class Importance(object):
def __init__(self,
scenario_file: Union[None, str] = None,
scenario: Union[None, Scenario] = None,
runhistory_file: Union[str, None] = None,
runhistory: Union[None, RunHistory] = None,
traj_file: Union[None, List[str]] = None,
incumbent: Union[None, Configuration] = None,
seed: int = 12345,
parameters_to_evaluate: int = -1,
margin: Union[None, float] = None,
save_folder: str = 'PIMP',
impute_censored: bool = False,
max_sample_size: int = -1):
"""
Importance Object. Handles the construction of the data and training of the model. Easy interface to the
different evaluators.
:param scenario_file: File to load the scenario from, if scenario is None.
:param scenario: Scenario Object to use if scenario_file is None
:param runhistory_file: File to load the runhistory from if runhistory is None.
:param runhistory: Runhistory Object to use if runhistory_file is None.
:param traj_file: File to load the trajectory from. If this is None but runhistory_file was specified,
the trajectory will be read from the same directory as the runhistory.
If both are None and incumbent is set, the incumbent configuration object will be used instead.
:param incumbent: Configuration Object to use if no other means of loading the trajectory are given.
:param seed: Seed used for the numpy random generator.
:param parameters_to_evaluate: int that specifies how many parameters have to be evaluated.
If set to -1 all parameters will be evaluated.
:param margin: float used in conjunction with influence models. Is the minimal improvement to accept a
parameter as important.
:param save_folder: Folder name to save the output to
:param impute_censored: boolean that specifies if censored data should be imputed. If not, censored data are
ignored.
"""
self.logger = logging.getLogger("Importance")
self.rng = np.random.RandomState(seed)
self._parameters_to_evaluate = parameters_to_evaluate
self._evaluator = None
self.margin = margin
self.threshold = None
self.seed = seed
self.impute = impute_censored
self._setup_scenario(scenario, scenario_file, save_folder)
self._load_runhist(runhistory, runhistory_file)
self._setup_model()
self._load_incumbent(traj_file, runhistory_file, incumbent)
if 0 < max_sample_size < len(self.X):
idx = list(range(len(self.X)))
np.random.shuffle(idx)
idx = idx[:max_sample_size]
self.X = self.X[idx]
self.y = self.y[idx]
self.logger.info('Remaining %d datapoints' % len(self.X))
self.model.train(self.X, self.y)
def _setup_scenario(self, scenario: Union[None, Scenario], scenario_file: Union[None, str], save_folder: str) -> \
None:
"""
Setup for the scenario
Helper method to have the init method less cluttered.
For parameter specifications, see __init__
"""
if scenario is not None:
self.scenario = scenario
elif scenario_file is not None:
self.logger.info(
'Reading Scenario file and files specified in the scenario')
self.scenario = Scenario(scenario=scenario_file,
cmd_args={'output_dir': ""},
run_id=1)
self.scenario.output_dir = save_folder
self.scenario.out_writer.write_scenario_file(self.scenario)
else:
raise Exception(
'Either a scenario has to be given or a file to load it from! Both were set to None!'
)
def _load_incumbent(self,
traj_file,
runhistory_file,
incumbent,
predict_best=True) -> None:
"""
Handles the loading of the incumbent according to the given parameters.
Helper method to have the init method less cluttered.
For parameter specifications, see __init__
"""
self.incumbent = (None, None)
if traj_file is not None:
self.incumbent = self._read_traj_file(traj_file)[0]
self.logger.debug('Incumbent %s' % str(self.incumbent))
elif traj_file is None and runhistory_file is not None:
traj_files = os.path.join(os.path.dirname(runhistory_file),
'traj_aclib2.json')
traj_files = sorted(glob.glob(traj_files, recursive=True))
incumbents = []
for traj_ in traj_files:
self.logger.info('Reading traj_file: %s' % traj_)
incumbents.append(self._read_traj_file(traj_))
incumbents[-1].extend(
self._model.predict_marginalized_over_instances(
np.array([
impute_inactive_values(
incumbents[-1][0]).get_array()
])))
self.logger.debug(incumbents[-1])
sort_idx = 2 if predict_best else 1
incumbents = sorted(incumbents, key=lambda x: x[sort_idx])
self.incumbent = incumbents[0][0]
self.logger.info('Incumbent %s' % str(self.incumbent))
elif incumbent is not None:
self.incumbent = incumbent
else:
raise Exception(
'No method specified to load an incumbent. Either give the incumbent directly or specify '
'a file to load it from!')
def _setup_model(self) -> None:
"""
Sets up all the necessary parameters used for the model.
Helper method to have the init method less cluttered.
For parameter specifications, see __init__
"""
self.logger.info('Converting Data and constructing Model')
self.X = None
self.y = None
self.types = None
self.bounds = None
self._model = None
self.logged_y = False
self._convert_data()
def _load_runhist(self, runhistory, runhistory_file) -> None:
"""
Handels loading of the runhistory/runhistories.
Helper method to have the init method less cluttered.
For parameter specifications, see __init__
"""
self.logger.debug(runhistory_file)
self.logger.debug(runhistory)
if runhistory is not None:
self.runhistory = runhistory
elif runhistory_file is not None:
self.logger.info('Reading Runhistory')
self.runhistory = RunHistory(aggregate_func=average_cost)
globed_files = glob.glob(runhistory_file)
self.logger.info('#RunHistories found: %d' % len(globed_files))
if not globed_files:
self.logger.error('No runhistory files found!')
sys.exit(1)
self.runhistory.load_json(globed_files[0], self.scenario.cs)
for rh_file in globed_files[1:]:
self.runhistory.update_from_json(rh_file, self.scenario.cs)
else:
raise Exception(
'Either a runhistory or files to load them from have to be specified! Both were set to '
'None!')
