"," + 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()
