def test_predict_marginalized_over_instances_no_features(self, rf_mock):
"""The RF should fall back to the regular predict() method."""
rs = np.random.RandomState(1)
X = rs.rand(20, 10)
Y = rs.rand(10, 1)
model = RandomForestWithInstances(np.zeros((10, ), dtype=np.uint))
model.train(X[:10], Y[:10])
model.predict(X[10:])
self.assertEqual(rf_mock.call_count, 1)
def test_predict_marginalized_over_instances_no_features(self, rf_mock):
"""The RF should fall back to the regular predict() method."""
rs = np.random.RandomState(1)
X = rs.rand(20, 10)
Y = rs.rand(10, 1)
model = RandomForestWithInstances(
configspace=self._get_cs(10),
types=np.zeros((10, ), dtype=np.uint),
bounds=list(map(lambda x: (0, 10), range(10))),
seed=1,
)
model.train(X[:10], Y[:10])
model.predict(X[10:])
self.assertEqual(rf_mock.call_count, 1)
def test_predict_with_actual_values(self):
print()
X = np.array([[0., 0., 0.], [0., 0., 1.], [0., 1., 0.], [0., 1., 1.],
[1., 0., 0.], [1., 0., 1.], [1., 1., 0.], [1., 1., 1.]],
dtype=np.float64)
y = np.array(
[[.1], [.2], [9], [9.2], [100.], [100.2], [109.], [109.2]],
dtype=np.float64)
# print(X.shape, y.shape)
model = RandomForestWithInstances(types=np.array([0, 0, 0],
dtype=np.uint),
bounds=np.array([(0, np.nan),
(0, np.nan),
(0, np.nan)],
dtype=object),
instance_features=None,
seed=12345)
model.train(np.vstack((X, X, X, X, X, X, X, X)),
np.vstack((y, y, y, y, y, y, y, y)))
# for idx, x in enumerate(X):
# print(model.rf.all_leaf_values(x))
# print(x, model.predict(np.array([x]))[0], y[idx])
y_hat, _ = model.predict(X)
for y_i, y_hat_i in zip(
y.reshape((1, -1)).flatten(),
y_hat.reshape((1, -1)).flatten()):
# print(y_i, y_hat_i)
self.assertAlmostEqual(y_i, y_hat_i, delta=0.1)
def test_predict_with_actual_values(self):
X = np.array([
[0., 0., 0.],
[0., 0., 1.],
[0., 1., 0.],
[0., 1., 1.],
[1., 0., 0.],
[1., 0., 1.],
[1., 1., 0.],
[1., 1., 1.]], dtype=np.float64)
y = np.array([
[.1],
[.2],
[9],
[9.2],
[100.],
[100.2],
[109.],
[109.2]], dtype=np.float64)
model = RandomForestWithInstances(
configspace=self._get_cs(3),
types=np.array([0, 0, 0], dtype=np.uint),
bounds=[(0, np.nan), (0, np.nan), (0, np.nan)],
instance_features=None,
seed=12345,
ratio_features=1.0,
)
model.train(np.vstack((X, X, X, X, X, X, X, X)), np.vstack((y, y, y, y, y, y, y, y)))
y_hat, _ = model.predict(X)
for y_i, y_hat_i in zip(y.reshape((1, -1)).flatten(), y_hat.reshape((1, -1)).flatten()):
self.assertAlmostEqual(y_i, y_hat_i, delta=0.1)
def test_predict_mocked(self, rf_mock):
"""Use mock to count the number of calls to _predict"""
class SideEffect(object):
def __init__(self):
self.counter = 0
def __call__(self, X):
self.counter += 1
# Return mean and variance
return self.counter, self.counter
rf_mock.side_effect = SideEffect()
rs = np.random.RandomState(1)
X = rs.rand(20, 10)
Y = rs.rand(10, 1)
model = RandomForestWithInstances(np.zeros((10, ), dtype=np.uint))
model.train(X[:10], Y[:10])
m_hat, v_hat = model.predict(X[10:])
self.assertEqual(m_hat.shape, (10, 1))
self.assertEqual(v_hat.shape, (10, 1))
self.assertEqual(rf_mock.call_count, 10)