self.logger.info('Combined number of Runhistory data points: %d' %
len(self.runhistory.data))
self.logger.info('Number of Configurations: %d' %
(len(self.runhistory.get_all_configs())))
def _read_traj_file(self, fn):
"""
Simple method to read in a trajectory file in the json format / aclib2 format
:param fn:
file name
:return:
tuple of (incumbent [Configuration], incumbent_cost [float])
"""
if not (os.path.exists(fn)
and os.path.isfile(fn)): # File existence check
raise FileNotFoundError('File %s not found!' % fn)
with open(fn, 'r') as fh:
for line in fh.readlines():
pass
line = line.strip()
incumbent_dict = json.loads(line)
inc_dict = {}
for key_val in incumbent_dict[
'incumbent']: # convert string to Configuration
key, val = key_val.replace("'", '').split('=')
if isinstance(self.scenario.cs.get_hyperparameter(key),
(CategoricalHyperparameter)):
inc_dict[key] = val
elif isinstance(self.scenario.cs.get_hyperparameter(key),
(FloatHyperparameter)):
inc_dict[key] = float(val)
elif isinstance(self.scenario.cs.get_hyperparameter(key),
(IntegerHyperparameter)):
inc_dict[key] = int(val)
incumbent = Configuration(self.scenario.cs, inc_dict)
incumbent_cost = incumbent_dict['cost']
return [incumbent, incumbent_cost]
@property
def model(self):
return self._model
@model.setter
def model(self, model_short_name='urfi'):
self.types, self.bounds = get_types(self.scenario.cs,
self.scenario.feature_array)
if model_short_name not in ['urfi', 'rfi']:
raise ValueError(
'Specified model %s does not exist or not supported!' %
model_short_name)
elif model_short_name == 'rfi':
self._model = RandomForestWithInstances(
self.types,
self.bounds,
instance_features=self.scenario.feature_array,
seed=self.rng.randint(99999))
elif model_short_name == 'urfi':
self._model = UnloggedRandomForestWithInstances(
self.types,
self.bounds,
self.scenario.feature_array,
seed=self.rng.randint(99999),
cutoff=self.cutoff,
threshold=self.threshold)
self._model.rf_opts.compute_oob_error = True
@property
def evaluator(self) -> AbstractEvaluator:
"""
Getter of the evaluator property. Returns the set evaluation method.
:return: AbstractEvaluator
"""
return self._evaluator
@evaluator.setter
def evaluator(self, evaluation_method: str) -> None:
"""
Setter of the evaluator property. The wanted evaluation method can be specified as string and the rest is
handled automatically here
:param evaluation_method: Name of the evaluation method to use
:return: None
"""
if self._model is None:
self._setup_model()
self.logger.info('Setting up Evaluation Method')
if evaluation_method not in [
'ablation', 'fanova', 'forward-selection', 'influence-model',
'incneighbor'
]:
raise ValueError('Specified evaluation method %s does not exist!' %
evaluation_method)
if evaluation_method == 'ablation':
if self.incumbent is None:
raise ValueError('Incumbent is %s!\n \
Incumbent has to be read from a trajectory file before ablation can be used!'
% self.incumbent)
self.logger.info('Using model %s' % str(self.model))
self.logger.info('X shape %s' % str(self.model.X.shape))
evaluator = Ablation(scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
incumbent=self.incumbent,
logy=self.logged_y,
rng=self.rng)
elif evaluation_method == 'influence-model':
self.logger.info('Using model %s' % str(self.model))
self.logger.info('X shape %s' % str(self.model.X.shape))
evaluator = InfluenceModel(
scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
margin=self.margin,
threshold=self.threshold,
rng=self.rng)
elif evaluation_method == 'fanova':
self.logger.info('Using model %s' % str(self.model))
self.logger.info('X shape %s' % str(self.model.X.shape))
evaluator = fANOVA(scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
runhist=self.runhistory,
rng=self.rng)
elif evaluation_method == 'incneighbor':
if self.incumbent is None:
raise ValueError('Incumbent is %s!\n \
Incumbent has to be read from a trajectory file before ablation can be used!'
% self.incumbent)
self.logger.info('Using model %s' % str(self.model))
self.logger.info('X shape %s' % str(self.model.X.shape))
evaluator = IncNeighbor(scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
incumbent=self.incumbent,
logy=self.logged_y,
rng=self.rng)
else:
self.logger.info('Using model %s' % str(self.model))
evaluator = ForwardSelector(
scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
rng=self.rng)
self._evaluator = evaluator
def _convert_data(self) -> None: # From Marius
'''
converts data from runhistory into EPM format
Parameters
----------
scenario: Scenario
smac.scenario.scenario.Scenario Object
runhistory: RunHistory
smac.runhistory.runhistory.RunHistory Object with all necessary data
Returns
-------
np.array
X matrix with configuartion x features for all observed samples
np.array
y matrix with all observations
np.array
types of X cols -- necessary to train our RF implementation
'''
params = self.scenario.cs.get_hyperparameters()
num_params = len(params)
if self.scenario.run_obj == "runtime":
self.cutoff = self.scenario.cutoff
self.threshold = self.scenario.cutoff * self.scenario.par_factor
self.model = 'urfi'
self.logged_y = True
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
cutoff = np.log10(self.scenario.cutoff)
threshold = np.log10(self.scenario.cutoff *
self.scenario.par_factor)
model = RandomForestWithInstances(
self.types,
self.bounds,
instance_features=self.scenario.feature_array,
seed=self.rng.randint(99999),
do_bootstrapping=True,
num_trees=80,
n_points_per_tree=50000)
imputor = RFRImputator(rng=self.rng,
cutoff=cutoff,
threshold=threshold,
model=model,
change_threshold=0.01,
max_iter=10)
rh2EPM = RunHistory2EPM4LogCost(
scenario=self.scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS,
],
impute_censored_data=self.impute,
impute_state=[StatusType.TIMEOUT, StatusType.CAPPED],
imputor=imputor)
else:
self.model = 'rfi'
rh2EPM = RunHistory2EPM4Cost(scenario=self.scenario,
num_params=num_params,
success_states=None,
impute_censored_data=self.impute,
impute_state=None)
self.logger.info('Using model %s' % str(self.model))
X, Y = rh2EPM.transform(self.runhistory)
self.X = X
self.y = Y
self.logger.info('Size of training X: %s' % str(self.X.shape))
self.logger.info('Size of training y: %s' % str(self.y.shape))
self.logger.info('Data was %s imputed' %
('not' if not self.impute else ''))
if not self.impute:
self.logger.info(
'Thus the size of X might be smaller than the datapoints in the RunHistory'
)
self.model.train(X, Y)
def evaluate_scenario(
self,
evaluation_method='all',
sort_by: int = 0
) -> Union[Tuple[Dict[str, Dict[str, float]], List[AbstractEvaluator]],
Dict[str, Dict[str, float]]]:
"""
Evaluate the given scenario
:param evaluation_method: name of the method to use
:param sort_by: int, determines the order (only used if evaluation_method == all)
0 => Ablation, fANOVA, Forward Selection
1 => Ablation, Forward Selection, fANOVA
2 => fANOVA, Forward Selection, Ablation
3 => fANOVA, Ablation, Forward Selection
4 => Forward Selection, Ablation, fANOVA
5 => Forward Selection, fANOVA, Ablation
:return: if evaluation all: Tupel of dictionary[evaluation_method] -> importance values, List ov evaluator
names, ordered according to sort_by
else:
dict[evalution_method] -> importance values
"""
# influence-model currently not supported
# influence-model currently not supported
methods = ['ablation', 'fanova', 'forward-selection', 'incneighbor']
if sort_by == 1:
methods = [
'ablation', 'forward-selection', 'fanova', 'incneighbor'
]
elif sort_by == 2:
methods = [
'fanova', 'forward-selection', 'ablation', 'incneighbor'
]
elif sort_by == 3:
methods = [
'fanova', 'ablation', 'forward-selection', 'incneighbor'
]
elif sort_by == 4:
methods = [
'forward-selection', 'ablation', 'fanova', 'incneighbor'
]
elif sort_by == 5:
methods = [
'forward-selection', 'fanova', 'ablation', 'incneighbor'
]
if evaluation_method == 'all':
evaluators = []
dict_ = {}
for method in methods:
self.logger.info('Running %s' % method)
self.evaluator = method
dict_[method] = self.evaluator.run()
evaluators.append(self.evaluator)
return dict_, evaluators
else:
self.evaluator = evaluation_method
self.logger.info('Running evaluation method %s' %
self.evaluator.name)
return {evaluation_method: self.evaluator.run()}
def plot_results(self,
name: Union[List[str], str, None] = None,
evaluators: Union[List[AbstractEvaluator], None] = None,
show: bool = True):
"""
Method to handle the plotting in case of plots for multiple evaluation methods or only one
:param name: name(s) to save the plot(s) with
:param evaluators: list of ealuators to generate the plots for
:param show: boolean. Specifies if the results have to additionally be shown and not just saved!
