def start_fix_exist_aliases_for_aliases_type(self, alias_type): pass self.logger = Logger(self.logger_file_name).get_log() self.logger.info("-----------------------start-----------------------") self.logger.info("generate api aliases for alias type=%d", alias_type) session = self.get_session()
def start_import(self, graphClient): self.logger = Logger(self.logger_file_name).get_log() if not self.session: self.session = EngineFactory.create_session() self.graphClient = graphClient all_relation_list = self.session.query(APIRelation).all() for api_relation in all_relation_list: self.import_one_relation(api_relation) print("import api entity relation complete")
def start_import(self, graphClient): self.logger = Logger(self.logger_file_name).get_log() if not self.session: self.session = EngineFactory.create_session() self.graphClient = graphClient all_apis = self.session.query(APIEntity).all() for api_entity in all_apis: self.import_one_api_entity(api_entity) print("import api entity complete")
def start_import(self, graphClient): self.logger = Logger(self.logger_file_name).get_log() if not self.session: self.session = EngineFactory.create_session() self.graphClient = graphClient all_apis = self.session.query(APIEntity).all() for api_entity in all_apis: api_id = api_entity.id api_document_website_list = APIDocumentWebsite.get_document_website_list_by_api_id( self.session, api_id) self.import_document_website_to_one_entity( api_id, api_document_website_list) print("import api doc url complete")
def init(self, vector_dir_path="./model/"): self.kg_models = KnowledgeGraphFeafureModels() self.kg_models.init(vector_dir_path=vector_dir_path) self._session = EngineFactory.create_session(echo=False) self._entity_extractor = EntityExtractor() # self._tf_idf_model = TFIDFModel() # self._tf_idf_model.load(dict_type=2) self.qa_searcher = QAEntitySearcher() client = GraphClient(server_number=4) self.semanticSearchAccessor = SemanticSearchAccessor(client) self.defaultAccessor = DefaultGraphAccessor(client) self._logger = Logger("QAResultSearch").get_log()
def __init__(self, sentence_list, logger=None): self._api_searcher = APISearcher() if logger is None: self.logger = Logger("TreeView").get_log() else: self.logger = logger self._tree = self.get_api_tree_from_sentence_list(sentence_list)
def start_import(self): self.logger = Logger(self.logger_file_name).get_log() if not self.session: self.session = EngineFactory.create_session() self.init_knowledge_table() cur = ConnectionFactory.create_cursor_by_knowledge_table( self.data_source_knowledge_table) select_sql = "select {primary_key_name},{html_column} from {table}".format( primary_key_name=self.primary_key_name, html_column=self.html_column, table=self.table) cur.execute(select_sql) data_list = cur.fetchall() result_tuples = [] for i in range(0, len(data_list)): row_data = data_list[i] primary_key = row_data[0] html_text = row_data[1] if KnowledgeTableColumnMapRecord.exist_import_record( session=self.session, start_knowledge_table=self.data_source_knowledge_table, end_knowledge_table=self.api_html_table, start_row_id=primary_key, start_row_name=self.html_column): self.logger.info("%d has been import to new table", primary_key) continue api_html_text = self.create_from_one_row_data( primary_key, html_text) if api_html_text: api_html_text = api_html_text.create(self.session, autocommit=False) result_tuples.append((api_html_text, primary_key)) else: self.logger.warn("None api_html_text fot %s", str(row_data)) continue if len(result_tuples) > self.commit_step: self.commit_to_server_for_column_map(map_tuples=result_tuples) result_tuples = [] self.commit_to_server_for_column_map(map_tuples=result_tuples) self.logger.info("import api html completed!") cur.close()
def start_generate_aliases_for_api_type(self, alias_type, api_type=ALL_API): self.logger = Logger(self.logger_file_name).get_log() self.logger.info("-----------------------start-----------------------") self.logger.info("generate api aliases for api type=%d alias type=%d", api_type, alias_type) session = self.get_session() api_alias_generator = APIAliasGeneratorFactory.create_generator( alias_type) if api_alias_generator is None: self.logger.error("Not Implemented Generator for %d ", alias_type) return if api_type == APIAliasesTableFuller.ALL_API: count_api = session.query(APIEntity).count() api_list_query = session.query(APIEntity) else: count_api = session.query(APIEntity).filter( APIEntity.api_type == api_type).count() api_list_query = session.query(APIEntity).filter( APIEntity.api_type == api_type) start_index_list = range(0, count_api, self.commit_step) for start_index in start_index_list: end_index = min(start_index + self.commit_step, count_api) for api in api_list_query.all()[start_index:end_index]: try: api_alias_list = api_alias_generator.generate_aliases(api) self.add_aliases_to_api_entity(api, api_alias_list, session) except Exception: traceback.print_exc() self.logger.info("complete %d-%d", start_index, end_index) session.commit() self.logger.info("complete all")
class APIRelationImporter: def __init__(self): self.logger_file_name = "import_api_relation_to_neo4j" self.logger = None self.graphClient = None self.session = None def start_import(self, graphClient): self.logger = Logger(self.logger_file_name).get_log() if not self.session: self.session = EngineFactory.create_session() self.graphClient = graphClient all_relation_list = self.session.query(APIRelation).all() for api_relation in all_relation_list: self.import_one_relation(api_relation) print("import api entity relation complete") def import_one_relation(self, api_relation): if api_relation is not None: relation_type = APIRelation.get_type_string( api_relation.relation_type) start_api_id = api_relation.start_api_id end_api_id = api_relation.end_api_id start_node = self.graphClient.find_node_by_api_id(start_api_id) end_node = self.graphClient.find_node_by_api_id(end_api_id) if start_node is not None and end_node is not None: if relation_type == "belong to": relation_type = self.transfer_belong_to_type(start_api_id) relationship = Relationship(end_node, relation_type, start_node) self.graphClient.graph.merge(relationship) else: relationship = Relationship(start_node, relation_type, end_node) self.graphClient.graph.merge(relationship) self.logger.info("create or merge relation" + str(relationship)) else: self.logger.warn( "fail create relation because start node or end node is none." ) else: self.logger.warn( "fail create relation because api relation is none.") def transfer_belong_to_type(self, start_api_id): if start_api_id is not None: start_api_entity = APIEntity.find_by_id(self.session, start_api_id) if start_api_entity is not None: start_api_type = start_api_entity.api_type type_str = APIEntity.get_simple_type_string(start_api_type) type_str = type_str.replace("api", "") relation_str = "has" + type_str return relation_str return None
