def run(self, viewer=None, maintain_history=False, checkpoint_every=None):
results = []
histories = {}
for idx, bmark_conf in enumerate(self.configs_to_run):
self.logger.info(
"Running config idx {} being {}/{}: Scenario {} of set \"{}\" for behavior \"{}\""
.format(bmark_conf.config_idx, idx,
len(self.benchmark_configs) - 1,
bmark_conf.scenario_idx, bmark_conf.scenario_set_name,
bmark_conf.behavior_config.behavior_name))
bmark_conf = copy.deepcopy(
bmark_conf) if self._deepcopy else bmark_conf
result_dict, scenario_history = self._run_benchmark_config(
bmark_conf, viewer, maintain_history)
results.append(result_dict)
histories[bmark_conf.config_idx] = scenario_history
if self.log_eval_avg_every and (idx +
1) % self.log_eval_avg_every == 0:
self._log_eval_average(results, self.configs_to_run)
if checkpoint_every and (idx + 1) % checkpoint_every == 0:
intermediate_result = BenchmarkResult(results, \
self.configs_to_run[0:idx+1], histories=histories)
checkpoint_file = os.path.join(self.checkpoint_dir,
self.get_checkpoint_file_name())
intermediate_result.dump(checkpoint_file,
dump_configs=True,
dump_histories=maintain_history)
self.logger.info("Saved checkpoint {}".format(checkpoint_file))
benchmark_result = BenchmarkResult(results,
self.configs_to_run,
histories=histories)
self.existing_benchmark_result.extend(benchmark_result)
return self.existing_benchmark_result
def test_dump_and_load_results(self):
result_data = random_result_data(size = 10)
br = BenchmarkResult(result_dict=result_data)
br.dump("./results")
br_loaded = BenchmarkResult.load("./results")
loaded_dict = br_loaded.get_result_dict()
self.assertEqual(result_data, loaded_dict)
def merge_checkpoint_benchmark_results(checkpoint_dir):
checkpoint_files = glob.glob(os.path.join(checkpoint_dir,
"**/*.ckpnt"),
recursive=True)
merged_result = BenchmarkResult()
# merge all checkpoints with new results
for checkpoint_file in checkpoint_files:
logging.info("Loading checkpoint {}".format(
os.path.abspath(checkpoint_file)))
next_result = BenchmarkResult.load(os.path.abspath(checkpoint_file), \
load_configs=True, load_histories=True)
merged_result.extend(next_result)
# dump merged result
if len(merged_result.get_result_dict()) > 0:
logging.info("Dumping merged result")
merged_result_filename = os.path.join(checkpoint_dir,
"merged_results.ckpnt")
merged_result.dump(merged_result_filename, \
dump_configs=True, dump_histories=True)
# delete checkpoints
for checkpoint_file in checkpoint_files:
if checkpoint_file == merged_result_filename:
continue
os.remove(checkpoint_file)
logging.info(
"Removed old checkpoint file {}".format(checkpoint_file))
return merged_result
def get_configs_to_run(benchmark_configs, existing_benchmark_result):
existing_inds = existing_benchmark_result.get_benchmark_config_indices()
required_inds = BenchmarkResult(benchmark_configs=benchmark_configs).get_benchmark_config_indices()
missing_inds = list(set(required_inds) - set(existing_inds))
filtered_configs = filter(lambda bc : bc.config_idx in missing_inds, benchmark_configs)
return list(filtered_configs)
def dummy_benchmark_results():
br = [
{
"config_idx": 24,
"collision": False,
"rss": True,
"behavior": "test1",
"scen_idx": 12
},
{
"config_idx": 1,
"collision": True,
"rss": False,
"behavior": "test",
"scen_idx": 10
},
{
"config_idx": 500,
"collision": True,
"rss": True,
"behavior": "test",
"scen_idx": 112
},
]
behaviorc = BehaviorConfig("behavior_name", "behavior")
benchmarkc = BenchmarkConfig(0, behaviorc, "test scenario", 0,
"scenario_set_name")
return BenchmarkResult(result_dict=br, benchmark_configs=[benchmarkc])
def test_dump_and_partial_load(self):
result_num = 100
confs, result_data, histories = random_benchmark_conf_data(result_num, 2000000, hist_size=1500000)
br = BenchmarkResult(result_dict=result_data,
benchmark_configs=confs, histories=histories)
br.dump("./results_all", dump_configs=True, dump_histories=True, max_mb_per_file = 5)
br_loaded = BenchmarkResult.load("./results_all")
loaded_dict = br_loaded.get_result_dict()
self.assertEqual(br.get_result_dict(), loaded_dict)