for i in range(10):
self.assertEqual(m_hat[i], i + 1)
self.assertEqual(v_hat[i], i + 1)
def test_predict(self):
rs = np.random.RandomState(1)
X = rs.rand(20, 10)
Y = rs.rand(10, 1)
model = RandomForestWithInstances(np.zeros((10, ), dtype=np.uint))
model.train(X[:10], Y[:10])
m_hat, v_hat = model.predict(X[10:])
self.assertEqual(m_hat.shape, (10, 1))
self.assertEqual(v_hat.shape, (10, 1))
def test_rf_on_sklearn_data(self):
import sklearn.datasets
X, y = sklearn.datasets.load_boston(return_X_y=True)
rs = np.random.RandomState(1)
types = np.zeros(X.shape[1])
bounds = [(np.min(X[:, i]), np.max(X[:, i]))
for i in range(X.shape[1])]
cv = sklearn.model_selection.KFold(shuffle=True,
random_state=rs,
n_splits=2)
for do_log in [False, True]:
if do_log:
targets = np.log(y)
model = RandomForestWithInstances(
configspace=self._get_cs(X.shape[1]),
types=types,
bounds=bounds,
seed=1,
ratio_features=1.0,
pca_components=100,
log_y=True,
)
maes = [0.43169704431695493156, 0.4267519520332511912]
else:
targets = y
model = RandomForestWithInstances(
configspace=self._get_cs(X.shape[1]),
types=types,
bounds=bounds,
seed=1,
ratio_features=1.0,
pca_components=100,
)
maes = [9.3298376833224042496, 9.348010654109179346]
for i, (train_split, test_split) in enumerate(cv.split(X,
targets)):
X_train = X[train_split]
y_train = targets[train_split]
X_test = X[test_split]
y_test = targets[test_split]
model.train(X_train, y_train)
y_hat, mu_hat = model.predict(X_test)
mae = np.mean(np.abs(y_hat - y_test), dtype=np.float128)
self.assertAlmostEqual(
mae,
maes[i],
msg=('Do log: %s, iteration %i' % (str(do_log), i)),
# We observe a difference of around 0.00017
# in github actions if doing log
places=3 if do_log else 7)
def test_predict(self):
rs = np.random.RandomState(1)
X = rs.rand(20, 10)
Y = rs.rand(10, 1)
model = RandomForestWithInstances(
types=np.zeros((10, ), dtype=np.uint),
bounds=list(map(lambda x: (0, 10), range(10))),
)
model.train(X[:10], Y[:10])
m_hat, v_hat = model.predict(X[10:])
self.assertEqual(m_hat.shape, (10, 1))
self.assertEqual(v_hat.shape, (10, 1))
def test_with_ordinal(self):
cs = smac.configspace.ConfigurationSpace()
_ = cs.add_hyperparameter(
CategoricalHyperparameter('a', [0, 1], default_value=0))
_ = cs.add_hyperparameter(
OrdinalHyperparameter('b', [0, 1], default_value=1))
_ = cs.add_hyperparameter(
UniformFloatHyperparameter('c',
lower=0.,
upper=1.,
default_value=1))
_ = cs.add_hyperparameter(
UniformIntegerHyperparameter('d',
lower=0,
upper=10,
default_value=1))
cs.seed(1)
feat_array = np.array([0, 0, 0]).reshape(1, -1)
types, bounds = get_types(cs, feat_array)
model = RandomForestWithInstances(
configspace=cs,
types=types,
bounds=bounds,
instance_features=feat_array,
seed=1,
ratio_features=1.0,
pca_components=9,
)
self.assertEqual(bounds[0][0], 2)
self.assertTrue(bounds[0][1] is np.nan)
self.assertEqual(bounds[1][0], 0)
self.assertEqual(bounds[1][1], 1)
self.assertEqual(bounds[2][0], 0.)
self.assertEqual(bounds[2][1], 1.)
self.assertEqual(bounds[3][0], 0.)
self.assertEqual(bounds[3][1], 1.)
