class MockPSMAC(PSMAC):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.ow = OutputWriter()
self.ow.write_scenario_file(self.scenario)
global MOCKCALLS
MOCKCALLS += 1
def optimize(self):
return np.array(
self.scenario.cs.sample_configuration(self.n_optimizers))
def get_best_incumbents_ids(self, incs):
cost_per_conf_v = cost_per_conf_e = {}
val_ids = est_ids = list(range(len(incs)))
global MOCKCALLS
for inc in incs:
# in successive runs will always be smaller -> hydra doesn't terminate early
cost_per_conf_v[inc] = cost_per_conf_e[inc] = {
inst: max(100 - MOCKCALLS, 0)
for inst in self.scenario.train_insts
}
if not self.validate:
cost_per_conf_v = val_ids = None
return cost_per_conf_v, val_ids, cost_per_conf_e, est_ids
class Scenario(object):
"""
Scenario contains the configuration of the optimization process and
constructs a scenario object from a file or dictionary.
All arguments set in the Scenario are set as attributes.
"""
def __init__(self, scenario, cmd_args: dict = None, run_id: int = 1):
"""Constructor
Parameters
----------
scenario : str or dict
If str, it will be interpreted as to a path a scenario file
If dict, it will be directly to get all scenario related information
cmd_args : dict
Command line arguments that were not processed by argparse
run_id: int
Run ID will be used as suffix for output_dir
"""
self.logger = logging.getLogger(self.__module__ + '.' +
self.__class__.__name__)
self.PCA_DIM = 7
self.in_reader = InputReader()
self.out_writer = OutputWriter()
if type(scenario) is str:
scenario_fn = scenario
self.logger.info("Reading scenario file: %s" % (scenario_fn))
scenario = self.in_reader.read_scenario_file(scenario_fn)
elif type(scenario) is dict:
scenario = copy.copy(scenario)
else:
raise TypeError(
"Wrong type of scenario (str or dict are supported)")
if cmd_args:
scenario.update(cmd_args)
self._arguments = {}
self._groups = defaultdict(set)
self._add_arguments()
# Make cutoff mandatory if run_obj is runtime
if scenario['run_obj'] == 'runtime':
self._arguments['cutoff_time']['required'] = True
# Parse arguments
parsed_arguments = {}
for key, value in self._arguments.items():
arg_name, arg_value = self._parse_argument(key, scenario, **value)
parsed_arguments[arg_name] = arg_value
if len(scenario) != 0:
raise ValueError('Could not parse the following arguments: %s' %
str(list(scenario.keys())))
for group, potential_members in self._groups.items():
n_members_in_scenario = 0
for pm in potential_members:
if pm in parsed_arguments:
n_members_in_scenario += 1
if n_members_in_scenario != 1:
raise ValueError('Exactly one of the following arguments must '
'be specified in the scenario file: %s' %
str(potential_members))
for arg_name, arg_value in parsed_arguments.items():
setattr(self, arg_name, arg_value)
self._transform_arguments()
if self.output_dir:
self.output_dir += "_run%d" % (run_id)
self.out_writer.write_scenario_file(self)
self.logger.debug("Scenario Options:")
for arg_name, arg_value in parsed_arguments.items():
if isinstance(arg_value, (int, str, float)):
self.logger.debug("%s = %s" % (arg_name, arg_value))
def add_argument(self,
name: str,
help: str,
callback=None,
default=None,
dest: str = None,
required: bool = False,
mutually_exclusive_group: str = None,
choice=None):
"""Add argument to the scenario object.
