def execute_voice(self,ref,count_pair,tensor1,tensor2=None,tensor_cross=None,mode="discrete",dt_max=5,N_samples=1000,rescale=1.0,thres=0.05):
model = DiscreteTimeStandardHawkesModel(K=self.K, dt_max=dt_max)
print self.data.shape
model.add_data(self.data)
calc_Fref(model,ref)
calc_Tensor(model,tensor1,(0,count_pair[0]),tensor_cross)
if count_pair[0] != count_pair[1]:
assert(tensor2 is not None)
calc_Tensor(model,tensor2,(count_pair[0],count_pair[1]))
model.fit_with_bfgs()
sol = reweight_model(model,0.1)
scale = sol[0]/sum(sol)
scale *= rescale
rate,rate_dist = compute_breakdown(model)
rate_ref,rate_dist_ref = compute_breakdown(model,ref=True)
#rate_pairs = sweep_series(rate_dist,rate_dist_ref,self.data,cutoff=0.05)
rate_pairs = sweep_series(rate_dist,rate_dist_ref,self.data,model.Tensor,scale,cutoff=thres,ref=True)
test_rate_dist = [[[0 for kk in range(self.data.shape[0])] for m in range(self.data.shape[1])] for k in range(self.data.shape[1])]
for k in rate_pairs:
for item in rate_pairs[k]:
test_rate_dist[k][item[0]][item[1]] = item[2]
cpair = count_pair
if count_pair[0] == count_pair[1]:
cpair = (0,count_pair[1])
topid = [t+cpair[0] for t in np.argsort(np.sum(rate,axis=0)[cpair[0]:cpair[1]])[::-1]]
return model.W, test_rate_dist, topid
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 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)
K = 5 # Number of nodes
T = 10000 # Number of time bins to simulate
dt = 1 # Time bin size
dt_max = 50 # Impulse response length
B = 1 # Number of basis functions
# Sample from a sparse network Hawkes model
S, true_model = sample_from_network_hawkes(K, T, dt, dt_max, B)
# Make a new model for inference
# test_basis = IdentityBasis(dt, dt_max, allow_instantaneous=False)
test_basis = true_model.basis
test_model = DiscreteTimeStandardHawkesModel(K=K, dt=dt, dt_max=dt_max+dt,
beta=1.0,
basis=test_basis,
allow_self_connections=True)
test_model.add_data(S)
# DEBUG: Initialize with the true parameters of the network Hawkes model
# test_model.initialize_with_gibbs_model(true_model)
test_model.fit_with_bfgs()
print("lambda0 true: ", true_model.bias_model.lambda0)
print("lambda0 test ", test_model.bias)
print("")
print("W true: ", true_model.weight_model.A * true_model.weight_model.W)
print("W test: ", test_model.W)
print("")
print("ll true: ", true_model.log_likelihood())
print("ll test: ", test_model.log_likelihood())
# test_model.plot_network()
# Plot the rates
plt.figure()
for k in range(3):
plt.subplot(3,1,k+1)
plt.plot(np.arange(T) * dt, true_model.compute_rate(proc=k), '-b')
plt.plot(np.arange(T) * dt, test_model.compute_rate(ks=k), '-r')
lim = plt.ylim()
plt.ylim(0, 1.25*lim[1])
plt.ioff()
plt.show()
def demo(seed=None):
"""
Create a discrete time Hawkes model and generate from it.
:return:
"""
raise NotImplementedError("This example needs to be updated.")
