def load_model(filename='model_cnn_', size='sm', path='/models', type_= '.bin'): root_path = utils.get_path_to_project_dir(os.getcwd()) full_path = root_path + path + '/' + filename + size + type_ if (os.path.isfile(full_path)): nlp = utils.read_obj(filename + size, path='/models', type_=type_) else: nlp = spacy.load("pt_core_news_" + size) utils.write_obj(nlp, filename + size, path='/models',type_=type_) return nlp
# get df_testemunhas by doc df = utils.get_df_testemunhas_by_doc(list_docs_testemunhas) utils.write_obj(df, 'df_excerpts', path=self.data_interim, type_=self.type) # get df with union of testemunhas by doc df_ = utils.df_union_testemunhas(df) utils.write_obj(df_, 'df_witnesses', path=self.data_interim, type_=self.type) return df_ if __name__ == "__main__": pred = Predictor(size='sm', type_='.bin', sample_size=6) pred.predict() print(f'tipo do obj:{type(pred)}') # print df_excerpts df_ = utils.read_obj('df_excerpts', path='data/interim', type_='.bin') print(df_.shape) print(df_) # print df_witnesses df_wit = utils.read_obj('df_witnesses', path='data/interim', type_='.bin') print(df_wit.shape) print(df_wit)
def test_read_obj(): lista = ['a', 'b'] utils.write_obj(lista, "lista") lista2 = utils.read_obj("lista") assert lista == lista2
def test_predict(): pred = Predictor(sample_size=6) df_wit = pred.predict() df_test2 = utils.read_obj('df_witnesses', '/test/core', type_='.bin') assert df_wit.columns == df_test2.columns
def test_load_model(): pred = Predictor(sample_size=6) ml = pred.load_model() ml2 = utils.read_obj('model_cnn_sm', '/test/core', type_='.bin') assert type(ml) == type(ml2)