def test():
# This piece of code was used to print index of the external entity that produces the best candidate.
for dataset in datasets:
queries = get_evaluated_queries(dataset, True, parameters)
for index, query in enumerate(queries):
external_entities = dict()
max_f1 = 0
for candidate in query.eval_candidates:
f1 = candidate.evaluation_result.f1
max_f1 = max(max_f1, f1)
for entity in candidate.query_candidate.matched_entities:
if entity.entity.external_entity:
count = entity.entity.external_entity_count
if entity.entity.name not in external_entities:
external_entities[entity.entity.name] = (count, f1)
if external_entities[entity.entity.name][1] < f1:
external_entities[entity.entity.name] = (count, f1)
external_entities = [(entry[0], entry[1][1]) for entry in sorted(external_entities.iteritems(), key=lambda e: e[1][0], reverse=True)]
if external_entities:
max_external_f1 = max(f1 for _, f1 in external_entities)
if max_external_f1 == max_f1:
position = [f1 for _, f1 in external_entities].index(max_external_f1)
positions.append((query.utterance, external_entities[position][0], position))
print "\n".join(map(str, positions))
positions = [pos for q, n, pos in positions]
print positions, min(positions), avg(positions), max(positions)
def extend_candidates(self, candidates):
"""
Extend the set of candidates with addition query candidates, which can be based on existing candidates.
For example, one way to extend is to add candidates containing additional filters, e.g. by notable type.
:param candidates: A set of candidates to extend.
:return: A new set of candidates.
"""
extra_candidates = []
add_types_filters = \
globals.config.get('QueryCandidateExtender', 'add-notable-types-filter-templates', '') == "True"
if add_types_filters and self.ngram_notable_types_npmi and candidates:
for candidate in candidates:
n_grams = set(get_n_grams_features(candidate))
notable_types = set(notable_type for notable_type in candidate.get_answer_notable_types()
if notable_type)
if len(notable_types) > 1:
notable_type_scores = dict()
for n_gram, notable_type in product(n_grams, notable_types):
pair = (n_gram, notable_type)
if notable_type not in notable_type_scores:
notable_type_scores[notable_type] = []
notable_type_scores[notable_type].append((n_gram, self.ngram_notable_types_npmi[pair])
if pair in self.ngram_notable_types_npmi
else ("", 0.0))
for notable_type, ngram_scores in notable_type_scores.iteritems():
scores = [score for ngram, score in ngram_scores]
max_score = max(scores)
if max_score > self.notable_types_npmi_threshold:
avg_score = avg(scores)
logger.info("Extending candidate with type filter:")
logger.info(candidate)
logger.info(notable_type)
logger.info(ngram_scores)
new_query_candidate = copy.deepcopy(candidate)
new_query_candidate.filter_answers_by_type(notable_type,
[max_score, avg_score])
extra_candidates.append(new_query_candidate)
logger.info(candidate.get_results_text())
logger.info(new_query_candidate.get_results_text())
return candidates + extra_candidates
def generate_text_based_features(candidate):
# Get candidate answers
answers = map(unicode.lower, candidate.get_results_text())
# Skip empty and extra-long answers.
if len(answers) == 0:
return dict()
# Get answers descriptions.
answers_descriptions = ['\n'.join(KBEntity.get_entity_descriptions_by_name(answer, keep_most_triples_only=True))
for answer in answers]
# Get question text.
question_text = candidate.query.original_query
question_tokens2pos = dict((token, [1, ]) for token in tokenize(question_text))
question_token_tfidf = SparseVector.from_2pos(question_tokens2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements)
# Get question entities
question_entities2pos = dict((entity.entity.name.lower(), [1, ]) for entity in candidate.matched_entities)
question_entitytoken2pos = dict((token, [1, ])
for entity in candidate.matched_entities
for token in tokenize(entity.entity.name))
question_entity_tfidf = SparseVector.from_2pos(question_entitytoken2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements)
# Get search results and check that they aren't empty
questions_search_results = get_questions_serps()
documents_vectors = []
snippets_vectors = []
fragment_vectors = []
combined_documents_vector = dict()
combined_document_snippets_vector = dict()
representations = ["entity_tfidf",
"token_tfidf",
# "entity",
# "token",
]
for r in representations:
combined_documents_vector[r] = dict()
combined_document_snippets_vector[r] = dict()
if question_text not in questions_search_results:
logger.warning("No search results found for the question %s" % question_text)
else:
documents_vectors, snippets_vectors, fragment_vectors, combined_documents_vector,\
combined_document_snippets_vector = generate_document_vectors(question_text,
question_tokens2pos,
questions_search_results)
answer_entity2pos = dict((answer_entity, [1, ]) for answer_entity in answers)
answer_token2pos = dict((answer_token, [1, ]) for answer_entity in answers
for answer_token in tokenize(answer_entity))
answers_vectors = {
"token_tfidf": SparseVector.from_2pos(answer_token2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements),
"entity_tfidf": SparseVector.from_2pos(answer_entity2pos,
element_calc_func=SparseVector.compute_tfidf_entity_elements),
# "entity": SparseVector.from_2pos(answer_entity2pos),
# "token": SparseVector.from_2pos(answer_token2pos),
}
answer_descriptions_token2pos = dict((token, [1, ]) for description in answers_descriptions
for token in tokenize(description))