loaded_configs_idx = list(range(10, 20))
processed_files = br_loaded.load_benchmark_configs(config_idx_list = loaded_configs_idx)
loaded_confs = br_loaded.get_benchmark_configs()
self.assertEqual(len(loaded_confs), 10)
# 2mb per conf, max 5 mb per file -> 2 confs per file -> 10/2 = 5files
self.assertEqual(len(processed_files), 5)
for conf_idx in loaded_configs_idx:
self.assertEqual(br_loaded.get_benchmark_config(conf_idx), confs[conf_idx])
loaded_configs_idx = list(range(10, 27))
processed_files = br_loaded.load_histories(config_idx_list = loaded_configs_idx)
loaded_histories = br_loaded.get_histories()
self.assertEqual(len(loaded_histories), 18) # one more as specified since it was in the last file
# 1.5mb per history, max 5 mb per file -> 3 confs per file -> 17/3 = 6files
self.assertEqual(len(processed_files), 6)
for conf_idx in loaded_configs_idx:
self.assertEqual(br_loaded.get_history(conf_idx), histories[conf_idx])
def run_benchmark_config(self, config_idx, **kwargs):
for idx, bmark_conf in enumerate(self.benchmark_configs):
if bmark_conf.config_idx == config_idx:
result_dict, scenario_history = self._run_benchmark_config(copy.deepcopy(bmark_conf), **kwargs)
return BenchmarkResult(result_dict, [bmark_conf], histories={config_idx : scenario_history})
self.logger.error("Config idx {} not found in benchmark configs. Skipping...".format(config_idx))
return
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 __init__(self,
benchmark_database=None,
evaluators=None,
terminal_when=None,
behaviors=None,
behavior_configs=None,
num_scenarios=None,
benchmark_configs=None,
scenario_generation=None,
logger_name=None,
log_eval_avg_every=None,
checkpoint_dir=None,
merge_existing=False,
deepcopy=True):
self.benchmark_database = benchmark_database
self.scenario_generation = scenario_generation
self.evaluators = evaluators if isinstance(evaluators, EvaluationConfig) else EvaluationConfig(evaluators)
self.terminal_when = terminal_when or []
if behaviors:
self.behavior_configs = BehaviorConfig.configs_from_dict(behaviors)
else:
self.behavior_configs = behavior_configs or {}
if benchmark_configs:
self.benchmark_configs = benchmark_configs
elif benchmark_database:
self.benchmark_configs = \
self._create_configurations_from_database(num_scenarios)
elif scenario_generation:
self.benchmark_configs = \
self._create_configurations_from_scenario_generation(num_scenarios)
else:
self.benchmark_configs = [] # to be compatible when benchmark runner is used in ray actor
self.logger = logging.getLogger(logger_name or "BenchmarkRunner")
self.logger.setLevel(logging.DEBUG)
self.logger.info("Total number of {} configs to run".format(len(self.benchmark_configs)))
self.configs_to_run = self.benchmark_configs
self._deepcopy = deepcopy
self.checkpoint_dir = checkpoint_dir or "checkpoints"
self.existing_benchmark_result = BenchmarkResult(file_name= \
BenchmarkRunner.get_merged_result_filename(self.checkpoint_dir))
if not os.path.exists(self.checkpoint_dir):
os.makedirs(self.checkpoint_dir)
if merge_existing:
self.existing_benchmark_result = \
BenchmarkRunner.merge_checkpoint_benchmark_results(checkpoint_dir)
self.logger.info("Merged {} processed configs in folder {}". \
format(len(self.existing_benchmark_result.get_benchmark_configs()), checkpoint_dir))
self.configs_to_run = self.get_configs_to_run(self.benchmark_configs, \
self.existing_benchmark_result)
self.logger.info("Remaining number of {} configs to run".format(len(self.configs_to_run)))
self.exceptions_caught = []
self.log_eval_avg_every = log_eval_avg_every
def test_make_scenarios_congruent(self):
brst = BenchmarkResult(result_dict=dummy_benchmark_results(),
benchmark_configs=None)
analyzer = BenchmarkAnalyzer(benchmark_result=brst)
# scenarios 5 and 12 in all lists
congruent_list = analyzer.make_scenarios_congruent(
configs_idx_lists=[[3, 2, 500, 11], [35, 1, 41],
[500, 41, 12, 121]])
self.assertEqual(congruent_list[0], [11, 2])
def test_dump_and_load_benchmark_configs(self):
result_num = 100
confs, result_data, _ = random_benchmark_conf_data(result_num, 2000000)