def init_importer(self, client=None): if client is None: return self.logger = Logger(self.logger_file_name).get_log() self.wikidata_accessor = WikiDataGraphAccessor(client) property_name_dict = {} session = EngineFactory.create_session() wikidata_property_list = session.query( WikiDataProperty.wd_item_id, WikiDataProperty.property_name).all() for wikipedia_property in wikidata_property_list: property_name_dict[wikipedia_property. wd_item_id] = wikipedia_property.property_name print("load all property name") self.wiki_creator = WikiDataNodeCreator( wikidata_graph_accessor=self.wikidata_accessor, init_property_from_file=False) self.wiki_creator.init_property_clean_Util( property_name_dict=property_name_dict) print("init wiki_creator")
class APIDocumentWebsiteImporter: def __init__(self): self.logger_file_name = "import_api_document_website_to_neo4j" self.logger = None self.graphClient = None self.session = None def start_import(self, graphClient): self.logger = Logger(self.logger_file_name).get_log() if not self.session: self.session = EngineFactory.create_session() self.graphClient = graphClient all_apis = self.session.query(APIEntity).all() for api_entity in all_apis: api_id = api_entity.id api_document_website_list = APIDocumentWebsite.get_document_website_list_by_api_id( self.session, api_id) self.import_document_website_to_one_entity( api_id, api_document_website_list) print("import api doc url complete") def import_document_website_to_one_entity(self, api_id, api_document_website_list): if api_id is not None and api_document_website_list is not None: node = self.graphClient.find_node_by_api_id(api_id) if node is not None: index = 1 for each in api_document_website_list: website = each[0] key = "api_document_website#" + str(index) index += 1 if key not in dict(node).keys(): node[key] = website self.graphClient.push_node(node) self.logger.info("add document website property " + str(node)) else: self.logger.warn( "fail to add document property because node is none") else: self.logger.warn( "fail to add document property because api id is none")
class AllAPIEntityImporter: def __init__(self): self.logger_file_name = "import_api_entity_to_neo4j" self.logger = None self.graphClient = None self.session = None def start_import(self, graphClient): self.logger = Logger(self.logger_file_name).get_log() if not self.session: self.session = EngineFactory.create_session() self.graphClient = graphClient all_apis = self.session.query(APIEntity).all() for api_entity in all_apis: self.import_one_api_entity(api_entity) print("import api entity complete") def import_one_api_entity(self, api_entity): api_type = APIEntity.get_simple_type_string(api_entity.api_type) print(api_type) property_dict = api_entity.__dict__ property_dict.pop("_sa_instance_state") property_dict["api_id"] = property_dict.pop("id") builder = NodeBuilder() builder.add_entity_label().add_property( **property_dict).add_api_label().add_label(api_type) node = builder.build() # when the node's qualifier name is "byte","int", the print will cause ValueError: Invalid identifier error # print node try: self.graphClient.create_or_update_api_node(node=node) self.logger.info('create node for api entity %s', property_dict['api_id']) except Exception, error: self.logger.warn('-%s- fail for create node for api entity ', property_dict['api_id']) self.logger.exception('this is an exception message')
from db.engine_factory import ConnectionFactory, EngineFactory from db.model import KnowledgeTableRowMapRecord, APIRelation, KnowledgeTableColumnMapRecord from db.model_factory import KnowledgeTableFactory from shared.logger_util import Logger logger = Logger("import_belong_to_relation_for_jdk_method").get_log() cur = ConnectionFactory.create_cursor_for_jdk_importer() session = EngineFactory.create_session() jdk_method_knowledge_table = KnowledgeTableFactory.get_jdk_method_table( session) jdk_class_knowledge_table = KnowledgeTableFactory.get_jdk_class_table(session) api_knowledge_table = KnowledgeTableFactory.get_api_entity_table(session) api_relation_table = KnowledgeTableFactory.get_api_relation_table(session) COMMIT_STEP = 5000 def is_imported(row_id): if KnowledgeTableColumnMapRecord.exist_import_record( session, jdk_method_knowledge_table, api_relation_table, row_id, "class_id"): return True else: return False def create_method_belong_to_relation(old_method_id, old_class_id): logger.info("old_method_id=%d old_class_id=%d", old_method_id,
from shared.logger_util import Logger from skgraph.graph.accessor.graph_accessor import GraphAccessor _logger = Logger("QAGraphAccessor").get_log() class QAGraphAccessor(GraphAccessor): def entity_linking_by_fulltext_search(self, name, top_number=50): """ linking a name to a node :param name: the name search :param top_number: the top number of search result,defalt is top 10 :return value is a list,each element is a dict d, get the node from d['node'],get the score of the result from d['weight'] """ try: query = "call apoc.index.search('entity', '{name}', {top_number}) YIELD node, weight return node,weight" query = query.format(name=name, top_number=top_number) record_list = self.graph.run(query) result_tuple = [] for record in record_list: result_tuple.append({ "node": record["node"], "weight": record["weight"] }) return result_tuple except Exception, error: _logger.exception("parameters=%r", name) return []
import codecs import json import os import random from WikiDataSPARQLWrapper import WikiDataSPARQLWrapper from WikiDataEntityIDStorage import WikiDataEntityIDStorage from shared.logger_util import Logger _logger = Logger(logger="wikidata_item_id_selector").get_log() class WikiDataItemSelector: wikiDataEntityIDStorage = WikiDataEntityIDStorage() seed_property_id_set = set(['P31', 'P279', 'P361', ]) ''' 'P31', # instance of 'P279', # subclass of 'P361', # part of ''' def __init__(self): self.logger = _logger self.wikiDataEntityIDStorage.load() start_deny_id_list_path = os.path.join('.', 'start_deny_id_list.txt') if os.path.isfile(start_deny_id_list_path): print "start deny id file exist" self.seed_deny_id_set = set(json.load(codecs.open(start_deny_id_list_path, 'r', 'utf-8'))) else: print "start deny id file not exist"
