def __init__(self,
kernel_support=None,
kernel_size=10,
kernel_discretization=None,
tol=1e-5,
max_iter=100,
print_every=10,
record_every=10,
verbose=False,
n_threads=1):
LearnerHawkesNoParam.__init__(self,
n_threads=n_threads,
verbose=verbose,
tol=tol,
max_iter=max_iter,
print_every=print_every,
record_every=record_every)
if kernel_discretization is not None:
self._learner = _HawkesEM(kernel_discretization, n_threads)
elif kernel_support is not None:
self._learner = _HawkesEM(kernel_support, kernel_size, n_threads)
else:
raise ValueError('Either kernel support or kernel discretization '
'must be provided')
self.baseline = None
self.kernel = None
self.history.print_order = ["n_iter", "rel_baseline", "rel_kernel"]
def __init__(self, max_mean_gaussian, n_gaussians=5, step_size=1e-7,
C=1e3, lasso_grouplasso_ratio=0.5, max_iter=50,
tol=1e-5, n_threads=1, verbose=False, print_every=10,
record_every=10, approx=0, em_max_iter=30,
em_tol=None):
LearnerHawkesNoParam.__init__(self, verbose=verbose, max_iter=max_iter,
print_every=print_every, tol=tol,
n_threads=n_threads,
record_every=record_every)
self.baseline = None
self.amplitudes = None
self.n_gaussians = n_gaussians
self.max_mean_gaussian = max_mean_gaussian
self.step_size = step_size
strength_lasso = lasso_grouplasso_ratio / C
strength_grouplasso = (1. - lasso_grouplasso_ratio) / C
self.em_max_iter = em_max_iter
self.em_tol = em_tol
self._learner = _HawkesSumGaussians(
n_gaussians, max_mean_gaussian, step_size, strength_lasso,
strength_grouplasso, em_max_iter, n_threads, approx)
self.verbose = verbose
self.history.print_order += ["rel_baseline", "rel_amplitudes"]
def __init__(self, integration_support, C=1e3, penalty='none',
solver='adam', step=1e-2, tol=1e-8, max_iter=1000,
verbose=False, print_every=100, record_every=10,
solver_kwargs=None, cs_ratio=None, elastic_net_ratio=0.95):
try:
import tensorflow
except ImportError:
raise ImportError('`tensorflow` >= 1.4.0 must be available to use '
'HawkesCumulantMatching')
self._tf_graph = tf.Graph()
LearnerHawkesNoParam.__init__(
self, tol=tol, verbose=verbose, max_iter=max_iter,
print_every=print_every, record_every=record_every)
self._elastic_net_ratio = None
self.C = C
self.penalty = penalty
self.elastic_net_ratio = elastic_net_ratio
self.step = step
self.cs_ratio = cs_ratio
self.solver_kwargs = solver_kwargs
if self.solver_kwargs is None:
self.solver_kwargs = {}
self._cumulant_computer = _HawkesCumulantComputer(
integration_support=integration_support)
self._learner = self._cumulant_computer._learner
self._solver = solver
self._tf_feed_dict = None
self._events_of_cumulants = None
self.history.print_order = ["n_iter", "objective", "rel_obj"]
def __init__(self, decay, C=1e3, lasso_nuclear_ratio=0.5, max_iter=50,
tol=1e-5, n_threads=1, verbose=False, print_every=10,
record_every=10, rho=.1, approx=0, em_max_iter=30,
em_tol=None):
LearnerHawkesNoParam.__init__(
self, verbose=verbose, max_iter=max_iter, print_every=print_every,
tol=tol, n_threads=n_threads, record_every=record_every)
self.baseline = None
self.adjacency = None
self._C = 0
self._lasso_nuclear_ratio = 0
self.decay = decay
self.rho = rho
self._prox_l1 = ProxL1(1.)
self._prox_nuclear = ProxNuclear(1.)
self.C = C
self.lasso_nuclear_ratio = lasso_nuclear_ratio
self.verbose = verbose
self.em_max_iter = em_max_iter
self.em_tol = em_tol
self._learner = _HawkesADM4(decay, rho, n_threads, approx)
# TODO add approx to model
self._model = ModelHawkesExpKernLogLik(self.decay,
n_threads=self.n_threads)
self.history.print_order += ["rel_baseline", "rel_adjacency"]
def __init__(self, kernel_support, n_basis=None, kernel_size=10, tol=1e-5,
C=1e-1, max_iter=100, verbose=False, print_every=10,
record_every=10, n_threads=1, ode_max_iter=100, ode_tol=1e-5):
LearnerHawkesNoParam.__init__(self, max_iter=max_iter, verbose=verbose,
tol=tol, print_every=print_every,
record_every=record_every,
n_threads=n_threads)
self.ode_max_iter = ode_max_iter
self.ode_tol = ode_tol
alpha = 1. / C
if n_basis is None:
n_basis = 0
self._learner = _HawkesBasisKernels(kernel_support, kernel_size,
n_basis, alpha, n_threads)
self._amplitudes_2d = None
self.history.print_order = [
"n_iter", "rel_baseline", "rel_amplitudes", "rel_basis_kernels"
]