def _prior_knowledge(self):
"""Create prior knowledge from arguments."""
self.priors_from_cli()
prior_indices, prior_labels = _merge_prior_knowledge(
self.prior_included, self.prior_excluded)
return np.array(prior_indices, dtype=np.int), np.array(prior_labels, dtype=np.int)
def _prior_knowledge(self, logger):
"""Get the prior knowledge, either from specific paper IDs,
and if they're not given from the number of in/exclusions."""
if self.prior_included is not None or self.prior_excluded is not None:
prior_indices, prior_labels = _merge_prior_knowledge(
self.prior_included, self.prior_excluded)
else:
# Create the prior knowledge
init_ind = sample_prior_knowledge(
self.y,
n_prior_included=self.n_prior_included,
n_prior_excluded=self.n_prior_excluded,
random_state=None # TODO
)
prior_indices, prior_labels = init_ind, self.y[init_ind, ]
self.classify(prior_indices, prior_labels, logger, method="initial")
def _prior_knowledge(self):
""" Get the prior knowledge, either from specific paper IDs,
and if they're not given from the number of in/exclusions. """
if self.prior_included or self.prior_excluded:
prior_indices, prior_labels = _merge_prior_knowledge(
self.prior_included, self.prior_excluded)
return prior_indices, prior_labels
# Create the prior knowledge
init_ind = sample_prior_knowledge(
self.y,
n_prior_included=self.n_prior_included,
n_prior_excluded=self.n_prior_excluded,
random_state=None # TODO
)
return init_ind, self.y[init_ind, ]
def _prior_knowledge(self):
if self.prior_included and self.prior_excluded:
prior_indices, prior_labels = _merge_prior_knowledge(
self.prior_included, self.prior_excluded)
return prior_indices, prior_labels
elif self.n_prior_included and self.n_prior_excluded:
# Create the prior knowledge
init_ind = sample_prior_knowledge(
self.y,
n_prior_included=self.n_prior_included,
n_prior_excluded=self.n_prior_excluded,
random_state=None # TODO
)
return init_ind, self.y[init_ind, ]
else:
raise ValueError("provide both prior_included and prior_excluded, "
"or n_prior_included and n_prior_excluded")