:return:
"""
if evaluators:
for eval, name_ in zip(evaluators, name):
eval.plot_result(name_, show)
else:
self.evaluator.plot_result(name, show)
def table_for_comparison(self,
evaluators: List[AbstractEvaluator],
name: Union[None, str] = None,
style='cmd'):
"""
Small Method that creates an output table for comparison either printed in a readable format for the command
line or in latex style
:param evaluators: All evaluators to put into the table
:param name: Name for the save file name
:param style: (cmd|latex) str to determine which format to use
:return: None
"""
if name:
f = open(name, 'w')
else:
f = sys.stderr
header = ['{:>{width}s}' for _ in range(len(evaluators) + 1)]
line = '-' if style == 'cmd' else '\hline'
join_ = ' | ' if style == 'cmd' else ' & '
body = OrderedDict()
_max_len_p = 1
_max_len_h = 1
for idx, e in enumerate(evaluators):
for p in e.evaluated_parameter_importance:
if p not in ['-source-', '-target-']:
if p not in body:
body[p] = ['-' for _ in range(len(evaluators))]
body[p][idx] = e.evaluated_parameter_importance[p]
_max_len_p = max(_max_len_p, len(p))
else:
body[p][idx] = e.evaluated_parameter_importance[p]
if e.name in ['Ablation', 'fANOVA']:
if body[p][idx] != '-':
body[p][idx] *= 100
_max_len_p = max(_max_len_p, len(p))
header[idx + 1] = e.name
_max_len_h = max(_max_len_h, len(e.name))
header[0] = header[0].format(' ', width=_max_len_p)
header[1:] = list(
map(lambda x: '{:^{width}s}'.format(x, width=_max_len_h),
header[1:]))
header = join_.join(header)
if style == 'latex':
print('\\begin{table}', file=f)
print('\\begin{tabular}{r%s}' % ('|r' * len(evaluators)), file=f)
print('\\toprule', file=f)
print(header, end='\n' if style == 'cmd' else '\\\\\n', file=f)
if style == 'cmd':
print(line * len(header), file=f)
else:
print(line, file=f)
for p in body:
if style == 'cmd':
b = ['{:>{width}s}'.format(p, width=_max_len_p)]
else:
b = ['{:<{width}s}'.format(p, width=_max_len_p)]
for x in body[p]:
try:
if style == 'latex':
b.append('${:> {width}.3f}$'.format(x,
width=_max_len_h -
2))
else:
b.append('{:> {width}.3f}'.format(x, width=_max_len_h))
except ValueError:
b.append('{:>{width}s}'.format(x, width=_max_len_h))
print(join_.join(b),
end='\n' if style == 'cmd' else '\\\\\n',
file=f)
cap = 'Parameter Importance values, obtained using the PIMP package. Ablation values are percentages ' \
'of improvement a single parameter change obtained between the default and an' \
' incumbent configuration.\n' \
'fANOVA values are percentages that show how much variance across the whole ConfigSpace can be ' \
'explained by that parameter.\n' \
'Forward Selection values are RMSE values obtained using only a subset of parameters for prediction.\n' \
'fANOVA and Forward Selection try to estimate the importances across the whole parameter space, while ' \
'ablation tries to estimate them between two given configurations.'
if self._parameters_to_evaluate > 0:
cap += """\nOnly the top %d parameters of each method are listed.
"-" represent that this parameter was not evaluated
using the given method but with another.
""" % self._parameters_to_evaluate
if style == 'latex':
print('\\bottomrule', file=f)
print('\end{tabular}', file=f)
print('\\caption{%s}' % cap, file=f)
print('\\label{tab:pimp}', file=f)
print('\end{table}', file=f)
else:
print('', file=f)
print(cap)
if name:
f.close()
class Importance(object):
def __init__(self, scenario_file: Union[None, str] = None, scenario: Union[None, Scenario] = None,
runhistory_file: Union[str, None] = None, runhistory: Union[None, RunHistory] = None,
traj_file: Union[None, List[str]] = None, incumbent: Union[None, Configuration] = None,
seed: int = 12345, parameters_to_evaluate: int = -1, margin: Union[None, float] = None,
save_folder: str = 'PIMP', impute_censored: bool = False, max_sample_size: int = -1,
fANOVA_cut_at_default=False, fANOVA_pairwise=True, forwardsel_feat_imp=False,
incn_quant_var=True, preprocess=False, forwardsel_cv=False, verbose: bool=True):
"""
Importance Object. Handles the construction of the data and training of the model. Easy interface to the
different evaluators.
:param scenario_file: File to load the scenario from, if scenario is None.
:param scenario: Scenario Object to use if scenario_file is None
:param runhistory_file: File to load the runhistory from if runhistory is None.
:param runhistory: Runhistory Object to use if runhistory_file is None.
:param traj_file: File to load the trajectory from. If this is None but runhistory_file was specified,
the trajectory will be read from the same directory as the runhistory. (Will be ignored if incumbent is
set)
:param incumbent: Configuration Object to use.
:param seed: Seed used for the numpy random generator.
:param parameters_to_evaluate: int that specifies how many parameters have to be evaluated.
If set to -1 all parameters will be evaluated.
:param margin: float used in conjunction with influence models. Is the minimal improvement to accept a
parameter as important.
:param save_folder: Folder name to save the output to
:param impute_censored: boolean that specifies if censored data should be imputed. If not, censored data are
ignored.