class SentenceLevelSemanticSearch: SORT_FUNCTION_ENTITIES_BRIDGE = 3 SORT_FUNCTION_AVERAGE_ENTITY_GRAPH_SIMILAR = 4 SORT_FUNCTION_AVERAGE_VECTOR = 2 SORT_FUNCTION_NOT_AVERAGE_GRAPH_VECTOR = 1 SORT_FUNCTION_SELECT_PART_ENTITY_LINK = 5 def __init__(self, ): self._session = None self.kg_models = None self._entity_extractor = None # self._tf_idf_model = None self.qa_searcher = None self.semanticSearchAccessor = None self.defaultAccessor = None self._logger = None def init(self, vector_dir_path="./model/"): self.kg_models = KnowledgeGraphFeafureModels() self.kg_models.init(vector_dir_path=vector_dir_path) self._session = EngineFactory.create_session(echo=False) self._entity_extractor = EntityExtractor() # self._tf_idf_model = TFIDFModel() # self._tf_idf_model.load(dict_type=2) self.qa_searcher = QAEntitySearcher() client = GraphClient(server_number=4) self.semanticSearchAccessor = SemanticSearchAccessor(client) self.defaultAccessor = DefaultGraphAccessor(client) self._logger = Logger("QAResultSearch").get_log() def semantic_search(self, query_text, each_np_candidate_entity_num=50, sort_function=SORT_FUNCTION_SELECT_PART_ENTITY_LINK, sentence_limit=20, weight_context_sim=0.6, weight_graph_sim=0.4, ): try: qa_info_manager = self.get_candidate_sentences(query_text, each_np_candidate_entity_num=each_np_candidate_entity_num) # sentence_list=qa_info_manager.get_candidate_sentence_list() # # entity_for_qa_set # entity_for_qa_set.print_informat() # entity_list = entity_for_qa_set.get_entity_node_list() # chunk_to_related_entity_list_map = entity_for_qa_set.keyword_2_entitynodemap self._logger.info("entity_list =%d sentence_list=%d" % ( qa_info_manager.get_entity_size(), qa_info_manager.get_sentence_size())) # for n in entity_list: # print("entity", n) new_sentence_list = [] # if sort_function == SentenceLevelSemanticSearch.SORT_FUNCTION_NOT_AVERAGE_GRAPH_VECTOR: # new_sentence_list = self.sort_sentence_by_build_graph_vector_for_query_in_semantic_weight(query_text, # sentence_list=sentence_list, # entity_list=entity_list, # weight_context_sim=weight_context_sim, # weight_graph_sim=weight_graph_sim) # # if sort_function == SentenceLevelSemanticSearch.SORT_FUNCTION_AVERAGE_VECTOR: # new_sentence_list = self.sort_sentence_by_build_average_graph_vector_for_query(query_text, # sentence_list=sentence_list, # entity_list=entity_list, # weight_context_sim=weight_context_sim, # weight_graph_sim=weight_graph_sim # ) # # if sort_function == SentenceLevelSemanticSearch.SORT_FUNCTION_ENTITIES_BRIDGE: # new_sentence_list = self.sort_sentence_by_entities_as_bridge(query_text, # sentence_list=sentence_list, # entity_list=entity_list, # weight_context_sim=weight_context_sim, # weight_graph_sim=weight_graph_sim) # # if sort_function == SentenceLevelSemanticSearch.SORT_FUNCTION_AVERAGE_ENTITY_GRAPH_SIMILAR: # new_sentence_list = self.sort_sentence_by_entities_for_graph_similarity_as_bridge(query_text, # sentence_list=sentence_list, # entity_list=entity_list, # weight_context_sim=weight_context_sim, # weight_graph_sim=weight_graph_sim) if sort_function == SentenceLevelSemanticSearch.SORT_FUNCTION_SELECT_PART_ENTITY_LINK: new_sentence_list = self.sort_sentence_by_select_part_entity_as_bridge(query_text, qa_info_manager=qa_info_manager, weight_context_sim=weight_context_sim, weight_graph_sim=weight_graph_sim, ) result_list = qa_info_manager.fill_api_id_in_result_list(new_sentence_list[:sentence_limit]) self._logger.info("result_list =%d " % len(result_list)) return result_list except Exception: self._logger.exception("----qaexception----") traceback.print_exc() return [] def get_candidate_sentences(self, query_text, each_np_candidate_entity_num=20): chunk_list = self.get_chunk_from_text(query_text) print("chunk num=%d %s" % (len(chunk_list), ",".join(chunk_list))) qa_info_manager = self.search_entity_by_fulltext(chunk_list, each_np_candidate_entity_num) qa_info_manager.start_create_node_info_collection() print("related entity for qa", qa_info_manager) entity_for_qa_list = qa_info_manager.get_all_entity_for_qa_list() print("entity_for_qa_list num=%d" % len(entity_for_qa_list)) sentence_list = self.search_sentence_by_entity_for_qa_list(entity_for_qa_list) print("sentence_list num=%d" % len(sentence_list)) qa_info_manager.add_sentence_node_list(sentence_list) return qa_info_manager def expand_the_chunk_by_words(self, final_chunk_list): final_set = [] for chunk in final_chunk_list: final_set.append(chunk) for word in chunk.split(" "): final_set.append(word) print("word set", final_set) return list(set(final_set)) def get_chunk_from_text(self, text): final_chunk_list = self._entity_extractor.get_all_possible_key_word_from_text(text) return final_chunk_list def search_entity_by_fulltext(self, chunk_list, each_np_candidate_entity_num=20): qa_info_manager = QACacheInfoManager(semanticSearchAccessor=self.semanticSearchAccessor, defaultSearchAccessor=self.defaultAccessor, kg_models=self.kg_models) for chunk in chunk_list: related_entity_list = self.qa_searcher.search_related_entity(chunk, each_np_candidate_entity_num) qa_info_manager.add(chunk, related_entity_list) related_entity_for_api = self.qa_searcher.search_related_entity_for_api(chunk, each_np_candidate_entity_num) qa_info_manager.add(chunk, related_entity_for_api) return qa_info_manager def search_all_entity_by_fulltext_by_half(self, chunk, each_np_candidate_entity_num=20): qa_info_manager = QACacheInfoManager(semanticSearchAccessor=self.semanticSearchAccessor, defaultSearchAccessor=self.defaultAccessor, kg_models=self.kg_models) related_entity_for_api = self.qa_searcher.search_related_entity_for_api(chunk, each_np_candidate_entity_num/2) qa_info_manager.add(chunk, related_entity_for_api) related_entity_list = self.qa_searcher.search_related_entity(chunk, each_np_candidate_entity_num/2) qa_info_manager.add(chunk, related_entity_list) return qa_info_manager def search_sentence_by_entity_for_qa_list(self, entity_for_qa_list): entity_id_string_list = [str(entity_for_qa.kg_id) for entity_for_qa in entity_for_qa_list] entity_id_string_list = list(set(entity_id_string_list)) return self.semanticSearchAccessor.search_sentence_by_entity_list(entity_id_string_list=entity_id_string_list) def get_relation_by_nodes(self, node_list): return self.semanticSearchAccessor.get_nodes_relation(node_list) def sort_sentence_by_entities_as_bridge(self, question, sentence_list, entity_list, weight_context_sim=0.5, weight_graph_sim=0.5 ): self._logger.info("run sort_sentence_by_entities_as_bridge get result=%d" % len(sentence_list)) question_vec = self.kg_models.get_question_entity_vector(question) entity_vec_list, entity_graph_vec_list = self.kg_models.get_vectors_for_entity_list(entity_list) sentence_vec_list, sentence_graph_vec_list = self.kg_models.get_vectors_for_entity_list(sentence_list) qe_sim_np = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_vec, entity_vec_list) qe_sim_np = qe_sim_np / qe_sim_np.sum() kg_context_sim = MatrixCalculation.compute_cossin_for_matrix_to_matrix_normalize(entity_vec_list, sentence_vec_list) kg_graph_sim = MatrixCalculation.compute_cossin_for_matrix_to_matrix_normalize(entity_graph_vec_list, sentence_graph_vec_list) qs_context_sim = weight_context_sim * qe_sim_np * kg_context_sim qs_graph_sim = weight_graph_sim * qe_sim_np * kg_graph_sim qs_sim = qs_context_sim + qs_graph_sim qs_sim = qs_sim.tolist()[0] qs_context_sim = qs_context_sim.tolist()[0] qs_graph_sim = qs_graph_sim.tolist()[0] for sum_sim, sentence, context_sim, graph_sim in zip(qs_sim, sentence_list, qs_context_sim, qs_graph_sim): sentence["qs_sim"] = sum_sim sentence["qs_context_sim"] = context_sim