def test_feature_input(self):
feature_fn = "test/test_files/features_example.csv"
in_reader = InputReader()
feats = in_reader.read_instance_features_file(fn=feature_fn)
self.assertEqual(feats[0], ["feature1", "feature2", "feature3"])
feats_original = {"inst1": [1.0, 2.0, 3.0],
"inst2": [1.5, 2.5, 3.5],
"inst3": [1.7, 1.8, 1.9]}
for i in feats[1]:
self.assertEqual(feats_original[i], list(feats[1][i]))
class CSV2RH(object):
def read_csv_to_rh(
self,
data,
cs: Union[None, str, ConfigurationSpace] = None,
id_to_config: Union[None, dict] = None,
train_inst: Union[None, str, list] = None,
test_inst: Union[None, str, list] = None,
instance_features: Union[None, str, dict] = None,
):
""" Interpreting a .csv-file as runhistory.
Valid values for the header of the csv-file/DataFrame are:
['seed', 'cost', 'time', 'status', 'budget', 'config_id', 'instance_id'] or any
parameter- or instance-feature-names.
Parameters
----------
data: str or pd.DataFrame
either string to csv-formatted runhistory-file or DataFrame
containing the same information
cs: str or ConfigurationSpace
config-space to use for this runhistory
id_to_config: dict
mapping ids to Configuration-objects
train_inst: str or list[str]
train instances or path to file
test_inst: str or list[str]
test instances or path to file
instance_features: str or dict
instance features as dict mapping instance-ids to feature-array or
file to appropriately formatted instance-feature-file
Returns:
--------
rh: RunHistory
runhistory with all the runs from the csv-file
"""
self.logger = logging.getLogger(self.__module__ + '.' +
self.__class__.__name__)
self.input_reader = InputReader()
self.train_inst = self.input_reader.read_instance_file(
train_inst) if type(train_inst) == str else train_inst
self.test_inst = self.input_reader.read_instance_file(
test_inst) if type(test_inst) == str else test_inst
feature_names = [] # names of instance-features
if type(instance_features) == str:
feature_names, instance_features = self.input_reader.read_instance_features_file(
instance_features)
# Read in data
if isinstance(data, str):
self.logger.debug("Detected path for csv-file (\'%s\')", data)
data = load_csv_to_pandaframe(data,
self.logger,
apply_numeric=False)
# Expecting header as described in docstring
self.valid_values = [
'seed', 'cost', 'time', 'status', 'budget', 'config_id',
'instance_id'
]
if isinstance(cs, str):
self.logger.debug("Reading PCS from %s", cs)
with open(cs, 'r') as fh:
cs = pcs.read(fh)
elif not cs:
self.logger.debug("No config-space provided, create from columns")
if id_to_config:
cs = np.random.choice(list(
id_to_config.values())).configuration_space
else:
parameters = set(data.columns)
parameters -= set(self.valid_values)
parameters -= set(feature_names)
parameters = list(parameters)
cs = self.create_cs_from_pandaframe(data[parameters])
parameters = cs.get_hyperparameter_names()
if not feature_names and not 'instance_id' in data.columns:
feature_names = [
c for c in data.columns
if not c.lower() in self.valid_values and not c in parameters
]
for c in set(self.valid_values).intersection(set(data.columns)):
# Cast to numeric
data[c] = data[c].apply(pd.to_numeric, errors='ignore')
data, id_to_config = self.extract_configs(data, cs, id_to_config)
data, id_to_inst_feats = self.extract_instances(
data, feature_names, instance_features)
self.logger.debug(
"Found: seed=%s, cost=%s, time=%s, status=%s, budget=%s", 'seed'
in data.columns, 'cost' in data.columns, 'time' in data.columns,
'status' in data.columns, 'budget' in data.columns)
# Create RunHistory
rh = RunHistory()
def add_to_rh(row):
new_status = self._interpret_status(
row['status']) if 'status' in row else StatusType.SUCCESS
rh.add(config=id_to_config[row['config_id']],
cost=row['cost'],
time=row['time'] if 'time' in row else -1,
status=new_status,
instance_id=row['instance_id']
if 'instance_id' in row else None,
seed=row['seed'] if 'seed' in row else None,
budget=row['budget'] if 'budget' in row else 0,
additional_info=None,
origin=DataOrigin.INTERNAL)