if seed is None:
seed = np.random.randint(2**32)
print "Setting seed to ", seed
np.random.seed(seed)
C = 1 # Number of clusters in the true data
K = 10 # Number of nodes
T = 1000 # Number of time bins to simulate
dt = 0.02 # Time bin size
dt_max = 0.08
B = 3 # Number of basis functions
# Sample from a sparse network Hawkes model
S, true_model = sample_from_network_hawkes(C, K, T, dt, dt_max, B)
# Make a new model for inference
test_basis = IdentityBasis(dt, dt_max, allow_instantaneous=False)
test_model = DiscreteTimeStandardHawkesModel(K=K, dt=dt, dt_max=dt_max+dt,
beta=1.0,
basis=test_basis,
allow_self_connections=True)
test_model.add_data(S)
# DEBUG: Initialize with the true parameters of the network Hawkes model
# test_model.initialize_with_gibbs_model(true_model)
test_model.fit_with_bfgs()
print "W true: ", true_model.weight_model.A * true_model.weight_model.W
print "lambda0 true: ", true_model.bias_model.lambda0
print "ll true: ", true_model.log_likelihood()
print ""
print "W test: ", test_model.W
print "lambda0 test ", test_model.bias
print "ll test: ", test_model.log_likelihood()
plot_network(np.ones((K,K)), test_model.W, vmax=0.5)
# Plot the rates
plt.figure()
for k in xrange(3):
plt.subplot(3,1,k+1)
plt.plot(np.arange(T) * dt, true_model.compute_rate(proc=k), '-b')
plt.plot(np.arange(T) * dt, test_model.compute_rate(ks=k), '-r')
plt.ioff()
plt.show()
def fit_standard_hawkes_model_bfgs_noxv(S, K, dt, dt_max, output_path, W_max=None):
"""
Fit
:param S:
:return:
"""
# Check for existing results
if os.path.exists(out_path + ".bfgs.pkl"):
print "Existing BFGS results found. Loading from file."
with open(output_path + ".bfgs.pkl", "r") as f:
init_model, init_time = cPickle.load(f)
else:
print "Fitting the data with a standard Hawkes model"
# We want the max W ~ -.025 and the mean to be around 0.01
# W ~ Gamma(alpha, beta) => E[W] = alpha/beta, so beta ~100 * alpha
alpha = 1.1
beta = alpha * 1.0 / 0.01
# Make a model to initialize the parameters
test_basis = IdentityBasis(dt, dt_max, allow_instantaneous=True)
init_model = DiscreteTimeStandardHawkesModel(
K=K,
dt=dt,
dt_max=dt_max,
alpha=alpha,
beta=beta,
basis=test_basis,
allow_self_connections=False,
W_max=W_max,
)
init_model.add_data(S)
# Initialize the background rates to their mean
init_model.initialize_to_background_rate()
start = time.clock()
init_model.fit_with_bfgs()
init_time = time.clock() - start
# Save the model (sans data)
with open(output_path + ".bfgs.pkl", "w") as f:
print "Saving BFGS results to ", (output_path + ".bfgs.pkl")
cPickle.dump((init_model, init_time), f, protocol=-1)
return init_model, init_time
def fit_standard_hawkes_model_sgd(S, K, B, dt, dt_max, init_model=None):
"""
Fit
:param S:
:return:
"""
print "Fitting the data with a standard Hawkes model using SGD"
# Make a new model for inference
test_model = DiscreteTimeStandardHawkesModel(K=K, dt=dt, dt_max=dt_max, B=B)
test_model.add_data(S, minibatchsize=256)
# Initialize the test model with the init model weights
if init_model is not None:
test_model.weights = init_model.weights
plt.ion()
im = plot_network(np.ones((K,K)), test_model.W, vmax=0.5)
plt.pause(0.001)
# Gradient descent
N_steps = 1000
samples = []
lls = []
timestamps = []
learning_rate = 0.01 * np.ones(N_steps)
momentum = 0.8 * np.ones(N_steps)
prev_velocity = None
for itr in xrange(N_steps):
# W,ll,grad = test_model.gradient_descent_step(stepsz=0.001)
W,ll,prev_velocity = test_model.sgd_step(prev_velocity, learning_rate[itr], momentum[itr])
samples.append(test_model.copy_sample())
lls.append(ll)
timestamps.append(time.clock())
if itr % 1 == 0:
print "Iteration ", itr, "\t LL: ", ll
im.set_data(np.ones((K,K)) * test_model.W)
plt.pause(0.001)
plt.ioff()
plt.figure()
plt.plot(np.arange(N_steps), lls)
plt.xlabel("Iteration")
plt.ylabel("Log likelihood")
plot_network(np.ones((K,K)), test_model.W)
plt.show()
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)
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_standard_hawkes_model_bfgs(S, K, dt, dt_max, output_path, W_max=None):
"""
Fit
:param S:
:return:
"""
# Check for existing results
if os.path.exists(out_path + ".bfgs.pkl"):
print "Existing BFGS results found. Loading from file."