Parameters
----------
name : str
Argument name
help : str
Help text which can be displayed in the documentation.
callback : callable, optional
If given, the callback will be called when the argument is
parsed. Useful for custom casting/typechecking.
default : object, optional
Default value if the argument is not given. Default to ``None``.
dest : str
Assign the argument to scenario object by this name.
required : bool
If True, the scenario will raise an error if the argument is not
given.
mutually_exclusive_group : str
Group arguments with similar behaviour by assigning the same string
value. The scenario will ensure that exactly one of the arguments is
given. Is used for example to ensure that either a configuration
space object or a parameter file is passed to the scenario. Can not
be used together with ``required``.
choice: list/set/tuple
List of possible string for this argument
"""
if not isinstance(required, bool):
raise TypeError("Argument 'required' must be of type 'bool'.")
if required is not False and mutually_exclusive_group is not None:
raise ValueError("Cannot make argument '%s' required and add it to"
" a group of mutually exclusive arguments." %
name)
if choice is not None and not isinstance(choice, (list, set, tuple)):
raise TypeError('Choice must be of type list/set/tuple.')
self._arguments[name] = {
'default': default,
'required': required,
'help': help,
'dest': dest,
'callback': callback,
'choice': choice
}
if mutually_exclusive_group:
self._groups[mutually_exclusive_group].add(name)
def _parse_argument(self,
name: str,
scenario: dict,
help: str,
callback=None,
default=None,
dest: str = None,
required: bool = False,
choice=None):
"""Search the scenario dict for a single allowed argument and parse it.
Side effect: the argument is removed from the scenario dict if found.
name : str
Argument name, as specified in the Scenario class.
scenario : dict
Scenario dict as provided by the user or as parsed by the cli
interface.
help : str
Help string of the argument
callback : callable, optional (default=None)
If given, will be called to transform the given argument.
default : object, optional (default=None)
Will be used as default value if the argument is not given by the
user.
dest : str, optional (default=None)
Will be used as member name of the scenario.
required : bool (default=False)
If ``True``, the scenario will raise an Exception if the argument is
not given.
choice : list, optional (default=None)
If given, the scenario checks whether the argument is in the
list. If not, it raises an Exception.
Returns
-------
str
Member name of the attribute.
object
Value of the attribute.
"""
normalized_name = name.lower().replace('-', '').replace('_', '')
value = None
# Allows us to pop elements in order to remove all parsed elements
# from the dictionary
for key in list(scenario.keys()):
# Check all possible ways to spell an argument
normalized_key = key.lower().replace('-', '').replace('_', '')
if normalized_key == normalized_name:
value = scenario.pop(key)
if dest is None:
dest = name.lower().replace('-', '_')
if required is True:
if value is None:
raise ValueError('Required scenario argument %s not given.' %
name)
if value is None:
value = default
if value is not None and callable(callback):
value = callback(value)
if value is not None and choice:
value = value.strip()
if value not in choice:
raise ValueError('Argument %s can only take a value in %s, '
'but is %s' % (name, choice, value))
return dest, value
def _add_arguments(self):
"""TODO"""
# Add allowed arguments
self.add_argument(
name='abort_on_first_run_crash',
help="If true, *SMAC* will abort if the first run of "
"the target algorithm crashes.",
default=True,
callback=_is_truthy)
self.add_argument(name='always_race_default',
default=False,
help="Race new incumbents always against default "
"configuration.",
callback=_is_truthy,
dest="always_race_default")
self.add_argument(
name='algo',
dest='ta',
callback=shlex.split,
help="Specifies the target algorithm call that *SMAC* "
"will optimize. Interpreted as a bash-command.")
self.add_argument(
name='execdir',
default='.',
help="Specifies the path to the execution-directory.")