:param verbose: Toggle output to stdout (not logging, but tqdm-progress bars)
"""
self.logger = logging.getLogger("pimp.Importance")
self.rng = np.random.RandomState(seed)
self._parameters_to_evaluate = parameters_to_evaluate
self._evaluator = None
self.margin = margin
self.threshold = None
self.seed = seed
self.impute = impute_censored
self.cut_def_fan = fANOVA_cut_at_default
self.pairiwse_fANOVA = fANOVA_pairwise
self.forwardsel_feat_imp = forwardsel_feat_imp
self.incn_quant_var = incn_quant_var
self.preprocess = preprocess
self._preprocessed = False
self.X_fanova = None
self.y_fanova = None
self.forwardsel_cv = forwardsel_cv
self.verbose = verbose
self.evaluators = []
self._setup_scenario(scenario, scenario_file, save_folder)
self._load_runhist(runhistory, runhistory_file)
self._setup_model()
self.best_dir = None
self._load_incumbent(traj_file, runhistory_file, incumbent)
self.logger.info('Best incumbent found in %s' % self.best_dir)
if 0 < max_sample_size < len(self.X):
self.logger.warning('Reducing the amount of datapoints!')
if self.best_dir:
self.logger.warning('Only using the runhistory that contains the incumbent!')
self.logger.info('Loading from %s' % self.best_dir)
self._load_runhist(None, os.path.join(self.best_dir, '*history*'))
self._convert_data(fit=False)
self._load_incumbent(glob.glob(os.path.join(self.best_dir, '*traj_aclib2*'), recursive=True)[0], None,
incumbent)
if max_sample_size < len(self.X):
self.logger.warning('Also downsampling as requested!')
idx = list(range(len(self.X)))
np.random.shuffle(idx)
idx = idx[:max_sample_size]
self.X = self.X[idx]
self.y = self.y[idx]
else:
self.logger.warning('Downsampling as requested!')
idx = list(range(len(self.X)))
np.random.shuffle(idx)
idx = idx[:max_sample_size]
self.X = self.X[idx]
self.y = self.y[idx]
self.logger.info('Remaining %d datapoints' % len(self.X))
self.model.train(self.X, self.y)
def _preprocess(self, runhistory):
"""
Method to marginalize over instances such that fANOVA can determine the parameter importance without
having to deal with instance features.
:param runhistory: RunHistory that knows all configurations that were run. For all these configurations
we have to marginalize away the instance features with which fANOVA will make it's
predictions
"""
self.logger.info('PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING PREPROCESSING')
self.logger.info('Marginalizing away all instances!')
configs = runhistory.get_all_configs()
X_non_hyper, X_prime, y_prime = [], [], []
for c_id, config in tqdm(enumerate(configs), ascii=True, desc='Completed: ', total=len(configs)):
config = impute_inactive_values(config).get_array()
X_prime.append(config)
X_non_hyper.append(config)
y_prime.append(self.model.predict_marginalized_over_instances(np.array([X_prime[-1]]))[0].flatten())
for idx, param in enumerate(self.scenario.cs.get_hyperparameters()):
if not isinstance(param, CategoricalHyperparameter):
X_non_hyper[-1][idx] = param._transform(X_non_hyper[-1][idx])
X_non_hyper = np.array(X_non_hyper)
X_prime = np.array(X_prime)
y_prime = np.array(y_prime)
# y_prime = np.array(self.model.predict_marginalized_over_instances(X_prime)[0])
self.X = X_prime
self.X_fanova = X_non_hyper
self.y_fanova = y_prime
self.y = y_prime
self.logger.info('Size of training X after preprocessing: %s' % str(self.X.shape))
self.logger.info('Size of training y after preprocessing: %s' % str(self.y.shape))
self.logger.info('Finished Preprocessing')
self._preprocessed = True
def _setup_scenario(self, scenario: Union[None, Scenario], scenario_file: Union[None, str], save_folder: str) -> \
None:
"""
Setup for the scenario
Helper method to have the init method less cluttered.
For parameter specifications, see __init__
"""
if scenario is not None:
self.scenario = scenario
elif scenario_file is not None:
self.logger.info('Reading Scenario file and files specified in the scenario')
self.scenario = Scenario(scenario=scenario_file)
self.scenario.output_dir = save_folder
self.scenario.output_dir_for_this_run = save_folder
written = self.scenario.out_writer.write_scenario_file(self.scenario)
else:
raise Exception('Either a scenario has to be given or a file to load it from! Both were set to None!')
def _load_incumbent(self, traj_file, runhistory_file, incumbent, predict_best=True) -> None:
"""
Handles the loading of the incumbent according to the given parameters.
Helper method to have the init method less cluttered.
For parameter specifications, see __init__
"""
self.incumbent = (None, None)
if incumbent is not None:
self.incumbent = incumbent
elif traj_file is not None:
self.logger.info('Reading traj_file: %s' % traj_file)
self.incumbent = self._read_traj_file(traj_file)[0]
self.logger.debug('Incumbent %s' % str(self.incumbent))
elif traj_file is None and runhistory_file is not None:
traj_files = os.path.join(os.path.dirname(runhistory_file), 'traj_aclib2.json')
traj_files = sorted(glob.glob(traj_files, recursive=True))
incumbents = []
for traj_ in traj_files:
self.logger.info('Reading traj_file: %s' % traj_)
incumbents.append(self._read_traj_file(traj_))
incumbents[-1].extend(self._model.predict_marginalized_over_instances(
np.array([impute_inactive_values(incumbents[-1][0]).get_array()])))
self.logger.debug(incumbents[-1])
sort_idx = 2 if predict_best else 1
incumbents = sorted(enumerate(incumbents), key=lambda x: x[1][sort_idx])
self.best_dir = os.path.dirname(traj_files[incumbents[0][0]])
self.incumbent = incumbents[0][1][0]
self.logger.info('Incumbent %s' % str(self.incumbent))
else:
raise Exception('No method specified to load an incumbent. Either give the incumbent directly or specify '
'a file to load it from!')
def _setup_model(self) -> None:
"""
Sets up all the necessary parameters used for the model.
Helper method to have the init method less cluttered.
For parameter specifications, see __init__
"""
self.logger.info('Converting Data and constructing Model')
self.X = None
self.y = None
self.types = None
self.bounds = None
self._model = None
self.logged_y = False
self._convert_data(fit=True)
if self.preprocess:
self._preprocess(self.runhistory)
if self.scenario.run_obj == "runtime":
self.y = np.log10(self.y)
self.model = 'urfi'
self.model.train(self.X, self.y)
def _load_runhist(self, runhistory, runhistory_file) -> None:
"""
Handels loading of the runhistory/runhistories.
Helper method to have the init method less cluttered.