br = BenchmarkResult(result_dict=result_data, benchmark_configs=confs)
br.dump("./results_with_confs", dump_configs=True, max_mb_per_file = 5)
br_loaded = BenchmarkResult.load("./results_with_confs")
br_loaded.load_benchmark_configs(config_idx_list = list(range(0, result_num)))
loaded_confs = br_loaded.get_benchmark_configs()
self.assertEqual(confs, loaded_confs)
loaded_dict = br_loaded.get_result_dict()
self.assertEqual(br.get_result_dict(), loaded_dict)
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()))
def run(self, viewer=None, maintain_history=False, checkpoint_every=None):
last_results = []
last_histories = {}
last_run_configs = []
results = []
checkpoint_file = os.path.abspath(os.path.join(self.checkpoint_dir, self.get_checkpoint_file_name()))
last_result_file = os.path.abspath(os.path.join(self.checkpoint_dir, "tmp_{}".format(self.get_checkpoint_file_name())))
checkpoint_result = BenchmarkResult(file_name=checkpoint_file)
for idx, bmark_conf in enumerate(self.configs_to_run):
self.logger.info("Running config idx {} being {}/{}: Scenario {} of set \"{}\" for behavior \"{}\"".format(
bmark_conf.config_idx, idx, len(self.benchmark_configs) - 1, bmark_conf.scenario_idx,
bmark_conf.scenario_set_name, bmark_conf.behavior_config.behavior_name))
bmark_conf = copy.deepcopy(bmark_conf) if self._deepcopy else bmark_conf
result_dict, scenario_history = self._run_benchmark_config(bmark_conf, viewer,
maintain_history)
results.append(result_dict)
last_results.append(result_dict)
last_histories[bmark_conf.config_idx] = scenario_history
last_run_configs.append(bmark_conf)
if self.log_eval_avg_every and (idx + 1) % self.log_eval_avg_every == 0:
self._log_eval_average(results, self.configs_to_run)
if checkpoint_every and (idx+1) % checkpoint_every == 0:
# append results since last checkpoint
last_benchmark_result = BenchmarkResult(result_dict=last_results, file_name = last_result_file, \
benchmark_configs=last_run_configs, histories=last_histories)
last_benchmark_result.dump(last_result_file, dump_configs=True, dump_histories=maintain_history, append=False)
checkpoint_result.extend(benchmark_result=last_benchmark_result, file_level=True)
self.logger.info("Extended checkpoint {} with last result.".format(checkpoint_file))
last_histories.clear()
last_run_configs.clear()
last_results.clear()
# append results of last run
last_benchmark_result = BenchmarkResult(result_dict=last_results, file_name = last_result_file, \
benchmark_configs=last_run_configs, histories=last_histories)
last_benchmark_result.dump(last_result_file, dump_configs=True, dump_histories=maintain_history, append=False)
checkpoint_result.extend(benchmark_result=last_benchmark_result, file_level=True)
os.remove(last_result_file)
self.logger.info("Extended checkpoint {} with final result.".format(checkpoint_file))
checkpoint_result.extend(benchmark_result=self.existing_benchmark_result, file_level=True)
return checkpoint_result
def test_dump_and_load_histories_one(self):
result_num = 2
result_data = random_result_data(size = result_num)
histories = random_history_data(result_num, 20)
br = BenchmarkResult(result_dict=result_data, histories=histories)
br.dump("./results_with_history", dump_histories=True, max_mb_per_file = 2)
br_loaded = BenchmarkResult.load("./results_with_history")
br_loaded.load_histories(config_idx_list = list(histories.keys()))
loaded_histories = br_loaded.get_histories()
self.assertEqual(histories, loaded_histories)
loaded_dict = br_loaded.get_result_dict()
self.assertEqual(result_data, loaded_dict)
def test_find_config_idx(self):
brst = BenchmarkResult(result_dict=dummy_benchmark_results(),
benchmark_configs=None)
analyzer = BenchmarkAnalyzer(benchmark_result=brst)
configs_found = analyzer.find_configs({"collision": lambda x: x})
self.assertEqual(configs_found, [1, 500, 11])
configs_found = analyzer.find_configs({"collision": lambda x: not x})
self.assertEqual(configs_found, [24, 2, 41, 3, 12, 121, 35, 42])
configs_found = analyzer.find_configs({"metric1": lambda x: x == 0.1})
self.assertEqual(configs_found, [24, 11])