self.add_argument(name='deterministic',
default=False,
help="If true, the optimization process will be "
"repeatable.",
callback=_is_truthy)
self.add_argument(name='intensification_percentage',
default=0.5,
help="The fraction of time to be used on "
"intensification (versus choice of next "
"Configurations).",
callback=float)
self.add_argument(name='paramfile',
help="Specifies the path to the "
"PCS-file.",
dest='pcs_fn',
mutually_exclusive_group='cs')
self.add_argument(name='run_obj',
help="Defines what metric to optimize. When "
"optimizing runtime, *cutoff_time* is "
"required as well.",
required=True,
choice=['runtime', 'quality'])
self.add_argument(name='overall_obj',
help="PARX, where X is an integer defining the "
"penalty imposed on timeouts (i.e. runtimes that "
"exceed the *cutoff-time*).",
default='par10')
self.add_argument(name='cost_for_crash',
default=float(MAXINT),
help="Defines the cost-value for crashed runs "
"on scenarios with quality as run-obj.",
callback=float)
self.add_argument(name='cutoff_time',
help="Maximum runtime, after which the "
"target algorithm is cancelled. **Required "
"if *run_obj* is runtime.**",
default=None,
dest='cutoff',
callback=float)
self.add_argument(name='memory_limit',
help="Maximum available memory the target algorithm "
"can occupy before being cancelled.")
self.add_argument(
name='tuner-timeout',
help="Maximum amount of CPU-time used for optimization.",
default=numpy.inf,
dest='algo_runs_timelimit',
callback=float)
self.add_argument(
name='wallclock_limit',
help="Maximum amount of wallclock-time used for optimization.",
default=numpy.inf,
callback=float)
self.add_argument(
name='always_race_default',
help="Race new incumbents always against default configuration.",
default=False,
callback=_is_truthy,
dest="always_race_default")
self.add_argument(
name='runcount_limit',
help="Maximum number of algorithm-calls during optimization.",
default=numpy.inf,
callback=float,
dest="ta_run_limit")
self.add_argument(name='minR',
help="Minimum number of calls per configuration.",
default=1,
callback=int,
dest='minR')
self.add_argument(name='maxR',
help="Maximum number of calls per configuration.",
default=2000,
callback=int,
dest='maxR')
self.add_argument(
name='instance_file',
help="Specifies the file with the training-instances.",
dest='train_inst_fn')
self.add_argument(name='test_instance_file',
help="Specifies the file with the test-instances.",
dest='test_inst_fn')
self.add_argument(
name='feature_file',
help="Specifies the file with the instance-features.",
dest='feature_fn')
self.add_argument(
name='output_dir',
help="Specifies the output-directory for all emerging "
"files, such as logging and results.",
default="smac3-output_%s" % (datetime.datetime.fromtimestamp(
time.time()).strftime('%Y-%m-%d_%H:%M:%S_(%f)')))
self.add_argument(
name='input_psmac_dirs',
default=None,
help="For parallel SMAC, multiple output-directories "
"are used.")
self.add_argument(name='shared_model',
help="Whether to run SMAC in parallel mode.",
default=False,
callback=_is_truthy)
self.add_argument(name='instances',
default=[[None]],
help=None,
dest='train_insts')
self.add_argument(name='test_instances',
default=[[None]],
help=None,
dest='test_insts')
self.add_argument(name='initial_incumbent',
default="DEFAULT",
help="DEFAULT is the default from the PCS.",
dest='initial_incumbent',
choice=['DEFAULT', 'RANDOM'])
# instance name -> feature vector
self.add_argument(name='features',
default={},
help=None,
dest='feature_dict')
# ConfigSpace object
self.add_argument(name='cs', help=None, mutually_exclusive_group='cs')
def _transform_arguments(self):
"""TODO"""
self.n_features = len(self.feature_dict)
self.feature_array = None
if self.overall_obj[:3] in ["PAR", "par"]:
par_str = self.overall_obj[3:]
elif self.overall_obj[:4] in ["mean", "MEAN"]:
par_str = self.overall_obj[4:]
# Check for par-value as in "par10"/ "mean5"
if len(par_str) > 0:
self.par_factor = int(par_str)
else:
self.logger.debug("No par-factor detected. Using 1 by default.")