For parameter specifications, see __init__
"""
self.logger.debug(runhistory_file)
self.logger.debug(runhistory)
if runhistory is not None:
self.runhistory = runhistory
elif runhistory_file is not None:
self.logger.info('Reading Runhistory')
self.runhistory = RunHistory()
globed_files = glob.glob(runhistory_file)
self.logger.info('#RunHistories found: %d' % len(globed_files))
if not globed_files:
self.logger.error('No runhistory files found!')
sys.exit(1)
self.runhistory.load_json(globed_files[0], self.scenario.cs)
for rh_file in globed_files[1:]:
self.runhistory.update_from_json(rh_file, self.scenario.cs)
else:
raise Exception('Either a runhistory or files to load them from have to be specified! Both were set to '
'None!')
self.logger.info('Combined number of Runhistory data points: %d' % len(self.runhistory.data))
self.logger.info('Number of Configurations: %d' % (len(self.runhistory.get_all_configs())))
def _read_traj_file(self, fn):
"""
Simple method to read in a trajectory file in the json format / aclib2 format
:param fn:
file name
:return:
tuple of (incumbent [Configuration], incumbent_cost [float])
"""
if not (os.path.exists(fn) and os.path.isfile(fn)): # File existence check
raise FileNotFoundError('File %s not found!' % fn)
with open(fn) as fp:
# In aclib2, the incumbent is a list of strings, in alljson it's a dictionary.
fileformat = 'aclib2' if isinstance(json.loads(fp.readline())["incumbent"], list) else 'alljson'
if fileformat == "aclib2":
self.logger.info("Format is 'aclib2'. This format has issues with recovering configurations properly. We "
"recommend to use the alljson-format.")
traj = TrajLogger.read_traj_aclib_format(fn, self.scenario.cs)
else:
traj = TrajLogger.read_traj_alljson_format(fn, self.scenario.cs)
incumbent_cost = traj[-1]['cost']
incumbent = traj[-1]['incumbent']
return [incumbent, incumbent_cost]
@property
def model(self):
return self._model
def _get_types(self, scenario, features):
types, bounds = get_types(scenario, features)
types = np.array(types, dtype='uint')
bounds = np.array(bounds, dtype='object')
return types, bounds
@model.setter
def model(self, model_short_name='urfi'):
if model_short_name not in ['urfi', 'rfi']:
raise ValueError('Specified model %s does not exist or not supported!' % model_short_name)
elif model_short_name == 'rfi':
self.types, self.bounds = self._get_types(self.scenario.cs, self.scenario.feature_array)
self._model = RandomForestWithInstances(self.scenario.cs, self.types, self.bounds, 12345,
instance_features=self.scenario.feature_array,
logged_y=self.logged_y)
elif model_short_name == 'urfi':
self.logged_y = True
if not self._preprocessed:
self.types, self.bounds = self._get_types(self.scenario.cs, self.scenario.feature_array)
self._model = UnloggedEPARXrfi(self.scenario.cs, self.types, self.bounds, 12345,
instance_features=self.scenario.feature_array,
cutoff=self.cutoff, threshold=self.threshold,
logged_y=self.logged_y)
else:
self.types, self.bounds = self._get_types(self.scenario.cs, None)
self._model = Unloggedrfwi(self.scenario.cs, self.types, self.bounds, 12345,
instance_features=None,
logged_y=self.logged_y)
self._model.rf_opts.compute_oob_error = True
@property
def evaluator(self) -> AbstractEvaluator:
"""
Getter of the evaluator property. Returns the set evaluation method.
:return: AbstractEvaluator
"""
return self._evaluator
@evaluator.setter
def evaluator(self, evaluation_method: str) -> None:
"""
Setter of the evaluator property. The wanted evaluation method can be specified as string and the rest is
handled automatically here
:param evaluation_method: Name of the evaluation method to use
:return: None
"""
if self._model is None:
self._setup_model()
self.logger.info('Setting up Evaluation Method')
if evaluation_method not in ['ablation', 'fanova', 'forward-selection', 'influence-model',
'incneighbor', 'lpi']:
raise ValueError('Specified evaluation method %s does not exist!' % evaluation_method)
if evaluation_method == 'ablation':
if self.incumbent is None:
raise ValueError('Incumbent is %s!\n \
Incumbent has to be read from a trajectory file before ablation can be used!'
% self.incumbent)
self.logger.info('Using model %s' % str(self.model))
self.logger.info('X shape %s' % str(self.model.X.shape))
evaluator = Ablation(scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
incumbent=self.incumbent,
logy=self.logged_y,
rng=self.rng,
verbose=self.verbose)
elif evaluation_method == 'influence-model':
self.logger.info('Using model %s' % str(self.model))
self.logger.info('X shape %s' % str(self.model.X.shape))
evaluator = InfluenceModel(scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
margin=self.margin,
threshold=self.threshold,
rng=self.rng,
verbose=self.verbose)
elif evaluation_method == 'fanova':
self.logger.info('Using model %s' % str(self.model))
self.logger.info('X shape %s' % str(self.model.X.shape))
mini = None
if self.cut_def_fan:
mini = True # TODO what about scenarios where we maximize?
evaluator = fANOVA(scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
runhist=self.runhistory,
rng=self.rng,
minimize=mini,
pairwise=self.pairiwse_fANOVA,
preprocessed_X=self.X_fanova,
preprocessed_y=self.y_fanova,
incumbents=self.incumbent,
verbose=self.verbose)
elif evaluation_method in ['incneighbor', 'lpi']:
if self.incumbent is None:
raise ValueError('Incumbent is %s!\n \
Incumbent has to be read from a trajectory file before LPI can be used!'