X = np.array(
[[0., 0., 0., 0., 0., 0., 0.], [0., 0., 1., 0., 0., 0., 0.],
[0., 1., 0., 9., 0., 0., 0.], [0., 1., 1., 4., 0., 0., 0.]],
dtype=np.float64)
y = np.array([0, 1, 2, 3], dtype=np.float64)
X_train = np.vstack((X, X, X, X, X, X, X, X, X, X))
y_train = np.vstack((y, y, y, y, y, y, y, y, y, y))
model.train(X_train, y_train.reshape((-1, 1)))
mean, _ = model.predict(X)
for idx, m in enumerate(mean):
self.assertAlmostEqual(y[idx], m, 0.05)
def validate_epm(
self,
config_mode: Union[str, typing.List[Configuration]] = 'def',
instance_mode: Union[str, typing.List[str]] = 'test',
repetitions: int = 1,
runhistory: typing.Optional[RunHistory] = None,
output_fn: typing.Optional[str] = None,
reuse_epm: bool = True,
) -> RunHistory:
"""
Use EPM to predict costs/runtimes for unknown config/inst-pairs.
side effect: if output is specified, saves runhistory to specified
output directory.
Parameters
----------
output_fn: str
path to runhistory to be saved. if the suffix is not '.json', will
be interpreted as directory and filename will be
'validated_runhistory_EPM.json'
config_mode: str or list<Configuration>
string or directly a list of Configuration, string from [def, inc, def+inc, wallclock_time, cpu_time, all].
time evaluates at cpu- or wallclock-timesteps of:
[max_time/2^0, max_time/2^1, max_time/2^3, ..., default] with max_time being the highest recorded time
instance_mode: str or list<str>
what instances to use for validation, either from
[train, test, train+test] or directly a list of instances
repetitions: int
number of repetitions in nondeterministic algorithms
runhistory: RunHistory
optional, RunHistory-object to reuse runs
reuse_epm: bool
if true (and if `self.epm`), reuse epm to validate runs
Returns
-------
runhistory: RunHistory
runhistory with predicted runs
"""
if not isinstance(runhistory, RunHistory) and (self.epm is None
or not reuse_epm):
raise ValueError(
"No runhistory specified for validating with EPM!")
elif not reuse_epm or self.epm is None:
# Create RandomForest
types, bounds = get_types(
self.scen.cs, self.scen.feature_array
) # type: ignore[attr-defined] # noqa F821
epm = RandomForestWithInstances(
configspace=self.scen.
cs, # type: ignore[attr-defined] # noqa F821
types=types,
bounds=bounds,
instance_features=self.scen.feature_array,
seed=self.rng.randint(MAXINT),
ratio_features=1.0,
)
# Use imputor if objective is runtime
imputor = None
impute_state = None
impute_censored_data = False
if self.scen.run_obj == 'runtime':
threshold = self.scen.cutoff * self.scen.par_factor # type: ignore[attr-defined] # noqa F821
imputor = RFRImputator(
rng=self.rng,
cutoff=self.scen.