with open(output_path + ".bfgs.pkl", "r") as f:
init_model, init_time = cPickle.load(f)
else:
print "Fitting the data with a standard Hawkes model"
# betas = np.logspace(-1,1.3,num=1)
# betas = [ 0.0 ]
# We want the max W ~ -.025 and the mean to be around 0.01
# W ~ Gamma(alpha, beta) => E[W] = alpha/beta, so beta ~100 * alpha
alpha = 1.1
betas = [alpha * 1.0 / 0.01]
init_models = []
xv_len = 10000
init_len = S.shape[0] - 10000
S_init = S[:init_len, :]
xv_ll = np.zeros(len(betas))
S_xv = S[init_len : init_len + xv_len, :]
# Make a model to initialize the parameters
test_basis = IdentityBasis(dt, dt_max, allow_instantaneous=True)
init_model = DiscreteTimeStandardHawkesModel(
K=K,
dt=dt,
dt_max=dt_max,
alpha=alpha,
beta=0.0,
basis=test_basis,
allow_self_connections=False,
W_max=W_max,
)
init_model.add_data(S_init)
# Initialize the background rates to their mean
init_model.initialize_to_background_rate()
start = time.clock()
for i, beta in enumerate(betas):
print "Fitting with BFGS on first ", init_len, " time bins, ", "beta = ", beta, "W_max = ", W_max
init_model.beta = beta
init_model.fit_with_bfgs()
init_models.append(init_model.copy_sample())
# Compute the heldout likelihood on the xv data
xv_ll[i] = init_model.heldout_log_likelihood(S_xv)
if not np.isfinite(xv_ll[i]):
xv_ll[i] = -np.inf
init_time = time.clock() - start
# Take the best model
print "XV predictive log likelihoods: "
for beta, ll in zip(betas, xv_ll):
print "Beta: %.2f\tLL: %.2f" % (beta, ll)
best_ind = np.argmax(xv_ll)
print "Best beta: ", betas[best_ind]
init_model = init_models[best_ind]
if best_ind == 0 or best_ind == len(betas) - 1:
print "WARNING: Best BFGS model was for extreme value of beta. " "Consider expanding the beta range."
# Save the model (sans data)
with open(output_path + ".bfgs.pkl", "w") as f:
print "Saving BFGS results to ", (output_path + ".bfgs.pkl")
cPickle.dump((init_model, init_time), f, protocol=-1)
return init_model, init_time
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)
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)
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
def fit_standard_hawkes_model_bfgs(S, K, B, dt, dt_max, output_path,
init_len=10000, xv_len=1000):
"""
Fit
:param S:
:return:
"""
# Check for existing results
if os.path.exists(out_path + ".bfgs.pkl"):
print "Existing BFGS results found. Loading from file."
with open(output_path + ".bfgs.pkl", 'r') as f:
init_model, init_time = cPickle.load(f)
else:
print "Fitting the data with a standard Hawkes model"
# betas = np.logspace(-3,-0.8,num=10)
betas = np.array([0.01, 0.1, 1.0, 10.0, 20.0])
# betas = np.concatenate(([0], betas))
init_models = []
S_init = S[:init_len,:]
xv_ll = np.zeros(len(betas))
S_xv = S[init_len:init_len+xv_len, :]
# Make a model to initialize the parameters
init_model = DiscreteTimeStandardHawkesModel(K=K, dt=dt, B=B, dt_max=dt_max, beta=0.0)
init_model.add_data(S_init)
# Initialize the background rates to their mean
init_model.initialize_to_background_rate()
start = time.clock()
for i,beta in enumerate(betas):
print "Fitting with BFGS on first ", init_len, " time bins, beta = ", beta
init_model.beta = beta
init_model.fit_with_bfgs()
init_models.append(init_model.copy_sample())
# Compute the heldout likelihood on the xv data
xv_ll[i] = init_model.heldout_log_likelihood(S_xv)
if not np.isfinite(xv_ll[i]):
xv_ll[i] = -np.inf
init_time = time.clock() - start
# Take the best model
print "XV predictive log likelihoods: "
for beta, ll in zip(betas, xv_ll):
print "Beta: %.2f\tLL: %.2f" % (beta, ll)
best_ind = np.argmax(xv_ll)
print "Best beta: ", betas[best_ind]
init_model = init_models[best_ind]
if best_ind == 0 or best_ind == len(betas) - 1:
print "WARNING: Best BFGS model was for extreme value of beta. " \
"Consider expanding the beta range."
