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 = pickle.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"))
pickle.dump((init_model, init_time), f, protocol=-1)
return init_model, init_time
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 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 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