self.par_factor = 1
# read instance files
if self.train_inst_fn:
if os.path.isfile(self.train_inst_fn):
self.train_insts = self.in_reader.read_instance_file(
self.train_inst_fn)
else:
self.logger.error("Have not found instance file: %s" %
(self.train_inst_fn))
sys.exit(1)
if self.test_inst_fn:
if os.path.isfile(self.test_inst_fn):
self.test_insts = self.in_reader.read_instance_file(
self.test_inst_fn)
else:
self.logger.error("Have not found test instance file: %s" %
(self.test_inst_fn))
sys.exit(1)
self.instance_specific = {}
def extract_instance_specific(instance_list):
insts = []
for inst in instance_list:
if len(inst) > 1:
self.instance_specific[inst[0]] = " ".join(inst[1:])
insts.append(inst[0])
return insts
self.train_insts = extract_instance_specific(self.train_insts)
if self.test_insts:
self.test_insts = extract_instance_specific(self.test_insts)
self.train_insts = self._to_str_and_warn(l=self.train_insts)
self.test_insts = self._to_str_and_warn(l=self.test_insts)
# read feature file
if self.feature_fn:
if os.path.isfile(self.feature_fn):
self.feature_dict = self.in_reader.read_instance_features_file(
self.feature_fn)[1]
if self.feature_dict:
self.feature_array = []
for inst_ in self.train_insts:
self.feature_array.append(self.feature_dict[inst_])
self.feature_array = numpy.array(self.feature_array)
self.n_features = self.feature_array.shape[1]
# read pcs file
if self.pcs_fn and os.path.isfile(self.pcs_fn):
with open(self.pcs_fn) as fp:
pcs_str = fp.readlines()
try:
self.cs = pcs.read(pcs_str)
except:
self.logger.debug(
"Could not parse pcs file with old format; trying new format next"
)
self.cs = pcs_new.read(pcs_str)
self.cs.seed(42)
elif self.pcs_fn:
self.logger.error("Have not found pcs file: %s" % (self.pcs_fn))
sys.exit(1)
# you cannot set output dir to None directly
# because None is replaced by default always
if self.output_dir == "":
self.output_dir = None
self.logger.debug("Deactivate output directory.")
else:
self.logger.info("Output to %s" % (self.output_dir))
if self.shared_model and self.input_psmac_dirs is None:
# per default, we assume that
# all psmac runs write to the same directory
self.input_psmac_dirs = [self.output_dir]
def __getstate__(self):
d = dict(self.__dict__)
del d['logger']
return d
def __setstate__(self, d):
self.__dict__.update(d)
self.logger = logging.getLogger(self.__module__ + '.' +
self.__class__.__name__)
def _to_str_and_warn(self, l: typing.List[typing.Any]):
warn_ = False
for i, e in enumerate(l):
if e is not None and not isinstance(e, str):
warn_ = True
try:
l[i] = str(e)
except ValueError:
raise ValueError("Failed to cast all instances to str")
if warn_:
self.logger.warn("All instances were casted to str.")
return l
def write_options_to_doc(self, path='scenario_options.rst'):
"""Writes the option-list to file for autogeneration in documentation.
The list is created in doc/conf.py and read in doc/options.rst.
Parameters
----------
path: string
Where to write to (relative to doc-folder since executed in conf.py)
"""
exclude = ['cs', 'features', 'instances', 'test_instances']
with open(path, 'w') as fh:
for arg in sorted(self._arguments.keys()):
if arg in exclude:
continue
fh.write(":{}: ".format(arg))
fh.write("{}".format(self._arguments[arg]['help']))
if self._arguments[arg]['default']:
fh.write(" Default: {}.".format(
self._arguments[arg]['default']))
if self._arguments[arg]['choice']:
fh.write(" Must be from: {}.".format(
self._arguments[arg]['choice']))
fh.write("\n")
fh.write("\n\n")
class Scenario(object):
"""
Scenario contains the configuration of the optimization process and
constructs a scenario object from a file or dictionary.
All arguments set in the Scenario are set as attributes.