cutoff, # type: ignore[attr-defined] # noqa F821
threshold=threshold,
model=epm)
impute_censored_data = True
impute_state = [StatusType.CAPPED]
success_states = [
StatusType.SUCCESS,
]
else:
success_states = [
StatusType.SUCCESS, StatusType.CRASHED, StatusType.MEMOUT
]
# Transform training data (from given rh)
rh2epm = RunHistory2EPM4Cost(
num_params=len(self.scen.cs.get_hyperparameters()
), # type: ignore[attr-defined] # noqa F821
scenario=self.scen,
rng=self.rng,
impute_censored_data=impute_censored_data,
imputor=imputor,
impute_state=impute_state,
success_states=success_states)
assert runhistory is not None # please mypy
X, y = rh2epm.transform(runhistory)
self.logger.debug("Training model with data of shape X: %s, y:%s",
str(X.shape), str(y.shape))
# Train random forest
epm.train(X, y)
else:
epm = typing.cast(RandomForestWithInstances, self.epm)
# Predict desired runs
runs, rh_epm = self._get_runs(config_mode, instance_mode, repetitions,
runhistory)
feature_array_size = len(self.scen.cs.get_hyperparameters()
) # type: ignore[attr-defined] # noqa F821
if self.scen.feature_array is not None:
feature_array_size += self.scen.feature_array.shape[1]
X_pred = np.empty((len(runs), feature_array_size))
for idx, run in enumerate(runs):
if self.scen.feature_array is not None and run.inst is not None:
X_pred[idx] = np.hstack([
convert_configurations_to_array([run.config])[0],
self.scen.feature_dict[run.inst]
])
else:
X_pred[idx] = convert_configurations_to_array([run.config])[0]
self.logger.debug("Predicting desired %d runs, data has shape %s",
len(runs), str(X_pred.shape))
y_pred = epm.predict(X_pred)
self.epm = epm
# Add runs to runhistory
for run, pred in zip(runs, y_pred[0]):
rh_epm.add(
config=run.config,
cost=float(pred),
time=float(pred),
status=StatusType.SUCCESS,
instance_id=run.inst,
seed=-1,
additional_info={"additional_info": "ESTIMATED USING EPM!"})
if output_fn:
self._save_results(rh_epm,
output_fn,
backup_fn="validated_runhistory_EPM.json")
return rh_epm
class Validator(object):
"""
Validator for the output of SMAC-scenarios.
Evaluates specified configurations on specified instances.
"""
def __init__(self,
scenario: Scenario,
trajectory: list,
rng: Union[np.random.RandomState, int] = None):
"""
Construct Validator for given scenario and trajectory.
Parameters
----------
scenario: Scenario
scenario object for cutoff, instances, features and specifics
trajectory: trajectory-list
trajectory to take incumbent(s) from
rng: np.random.RandomState or int
Random number generator or seed
"""
self.logger = logging.getLogger(self.__module__ + "." +
self.__class__.__name__)
self.traj = trajectory
self.scen = scenario
self.epm = None
if isinstance(rng, np.random.RandomState):
self.rng = rng
elif isinstance(rng, int):
self.rng = np.random.RandomState(seed=rng)
else:
self.logger.debug('no seed given, using default seed of 1')
num_run = 1
self.rng = np.random.RandomState(seed=num_run)
def _save_results(self, rh: RunHistory, output_fn, backup_fn=None):
""" Helper to save results to file
Parameters
----------
rh: RunHistory
runhistory to save
output_fn: str
if ends on '.json': filename to save history to
else: directory to save runhistory to (filename is backup_fn)
backup_fn: str
if output_fn does not end on '.json', treat output_fn as dir and
append backup_fn as filename (if output_fn ends on '.json', this
argument is ignored)
"""
if output_fn == "":
self.logger.info(
"No output specified, validated runhistory not saved.")
return
# Check if a folder or a file is specified as output
if not output_fn.endswith('.json'):
output_dir = output_fn
output_fn = os.path.join(output_dir, backup_fn)
self.logger.debug("Output is \"%s\", changing to \"%s\"!",
output_dir, output_fn)
base = os.path.split(output_fn)[0]
if not base == "" and not os.path.exists(base):
self.logger.debug("Folder (\"%s\") doesn't exist, creating.", base)
os.makedirs(base)
rh.save_json(output_fn)
self.logger.info("Saving validation-results in %s", output_fn)
def validate(
self,
config_mode: Union[str, typing.List[Configuration]] = 'def',
instance_mode: Union[str, typing.List[str]] = 'test',
repetitions: int = 1,
n_jobs: int = 1,
backend: str = 'threading',
runhistory: RunHistory = None,
tae: ExecuteTARun = None,
output_fn: str = "",
) -> RunHistory:
"""
Validate configs on instances and save result in runhistory.
If a runhistory is provided as input it is important that you run it on the same/comparable hardware.
side effect: if output is specified, saves runhistory to specified
output directory.
