"," + str(rq2_recall) + "," + str(rq2_f1) + "," + str(rq2_average_precision) + "," + str(elapsed_time) + "\n") write_result(rq1_outfile, rq1_result_str) write_result(rq2_outfile, rq2_result_str) gc.collect() except Exception as ex: print( "Skipping combination layer: {}, emb_output: {}, lstm_units: {}, epoch: {}" .format(layer, emb_output, lstm_units, epoch)) print(ex) cur_iter += 1 if __name__ == "__main__": smell_list = { "ComplexMethod", "EmptyCatchBlock", "MagicNumber", "MultifacetedAbstraction" } # smell_list = {"ComplexMethod"} for smell in smell_list: # data_path is for both training and rq1_evaluation data_path = os.path.join( os.path.join(TRAINING_TOKENIZER_OUT_PATH, smell), DIM) rq2_eval_data_path = os.path.join( os.path.join(EVAL_TOKENIZER_OUT_PATH, smell), DIM) inputs.preprocess_data(data_path) inputs.preprocess_data(rq2_eval_data_path) main(data_path, rq2_eval_data_path, smell)
import os import rq1_rnn_emb_lstm as rnn import inputs # --- Parameters -- DIM = "1d" # TOKENIZER_OUT_PATH = "../../data/tokenizer_out_cs/" TOKENIZER_OUT_PATH = "/users/pa18/tushar/smellDetectionML/data/tokenizer_out_cs/" # --- smell = "MultifacetedAbstraction" data_path = os.path.join(os.path.join(TOKENIZER_OUT_PATH, smell), DIM) inputs.preprocess_data(data_path) rnn.main(data_path, smell)
str(accuracy) + "," + str(precision) + "," + str(recall) + "," + str(f1) + "," + str(average_precision) + "," + str(elapsed_time) + "\n") write_result(outfile, result_str) except Exception as ex: print( "Skipping combination layer: {}, emb_output: {}, lstm_units: {}, epoch: {}" .format(layer, emb_output, lstm_units, epoch)) print(ex) cur_iter += 1 gc.collect() if __name__ == "__main__": smell_list = { "ComplexMethod", "EmptyCatchBlock", "MagicNumber", "MultifacetedAbstraction" } # smell_list = {"ComplexMethod"} for smell in smell_list: training_data_path = os.path.join( os.path.join(TRAINING_TOKENIZER_OUT_PATH, smell), DIM) eval_data_path = os.path.join( os.path.join(EVAL_TOKENIZER_OUT_PATH, smell), DIM) inputs.preprocess_data(training_data_path) inputs.preprocess_data(eval_data_path) main()