sentence["qs_graph_sim"] = graph_sim result = [] for sentence in sentence_list: result.append({ "kg_id": self.defaultAccessor.get_id_for_node(sentence), "sentence_id": sentence["sentence_id"], "sentence_type": sentence["sentence_type_code"], "text": sentence["sentence_text"], "qs_sim": sentence["qs_sim"], "qs_context_sim": sentence["qs_context_sim"], "qs_graph_sim": sentence["qs_graph_sim"] }) self._logger.info("run sort_sentence_by_entities_as_bridge get result num=%d" % len(result)) result.sort(key=lambda k: (k.get('qs_sim', 0)), reverse=True) return result def sort_sentence_by_entities_for_graph_similarity_as_bridge(self, question, sentence_list, entity_list, weight_context_sim=0.5, weight_graph_sim=0.5 ): self._logger.info( "run sort_sentence_by_entities_for_graph_similarity_as_bridge get result=%d" % len(sentence_list)) question_context_vec = self.kg_models.get_question_entity_vector(question) entity_vec_list, entity_graph_vec_list = self.kg_models.get_vectors_for_entity_list(entity_list) sentence_vec_list, sentence_graph_vec_list = self.kg_models.get_vectors_for_entity_list(sentence_list) qe_sim_np = np.ones((1, len(entity_list))) qe_sim_np = qe_sim_np / qe_sim_np.sum() qs_context_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_context_vec, sentence_vec_list) kg_graph_sim = MatrixCalculation.compute_cossin_for_matrix_to_matrix_normalize(entity_graph_vec_list, sentence_graph_vec_list) qs_context_sim = weight_context_sim * qs_context_sim qs_graph_sim = weight_graph_sim * qe_sim_np * kg_graph_sim qs_sim = qs_context_sim + qs_graph_sim qs_sim = qs_sim.tolist()[0] qs_context_sim = qs_context_sim.tolist()[0] qs_graph_sim = qs_graph_sim.tolist()[0] for sum_sim, sentence, context_sim, graph_sim in zip(qs_sim, sentence_list, qs_context_sim, qs_graph_sim): sentence["qs_sim"] = sum_sim sentence["qs_context_sim"] = context_sim sentence["qs_graph_sim"] = graph_sim result = [] for sentence in sentence_list: result.append({ "kg_id": self.defaultAccessor.get_id_for_node(sentence), "sentence_id": sentence["sentence_id"], "sentence_type": sentence["sentence_type_code"], "text": sentence["sentence_text"], "qs_sim": sentence["qs_sim"], "qs_context_sim": sentence["qs_context_sim"], "qs_graph_sim": sentence["qs_graph_sim"] }) self._logger.info("run sort_sentence_by_entities_as_bridge get result num=%d" % len(result)) result.sort(key=lambda k: (k.get('qs_sim', 0)), reverse=True) print("sorted result") for t in result: print("test sort", t) print(result[:100]) return result def sort_sentence_by_build_average_graph_vector_for_query(self, question, sentence_list, entity_list, weight_context_sim=0.5, weight_graph_sim=0.5 ): self._logger.info( "run sort_sentence_by_build_average_graph_vector_for_query get sentence_list=%d" % len(sentence_list)) kg_models = self.kg_models question_context_vec = kg_models.get_question_entity_vector(question) entity_vec_list, entity_graph_vec_list = self.kg_models.get_vectors_for_entity_list(entity_list) sentence_vec_list, sentence_graph_vec_list = self.kg_models.get_vectors_for_entity_list(sentence_list) entity_list, entity_vec_list, entity_graph_vec_list = self.remove_the_not_related_entity(entity_graph_vec_list, entity_list, entity_vec_list, question_context_vec) query_graph_vector = kg_models.get_question_graph_vector_by_average_all_entities( question=question, entity_graph_vec_list=entity_graph_vec_list) qs_context_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_context_vec, sentence_vec_list) qs_graph_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(query_graph_vector, sentence_graph_vec_list) qs_context_sim = weight_context_sim * qs_context_sim qs_graph_sim = weight_graph_sim * qs_graph_sim qs_sim = qs_context_sim + qs_graph_sim qs_sim = qs_sim.tolist()[0] qs_context_sim = qs_context_sim.tolist()[0] qs_graph_sim = qs_graph_sim.tolist()[0] for sum_sim, sentence, context_sim, graph_sim in zip(qs_sim, sentence_list, qs_context_sim, qs_graph_sim): sentence["qs_sim"] = sum_sim sentence["qs_context_sim"] = context_sim sentence["qs_graph_sim"] = graph_sim result = [] for sentence in sentence_list: result.append({ "kg_id": self.defaultAccessor.get_id_for_node(sentence), "sentence_id": sentence["sentence_id"], "text": sentence["sentence_text"], "sentence_type": sentence["sentence_type_code"], "qs_sim": sentence["qs_sim"], "qs_context_sim": sentence["qs_context_sim"], "qs_graph_sim": sentence["qs_graph_sim"] }) self._logger.info("run sort_sentence_by_build_average_graph_vector_for_query get result num=%d" % len(result)) result.sort(key=lambda k: (k.get('qs_sim', 0)), reverse=True) return result def sort_sentence_by_select_part_entity_as_bridge(self, question, qa_info_manager, # sentence_list, # entity_list, weight_context_sim=0.6, weight_graph_sim=0.4, # chunk_to_related_entity_list_map=None, ): self._logger.info( "run sort part entity result=%d" % qa_info_manager.get_sentence_size()) print("entity for node") qa_info_manager.print_entities() print("sentence for node") # qa_info_manager.print_sentences() entity_info_collection = qa_info_manager.get_entity_info_collection() sentence_info_collection = qa_info_manager.get_sentence_info_collection() entity_info_collection.init_vectors(self.kg_models) sentence_info_collection.init_vectors(self.kg_models) sentence_list = sentence_info_collection.get_entity_list() entity_vec_list = entity_info_collection.get_entity_context_list() entity_graph_vec_list = entity_info_collection.get_entity_graph_list() entity_list = entity_info_collection.get_entity_list() sentence_vec_list = sentence_info_collection.get_entity_context_list() sentence_graph_vec_list = sentence_info_collection.get_entity_graph_list() question_context_vec = self.kg_models.get_question_entity_vector(question) entity_list, entity_vec_list, entity_graph_vec_list = self.get_top_related_entity_info_list( question_context_vec=question_context_vec, qa_info_manager=qa_info_manager) # entity_list, entity_vec_list, entity_graph_vec_list = self.remove_the_not_related_entity_by_only_save_one_for_each( # entity_graph_vec_list=entity_graph_vec_list, entity_vec_list=entity_vec_list, entity_list=entity_list, # question_context_vec=question_context_vec, # qa_info_manager=qa_info_manager # # ) qs_context_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_context_vec, sentence_vec_list) # todo:change to the average graph similarity # qs_graph_sim = self.get_graph_similarity_by_average_entity_graph_vector(entity_graph_vec_list, question, # sentence_graph_vec_list) qs_graph_sim = self.get_query_to_sentence_graph_sim_by_select_top_enttity(entity_graph_vec_list, entity_list, entity_vec_list, sentence_graph_vec_list, sentence_vec_list) qs_context_sim = weight_context_sim * qs_context_sim qs_graph_sim = weight_graph_sim * qs_graph_sim qs_sim = qs_context_sim + qs_graph_sim qs_sim = qs_sim.tolist()[0] qs_context_sim = qs_context_sim.tolist()[0] qs_graph_sim = qs_graph_sim.tolist()[0] for