% self.incumbent)
self.logger.info('Using model %s' % str(self.model))
self.logger.info('X shape %s' % str(self.model.X.shape))
evaluator = LPI(scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
incumbent=self.incumbent,
logy=self.logged_y,
rng=self.rng,
quant_var=self.incn_quant_var,
verbose=self.verbose)
else:
self.logger.info('Using model %s' % str(self.model))
evaluator = ForwardSelector(scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
rng=self.rng,
feature_imp=self.forwardsel_feat_imp,
cv=self.forwardsel_cv,
verbose=self.verbose)
self._evaluator = evaluator
def _convert_data(self, fit=True) -> None: # From Marius
'''
converts data from runhistory into EPM format
Parameters
----------
scenario: Scenario
smac.scenario.scenario.Scenario Object
runhistory: RunHistory
smac.runhistory.runhistory.RunHistory Object with all necessary data
Returns
-------
np.array
X matrix with configuartion x features for all observed samples
np.array
y matrix with all observations
np.array
types of X cols -- necessary to train our RF implementation
'''
params = self.scenario.cs.get_hyperparameters()
num_params = len(params)
self.logger.debug("Counted %d hyperparameters", num_params)
if self.scenario.run_obj == "runtime":
self.cutoff = self.scenario.cutoff
self.threshold = self.scenario.cutoff * self.scenario.par_factor
self.model = 'urfi'
self.logged_y = True
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
cutoff = np.log10(self.scenario.cutoff)
threshold = np.log10(self.scenario.cutoff *
self.scenario.par_factor)
model = RandomForestWithInstances(self.scenario.cs,
self.types, self.bounds, 12345,
instance_features=self.scenario.feature_array,
)
imputor = RFRImputator(rng=self.rng,
cutoff=cutoff,
threshold=threshold,
model=model,
change_threshold=0.01,
max_iter=10)
rh2EPM = RunHistory2EPM4LogCost(scenario=self.scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS, ],
impute_censored_data=self.impute,
impute_state=[
StatusType.TIMEOUT, StatusType.CAPPED],
imputor=imputor)
else:
self.model = 'rfi'
rh2EPM = RunHistory2EPM4Cost(scenario=self.scenario,
num_params=num_params,
success_states=[StatusType.SUCCESS],
impute_censored_data=self.impute,
impute_state=None)
self.logger.info('Using model %s' % str(self.model))
X, Y = rh2EPM.transform(self.runhistory)
self.X = X
self.y = Y
self.logger.info('Size of training X: %s' % str(self.X.shape))
self.logger.info('Size of training y: %s' % str(self.y.shape))
self.logger.info('Data was %s imputed' % ('not' if not self.impute else ''))
if not self.impute:
self.logger.info('Thus the size of X might be smaller than the datapoints in the RunHistory')
if fit:
self.logger.info('Fitting Model')
self.model.train(X, Y)
def evaluate_scenario(self, methods, save_folder=None, plot_pyplot=True, plot_bokeh=False) -> Union[
Tuple[Dict[str, Dict[str, float]], List[AbstractEvaluator]], Dict[str, Dict[str, float]]]:
"""
the given scenario
:param evaluation_method: name of the method to use
:param sort_by: int, determines the order (only used if evaluation_method == all)
0 => Ablation, fANOVA, Forward Selection
1 => Ablation, Forward Selection, fANOVA
2 => fANOVA, Forward Selection, Ablation
3 => fANOVA, Ablation, Forward Selection
4 => Forward Selection, Ablation, fANOVA
5 => Forward Selection, fANOVA, Ablation
:param plot_pyplot: whether to perform standard matplotlib- plotting
:param plot_bokeh: whether to perform advanced bokeh plotting
:return: if evaluation all: Tupel of dictionary[evaluation_method] -> importance values, List ov evaluator
names, ordered according to sort_by
else:
dict[evalution_method] -> importance values
"""
# influence-model currently not supported
if not len(methods) >= 1:
raise ValueError("Specify at least one method to evaluate the scenario!")
fn = os.path.join(save_folder, 'pimp_results.json')
load = os.path.exists(fn)
dict_ = {}
for rnd, method in enumerate(methods):
self.logger.info('Running %s' % method)
self.evaluator = method
dict_[self.evaluator.name.lower()] = self.evaluator.run()
self.evaluators.append(self.evaluator)
if save_folder and plot_pyplot:
self.evaluator.plot_result(os.path.join(save_folder, self.evaluator.name.lower()), show=False)
if save_folder and plot_bokeh:
self.evaluator.plot_bokeh(os.path.join(save_folder, self.evaluator.name.lower() + "_bokeh"))
if load:
with open(fn, 'r') as in_file:
doct = json.load(in_file)
for key in doct:
dict_[key] = doct[key]
if save_folder:
with open(fn, 'w') as out_file:
json.dump(dict_, out_file, sort_keys=True, indent=4, separators=(',', ': '))
load = True
return dict_, self.evaluators
def plot_results(self, name: Union[List[str], str, None] = None, evaluators: Union[List[AbstractEvaluator],
None] = None,
show: bool = True):
"""
Method to handle the plotting in case of plots for multiple evaluation methods or only one
:param name: name(s) to save the plot(s) with
:param evaluators: list of ealuators to generate the plots for
:param show: boolean. Specifies if the results have to additionally be shown and not just saved!
:return:
"""
if evaluators:
for eval, name_ in zip(evaluators, name):
eval.plot_result(name_, show)
else:
self.evaluator.plot_result(name, show)
def table_for_comparison(self, evaluators: List[AbstractEvaluator], name: Union[None, str] = None, style='cmd'):
"""
Small Method that creates an output table for comparison either printed in a readable format for the command
line or in latex style
:param evaluators: All evaluators to put into the table
:param name: Name for the save file name
:param style: (cmd|latex) str to determine which format to use
:return: None
"""
if name:
f = open(name, 'w')
else:
f = sys.stderr
header = ['{:>{width}s}' for _ in range(len(evaluators) + 1)]
line = '-' if style == 'cmd' else '\hline'
join_ = ' | ' if style == 'cmd' else ' & '
body = OrderedDict()
_max_len_p = 1
_max_len_h = 1
for idx, e in enumerate(evaluators):
for p in e.evaluated_parameter_importance:
if p not in ['-source-', '-target-']:
if p not in body:
body[p] = ['-' for _ in range(len(evaluators))]
body[p][idx] = e.evaluated_parameter_importance[p]
_max_len_p = max(_max_len_p, len(p))
else:
body[p][idx] = e.evaluated_parameter_importance[p]
if e.name in ['Ablation', 'fANOVA', 'LPI']:
if body[p][idx] != '-':
body[p][idx] *= 100
_max_len_p = max(_max_len_p, len(p))
header[idx + 1] = e.name
_max_len_h = max(_max_len_h, len(e.name))
header[0] = header[0].format(' ', width=_max_len_p)
header[1:] = list(map(lambda x: '{:^{width}s}'.format(x, width=_max_len_h), header[1:]))
header = join_.join(header)
if style == 'latex':
print('\\begin{table}', file=f)
print('\\begin{tabular}{r%s}' % ('|r' * len(evaluators)), file=f)
print('\\toprule', file=f)
print(header, end='\n' if style == 'cmd' else '\\\\\n', file=f)
if style == 'cmd':
print(line * len(header), file=f)
else:
print(line, file=f)
for p in body:
if style == 'cmd':
b = ['{:>{width}s}'.format(p, width=_max_len_p)]
else:
b = ['{:<{width}s}'.format(p, width=_max_len_p)]
for x in body[p]:
try:
if style == 'latex':
b.append('${:> {width}.3f}$'.format(x, width=_max_len_h - 2))
else:
b.append('{:> {width}.3f}'.format(x, width=_max_len_h))
except ValueError:
b.append('{:>{width}s}'.format(x, width=_max_len_h))
print(join_.join(b), end='\n' if style == 'cmd' else '\\\\\n', file=f)
cap = 'Parameter Importance values, obtained using the PIMP package. Ablation values are percentages ' \
'of improvement a single parameter change obtained between the default and an' \
' incumbent configuration.\n' \
'fANOVA values are percentages that show how much variance across the whole ConfigSpace can be ' \
'explained by that parameter.\n' \
'Forward Selection values are RMSE values obtained using only a subset of parameters for prediction.\n' \
'fANOVA and Forward Selection try to estimate the importances across the whole parameter space, while ' \
'ablation tries to estimate them between two given configurations.'
if self._parameters_to_evaluate > 0:
cap += """\nOnly the top %d parameters of each method are listed.
"-" represent that this parameter was not evaluated
using the given method but with another.