answer_description_vectors = {
"token_tfidf": SparseVector.from_2pos(answer_descriptions_token2pos,
element_calc_func=SparseVector.compute_tfidf_token_elements),
"entity_tfidf": SparseVector(dict()),
# Keeping only tf-idf similarities. This seems to be enough.
# "token": SparseVector.from_2pos(answer_descriptions_token2pos),
# "entity": SparseVector(dict()),
}
similarity_functions = [
("cosine", Similarity.cosine_similarity),
# ("itersection", Similarity.intersection_similarity),
# ("normalized_intersection", Similarity.normalized_intersection_similarity),
# ("bm25", Similarity.bm25_similarity),
]
features = dict()
for similarity_name, similarity in similarity_functions:
# Computing document-answer similarities for each representation.
document_answer_similarities = {}
for representation in representations:
if representation not in document_answer_similarities:
document_answer_similarities[representation] = []
for doc_vector in documents_vectors:
document_answer_similarities[representation].append(similarity(representation,
doc_vector[representation],
answers_vectors[representation]))
for representation in representations:
features.update({
"text_features:avg_document_answer_%s_%s" % (representation, similarity_name):
avg(document_answer_similarities[representation]),
"text_features:max_document_answer_%s_%s" % (representation, similarity_name):
max(document_answer_similarities[representation]) if document_answer_similarities[representation]
else 0.0,
})
# logger.info("Snippet-answer similarity...")
# Computing snippet-answer similarities for each representation.
snippet_answer_similarities = {}
for representation in representations:
if representation not in snippet_answer_similarities:
snippet_answer_similarities[representation] = []
for snippet_vector in snippets_vectors:
snippet_answer_similarities[representation].append(similarity(representation,
snippet_vector[representation],
answers_vectors[representation]))
for representation in representations:
features.update({
"text_features:avg_snippet_answer_%s_%s" % (representation, similarity_name):
avg(snippet_answer_similarities[representation]),
"text_features:max_snippet_answer_%s_%s" % (representation, similarity_name):
max(snippet_answer_similarities[representation]) if snippet_answer_similarities[representation] else 0.0,
})
# logger.info("Fragment-answer similarity...")
# Best BM25 fragment-answer similarities.
# Weren't very efficient and therefore I remove this features. There is a chance that there is a bug in the features.
# fragment_answer_similarities = {}
# for fragment_vector in fragment_vectors:
# for representation in representations:
# if representation not in fragment_answer_similarities:
# fragment_answer_similarities[representation] = []
# fragment_answer_similarities[representation].append(similarity(representation,
# fragment_vector[representation],
# answers_vectors[representation]))
#
# for representation in representations:
# features.update({
# "text_features:avg_fragment_answer_%s_%s" % (representation, similarity_name):
# avg(fragment_answer_similarities[representation]),
# "text_features:max_fragment_answer_%s_%s" % (representation, similarity_name):
# max(fragment_answer_similarities[representation]) if fragment_answer_similarities[representation] else 0.0,
# })
# logger.info("Combined document-answer similarity...")
# Combined documents answer similarity
for representation in representations:
combineddoc_answer_similarity = similarity(representation,
combined_documents_vector[representation],
answers_vectors[representation])
features.update({
"text_features:combdocument_answer_%s_%s" % (representation, similarity_name):
combineddoc_answer_similarity,
})
# logger.info("Combined snippet-answer similarity...")
for representation in representations:
combineddocsnippet_answer_similarity = similarity(representation,
combined_document_snippets_vector[representation],
answers_vectors[representation])
features.update({
"text_features:combdocument_snippet_answer_%s_%s" % (representation, similarity_name):
combineddocsnippet_answer_similarity,
})
# logger.info("Description-question similarity...")
# These features aren't very efficient either. The next candidate for removal.
description_question_entity_similarity = similarity("token_tfidf", question_entity_tfidf,
answer_description_vectors["token_tfidf"])
description_question_token_similarity = similarity("token_tfidf", question_token_tfidf,
answer_description_vectors["token_tfidf"])
features.update({
"text_features:description_question_entitytoken_%s" % similarity_name:
description_question_entity_similarity,
"text_features:description_question_token_%s" % similarity_name: description_question_token_similarity,
})
# Description - question embedding similarity.
description_question_token_embedding_avg_similarity = Similarity.embedding_avg_idf_similarity(
"token_tfidf", question_token_tfidf, answer_description_vectors["token_tfidf"])
description_question_token_embedding_n_similarity = Similarity.embedding_avg_idf_similarity(
"token_tfidf", question_token_tfidf, answer_description_vectors["token_tfidf"])
features.update({
"text_features:description_question_token_avg_idf_embeddings":
description_question_token_embedding_avg_similarity,
"text_features:description_question_token_n_embeddings":
description_question_token_embedding_n_similarity,
})
# Remove features with 0 score.
features = dict((feature, value) for feature, value in features.iteritems() if value != 0.0)
return features