sum_sim, sentence, context_sim, graph_sim in zip(qs_sim, sentence_list, qs_context_sim, qs_graph_sim): sentence["qs_sim"] = sum_sim sentence["qs_context_sim"] = context_sim sentence["qs_graph_sim"] = graph_sim result = [] for sentence in sentence_list: result.append({ "kg_id": self.defaultAccessor.get_id_for_node(sentence), "sentence_id": sentence["sentence_id"], "sentence_type": sentence["sentence_type_code"], "text": sentence["sentence_text"], "qs_sim": sentence["qs_sim"], "qs_context_sim": sentence["qs_context_sim"], "qs_graph_sim": sentence["qs_graph_sim"] }) self._logger.info("run sort_sentence_by_entities_as_bridge get result num=%d" % len(result)) result.sort(key=lambda k: (k.get('qs_sim', 0)), reverse=True) print(result[:100]) return result def get_graph_similarity_by_average_entity_graph_vector(self, entity_graph_vec_list, question, sentence_graph_vec_list): query_graph_vector = self.kg_models.get_question_graph_vector_by_average_all_entities( question=question, entity_graph_vec_list=entity_graph_vec_list) qs_graph_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(query_graph_vector, sentence_graph_vec_list) return qs_graph_sim def get_graph_similarity_average_entity_graph_vector_similarity(self, entity_graph_vec_list, question, sentence_graph_vec_list): # query_graph_vector = self.kg_models.get_question_graph_vector_by_average_all_entities( # question=question, # entity_graph_vec_list=entity_graph_vec_list) qs_graph_sim = MatrixCalculation.compute_cossin_for_matrix_to_matrix_normalize(sentence_graph_vec_list, entity_graph_vec_list) return np.mean(qs_graph_sim, axis=1) def get_query_to_sentence_graph_sim_by_select_top_enttity(self, entity_graph_vec_list, entity_list, entity_vec_list, sentence_graph_vec_list, sentence_vec_list): # kg_se_graph_sim = MatrixCalculation.compute_cossin_for_matrix_to_matrix_normalize(sentence_graph_vec_list, # entity_graph_vec_list, # ) kg_se_context_sim = MatrixCalculation.compute_cossin_for_matrix_to_matrix_normalize( sentence_vec_list, entity_vec_list) # TODO # kg_se_sim = 0.5 * kg_se_graph_sim + 0.5 * kg_se_context_sim kg_se_sim = kg_se_context_sim print("final entity list", len(entity_list), entity_list) select_linking_entity_num = min(5, len(entity_list)) onehot_maxsim_se_matrix = MatrixCalculation.get_most_similar_top_n_entity_as_matrix( top_n=select_linking_entity_num, s_e_similarity_matrix=kg_se_sim) s_query_graph_vec_matrix = onehot_maxsim_se_matrix * np.matrix( entity_graph_vec_list) / select_linking_entity_num qs_graph_sim = MatrixCalculation.compute_cossin_for_one_to_one_in_two_list_normalize(sentence_graph_vec_list, s_query_graph_vec_matrix.getA()) return qs_graph_sim def remove_the_not_related_entity_by_only_save_one_for_each(self, entity_graph_vec_list, entity_list, entity_vec_list, question_context_vec, qa_info_manager): chunk_to_related_entity_list_map = qa_info_manager.keyword_2_entitynodemap qe_sim_np = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_context_vec, entity_vec_list) entity_info_sumary_list = [] for (entity, sim, entity_vec, entity_graph_vec) in zip(entity_list, qe_sim_np.getA()[0], entity_vec_list, entity_graph_vec_list): print("after first removing sim=", sim, "entity=", entity) entity_info_sumary_list.append({"entity": entity, "sim": sim, "entity_vec": entity_vec, "entity_graph_vec": entity_graph_vec }) entity_info_sumary_list.sort(key=lambda k: (k.get('sim', 0)), reverse=True) valid_word_set = set([]) word_to_related_entity_list_map = {} for chunk, related_entity_list in chunk_to_related_entity_list_map.items(): word = chunk if word not in valid_word_set: valid_word_set.add(word) word_to_related_entity_list_map[word] = related_entity_list else: word_to_related_entity_list_map[word].extend(related_entity_list) # clean_entity_info_list = self.get_clean_entity_for_each_word_by_max_similarity(entity_info_sumary_list, # word_to_related_entity_list_map) # clean_entity_info_list = self.get_clean_entity_for_each_word_by_max_n_similarity(entity_info_sumary_list, word_to_related_entity_list_map) new_entity_list = [] new_entity_graph_vec_list = [] new_entity_vec_list = [] for entity_info_sumary in clean_entity_info_list: new_entity_list.append(entity_info_sumary["entity"]) new_entity_graph_vec_list.append(entity_info_sumary["entity_graph_vec"]) new_entity_vec_list.append(entity_info_sumary["entity_vec"]) print("final save sim=", entity_info_sumary["sim"], "entity=", entity_info_sumary["entity"]) return new_entity_list, new_entity_vec_list, new_entity_graph_vec_list def get_top_related_entity_info_list(self, question_context_vec, qa_info_manager): node_info_collection = qa_info_manager.get_node_info_collection() node_info_collection.fill_each_entity_with_similary_to_question(question_context_vec) node_info_collection.sort_by_qe_sim() # selected_entity_info_list = qa_info_manager.get_top_node_info_by_each_keywords_three_different_type() selected_entity_info_list = qa_info_manager.get_top_node_info_by_each_keywords() new_entity_list = [] new_entity_vec_list = [] new_entity_graph_vec_list = [] for node_info in selected_entity_info_list: new_entity_list.append(node_info.entity_node) new_entity_vec_list.append(node_info.entity_context_vec) new_entity_graph_vec_list.append(node_info.entity_graph_vec) return new_entity_list, new_entity_vec_list, new_entity_graph_vec_list def get_clean_entity_for_each_word_by_max_n_similarity(self, entity_info_sumary_list, word_to_related_entity_list_map): clean_entity_kg_id_list = set([]) print("start get_clean_entity_infi_sumary_list ") word_name_entity_mark = {} for valid_word, related_entity_list in word_to_related_entity_list_map.items(): print("valid word=", valid_word) entity_info_list = self.get_first_from_entity_info_sumary_list_and_in_related_entity_list( entity_info_sumary_list, related_entity_list, 3) # for entity_info in entity_info_list: print("get candidate for word=", valid_word, entity_info_list) word_name_entity_mark[valid_word] = entity_info_list clean_entity_info_list = [] clean_entity_kg_id_list = set([]) for word, entity_info_list in word_name_entity_mark.items(): for entity_info in entity_info_list: kg_id = self.defaultAccessor.get_id_for_node(entity_info["entity"]) if kg_id not in clean_entity_kg_id_list: clean_entity_info_list.append(entity_info) clean_entity_kg_id_list.add(kg_id) print("valid word=", word, entity_info["entity"]) return clean_entity_info_list def get_clean_entity_for_each_word_by_max_similarity(self, entity_info_sumary_list, word_to_related_entity_list_map): clean_entity_kg_id_list = set([]) print("start get_clean_entity_infi_sumary_list ") word_name_entity_mark = {} for valid_word, related_entity_list in word_to_related_entity_list_map.items(): print("valid word=", valid_word) entity_info_list = self.get_first_from_entity_info_sumary_list_and_in_related_entity_list( entity_info_sumary_list, related_entity_list) for entity_info in entity_info_list: print("get