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_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:
"""
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)
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 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