# Save the model (sans data)
with open(output_path + ".bfgs.pkl", 'w') as f:
print "Saving BFGS results to ", (output_path + ".bfgs.pkl")
cPickle.dump((init_model, init_time), f, protocol=-1)
return init_model, init_time
def demo(seed=None):
"""
Suppose we have a very long recording such that computing gradients of
the log likelihood is quite expensive. Here we explore the use of
stochastic gradient descent to fit the standard Hawkes model, which has
a convex log likelihood. We first initialize the parameters using BFGS
on a manageable subset of the data. Then we use SGD to refine the parameters
on the entire dataset.
:return:
"""
if seed is None:
seed = np.random.randint(2**32)
print "Setting seed to ", seed
np.random.seed(seed)
C = 1 # Number of clusters in the true data
K = 10 # Number of nodes
T = 10000 # Number of time bins to simulate
dt = 1.0 # Time bin size
B = 3 # Number of basis functions
# Sample from the network Hawkes model
S, R, true_model = sample_from_network_hawkes(C, K, T, dt, B)
# Make a model to initialize the parameters
init_len = 256
init_model = DiscreteTimeStandardHawkesModel(K=K, dt=dt, B=B, beta=1.0)
init_model.add_data(S[:init_len, :])
print "Initializing with BFGS on first ", init_len, " time bins."
init_model.fit_with_bfgs()
# Make another model for inference
test_model = DiscreteTimeStandardHawkesModel(K=K, dt=dt, B=B, beta=1.0)
# Initialize with the BFGS parameters
test_model.weights = init_model.weights
# Add the data in minibatches
test_model.add_data(S, minibatchsize=256)
# Plot the true and inferred firing rate
kplt = 0
plt.figure()
plt.plot(np.arange(256), R[:256, kplt], '-k', lw=2)
plt.ion()
ln = plt.plot(np.arange(256),
test_model.compute_rate(ks=kplt)[:256], '-r')[0]
plt.show()
# Gradient descent
N_steps = 10000
lls = []
learning_rate = 0.01 * np.ones(N_steps)
momentum = 0.8 * np.ones(N_steps)
prev_velocity = None
for itr in xrange(N_steps):
W, ll, prev_velocity = test_model.sgd_step(prev_velocity,
learning_rate[itr],
momentum[itr])
lls.append(ll)
# Update plot
if itr % 5 == 0:
ln.set_data(np.arange(256), test_model.compute_rate(ks=kplt))
plt.title("Iteration %d" % itr)
plt.pause(0.001)
plt.ioff()
print "W true: ", true_model.weight_model.A * true_model.weight_model.W
print "lambda0 true: ", true_model.bias_model.lambda0
print ""
print "W test: ", test_model.W
print "lambda0 test ", test_model.bias
plt.figure()
plt.plot(np.arange(N_steps), lls)
plt.xlabel("Iteration")
plt.ylabel("Log likelihood")
plot_network(np.ones((K, K)), test_model.W)
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 = 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_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 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 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):
"""
Create a discrete time Hawkes model and generate from it.
:return:
"""
raise NotImplementedError("This example needs to be updated.")