candidate for word=", valid_word, entity_info["entity"]) word_name_entity_mark[valid_word] = entity_info clean_entity_info_list = [] clean_entity_kg_id_list = set([]) for word, entity_info in word_name_entity_mark.items(): kg_id = self.defaultAccessor.get_id_for_node(entity_info["entity"]) if kg_id not in clean_entity_kg_id_list: clean_entity_info_list.append(entity_info) clean_entity_kg_id_list.add(kg_id) print("valid word=", word, entity_info["entity"]) return clean_entity_info_list def get_clean_entity_infi_sumary_list(self, entity_info_sumary_list, word_to_related_entity_list_map): clean_entity_kg_id_list = set([]) print("start get_clean_entity_infi_sumary_list ") word_name_entity_mark = {} for valid_word, related_entity_list in word_to_related_entity_list_map.items(): print("valid word=", valid_word) entity_info_list = self.get_first_from_entity_info_sumary_list_and_in_related_entity_list( entity_info_sumary_list, related_entity_list) for entity_info in entity_info_list: kg_id = self.defaultAccessor.get_id_for_node(entity_info["entity"]) print("get candidate for word=", valid_word, entity_info["entity"]) if kg_id not in clean_entity_kg_id_list: if valid_word not in word_name_entity_mark.keys(): word_name_entity_mark[valid_word] = entity_info else: old_entity_info = word_name_entity_mark[valid_word] if entity_info["sim"] > old_entity_info["sim"]: word_name_entity_mark[valid_word] = entity_info for seperate_name in valid_word.split(" "): if seperate_name not in word_name_entity_mark.keys(): word_name_entity_mark[seperate_name] = entity_info else: old_entity_info = word_name_entity_mark[seperate_name] if entity_info["sim"] > old_entity_info["sim"]: word_name_entity_mark[seperate_name] = entity_info clean_entity_kg_id_list.add(kg_id) clean_entity_info_list = [] clean_entity_kg_id_list = set([]) for word, entity_info in word_name_entity_mark.items(): kg_id = self.defaultAccessor.get_id_for_node(entity_info["entity"]) if kg_id not in clean_entity_kg_id_list: clean_entity_info_list.append(entity_info) clean_entity_kg_id_list.add(kg_id) print("valid word=", word, entity_info["entity"]) return clean_entity_info_list def remove_the_not_related_entity(self, entity_graph_vec_list, entity_list, entity_vec_list, question_context_vec): qe_sim_np = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_context_vec, entity_vec_list) print("qeustion to entity similary") new_entity_list = [] new_entity_vec_list = [] new_entity_graph_vec_list = [] qe_sim_clean = [] for (entity, sim, entity_vec, entity_graph_vec) in zip(entity_list, qe_sim_np.getA()[0], entity_vec_list, entity_graph_vec_list): print("sim=", sim, "entity=", entity) if sim > MIN_RELATED_ENTITY_SIMILARITY: print("adding ", entity) new_entity_list.append(entity) new_entity_vec_list.append(entity_vec) new_entity_graph_vec_list.append(entity_graph_vec) qe_sim_clean.append(sim) entity_list = new_entity_list entity_vec_list = new_entity_vec_list entity_graph_vec_list = new_entity_graph_vec_list new_entity_list = [] new_entity_vec_list = [] new_entity_graph_vec_list = [] entity_info_sumary_list = [] for (entity, sim, entity_vec, entity_graph_vec) in zip(entity_list, qe_sim_clean, entity_vec_list, entity_graph_vec_list): print("after first removing sim=", sim, "entity=", entity) entity_info_sumary_list.append({"entity": entity, "sim": sim, "entity_vec": entity_vec, "entity_graph_vec": entity_graph_vec }) entity_info_sumary_list.sort(key=lambda k: (k.get('sim', 0)), reverse=True) api_class_name_set = set([]) new_entity_info_sumary_list = [] for entity_info_sumary in entity_info_sumary_list: if entity_info_sumary["entity"].has_label("api"): qualified_name = entity_info_sumary["entity"]["qualified_name"] if qualified_name in api_class_name_set: continue if "(" in qualified_name: simple_name = qualified_name.split("(")[0] class_name = ".".join(simple_name.split(".")[:-1]) if class_name in api_class_name_set: continue else: api_class_name_set.add(class_name) new_entity_info_sumary_list.append(entity_info_sumary) else: api_class_name_set.add(qualified_name) new_entity_info_sumary_list.append(entity_info_sumary) else: new_entity_info_sumary_list.append(entity_info_sumary) for entity_info_sumary in new_entity_info_sumary_list: new_entity_list.append(entity_info_sumary["entity"]) new_entity_graph_vec_list.append(entity_info_sumary["entity_graph_vec"]) new_entity_vec_list.append(entity_info_sumary["entity_vec"]) print("final save sim=", entity_info_sumary["sim"], "entity=", entity_info_sumary["entity"]) return new_entity_list, new_entity_vec_list, new_entity_graph_vec_list def sort_sentence_by_build_graph_vector_for_query_in_semantic_weight(self, question, sentence_list, entity_list, weight_context_sim=0.5, weight_graph_sim=0.5 ): self._logger.info( "run sort_sentence_by_build_graph_vector_for_query_in_semantic_weight get sentence_list=%d" % len( sentence_list)) kg_models = self.kg_models question_context_vec = kg_models.get_question_entity_vector(question) entity_vec_list, entity_graph_vec_list = self.kg_models.get_vectors_for_entity_list(entity_list) sentence_vec_list, sentence_graph_vec_list = self.kg_models.get_vectors_for_entity_list(sentence_list) query_graph_vector = kg_models.get_question_graph_vector_by_semantic_weight_all_entities( question_context_vec=question_context_vec, entity_context_vec_list=entity_vec_list, entity_graph_vec_list=entity_graph_vec_list) qs_context_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(question_context_vec, sentence_vec_list) qs_graph_sim = MatrixCalculation.compute_cossin_for_vec_to_matrix_normalize(query_graph_vector, sentence_graph_vec_list) qs_context_sim = weight_context_sim * qs_context_sim qs_graph_sim = weight_graph_sim * qs_graph_sim qs_sim = qs_context_sim + qs_graph_sim qs_sim = qs_sim.tolist()[0] qs_context_sim = qs_context_sim.tolist()[0] qs_graph_sim = qs_graph_sim.tolist()[0] for sum_sim, sentence, context_sim, graph_sim in zip(qs_sim, sentence_list, qs_context_sim, qs_graph_sim): sentence["qs_sim"] = sum_sim sentence["qs_context_sim"] = context_sim sentence["qs_graph_sim"] = graph_sim result = [] for sentence in sentence_list: result.append({ "kg_id": self.defaultAccessor.get_id_for_node(sentence), "sentence_id": sentence["sentence_id"], "text": sentence["sentence_text"], "qs_sim": sentence["qs_sim"], "qs_context_sim": sentence["qs_context_sim"], "qs_graph_sim": sentence["qs_graph_sim"] }) self._logger.info( "run sort_sentence_by_build_graph_vector_for_query_in_semantic_weight get result=%d" % len(result)) result.sort(key=lambda k: (k.get('qs_sim', 0)), reverse=True) return result def get_all_entity(self, entity_for_qa_list): entity_id_string_list = [str(entity_for_qa.kg_id) for entity_for_qa in entity_for_qa_list] entity_id_string_list = list(set(entity_id_string_list)) return self.semanticSearchAccessor.get_all_entity(entity_id_string_list=entity_id_string_list) def get_first_from_entity_info_sumary_list_and_in_related_entity_list(self, entity_info_sumary_list, related_entity_list, top_relate_entity_num=1): return_result_list = [] for entity_info in entity_info_sumary_list: kg_id = self.defaultAccessor.get_id_for_node(entity_info["entity"]) entity = self.get_entity_from_entity_list_by_kgid(kg_id, related_entity_list) if entity is not None: return_result_list.append(entity_info) if len(return_result_list) >= top_relate_entity_num: return return_result_list return [] def get_entity_from_entity_list_by_kgid(self, kg_id, related_entity_list): for related_entity in related_entity_list: if related_entity.kg_id == kg_id: return related_entity return None
