_model.weight_model.W)
# Plot the block probabilities
plt.figure()
im = plt.imshow(test_model.network.mf_m[perm,:],
interpolation="none", cmap="Greys",
aspect=float(C)/K)
plt.xlabel('C')
plt.ylabel('K')
plt.show()
plt.pause(0.001)
# Run mean field updates for the SBM given a fixed network
N_iters = 50
c_samples = []
vlbs = []
for itr in xrange(N_iters):
if itr % 5 == 0:
print "Iteration: ", itr
# Update the plot
im.set_data(test_model.network.mf_m[perm,:])
plt.pause(0.001)
# Resample from meanfield distribution
test_model.network.resample_from_mf()
c_samples.append(copy.deepcopy(test_model.network.c))
vlbs.append(test_model.network.get_vlb() + test_model.weight_model.get_vlb())
if itr > 0:
if vlbs[-1] - vlbs[-2] < -1e-3:
print "VLBS are not increasing"
print np.array(vlbs)
# import pdb; pdb.set_trace()
# raise Exception("VLBS are not increasing!")
# Take a mean field step
test_model.network.meanfieldupdate(test_model.weight_model)
plt.ioff()
# Compute sample statistics for second half of samples
c_samples = np.array(c_samples)
vlbs = np.array(vlbs)
print "True c: ", true_model.network.c
print "Test c: ", c_samples[-10:, :]
# Compute the adjusted mutual info score of the clusterings
amis = []
arss = []
for c in c_samples:
amis.append(adjusted_mutual_info_score(true_model.network.c, c))
arss.append(adjusted_rand_score(true_model.network.c, c))
plt.figure()
plt.plot(np.arange(N_iters), amis, '-r')
plt.plot(np.arange(N_iters), arss, '-b')
plt.xlabel("Iteration")
plt.ylabel("Clustering score")
plt.figure()
plt.plot(np.arange(N_iters), vlbs)
plt.xlabel("Iteration")
plt.ylabel("VLB")
plt.show()
def fit_network_hawkes_svi(S,
K,
C,
B,
dt,
dt_max,
output_path,
standard_model=None):
samples_and_timestamps = load_partial_results(output_path, typ="svi2")
if samples_and_timestamps is not None:
samples, timestamps = samples_and_timestamps
else:
print("Fitting the data with a network Hawkes model using SVI")
# Make a new model for inference
network_hypers = {'C': C, 'alpha': 1.0, 'beta': 1.0 / 20.0}
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(
K=K, dt=dt, dt_max=dt_max, B=B, network_hypers=network_hypers)
# 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)
# TODO: Add the data in minibatches
minibatchsize = 500
import pdb
pdb.set_trace()
test_model.add_data(S)
# Stochastic variational inference
N_iters = 10000
samples = []
delay = 1.0
forgetting_rate = 0.5
stepsize = (np.arange(N_iters) + delay)**(-forgetting_rate)
start = time.clock()
timestamps = []
for itr in range(N_iters):
print("SVI Iter: ", itr, "\tStepsize: ", stepsize[itr])
test_model.sgd_step(minibatchsize=minibatchsize,
stepsize=stepsize[itr])
test_model.resample_from_mf()
samples.append(test_model.copy_sample())
timestamps.append(time.clock())
if itr % 1 == 0:
im.set_data(test_model.weight_model.expected_W())
plt.pause(0.001)
# Save this sample
with open(output_path + ".svi.itr%04d.pkl" % itr, 'w') as f:
pickle.dump((samples[-1], timestamps[-1] - start),
f,
protocol=-1)
# Save the Gibbs samples
timestamps = np.array(timestamps)
with gzip.open(output_path + ".svi.pkl.gz", 'w') as f:
print("Saving SVI samples to ", (output_path + ".svi.pkl.gz"))
pickle.dump((samples, timestamps - start), f, protocol=-1)
return samples, timestamps
def demo(seed=None):
"""
Fit a weakly sparse
:return:
"""
import warnings
warnings.warn("This test runs but the parameters need to be tuned. "
"Right now, the SVI algorithm seems to walk away from "
"the MAP estimate and yield suboptimal results. "
"I'm not convinced the variational inference with the "
"gamma mixture provides the best estimates of the sparsity.")
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
###########################################################
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 weak 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'] = 1
network_hypers['c'] = None
network_hypers['v'] = None
network_hypers['m'] = None
test_network = StochasticBlockModel(K=K, **network_hypers)
test_model = DiscreteTimeNetworkHawkesModelGammaMixture(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)
###########################################################
# Fit the test model with stochastic variational inference
###########################################################
N_iters = 500
minibatchsize = 1000
delay = 1.0
forgetting_rate = 0.5
stepsize = (np.arange(N_iters) + delay)**(-forgetting_rate)
samples = []
for itr in range(N_iters):
print("SVI Iter: ", itr, "\tStepsize: ", stepsize[itr])
test_model.sgd_step(minibatchsize=minibatchsize, stepsize=stepsize[itr])
test_model.resample_from_mf()
samples.append(test_model.copy_sample())
###########################################################
# Analyze the samples
###########################################################
analyze_samples(true_model, init_model, samples)
from pyhawkes.models import DiscreteTimeNetworkHawkesModelGammaMixture
if __name__ == "__main__":
"""
Create a discrete time Hawkes model and generate from it.
:return:
"""
T = 50
dt = 1.0
dt_max = 3.0
network_hypers = {'c': np.array([0], dtype=np.int),
'p': 0.5, 'kappa': 3.0, 'v': 15.0}
weight_hypers = {"kappa_0": 3.0, "nu_0": 15.0}
model = DiscreteTimeNetworkHawkesModelGammaMixture(K=1, dt=dt, dt_max=dt_max,
weight_hypers=weight_hypers,
network_hypers=network_hypers)
model.generate(T=T)
# Gibbs sample and then generate new data
N_samples = 10000
samples = []
lps = []
for itr in progprint_xrange(N_samples, perline=50):
# Resample the model
model.resample_model(resample_network=False)
samples.append(model.copy_sample())
lps.append(model.log_probability())
# Geweke step
model.data_list.pop()
def demo(seed=None):
"""
Fit a weakly sparse
: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 weak 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_model = DiscreteTimeNetworkHawkesModelGammaMixture(
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_hypers=network_hypers)
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 = 500
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)