Parameters
----------
config_mode: str or list<Configuration>
string or directly a list of Configuration.
string from [def, inc, def+inc, wallclock_time, cpu_time, all].
time evaluates at cpu- or wallclock-timesteps of:
[max_time/2^0, max_time/2^1, max_time/2^3, ..., default]
with max_time being the highest recorded time
instance_mode: str or list<str>
what instances to use for validation, either from
[train, test, train+test] or directly a list of instances
repetitions: int
number of repetitions in nondeterministic algorithms
n_jobs: int
number of parallel processes used by joblib
backend: str
what backend joblib should use for parallel runs
runhistory: RunHistory
optional, RunHistory-object to reuse runs
tae: ExecuteTARun
tae to be used. if None, will initialize ExecuteTARunOld
output_fn: str
path to runhistory to be saved. if the suffix is not '.json', will
be interpreted as directory and filename will be
'validated_runhistory.json'
Returns
-------
runhistory: RunHistory
runhistory with validated runs
"""
self.logger.debug(
"Validating configs '%s' on instances '%s', repeating %d times"
" with %d parallel runs on backend '%s'.", config_mode,
instance_mode, repetitions, n_jobs, backend)
# Get all runs to be evaluated as list
runs, validated_rh = self._get_runs(config_mode, instance_mode,
repetitions, runhistory)
# Create new Stats without limits
inf_scen = Scenario({
'run_obj': self.scen.run_obj,
'cutoff_time': self.scen.cutoff,
'output_dir': ""
})
inf_stats = Stats(inf_scen)
inf_stats.start_timing()
# Create TAE
if not tae:
tae = ExecuteTARunOld(ta=self.scen.ta,
stats=inf_stats,
run_obj=self.scen.run_obj,
par_factor=self.scen.par_factor,
cost_for_crash=self.scen.cost_for_crash)
else:
# Inject endless-stats
tae.stats = inf_stats
# Validate!
run_results = self._validate_parallel(tae, runs, n_jobs, backend)
# tae returns (status, cost, runtime, additional_info)
# Add runs to RunHistory
idx = 0
for result in run_results:
validated_rh.add(config=runs[idx].config,
cost=result[1],
time=result[2],
status=result[0],
instance_id=runs[idx].inst,
seed=runs[idx].seed,
additional_info=result[3])
idx += 1
if output_fn:
self._save_results(validated_rh,
output_fn,
backup_fn="validated_runhistory.json")
return validated_rh
def _validate_parallel(self, tae: ExecuteTARun, runs: typing.List[_Run],
n_jobs: int, backend: str):
"""
Validate runs with joblibs Parallel-interface
Parameters
----------
tae: ExecuteTARun
tae to be used for validation
runs: list<_Run>
list with _Run-objects
[_Run(config=CONFIG1,inst=INSTANCE1,seed=SEED1,inst_specs=INST_SPECIFICS1), ...]
n_jobs: int
number of cpus to use for validation (-1 to use all)
backend: str
what backend to use for parallelization
Returns
-------
run_results: list<tuple(tae-returns)>
results as returned by tae
"""
# Runs with parallel
run_results = Parallel(n_jobs=n_jobs, backend=backend)(
delayed(_unbound_tae_starter)(tae,
run.config,
run.inst,
self.scen.cutoff,
run.seed,
run.inst_specs,
capped=False) for run in runs)
return run_results
def validate_epm(
self,
config_mode: Union[str, typing.List[Configuration]] = 'def',
instance_mode: Union[str, typing.List[str]] = 'test',
repetitions: int = 1,
runhistory: RunHistory = None,
output_fn="",
reuse_epm=True,
) -> RunHistory:
"""
Use EPM to predict costs/runtimes for unknown config/inst-pairs.
side effect: if output is specified, saves runhistory to specified
output directory.