data.apply(add_to_rh, axis=1)
return rh
def create_cs_from_pandaframe(self, data):
# TODO use from pyimp after https://github.com/automl/ParameterImportance/issues/72 is implemented
warnings.warn(
"No parameter configuration space (pcs) provided! "
"Interpreting all parameters as floats. This might lead "
"to suboptimal analysis.", RuntimeWarning)
self.logger.debug("Interpreting as parameters: %s", data.columns)
minima = data.min() # to define ranges of hyperparameter
maxima = data.max()
cs = ConfigurationSpace(seed=42)
for p in data.columns:
cs.add_hyperparameter(
UniformFloatHyperparameter(p,
lower=minima[p] - 1,
upper=maxima[p] + 1))
return cs
def _interpret_status(self, status, types=None):
"""
Parameters
----------
status: str
status-string
types: dict[str:StatusType]
optional, mapping to use
Returns
-------
status: StatusType
interpreted status-type
"""
if not types:
types = {
"SAT": StatusType.SUCCESS,
"UNSAT": StatusType.SUCCESS,
"SUCCESS": StatusType.SUCCESS,
"STATUSTYPE.SUCCESS": StatusType.SUCCESS,
"TIMEOUT": StatusType.TIMEOUT,
"STATUSTYPE.TIMEOUT": StatusType.TIMEOUT,
"CRASHED": StatusType.CRASHED,
"STATUSTYPE.CRASHED": StatusType.CRASHED,
"MEMOUT": StatusType.MEMOUT,
"STATUSTYPE.MEMOUT": StatusType.MEMOUT,
"ABORT": StatusType.ABORT,
"STATUSTYPE.ABORT": StatusType.ABORT,
}
status = status.strip().upper()
if status in types:
status = types[status]
else:
self.logger.warning(
"Could not parse %s as a status. Valid values "
"are: %s. Treating as CRASHED run.", status, types.keys())
status = StatusType.CRASHED
return status
def extract_configs(self, data, cs: ConfigurationSpace, id_to_config=None):
"""
After completion, every unique configuration in the data will have a
corresponding id in the data-frame.
The data-frame is expected to either contain a column for config-id OR
columns for each individual hyperparameter. Parameter-names will be used
from the provided configspace.
If a mapping of ids to configurations already exists, it will be used.
Parameters
----------
data: pd.DataFrame
pandas dataframe containing either a column called `config_id` or a
column for every individual parameter
cs: ConfigurationSpace
optional, if provided the `parameters`-argument will be ignored
id_to_config: dict[int:Configuration]
optional, mapping ids to Configurations (necessary when using
`config_id`-column)
Returns
-------
data: pd.DataFrame
if no config-id-columns was there before, there is one now.
id_to_config: dict
mapping every id to a configuration
"""
if id_to_config:
config_to_id = {conf: name for name, conf in id_to_config.items()}
else:
id_to_config = {}
config_to_id = {}
parameters = cs.get_hyperparameter_names()
if 'config_id' in data.columns and not id_to_config:
raise ValueError("When defining configs with \"config_id\" "
"in header, you need to provide the argument "
"\"configurations\" to the CSV2RH-object - "
"either as a dict, mapping the id's to "
"Configurations or as a path to a csv-file "
"containing the necessary information.")
if 'config_id' not in data.columns:
# Map to configurations
ids_in_order = []
data['config_id'] = -1
def add_config(row):
values = {
name: row[name]
for name in parameters if row[name] != ''
}
config = deactivate_inactive_hyperparameters(
fix_types(values, cs), cs)
if config not in config_to_id:
config_to_id[config] = len(config_to_id)
row['config_id'] = config_to_id[config]
return row
data = data.apply(add_config, axis=1)
id_to_config = {conf: name for name, conf in config_to_id.items()}
data["config_id"] = pd.to_numeric(data["config_id"])
# Check whether all config-ids are present
if len(set(data['config_id']) - set(id_to_config.keys())) > 0:
raise ValueError(
"config id {} cannot be identified (is your configurations.csv complete? Or maybe "
"this is a type-issue...".format(
set(data['config_id']) - set(id_to_config.keys())))
return data, id_to_config
def extract_instances(self, data, feature_names, features):
"""
After completion, every unique instance in the data will have a
corresponding id in the data-frame.