configs_found = analyzer.find_configs({
"collision": lambda x: not x,
"metric1": lambda x: x == 0.1
})
self.assertEqual(configs_found, [24])
configs_found = analyzer.find_configs({
"collision": lambda x: x,
"metric1": lambda x: x > 1
})
self.assertEqual(configs_found, [1, 500])
configs_found = analyzer.find_configs({
"collision":
lambda x: x,
"behavior":
lambda x: x == "test2"
})
self.assertEqual(configs_found, [])
configs_found = analyzer.find_configs(scenario_idx_list=[4, 10, 7])
self.assertEqual(configs_found, [121, 1, 42])
configs_found = analyzer.find_configs({"collision": lambda x: not x},
scenario_idx_list=[4, 10, 7])
self.assertEqual(configs_found, [121, 42])
configs_found = analyzer.find_configs(
{"collision": lambda x: x},
scenarios_as_in_configs=[24, 11, 121, 1])
self.assertEqual(configs_found, [11, 1])
configs_found = analyzer.find_configs(scenario_idx_list=[4, 10, 7],
in_configs=[1, 42])
self.assertEqual(configs_found, [1, 42])
def run(self, viewer=None, maintain_history=False, checkpoint_every=None):
results_tmp = ray.get([
actor.run.remote(viewer, maintain_history, checkpoint_every)
for actor in self.actors
])
result_dict = []
benchmark_configs = []
histories = {}
for result_tmp in results_tmp:
result_dict.extend(result_tmp.get_result_dict())
benchmark_configs.extend(result_tmp.get_benchmark_configs())
histories.update(result_tmp.get_histories())
benchmark_result = BenchmarkResult(result_dict,
benchmark_configs,
histories=histories)
self.existing_benchmark_result.extend(benchmark_result)
return self.existing_benchmark_result
def __init__(self,
benchmark_database=None,
evaluators=None,
terminal_when=None,
behaviors=None,
behavior_configs=None,
num_scenarios=None,
benchmark_configs=None,
logger_name=None,
log_eval_avg_every=None,
checkpoint_dir=None,
merge_existing=False):
self.benchmark_database = benchmark_database
self.evaluators = evaluators or {}
self.terminal_when = terminal_when or []
if behaviors:
self.behavior_configs = BehaviorConfig.configs_from_dict(behaviors)
else:
self.behavior_configs = behavior_configs or {}
self.benchmark_configs = benchmark_configs or \
self._create_configurations(num_scenarios)
self.logger = logging.getLogger(logger_name or "BenchmarkRunner")
self.logger.setLevel(logging.DEBUG)
self.logger.info("Total number of {} configs to run".format(len(self.benchmark_configs)))
self.existing_benchmark_result = BenchmarkResult()
self.configs_to_run = self.benchmark_configs
self.checkpoint_dir = checkpoint_dir or "checkpoints"
if not os.path.exists(self.checkpoint_dir):
os.makedirs(self.checkpoint_dir)
if merge_existing:
self.existing_benchmark_result = \
BenchmarkRunner.merge_checkpoint_benchmark_results(checkpoint_dir)
self.logger.info("Merged {} processed configs in folder {}". \
format(len(self.existing_benchmark_result.get_benchmark_configs()), checkpoint_dir))
self.configs_to_run = self.get_configs_to_run(self.benchmark_configs, \
self.existing_benchmark_result)
self.logger.info("Remaining number of {} configs to run".format(len(self.configs_to_run)))
self.exceptions_caught = []
self.log_eval_avg_every = log_eval_avg_every
def merge_checkpoint_benchmark_results(checkpoint_dir):
checkpoint_files = glob.glob(os.path.join(checkpoint_dir, "**/*.ckpnt"), recursive=True)
merged_result_filename = BenchmarkRunner.get_merged_result_filename(checkpoint_dir)
if os.path.exists(merged_result_filename):
merged_result = BenchmarkResult.load_results(filename=merged_result_filename)
else:
merged_result = BenchmarkResult(file_name=merged_result_filename)
# merge all checkpoints with new results
for checkpoint_file in checkpoint_files:
loaded_result = BenchmarkResult.load(os.path.abspath(checkpoint_file))
merged_result.extend(loaded_result, file_level=True)
logging.info("Extending with checkpoint {}".format(checkpoint_file))
# delete checkpoints
for checkpoint_file in checkpoint_files:
if "merged_result" in checkpoint_file:
continue
os.remove(checkpoint_file)
logging.info("Removed old checkpoint file {}".format(checkpoint_file))
return merged_result