""" % self._parameters_to_evaluate
if style == 'latex':
print('\\bottomrule', file=f)
print('\end{tabular}', file=f)
print('\\caption{%s}' % cap,
file=f)
print('\\label{tab:pimp}', file=f)
print('\end{table}', file=f)
else:
print('', file=f)
print(cap)
if name:
f.close()
class Importance(object):
"""
Importance Object. Handles the construction of the data and training of the model. Easy interface to the different
evaluators
"""
def __init__(self,
scenario_file,
runhistory_files,
seed: int = 12345,
parameters_to_evaluate: int = -1,
traj_file=None,
threshold=None,
margin=None,
save_folder='PIMP',
impute_censored: bool = False):
self.rng = np.random.RandomState(seed)
self.logger = logging.getLogger("Importance")
self.impute = impute_censored
self.logger.info(
'Reading Scenario file and files specified in the scenario')
self.scenario = Scenario(scenario=scenario_file,
cmd_args={'output_dir': save_folder},
run_id=1)
self.logger.info('Reading Runhistory')
self.runhistory = RunHistory(aggregate_func=average_cost)
globed_files = glob.glob(runhistory_files)
self.logger.info('#RunHistories found: %d' % len(globed_files))
if not globed_files:
self.logger.error('No runhistory files found!')
sys.exit(1)
self.runhistory.load_json(globed_files[0], self.scenario.cs)
for rh_file in globed_files[1:]:
self.runhistory.update_from_json(rh_file, self.scenario.cs)
self.logger.info('Combined number of Runhistory data points: %d' %
len(self.runhistory.data))
self.seed = seed
self.logger.info('Number of Configurations: %d' %
(len(self.runhistory.get_all_configs())))
self.logger.info('Converting Data and constructing Model')
self.X = None
self.y = None
self.types = None
self.bounds = None
self._model = None
self.incumbent = (None, None)
self.logged_y = False
self._convert_data()
self._evaluator = None
if traj_file is not None:
self.incumbent = self._read_traj_file(traj_file)
self.logger.debug('Incumbent %s' % str(self.incumbent))
else:
traj_files = glob.glob('**/traj_aclib2.json', recursive=True)
incumbents = []
for traj_ in traj_files:
self.logger.info('Reading traj_file: %s' % traj_)
incumbents.append(self._read_traj_file(traj_))
self.logger.debug(incumbents[-1])
incumbents = sorted(incumbents, key=lambda x: x[1])
self.incumbent = incumbents[0]
self.logger.info('Incumbent %s' % str(self.incumbent))
self.logger.info('Setting up Evaluation Method')
self._parameters_to_evaluate = parameters_to_evaluate
self.margin = margin
self.threshold = threshold
# self.evaluator = evaluation_method
def _read_traj_file(self, fn):
"""
Simple method to read in a trajectory file in the json format / aclib2 format
:param fn:
file name
:return:
tuple of (incumbent [Configuration], incumbent_cost [float])
"""
if not (os.path.exists(fn)
and os.path.isfile(fn)): # File existence check
raise FileNotFoundError('File %s not found!' % fn)
with open(fn, 'r') as fh:
for line in fh.readlines():
pass
line = line.strip()
incumbent_dict = json.loads(line)
inc_dict = {}
for key_val in incumbent_dict[
'incumbent']: # convert string to Configuration
key, val = key_val.replace("'", '').split('=')
if isinstance(self.scenario.cs.get_hyperparameter(key),
(CategoricalHyperparameter)):
inc_dict[key] = val
elif isinstance(self.scenario.cs.get_hyperparameter(key),
(FloatHyperparameter)):
inc_dict[key] = float(val)
elif isinstance(self.scenario.cs.get_hyperparameter(key),
(IntegerHyperparameter)):
inc_dict[key] = int(val)
incumbent = Configuration(self.scenario.cs, inc_dict)
incumbent_cost = incumbent_dict['cost']
return incumbent, incumbent_cost
@property
def model(self):
return self._model
@model.setter
def model(self, model_short_name='urfi'):
self.types, self.bounds = get_types(self.scenario.cs,
self.scenario.feature_array)
if model_short_name not in ['urfi', 'rfi']:
raise ValueError(
'Specified model %s does not exist or not supported!' %
model_short_name)
elif model_short_name == 'rfi':
self._model = RandomForestWithInstances(
self.types,
self.bounds,
instance_features=self.scenario.feature_array,
seed=self.rng.randint(99999),
num_trees=100)
elif model_short_name == 'urfi':
self._model = UnloggedRandomForestWithInstances(
self.types,
self.bounds,
self.scenario.feature_array,
seed=self.rng.randint(99999),
cutoff=self.cutoff,
threshold=self.threshold,
num_trees=100)
self._model.rf_opts.compute_oob_error = True
@property
def evaluator(self):
return self._evaluator
@evaluator.setter
def evaluator(self, evaluation_method):
if evaluation_method not in [
'ablation', 'fanova', 'forward-selection', 'influence-model'
]:
raise ValueError('Specified evaluation method %s does not exist!' %
evaluation_method)
if evaluation_method == 'ablation':
if self.scenario.run_obj == "runtime":
self.cutoff = self.scenario.cutoff
self.threshold = self.scenario.cutoff * self.scenario.par_factor
self.model = 'urfi'
self.logged_y = True
else:
self.model = 'rfi'
self.model.train(self.X, self.y)
if self.incumbent[0] is None:
raise ValueError('Incumbent is %s!\n \
Incumbent has to be read from a trajectory file before ablation can be used!'