if seed is None:
seed = np.random.randint(2**32)
print "Setting seed to ", seed
np.random.seed(seed)
C = 1
K = 10
T = 1000
dt = 1.0
B = 3
# Create a true model
p = 0.8 * np.eye(C)
v = 10.0 * np.eye(C) + 20.0 * (1-np.eye(C))
# m = 0.5 * np.ones(C)
c = (0.0 * (np.arange(K) < 10) + 1.0 * (np.arange(K) >= 10)).astype(np.int)
true_model = DiscreteTimeNetworkHawkesModelSpikeAndSlab(C=C, K=K, dt=dt, B=B, c=c, p=p, v=v)
# Plot the true network
plt.ion()
plot_network(true_model.weight_model.A,
true_model.weight_model.W,
vmax=0.5)
plt.pause(0.001)
# Sample from the true model
S,R = true_model.generate(T=T)
# Make a new model for inference
test_model = DiscreteTimeStandardHawkesModel(K=K, dt=dt, B=B, beta=1.0)
test_model.add_data(S)
# Plot the true and inferred firing rate
kplt = 0
plt.figure()
plt.plot(np.arange(T), R[:,kplt], '-k', lw=2)
plt.ion()
ln = plt.plot(np.arange(T), test_model.compute_rate(ks=kplt), '-r')[0]
plt.show()
# Gradient descent
N_steps = 10000
lls = []
for itr in xrange(N_steps):
W,ll,grad = test_model.gradient_descent_step(stepsz=0.001)
lls.append(ll)
# Update plot
if itr % 5 == 0:
ln.set_data(np.arange(T), test_model.compute_rate(ks=kplt))
plt.title("Iteration %d" % itr)
plt.pause(0.001)
plt.ioff()
print "W true: ", true_model.weight_model.A * true_model.weight_model.W
print "lambda0 true: ", true_model.bias_model.lambda0
print "ll true: ", true_model.log_likelihood()
print ""
print "W test: ", test_model.W
print "lambda0 test ", test_model.bias
print "ll test: ", test_model.log_likelihood()
plt.figure()
plt.plot(np.arange(N_steps), lls)
plt.xlabel("Iteration")
plt.ylabel("Log likelihood")
plot_network(np.ones((K,K)), test_model.W, vmax=0.5)
plt.show()
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)
def demo(seed=None):
"""
Suppose we have a very long recording such that computing gradients of
the log likelihood is quite expensive. Here we explore the use of
stochastic gradient descent to fit the standard Hawkes model, which has
a convex log likelihood. We first initialize the parameters using BFGS
on a manageable subset of the data. Then we use SGD to refine the parameters
on the entire dataset.
:return:
"""
if seed is None:
seed = np.random.randint(2**32)
print "Setting seed to ", seed
np.random.seed(seed)
C = 1 # Number of clusters in the true data
K = 10 # Number of nodes
T = 10000 # Number of time bins to simulate
dt = 1.0 # Time bin size
B = 3 # Number of basis functions
# Sample from the network Hawkes model
S, R, true_model = sample_from_network_hawkes(C, K, T, dt, B)
# Make a model to initialize the parameters
init_len = 256
init_model = DiscreteTimeStandardHawkesModel(K=K, dt=dt, B=B, beta=1.0)
init_model.add_data(S[:init_len, :])
print "Initializing with BFGS on first ", init_len, " time bins."
init_model.fit_with_bfgs()
# Make another model for inference
test_model = DiscreteTimeStandardHawkesModel(K=K, dt=dt, B=B, beta=1.0)
# Initialize with the BFGS parameters
test_model.weights = init_model.weights
# Add the data in minibatches
test_model.add_data(S, minibatchsize=256)
# Plot the true and inferred firing rate
kplt = 0
plt.figure()
plt.plot(np.arange(256), R[:256,kplt], '-k', lw=2)
plt.ion()
ln = plt.plot(np.arange(256), test_model.compute_rate(ks=kplt)[:256], '-r')[0]
plt.show()
# Gradient descent
N_steps = 10000
lls = []
learning_rate = 0.01 * np.ones(N_steps)
momentum = 0.8 * np.ones(N_steps)
prev_velocity = None
for itr in xrange(N_steps):
W,ll,prev_velocity = test_model.sgd_step(prev_velocity, learning_rate[itr], momentum[itr])
lls.append(ll)
# Update plot
if itr % 5 == 0:
ln.set_data(np.arange(256), test_model.compute_rate(ks=kplt))
plt.title("Iteration %d" % itr)
plt.pause(0.001)
plt.ioff()
print "W true: ", true_model.weight_model.A * true_model.weight_model.W
print "lambda0 true: ", true_model.bias_model.lambda0
print ""
print "W test: ", test_model.W
print "lambda0 test ", test_model.bias
plt.figure()
plt.plot(np.arange(N_steps), lls)
plt.xlabel("Iteration")
plt.ylabel("Log likelihood")
plot_network(np.ones((K,K)), test_model.W)
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 = 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'] = 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 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())
###########################################################
# Analyze the samples
###########################################################
analyze_samples(true_model, init_model, samples)