from db.engine_factory import EngineFactory from db.cursor_factory import ConnectionFactory from db.model import KnowledgeTableRowMapRecord, APIDocumentWebsite, KnowledgeTableColumnMapRecord from db.model_factory import KnowledgeTableFactory from shared.logger_util import Logger logger = Logger("import_doc_website_for_jdk_package").get_log() cur = ConnectionFactory.create_cursor_for_jdk_importer() session = EngineFactory.create_session() jdk_package_knowledge_table = KnowledgeTableFactory.get_jdk_package_table( session) api_knowledge_table = KnowledgeTableFactory.get_api_entity_table(session) api_document_website_table = KnowledgeTableFactory.get_api_document_website_table( session) def create_doc_website_relation(old_package_id, doc_website): if doc_website is None: logger.error("no doc_website for %d", old_package_id) return None new_package_api_entity_id = KnowledgeTableRowMapRecord.get_end_row_id( session=session, start_knowledge_table=jdk_package_knowledge_table, end_knowledge_table=api_knowledge_table, start_row_id=old_package_id) if new_package_api_entity_id is None: logger.error("no new_package_api_entity_id for %d", old_package_id) return None
from tagme import wiki_title from factory import NodeBuilder from graph_accessor import GraphAccessor, DefaultGraphAccessor from shared.logger_util import Logger _logger = Logger("WikipediaGraphAccessor").get_log() class WikipediaGraphAccessor(GraphAccessor): def create_wikipedia_item_entity_by_url(self, url): if url.startswith("https://en.wikipedia.org/") == False: return None accessor = DefaultGraphAccessor(self) node = accessor.get_node_by_wikipedia_link(url) if node is None: property_dict = { "name": wiki_title(url.split("/")[-1]), "url": url, "site:enwiki": url } if "(" in property_dict["name"]: alias = [(property_dict["name"].split(" ("))[0]] property_dict["alias"] = alias node = NodeBuilder().add_entity_label().add_label( "wikipedia").add_property(**property_dict).build() self.graph.merge(node) _logger.info("create wikipedia node" + str(property_dict)) return node
from graph_accessor import DefaultGraphAccessor from shared.logger_util import Logger _logger = Logger("APIGraphAccessor").get_log() class APIGraphAccessor(DefaultGraphAccessor): """ a GraphAccessor for API node query """ def get_parameter_nodes_of_method(self, method_node_id): """ get all parameter nodes belong to one method :param method_node_id: method node id :return: parameter nodes list """ try: query = 'Match (n:`java method parameter`)-[r:`belong to`]->(m:`java method`) where ID(m)={method_node_id} return distinct n'.format( method_node_id=method_node_id) node_list = [] result = self.graph.run(query) for n in result: node_list.append(n['n']) return node_list except Exception, error: _logger.exception() return [] def get_parent_class_node_for_method_node(self, method_node_id): """
from py2neo import Relationship from factory import NodeBuilder from graph_accessor import GraphAccessor from shared.logger_util import Logger _logger = Logger("AliasGraphAccessor").get_log() class AliasGraphAccessor(GraphAccessor): def find_root_entity_by_alias_name(self, alias): query = "match (n:alias)-[:alias]-(m) where n.name='{alias}' return m" query = query.format(alias=alias) return self.graph.run(query) def build_alias_relation(self, alias, node): end_node = self.find_or_create_alias_node(alias) relation = Relationship(node, 'alias', end_node) self.graph.merge(relation) def find_or_create_alias_node(self, alias): end_node = NodeBuilder().add_as_alias().add_one_property( property_name='name', property_value=alias).build() self.graph.merge(end_node) return end_node def create_alias_node_for_name(self, name, node_id): alias_node = NodeBuilder().add_as_alias().add_one_property( "name", name).build() self.graph.merge(alias_node) alias_node_link_id_list = alias_node["link_id"]
import gc from db.engine_factory import ConnectionFactory, EngineFactory from db.model import KnowledgeTableRowMapRecord, APIEntity, APIDocumentWebsite from db.model_factory import KnowledgeTableFactory from shared.logger_util import Logger logger = Logger("import_doc_website_for_jdk_method").get_log() cur = ConnectionFactory.create_cursor_for_jdk_importer() session = EngineFactory.create_session() jdk_method_knowledge_table = KnowledgeTableFactory.get_jdk_method_table( session) api_knowledge_table = KnowledgeTableFactory.get_api_entity_table(session) def create_doc_website_relation(method_name, full_declaration, qualified_name, class_website): # print class_website if "http://docs.oracle.com/javase/7/docs/api/" in class_website: return None if full_declaration: if method_name[0] == "_": method_name = "Z:Z" + method_name if "(" in qualified_name and ")" in qualified_name: left_bracket_index = qualified_name.find("(") right_bracket_index = qualified_name.find(")") param_str = qualified_name[left_bracket_index + 1:right_bracket_index] if "," in param_str: param_list = param_str.split(",")
from db.engine_factory import ConnectionFactory, EngineFactory from db.model import KnowledgeTableRowMapRecord, APIRelation, KnowledgeTableColumnMapRecord from db.model_factory import KnowledgeTableFactory from shared.logger_util import Logger logger = Logger("import_belong_to_relation_for_jdk_class").get_log() cur = ConnectionFactory.create_cursor_for_jdk_importer() session = EngineFactory.create_session() jdk_package_knowledge_table = KnowledgeTableFactory.get_jdk_package_table( session) jdk_class_knowledge_table = KnowledgeTableFactory.get_jdk_class_table(session) api_knowledge_table = KnowledgeTableFactory.get_api_entity_table(session) api_relation_table = KnowledgeTableFactory.get_api_relation_table(session) def create_class_belong_to_relation(old_class_id, old_package_id): if old_package_id is None: logger.error("no old_package_id for %d", old_class_id) return None new_class_api_entity_id = KnowledgeTableRowMapRecord.get_end_row_id( session=session, start_knowledge_table=jdk_class_knowledge_table, end_knowledge_table=api_knowledge_table, start_row_id=old_class_id) if new_class_api_entity_id is None: logger.error("no new_class_api_entity_id for %d", old_class_id) return None new_package_api_entity_id = KnowledgeTableRowMapRecord.get_end_row_id( session=session,