% self.incumbent[0])
evaluator = Ablation(scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
incumbent=self.incumbent[0],
logy=self.logged_y,
target_performance=self.incumbent[1])
elif evaluation_method == 'influence-model':
self.model = 'rfi'
self.model.train(self.X, self.y)
evaluator = InfluenceModel(
scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
margin=self.margin,
threshold=self.threshold)
elif evaluation_method == 'fanova':
self.model = 'rfi'
self.model.train(self.X, self.y)
evaluator = fANOVA(scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate,
runhist=self.runhistory)
else:
self.model = 'rfi'
self.model.train(self.X, self.y)
evaluator = ForwardSelector(
scenario=self.scenario,
cs=self.scenario.cs,
model=self._model,
to_evaluate=self._parameters_to_evaluate)
self._evaluator = evaluator
def _convert_data(self): # From Marius
'''
converts data from runhistory into EPM format
Parameters
----------
scenario: Scenario
smac.scenario.scenario.Scenario Object
runhistory: RunHistory
smac.runhistory.runhistory.RunHistory Object with all necessary data
Returns
-------
np.array
X matrix with configuartion x features for all observed samples
np.array
y matrix with all observations
np.array
types of X cols -- necessary to train our RF implementation
'''
params = self.scenario.cs.get_hyperparameters()
num_params = len(params)
if self.scenario.run_obj == "runtime":
self.cutoff = self.scenario.cutoff
self.threshold = self.scenario.cutoff * self.scenario.par_factor
self.model = 'urfi'
self.logged_y = True
# if we log the performance data,
# the RFRImputator will already get
# log transform data from the runhistory
cutoff = np.log10(self.scenario.cutoff)
threshold = np.log10(self.scenario.cutoff *
self.scenario.par_factor)
model = RandomForestWithInstances(
self.types,
self.bounds,
instance_features=self.scenario.feature_array,
seed=self.rng.randint(99999),
do_bootstrapping=True,
num_trees=80,
n_points_per_tree=50000)
imputor = RFRImputator(rs=self.rng,
cutoff=cutoff,
threshold=threshold,
model=model,
change_threshold=0.01,
max_iter=10)
rh2EPM = RunHistory2EPM4LogCost(
scenario=self.scenario,
num_params=num_params,
success_states=[
StatusType.SUCCESS,
],
impute_censored_data=self.impute,
impute_state=[StatusType.TIMEOUT, StatusType.CAPPED],
imputor=imputor)
else:
self.model = 'rfi'
rh2EPM = RunHistory2EPM4Cost(scenario=self.scenario,
num_params=num_params,
success_states=None,
impute_censored_data=self.impute,
impute_state=None)
X, Y = rh2EPM.transform(self.runhistory)
self.X = X
self.y = Y
self.logger.info('Size of training X: %s' % str(self.X.shape))
self.logger.info('Size of training y: %s' % str(self.y.shape))
self.logger.info('Data was %s imputed' %
('not' if not self.impute else ''))
if not self.impute:
self.logger.info(
'Thus the size of X might be smaller than the datapoints in the RunHistory'
)
self.model.train(X, Y)
def evaluate_scenario(self, evaluation_method='all', sort_by: int = 0):
"""
Evaluate the given scenario
:param evaluation_method: name of the method to use
:param sort_by: int, determines the order (only used if evaluation_method == all)
0 => Ablation, fANOVA, Forward Selection
1 => Ablation, Forward Selection, fANOVA
2 => fANOVA, Forward Selection, Ablation
3 => fANOVA, Ablation, Forward Selection
4 => Forward Selection, Ablation, fANOVA
5 => Forward Selection, fANOVA, Ablation
:return:
"""
# influence-model currently not supported
# influence-model currently not supported
methods = ['ablation', 'fanova', 'forward-selection']
if sort_by == 1:
methods = ['ablation', 'forward-selection', 'fanova']
elif sort_by == 2:
methods = ['fanova', 'forward-selection', 'ablation']
elif sort_by == 3:
methods = ['fanova', 'ablation', 'forward-selection']
elif sort_by == 4:
methods = ['forward-selection', 'ablation', 'fanova']
elif sort_by == 5:
methods = ['forward-selection', 'fanova', 'ablation']
if evaluation_method == 'all':
evaluators = []
dict_ = {}
for method in methods:
self.evaluator = method
dict_[method] = self.evaluator.run()
evaluators.append(self.evaluator)
return dict_, evaluators
else:
self.evaluator = evaluation_method
self.logger.info('Running evaluation method %s' %
self.evaluator.name)
return {evaluation_method: self.evaluator.run()}
def plot_results(self, name=None, evaluators=None):
if evaluators:
for eval, name_ in zip(evaluators, name):
eval.plot_result(name_)
else:
self.evaluator.plot_result(name)
def table_for_comparison(self,
evaluators: List[AbstractEvaluator],
name: Union[None, str] = None,
style='cmd'):
"""
Small Method that creates an output table for comparison either printed in a readable format for the command
line or in latex style
:param evaluators: All evaluators to put into the table
:param name: Name for the save file name
:param style: (cmd|latex) str to determine which format to use
:return: None
"""
if name:
f = open(name, 'w')
else:
f = sys.stderr
header = ['{:>{width}s}' for _ in range(len(evaluators) + 1)]
line = '-' if style == 'cmd' else '\hline'
join_ = ' | ' if style == 'cmd' else ' & '
body = OrderedDict()
_max_len_p = 1
_max_len_h = 1
for idx, e in enumerate(evaluators):
for p in e.evaluated_parameter_importance:
if p not in ['-source-', '-target-']:
if p not in body:
body[p] = ['-' for _ in range(len(evaluators))]
body[p][idx] = e.evaluated_parameter_importance[p]
_max_len_p = max(_max_len_p, len(p))
else:
body[p][idx] = e.evaluated_parameter_importance[p]
if e.name in ['Ablation', 'fANOVA']:
if body[p][idx] != '-':
body[p][idx] *= 100
_max_len_p = max(_max_len_p, len(p))
header[idx + 1] = e.name
_max_len_h = max(_max_len_h, len(e.name))
header[0] = header[0].format(' ', width=_max_len_p)
header[1:] = list(
map(lambda x: '{:^{width}s}'.format(x, width=_max_len_h),
header[1:]))
header = join_.join(header)
if style == 'latex':
print('\\begin{table}', file=f)
print('\\begin{tabular}{r%s}' % ('|r' * len(evaluators)), file=f)
print('\\toprule', file=f)
print(header, end='\n' if style == 'cmd' else '\\\\\n', file=f)
if style == 'cmd':
print(line * len(header), file=f)
else:
print(line, file=f)
for p in body:
if style == 'cmd':
b = ['{:>{width}s}'.format(p, width=_max_len_p)]
else:
b = ['{:<{width}s}'.format(p, width=_max_len_p)]
for x in body[p]:
try:
if style == 'latex':
b.append('${:> {width}.3f}$'.format(x,
width=_max_len_h -
2))
else:
b.append('{:> {width}.3f}'.format(x, width=_max_len_h))
except ValueError:
b.append('{:>{width}s}'.format(x, width=_max_len_h))
print(join_.join(b),
end='\n' if style == 'cmd' else '\\\\\n',
file=f)
cap = 'Parameter Importance values, obtained using the PIMP package. Ablation values are percentages ' \
'of improvement a single parameter change obtained between the default and an' \
' incumbent configuration.\n' \
'fANOVA values are percentages that show how much variance across the whole ConfigSpace can be ' \
'explained by that parameter.\n' \
'Forward Selection values are RMSE values obtained using only a subset of parameters for prediction.\n' \
'fANOVA and Forward Selection try to estimate the importances across the whole parameter space, while ' \
'ablation tries to estimate them between two given configurations.'
if self._parameters_to_evaluate > 0:
cap += """\nOnly the top %d parameters of each method are listed.
"-" represent that this parameter was not evaluated
using the given method but with another.
""" % self._parameters_to_evaluate
if style == 'latex':
print('\\bottomrule', file=f)
print('\end{tabular}', file=f)
print('\\caption{%s}' % cap, file=f)
print('\\label{tab:pimp}', file=f)
print('\end{table}', file=f)
else:
print('', file=f)
print(cap)
if name:
f.close()