The data-frame is expected to either contain a column for instance-id OR
columns for each individual instance-feature. Parameter-names will be used
from the provided configspace.
If a mapping of ids to configurations already exists, it will be used.
Parameters
----------
data: pd.DataFrame
pandas dataframe containing either a column called `instance_id` or a
column for every individual instance-features
feature_names: list[str]
optional, list of feature-names
features: dict[int:np.array]
optional, mapping ids to instance-feature vectors (necessary when using
`instance_id`-column)
Returns
-------
data: pd.DataFrame
if no instance_id-columns was there before, there is one now.
id_to_inst_feats: dict
mapping every id to instance-features
"""
id_to_inst_feats = {}
inst_feats_to_id = {}
if features:
id_to_inst_feats = {
i: tuple([str(f) for f in feat])
for i, feat in features.items()
}
inst_feats_to_id = {
feat: i
for i, feat in id_to_inst_feats.items()
}
if 'instance_id' in data.columns and not features:
raise ValueError(
"Instances defined via \'instance_id\'-column, but no instance features available."
)
elif 'instance_id' not in data.columns and feature_names:
# Add new column for instance-ids
data['instance_id'] = -1
self.old = None
def add_instance(row):
row_features = tuple([str(row[idx]) for idx in feature_names])
if row_features not in inst_feats_to_id:
new_id = len(inst_feats_to_id)
inst_feats_to_id[row_features] = new_id
id_to_inst_feats[new_id] = features
row['instance_id'] = inst_feats_to_id[row_features]
self.old = row_features
return row
data = data.apply(add_instance, axis=1)
else:
self.logger.info("No instances detected.")
id_to_inst_feats = {
i: np.array(f).astype('float64')
for i, f in id_to_inst_feats.items()
}
return data, id_to_inst_feats
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):
'''
main class of SMAC
'''
def __init__(self, scenario, cmd_args=None):
"""Construct scenario object from file or dictionary.
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
"""
self.logger = logging.getLogger("scenario")
self.in_reader = InputReader()
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:
pass
else:
raise TypeError(
"Wrong type of scenario (str or dict are supported)")
if cmd_args:
scenario.update(cmd_args)
self._arguments = {}
self._add_arguments()
# 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 arg_name, arg_value in parsed_arguments.items():
setattr(self, arg_name, arg_value)
self._transform_arguments()
def add_argument(self,
name,
help,
callback=None,
default=None,
dest=None,
required=False):
if not isinstance(required, bool):
raise TypeError("Argument required must be of type 'bool'.")