Parameters
----------
output_fn: str
path to runhistory to be saved. if the suffix is not '.json', will
be interpreted as directory and filename will be
'validated_runhistory_EPM.json'
config_mode: str or list<Configuration>
string or directly a list of Configuration, string from [def, inc, def+inc, wallclock_time, cpu_time, all].
time evaluates at cpu- or wallclock-timesteps of:
[max_time/2^0, max_time/2^1, max_time/2^3, ..., default] with max_time being the highest recorded time
instance_mode: str or list<str>
what instances to use for validation, either from
[train, test, train+test] or directly a list of instances
repetitions: int
number of repetitions in nondeterministic algorithms
runhistory: RunHistory
optional, RunHistory-object to reuse runs
reuse_epm: bool
if true (and if `self.epm`), reuse epm to validate runs
Returns
-------
runhistory: RunHistory
runhistory with predicted runs
"""
if not isinstance(runhistory, RunHistory) and (self.epm is None
or reuse_epm is False):
raise ValueError(
"No runhistory specified for validating with EPM!")
elif reuse_epm is False or self.epm is None:
# Create RandomForest
types, bounds = get_types(self.scen.cs, self.scen.feature_array)
self.epm = RandomForestWithInstances(
types=types,
bounds=bounds,
instance_features=self.scen.feature_array,
seed=self.rng.randint(MAXINT),
ratio_features=1.0)
# Use imputor if objective is runtime
imputor = None
impute_state = None
impute_censored_data = False
if self.scen.run_obj == 'runtime':
threshold = self.scen.cutoff * self.scen.par_factor
imputor = RFRImputator(rng=self.rng,
cutoff=self.scen.cutoff,
threshold=threshold,
model=self.epm)
impute_censored_data = True
impute_state = [StatusType.CAPPED]
# Transform training data (from given rh)
rh2epm = RunHistory2EPM4Cost(
num_params=len(self.scen.cs.get_hyperparameters()),
scenario=self.scen,
rng=self.rng,
impute_censored_data=impute_censored_data,
imputor=imputor,
impute_state=impute_state)
X, y = rh2epm.transform(runhistory)
self.logger.debug("Training model with data of shape X: %s, y:%s",
str(X.shape), str(y.shape))
# Train random forest
self.epm.train(X, y)
# Predict desired runs
runs, rh_epm = self._get_runs(config_mode, instance_mode, repetitions,
runhistory)
feature_array_size = len(self.scen.cs.get_hyperparameters())
if self.scen.feature_array is not None:
feature_array_size += self.scen.feature_array.shape[1]
X_pred = np.empty((len(runs), feature_array_size))
for idx, run in enumerate(runs):
if self.scen.feature_array is not None and run.inst is not None:
X_pred[idx] = np.hstack([
convert_configurations_to_array([run.config])[0],
self.scen.feature_dict[run.inst]
])
else:
X_pred[idx] = convert_configurations_to_array([run.config])[0]
self.logger.debug("Predicting desired %d runs, data has shape %s",
len(runs), str(X_pred.shape))
y_pred = self.epm.predict(X_pred)
# Add runs to runhistory
for run, pred in zip(runs, y_pred[0]):
rh_epm.add(
config=run.config,
cost=float(pred),
time=float(pred),
status=StatusType.SUCCESS,
instance_id=run.inst,
seed=-1,
additional_info={"additional_info": "ESTIMATED USING EPM!"})
if output_fn:
self._save_results(rh_epm,
output_fn,
backup_fn="validated_runhistory_EPM.json")
return rh_epm
def _get_runs(
self,
configs: Union[str, typing.List[Configuration]],
insts: Union[str, typing.List[str]],
repetitions: int = 1,
runhistory: RunHistory = None,
) -> typing.Tuple[typing.List[_Run], RunHistory]:
"""
Generate list of SMAC-TAE runs to be executed. This means
combinations of configs with all instances on a certain number of seeds.
side effect: Adds runs that don't need to be reevaluated to self.rh!
Parameters
----------
configs: str or list<Configuration>
string or directly a list of Configuration
str from [def, inc, def+inc, wallclock_time, cpu_time, all]
time evaluates at cpu- or wallclock-timesteps of:
[max_time/2^0, max_time/2^1, max_time/2^3, ..., default]
with max_time being the highest recorded time
insts: str or list<str>
what instances to use for validation, either from
[train, test, train+test] or directly a list of instances
repetitions: int
number of seeds per instance/config-pair to be evaluated
runhistory: RunHistory
optional, try to reuse this runhistory and save some runs
Returns
-------
runs: list<_Run>
list with _Runs
[_Run(config=CONFIG1,inst=INSTANCE1,seed=SEED1,inst_specs=INST_SPECIFICS1),
_Run(config=CONFIG2,inst=INSTANCE2,seed=SEED2,inst_specs=INST_SPECIFICS2),
...]
