def __init__(self, pro_name, version):
self.model_dir_path = PathUtil.sim_model(pro_name=pro_name,
version=version,
model_type="svm")
self.model = FilterSemanticTFIDFNode2VectorModel(
name="svm", model_dir_path=self.model_dir_path)
self.document_collection_path = PathUtil.doc(pro_name, version)
self.collection = MultiFieldDocumentCollection.load(
str(self.document_collection_path))
self.processor = Preprocessor()
self.doc_collection = PreprocessMultiFieldDocumentCollection.create_from_doc_collection(
self.processor, self.collection)
self.pretrain_node2vec_path = PathUtil.node2vec(pro_name=pro_name,
version=version,
weight="unweight")
self.kg_name_searcher_path = PathUtil.name_searcher(pro_name, version)
self.doc_sim_model_path = PathUtil.sim_model(pro_name=pro_name,
version=version,
model_type="avg_w2v")
def train_model(pro_name, version, weight):
document_collection_path = PathUtil.doc(pro_name, version)
collection = MultiFieldDocumentCollection.load(str(document_collection_path))
processor = CodeDocPreprocessor()
doc_collection = PreprocessMultiFieldDocumentCollection.create_from_doc_collection(processor, collection)
graph_data_path = PathUtil.graph_data(pro_name=pro_name, version=version)
pretrain_node2vec_path = PathUtil.node2vec(pro_name=pro_name, version=version, weight=weight)
embedding_size = 100
kg_name_searcher_path = PathUtil.name_searcher(pro_name=pro_name, version=version)
model_dir_path = PathUtil.sim_model(pro_name=pro_name, version=version, model_type="avg_n2v")
model = AVGNode2VectorModel.train(model_dir_path=model_dir_path,
doc_collection=doc_collection,
embedding_size=embedding_size,
pretrain_node2vec_path=pretrain_node2vec_path,
graph_data_path=graph_data_path,
kg_name_searcher_path=kg_name_searcher_path,
)
return model_dir_path