def execute(self):
logging.info("started extracting word_embeddings feature generator:")
for counter, target_author_word_embeddings_dict in enumerate(
self._targeted_author_word_embeddings):
targeted_table = target_author_word_embeddings_dict["table_name"]
targeted_field_name = target_author_word_embeddings_dict[
"targeted_field_name"]
targeted_word_embedding_type = target_author_word_embeddings_dict[
"word_embedding_type"]
targeted_word_embeddings_combination = targeted_table + "_" + targeted_field_name + "_" + targeted_word_embedding_type
logging.info("currently extracting features of " +
targeted_word_embeddings_combination + ": " +
str(counter + 1) + " out of " +
str(len(self._targeted_author_word_embeddings)))
author_guid_word_embeding_dict = self.load_author_guid_word_embedding_dict(
targeted_field_name, targeted_table,
targeted_word_embedding_type)
Vector_Operations.create_features_from_word_embedding_dict(
author_guid_word_embeding_dict, targeted_table,
targeted_field_name, targeted_word_embedding_type,
self._word_embedding_table_name, self._window_start,
self._window_end, self._db, self._max_objects_without_saving,
self.__class__.__name__ + '_')
def execute(self):
logging.info("started extracting word_embbeddings feature generator:")
counter = 0
authors_features = []
for target_author_word_embeddings_dict in self._targeted_author_word_embeddings:
counter += 1
targeted_table = target_author_word_embeddings_dict["table_name"]
targeted_field_name = target_author_word_embeddings_dict[
"targeted_field_name"]
targeted_word_embedding_type = target_author_word_embeddings_dict[
"word_embedding_type"]
targeted_word_embeddings_combination = targeted_table + "_" + targeted_field_name + "_" + targeted_word_embedding_type
logging.info("currently extracting features of " +
targeted_word_embeddings_combination + ": " +
str(counter) + " out of " +
str(len(self._targeted_author_word_embeddings)))
author_guid_word_embeding_dict = self._db.get_author_guid_word_embedding_vector_dict(
targeted_table, targeted_field_name,
targeted_word_embedding_type)
Vector_Operations.create_features_from_word_embedding_dict(
author_guid_word_embeding_dict, targeted_table,
targeted_field_name, targeted_word_embedding_type,
self._window_start, self._window_end, self._db,
self._max_objects_without_saving)