self._arguments[name] = {
'default': default,
'required': required,
'help': help,
'dest': dest,
'callback': callback
}
def _parse_argument(self,
name,
scenario,
help,
callback=None,
default=None,
dest=None,
required=False):
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 argument %s not given.' % name)
if value is None:
value = default
if value is not None and callable(callback):
value = callback(value)
return dest, value
def _add_arguments(self):
# Add allowed arguments
self.add_argument(name='algo',
help=None,
dest='ta',
callback=lambda arg: shlex.split(arg))
self.add_argument(name='execdir', default='.', help=None)
self.add_argument(name='deterministic',
default="0",
help=None,
callback=lambda arg: arg in ["1", "true", True])
self.add_argument(name='paramfile', help=None, dest='pcs_fn')
self.add_argument(name='run_obj', help=None, default='runtime')
self.add_argument(name='overall_obj', help=None, default='par10')
self.add_argument(name='cutoff_time',
help=None,
default=None,
dest='cutoff',
callback=lambda arg: float(arg))
self.add_argument(name='memory_limit', help=None)
self.add_argument(name='tuner-timeout',
help=None,
default=numpy.inf,
dest='algo_runs_timelimit',
callback=lambda arg: float(arg))
self.add_argument(name='wallclock_limit',
help=None,
default=numpy.inf,
callback=lambda arg: float(arg))
self.add_argument(name='runcount_limit',
help=None,
default=numpy.inf,
callback=lambda arg: float(arg),
dest="ta_run_limit")
self.add_argument(name='instance_file',
help=None,
dest='train_inst_fn')
self.add_argument(name='test_instance_file',
help=None,
dest='test_inst_fn')
self.add_argument(name='feature_file', help=None, dest='feature_fn')
self.add_argument(name='output_dir',
help=None,
default="smac3-output_%s" %
(datetime.datetime.fromtimestamp(
time.time()).strftime('%Y-%m-%d_%H:%M:%S')))
self.add_argument(name='shared_model',
help=None,
default='0',
callback=lambda arg: arg in ['1', 'true', True])
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')
# instance name -> feature vector
self.add_argument(name='features',
default={},
help=None,
dest='feature_dict')
self.add_argument(name='cs', help=None) # ConfigSpace object
def _transform_arguments(self):
self.n_features = len(self.feature_dict)
self.feature_array = None
if self.overall_obj[:3] in ["PAR", "par"]:
self.par_factor = int(self.overall_obj[3:])
elif self.overall_obj[:4] in ["mean", "MEAN"]:
self.par_factor = int(self.overall_obj[4:])
else:
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)
# 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.n_features = len(self.feature_dict[list(
self.feature_dict.keys())[0]])
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)
# 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()
self.cs = pcs.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)
self.logger.info("Output to %s" % (self.output_dir))
def feature_analysis(self,
box_violin=False,
correlation=False,
clustering=False,
importance=False):
if not (box_violin or correlation or clustering or importance):
self.logger.debug("No feature analysis.")
return
# FEATURE ANALYSIS (ASAPY)
# TODO make the following line prettier
# TODO feat-names from scenario?
in_reader = InputReader()
feat_fn = self.scenario.feature_fn
if not self.scenario.feature_names:
with changedir(self.ta_exec_dir if self.ta_exec_dir else '.'):
if not feat_fn or not os.path.exists(feat_fn):
self.logger.warning(
"Feature Analysis needs valid feature "
"file! Either {} is not a valid "
"filename or features are not saved in "
"the scenario.")
self.logger.error("Skipping Feature Analysis.")
return
else:
feat_names = in_reader.read_instance_features_file(
self.scenario.feature_fn)[0]
else:
feat_names = copy.deepcopy(self.scenario.feature_names)
self.website["Feature Analysis"] = OrderedDict([])
# feature importance using forward selection
if importance:
self.website["Feature Analysis"][
"Feature Importance"] = OrderedDict()
imp, plots = self.analyzer.feature_importance()
imp = DataFrame(data=list(imp.values()),
index=list(imp.keys()),
columns=["Error"])
imp = imp.to_html() # this is a table with the values in html
self.website["Feature Analysis"]["Feature Importance"]["Table"] = {
"table": imp
}
for p in plots:
name = os.path.splitext(os.path.basename(p))[0]
self.website["Feature Analysis"]["Feature Importance"][
name] = {
"figure": p
}
# box and violin plots
if box_violin:
name_plots = self.analyzer.feature_analysis(
'box_violin', feat_names)
self.website["Feature Analysis"][
"Violin and Box Plots"] = OrderedDict()
for plot_tuple in name_plots:
key = "%s" % (plot_tuple[0])
self.website["Feature Analysis"]["Violin and Box Plots"][
key] = {
"figure": plot_tuple[1]
}
# correlation plot
if correlation:
correlation_plot = self.analyzer.feature_analysis(
'correlation', feat_names)
if correlation_plot:
self.website["Feature Analysis"]["Correlation"] = {
"figure": correlation_plot
}
# cluster instances in feature space
if clustering:
cluster_plot = self.analyzer.feature_analysis(
'clustering', feat_names)
self.website["Feature Analysis"]["Clustering"] = {
"figure": cluster_plot
}
self.build_website()