def __init__(self,
perf_fn: str,
feat_fn: str,
objective: str = "solution_quality",
runtime_cutoff: float = None,
maximize: bool = True,
cv_fn: str = None,
seed: int = 12345):
""" Constructor """
self.scenario = ASlibScenario()
self.scenario.read_from_csv(perf_fn=perf_fn,
feat_fn=feat_fn,
objective=objective,
runtime_cutoff=runtime_cutoff,
maximize=maximize,
cv_fn=cv_fn)
self.seed = seed
self.af = AutoFolio(random_seed=seed)
self.logger = logging.getLogger("AF Facade")
self.feature_pre_pipeline = None
self.pre_solver = None
self.selector = None
self.config = None
self.cs = None
def run_cli(self):
'''
main method of AutoFolio based on command line interface
'''
cmd_parser = CMDParser()
args_, self.overwrite_args = cmd_parser.parse()
self._root_logger.setLevel(args_.verbose)
if args_.load:
pred = self.read_model_and_predict(
model_fn=args_.load,
feature_vec=list(map(float, args_.feature_vec.split(" "))))
print("Selected Schedule [(algorithm, budget)]: %s" % (pred))
else:
scenario = ASlibScenario()
if args_.scenario:
scenario.read_scenario(args_.scenario)
elif args_.performance_csv and args_.feature_csv:
scenario.read_from_csv(perf_fn=args_.performance_csv,
feat_fn=args_.feature_csv,
objective=args_.objective,
runtime_cutoff=args_.runtime_cutoff,
maximize=args_.maximize,
cv_fn=args_.cv_csv)
else:
raise ValueError("Missing inputs to read scenario data.")
test_scenario = None
if args_.performance_test_csv and args_.feature_test_csv:
test_scenario = ASlibScenario()
test_scenario.read_from_csv(
perf_fn=args_.performance_test_csv,
feat_fn=args_.feature_test_csv,
objective=args_.objective,
runtime_cutoff=args_.runtime_cutoff,
maximize=args_.maximize,
cv_fn=None)
config = {}
if args_.config is not None:
self.logger.info("Reading yaml config file")
config = yaml.load(open(args_.config))
if not config.get("wallclock_limit"):
config["wallclock_limit"] = args_.wallclock_limit
if not config.get("runcount_limit"):
config["runcount_limit"] = args_.runcount_limit
if not config.get("output-dir"):
config["output-dir"] = args_.output_dir
self.cs = self.get_cs(scenario, config)
if args_.outer_cv:
self._outer_cv(scenario,
config,
args_.outer_cv_fold,
args_.out_template,
smac_seed=args_.smac_seed)
return 0
if args_.tune:
config = self.get_tuned_config(
scenario,
wallclock_limit=args_.wallclock_limit,
runcount_limit=args_.runcount_limit,
autofolio_config=config,
seed=args_.smac_seed)
else:
config = self.cs.get_default_configuration()
self.logger.debug(config)
if args_.save:
feature_pre_pipeline, pre_solver, selector = self.fit(
scenario=scenario, config=config)
self._save_model(args_.save, scenario, feature_pre_pipeline,
pre_solver, selector, config)
else:
self.run_cv(config=config,
scenario=scenario,
folds=int(scenario.cv_data.max().max()))
if test_scenario is not None:
stats = self.run_fold(config=config,
fold=0,
return_fit=False,
scenario=scenario,
test_scenario=test_scenario)