"""
def __init__(self, scenario=None, cmd_options: dict = None):
""" Creates a scenario-object. The output_dir will be
"output_dir/run_id/" and if that exists already, the old folder and its
content will be moved (without any checks on whether it's still used by
another process) to "output_dir/run_id.OLD". If that exists, ".OLD"s
will be appended until possible.
Parameters
----------
scenario : str or dict or None
If str, it will be interpreted as to a path a scenario file
If dict, it will be directly to get all scenario related information
If None, only cmd_options will be used
cmd_options : dict
Options from parsed command line arguments
"""
self.logger = logging.getLogger(self.__module__ + '.' +
self.__class__.__name__)
self.PCA_DIM = 7
self.in_reader = InputReader()
self.out_writer = OutputWriter()
self.output_dir_for_this_run = None
self._arguments = {}
self._arguments.update(CMDReader().scen_cmd_actions)
if scenario is None:
scenario = {}
if isinstance(scenario, str):
scenario_fn = scenario
scenario = {}
if cmd_options:
scenario.update(cmd_options)
cmd_reader = CMDReader()
self.logger.info("Reading scenario file: %s", scenario_fn)
smac_args_, scen_args_ = cmd_reader.read_smac_scenario_dict_cmd(
scenario, scenario_fn)
scenario = {}
scenario.update(vars(smac_args_))
scenario.update(vars(scen_args_))
elif isinstance(scenario, dict):
scenario = copy.copy(scenario)
if cmd_options:
scenario.update(cmd_options)
cmd_reader = CMDReader()
smac_args_, scen_args_ = cmd_reader.read_smac_scenario_dict_cmd(
scenario)
scenario = {}
scenario.update(vars(smac_args_))
scenario.update(vars(scen_args_))
else:
raise TypeError(
"Wrong type of scenario (str or dict are supported)")
for arg_name, arg_value in scenario.items():
setattr(self, arg_name, arg_value)
self._transform_arguments()
self.logger.debug("SMAC and Scenario Options:")
if cmd_options:
for arg_name, arg_value in cmd_options.items():
if isinstance(arg_value, (int, str, float)):
self.logger.debug("%s = %s" % (arg_name, arg_value))
def _transform_arguments(self):
"""TODO"""
self.n_features = len(self.feature_dict)
self.feature_array = None
self.instance_specific = {}
if self.run_obj == "runtime":
self.logy = True
def extract_instance_specific(instance_list):
insts = []
for inst in instance_list:
if len(inst) > 1:
self.instance_specific[inst[0]] = " ".join(inst[1:])
insts.append(inst[0])
return insts
self.train_insts = extract_instance_specific(self.train_insts)
if self.test_insts:
self.test_insts = extract_instance_specific(self.test_insts)
self.train_insts = self._to_str_and_warn(l=self.train_insts)
self.test_insts = self._to_str_and_warn(l=self.test_insts)
if self.feature_dict:
self.feature_array = []
for inst_ in self.train_insts:
self.feature_array.append(self.feature_dict[inst_])
self.feature_array = np.array(self.feature_array)
self.n_features = self.feature_array.shape[1]
if self.use_ta_time:
if self.algo_runs_timelimit is None or not np.isfinite(
self.algo_runs_timelimit):
self.algo_runs_timelimit = self.wallclock_limit
self.wallclock_limit = np.inf
def __getstate__(self):
d = dict(self.__dict__)
del d['logger']
return d
def __setstate__(self, d):
self.__dict__.update(d)
self.logger = logging.getLogger(self.__module__ + '.' +
self.__class__.__name__)
def _to_str_and_warn(self, l: typing.List[typing.Any]):
warn_ = False
for i, e in enumerate(l):
if e is not None and not isinstance(e, str):
warn_ = True
try:
l[i] = str(e)
except ValueError:
raise ValueError("Failed to cast all instances to str")
if warn_:
self.logger.warning("All instances were casted to str.")
return l
def write(self):
""" Write scenario to self.output_dir/scenario.txt. """
self.out_writer.write_scenario_file(self)