def process(self, payload, distribution, topic_id):
terms = list(map(lambda id: self.tokens[id], payload))
term_entropies = compute_normalised_entropy(
distribution, base=2)
distribution = inference.aggregate_distribution(
distribution, mode='product', axis=0)
assert distribution.ndim == 1
distribution /= distribution.sum()
if not np.isclose(distribution.sum(), 1.0):
logging.error('Encountered non-normalized '
'distribution for topic "%s" '
'(mass=%.10f).',
topic_id, distribution.sum())
self.f_debug_out.write('Topic {0} {1}: {2}\n'.format(
topic_id,
math_utils.entropy(
distribution, base=2, normalize=True),
zip(terms, term_entropies)))
ranked_indices = np.argsort(distribution)
top_ranked_indices = ranked_indices[::-1]
top_ranked_values = distribution[top_ranked_indices]
self.rank_callback(topic_id, top_ranked_indices, top_ranked_values)
def process(self, payload, distribution, topic_id):
terms = list(map(lambda id: self.tokens[id], payload))
term_entropies = compute_normalised_entropy(distribution, base=2)
distribution = inference.aggregate_distribution(distribution,
mode='product',
axis=0)
assert distribution.ndim == 1
distribution /= distribution.sum()
if not np.isclose(distribution.sum(), 1.0):
logging.error(
'Encountered non-normalized '
'distribution for topic "%s" '
'(mass=%.10f).', topic_id, distribution.sum())
self.f_debug_out.write('Topic {0} {1}: {2}\n'.format(
topic_id, math_utils.entropy(distribution, base=2, normalize=True),
zip(terms, term_entropies)))
ranked_indices = np.argsort(distribution)
top_ranked_indices = ranked_indices[::-1]
top_ranked_values = distribution[top_ranked_indices]
self.rank_callback(topic_id, top_ranked_indices, top_ranked_values)
def process(self, payload, result, topic_id):
terms = list(map(lambda id: self.tokens[id], payload))
term_projections = inference.aggregate_distribution(result,
mode='identity',
axis=0)
if term_projections.ndim == 1:
term_projections = term_projections.reshape(1, -1)
_, entity_representation_size = term_projections.shape
assert (entity_representation_size ==
self.model_args.entity_representation_size)
if self.normalize_representations:
term_projections_l2_norm = \
np.linalg.norm(term_projections, axis=1)[:, np.newaxis]
term_projections /= term_projections_l2_norm
logging.debug('Querying kneighbors for %s.', terms)
distances, indices = self.query(term_projections)
assert indices.shape[0] == term_projections.shape[0]
candidates = collections.defaultdict(float)
assert indices.shape[0] == 1
for term in range(indices.shape[0]):
term_indices = indices[term, :]
for rank, candidate in enumerate(term_indices):
matching_score = np.sum(
self.entity_representations[candidate, :] *
term_projections[term, :])
if self.normalize_representations:
matching_score = (matching_score + 1.0) / 2.0
candidates[candidate] += matching_score
top_ranked_indices, top_ranked_values = \
map(np.array, zip(
*sorted(candidates.items(),
reverse=True,
key=operator.itemgetter(1))))
self.rank_callback(topic_id, top_ranked_indices, top_ranked_values)
def process(self, payload, result, topic_id):
terms = list(map(lambda id: self.tokens[id], payload))
term_projections = inference.aggregate_distribution(
result, mode='identity', axis=0)
if term_projections.ndim == 1:
term_projections = term_projections.reshape(1, -1)
_, entity_representation_size = term_projections.shape
assert(entity_representation_size ==
self.model_args.entity_representation_size)
if self.normalize_representations:
term_projections_l2_norm = \
np.linalg.norm(term_projections, axis=1)[:, np.newaxis]
term_projections /= term_projections_l2_norm
logging.debug('Querying kneighbors for %s.', terms)
distances, indices = self.query(term_projections)
assert indices.shape[0] == term_projections.shape[0]
candidates = collections.defaultdict(float)
assert indices.shape[0] == 1
for term in range(indices.shape[0]):
term_indices = indices[term, :]
for rank, candidate in enumerate(term_indices):
matching_score = np.sum(
self.entity_representations[candidate, :] *
term_projections[term, :])
if self.normalize_representations:
matching_score = (matching_score + 1.0) / 2.0
candidates[candidate] += matching_score
top_ranked_indices, top_ranked_values = \
map(np.array, zip(
*sorted(candidates.items(),
reverse=True,
key=operator.itemgetter(1))))
self.rank_callback(topic_id, top_ranked_indices, top_ranked_values)