from db.engine_factory import ConnectionFactory, EngineFactory from db.model import KnowledgeTableRowMapRecord, APIEntity from db.model_factory import KnowledgeTableFactory from db.util.code_text_process import clean_html_text from shared.logger_util import Logger session = EngineFactory.create_session() logger = Logger("import_android_class").get_log() IMPORT_DATA_SOURCE_TABLE_NAME = "androidAPI_class" package_knowledge_table = KnowledgeTableFactory.find_knowledge_table_by_name( session=session, name="androidAPI_package") class_knowledge_table = KnowledgeTableFactory.find_knowledge_table_by_name( session=session, name=IMPORT_DATA_SOURCE_TABLE_NAME) api_knowledge_table = KnowledgeTableFactory.find_knowledge_table_by_name( session=session, name=APIEntity.__tablename__) def get_package_full_name_by_old_package_id(package_id): return KnowledgeTableRowMapRecord.get_end_row_id( session=session, start_knowledge_table=package_knowledge_table, end_knowledge_table=api_knowledge_table, start_row_id=package_id) def get_qualified_name_of_package(package_id): api_entity_id = get_package_full_name_by_old_package_id( package_id=package_id) api_entity = APIEntity.find_by_id(session, api_entity_id) if api_entity:
from db.engine_factory import EngineFactory from db.model import DocumentText, DocumentAnnotationStatus from shared.logger_util import Logger logger = Logger("import_return_value_relation_for_jdk_method").get_log() session = EngineFactory.create_session() def import_jdk_document_annotation_status(): doc_id_list = DocumentText.get_doc_id_list(session) if doc_id_list is not None: for each in doc_id_list: doc_id = each[0] print doc_id document_annotation_status = DocumentAnnotationStatus(doc_id, DocumentAnnotationStatus.STATUS_TO_ANNOTATE) print document_annotation_status document_annotation_status.create(session, autocommit=False) session.commit() if __name__ == "__main__": import_jdk_document_annotation_status()
def __init__(self, session=None, logger=None): self.__session = session if logger is None: self.logger = Logger("DBSearcher").get_log() else: self.logger = logger
import MySQLdb # Mysql connect from py2neo import Relationship from skgraph.graph.accessor.graph_accessor import GraphClient from skgraph.graph.accessor.graph_client_for_wikidata import WikiDataGraphAccessor from shared.logger_util import Logger conn = None cur = None graphClient = WikiDataGraphAccessor(GraphClient(server_number=1)) # neo4j connect connect_graph = graphClient.graph logger = Logger("android_sdk_importer").get_log() # buffer q = Queue.Queue() n = 0 def get_android_sdk_node(): labels = ["library"] property_dict = {"name": "android API"} from skgraph.graph.accessor.factory import NodeBuilder nodeBuilder = NodeBuilder().add_labels(*labels).add_property( **property_dict) android_sdk_node = nodeBuilder.build() connect_graph.merge(android_sdk_node, "library", "name") return android_sdk_node
from py2neo import Relationship from graph_accessor import GraphAccessor from shared.logger_util import Logger from skgraph.graph.accessor.factory import NodeBuilder _logger = Logger("SentenceAccessor").get_log() class DomainEntityAccessor(GraphAccessor): def get_all_domain_entity(self): try: query = "MATCH (n:`domain entity`) return n" result = self.graph.run(query) node_list = [] for n in result: node_list.append(n['n']) return node_list except Exception, error: _logger.exception("------") return [] def create_entity_to_general_concept_relation(self, entity, wikipedia_entity): relation = Relationship(entity, 'may link', wikipedia_entity) self.graph.merge(relation) def delete_all_domain_entity_to_wikipedia_relation(self): try: query = "MATCH (n:`domain entity`)-[r:`may link`]-(m:wikipedia) delete r" self.graph.run(query)
from shared.logger_util import SQLAlchemyHandler from skgraph.graph.accessor.graph_accessor import DefaultGraphAccessor, GraphClient from skgraph.graph.apientitylinking import APIEntityLinking from skgraph.graph.label_util import LabelUtil from skgraph.graph.node_cleaner import GraphJsonParser reload(sys) sys.setdefaultencoding("utf-8") app = Flask(__name__) CORS(app) db_handler = SQLAlchemyHandler() db_handler.setLevel(logging.WARN) # Only serious messages app.logger.addHandler(db_handler) logger = Logger("neo4jServer").get_log() logger.info("create logger") graphClient = DefaultGraphAccessor(GraphClient(server_number=1)) logger.info("create graphClient") api_entity_linker = APIEntityLinking() logger.info("create api_entity_linker object") questionAnswerSystem = QuestionAnswerSystem() logger.info("create questionAnswerSystem") dbSOPostSearcher = SOPostSearcher(EngineFactory.create_so_session(), logger=app.logger) logger.info("create SO POST Searcher")
import codecs import json from py2neo import Relationship from skgraph.graph.accessor.graph_accessor import GraphClient from skgraph.graph.accessor.graph_client_for_awesome import AwesomeGraphAccessor from shared.logger_util import Logger _logger = Logger("AwesomeImporter").get_log() awesomeGraphAccessor = AwesomeGraphAccessor(GraphClient(server_number=0)) baseGraphClient = awesomeGraphAccessor.graph file_name = "complete_list_of_awesome_list_collect_relation.json" with codecs.open(file_name, 'r', 'utf-8') as f: relation_list = json.load(f) for relation in relation_list: start_url = relation["start_url"] relation_str = relation["relation"] end_url = relation["end_url"] start_node = awesomeGraphAccessor.find_awesome_list_entity(start_url) end_node = awesomeGraphAccessor.find_awesome_list_entity(end_url) if start_node is not None and end_node is not None: relationship = Relationship(start_node, relation_str, end_node) baseGraphClient.merge(relationship) _logger.info("create or merge relation" + str(relation)) else: _logger.warn("fail create relation" + str(relation))
from bs4 import BeautifulSoup from db.engine_factory import EngineFactory from db.cursor_factory import ConnectionFactory from db.model import KnowledgeTableRowMapRecord, APIEntity from db.model_factory import KnowledgeTableFactory from db.util.code_text_process import parse_declaration_html_with_full_qualified_type, clean_declaration_html, \ clean_html_text_with_format from shared.logger_util import Logger session = EngineFactory.create_session() logger = Logger("import_android_method").get_log() IMPORT_DATA_SOURCE_TABLE_NAME = "androidAPI_method" class_knowledge_table = KnowledgeTableFactory.find_knowledge_table_by_name( session=session, name="androidAPI_class") method_knowledge_table = KnowledgeTableFactory.find_knowledge_table_by_name( session=session, name="androidAPI_method") api_knowledge_table = KnowledgeTableFactory.find_knowledge_table_by_name( session=session, name=APIEntity.__tablename__) def get_api_entity_id_by_old_class_id(old_class_id): return KnowledgeTableRowMapRecord.get_end_row_id( session=session, start_knowledge_table=class_knowledge_table, end_knowledge_table=api_knowledge_table, start_row_id=old_class_id)