def _log_eval_average(self, result_dct_list, configs):
bresult = BenchmarkResult(result_dct_list, configs)
df = bresult.get_data_frame()
grouped = df.apply(pd.to_numeric, errors='ignore').groupby(bresult.get_evaluation_groups()).mean()[
self._evaluation_criteria()]
self.logger.info("\n------------------- Current Evaluation Results ---------------------- \n Num. Results:{}\n {} \n \
---------------------------------------------------------------------".format(len(result_dct_list),
grouped.to_string()))
def test_extend_from_file(self):
try:
os.remove("./br1")
os.remove("./br2")
os.remove("./br3")
except:
pass
result_num = 100
confs, result_data, histories1 = random_benchmark_conf_data(result_num, 2000000, hist_size=1500000, offset=0)
br1 = BenchmarkResult(result_dict=result_data,
benchmark_configs=confs, histories=histories1)
br1.dump("./br1", dump_histories=True, dump_configs=True)
br1_df = br1.get_data_frame().copy()
result_num = 30
confs2, result_data2, histories2 = random_benchmark_conf_data(result_num, 2000000, hist_size=1500000, offset=200)
br2 = BenchmarkResult(result_dict=result_data2,
benchmark_configs=confs2, histories=histories2)
br2.dump(filename="./br2", dump_histories=True, dump_configs=True)
result_num = 10
confs3, result_data3, histories3 = random_benchmark_conf_data(result_num, 2000000, hist_size=1500000, offset=400)
br3 = BenchmarkResult(result_dict=result_data3,
benchmark_configs=confs3, histories=histories3)
br3.dump(filename="./br3", dump_histories=True, dump_configs=True)
br1.extend(benchmark_result=br2, file_level=True)
br1.extend(benchmark_result=br3, file_level=True)
br_loaded = BenchmarkResult.load("./br1", load_histories=True, load_configs=True)
df_desired = br1_df
df_desired = pd.concat([df_desired, br2.get_data_frame()])
df_desired = pd.concat([df_desired, br3.get_data_frame()])
self.assertEqual(len(br_loaded.get_data_frame().index), len(df_desired.index))
extended_confs = br_loaded.get_benchmark_configs()
self.assertEqual(len(extended_confs), 140)
extended_histories = br_loaded.get_histories()
self.assertEqual(len(extended_histories), 140)
extended_histories = histories1
extended_histories.update(histories2)
extended_histories.update(histories3)
for bc in extended_confs:
self.assertEqual(br_loaded.get_history(bc.config_idx), extended_histories[bc.config_idx])
def _log_eval_average(self, result_dct_list, configs):
bresult = BenchmarkResult(result_dct_list, configs)
df = bresult.get_data_frame()
for eval_group in bresult.get_evaluation_groups():
if eval_group not in df.columns:
df[eval_group] = np.nan
df.fillna(-1, inplace=True)
grouped = df.apply(pd.to_numeric, errors='ignore').groupby(bresult.get_evaluation_groups()).mean()
self.logger.info("\n------------------- Current Evaluation Results ---------------------- \n Num. Results:{}\n {} \n \
---------------------------------------------------------------------".format(len(result_dct_list),
grouped.to_string()))