"""
# Get relevant configurations and instances
if isinstance(configs, str):
configs = self._get_configs(configs)
if isinstance(insts, str):
insts = self._get_instances(insts)
# If no instances are given, fix the instances to one "None" instance
if not insts:
insts = [None]
# If algorithm is deterministic, fix repetitions to 1
if self.scen.deterministic and repetitions != 1:
self.logger.warning(
"Specified %d repetitions, but fixing to 1, "
"because algorithm is deterministic.", repetitions)
repetitions = 1
# Extract relevant information from given runhistory
inst_seed_config = self._process_runhistory(configs, insts, runhistory)
# Now create the actual run-list
runs = []
# Counter for runs without the need of recalculation
runs_from_rh = 0
# If we reuse runs, we want to return them as well
new_rh = RunHistory(average_cost)
for i in sorted(insts):
for rep in range(repetitions):
# First, find a seed and add all the data we can take from the
# given runhistory to "our" validation runhistory.
configs_evaluated = []
if runhistory and i in inst_seed_config:
# Choose seed based on most often evaluated inst-seed-pair
seed, configs_evaluated = inst_seed_config[i].pop(0)
# Delete inst if all seeds are used
if not inst_seed_config[i]:
inst_seed_config.pop(i)
# Add runs to runhistory
for c in configs_evaluated[:]:
runkey = RunKey(runhistory.config_ids[c], i, seed)
cost, time, status, additional_info = runhistory.data[
runkey]
if status in [
StatusType.CRASHED, StatusType.ABORT,
StatusType.CAPPED
]:
# Not properly executed target algorithm runs should be repeated
configs_evaluated.remove(c)
continue
new_rh.add(c,
cost,
time,
status,
instance_id=i,
seed=seed,
additional_info=additional_info)
runs_from_rh += 1
else:
# If no runhistory or no entries for instance, get new seed
seed = self.rng.randint(MAXINT)
# We now have a seed and add all configs that are not already
# evaluated on that seed to the runs-list. This way, we
# guarantee the same inst-seed-pairs for all configs.
for config in [
c for c in configs if not c in configs_evaluated
]:
# Only use specifics if specific exists, else use string "0"
specs = self.scen.instance_specific[
i] if i and i in self.scen.instance_specific else "0"
runs.append(
_Run(config=config,
inst=i,
seed=seed,
inst_specs=specs))
self.logger.info(
"Collected %d runs from %d configurations on %d "
"instances with %d repetitions. Reusing %d runs from "
"given runhistory.", len(runs), len(configs), len(insts),
repetitions, runs_from_rh)
return runs, new_rh
def _process_runhistory(self, configs: typing.List[Configuration],
insts: typing.List[str], runhistory: RunHistory):
"""
Processes runhistory from self._get_runs by extracting already evaluated
(relevant) config-inst-seed tuples.
Parameters
----------
configs: list(Configuration)
list of configs of interest
insts: list(str)
list of instances of interest
runhistory: RunHistory
runhistory to extract runs from
Returns
-------
inst_seed_config: dict<str : list(tuple(int, tuple(configs)))>
dictionary mapping instances to a list of tuples of already used
seeds and the configs that this inst-seed-pair has been evaluated
on, sorted by the number of configs
"""
# We want to reuse seeds that have been used on most configurations
# To this end, we create a dictionary as {instances:{seed:[configs]}}
# Like this we can easily retrieve the most used instance-seed pairs to
# minimize the number of runs to be evaluated
inst_seed_config = {}
if runhistory:
relevant = dict()
for key in runhistory.data:
if (runhistory.ids_config[key.config_id] in configs
and key.instance_id in insts):
relevant[key] = runhistory.data[key]
# Change data-structure to {instances:[(seed1, (configs)), (seed2, (configs), ... ]}
# to make most used seed easily accessible, we sort after length of configs
for key in relevant:
inst, seed = key.instance_id, key.seed
config = runhistory.ids_config[key.config_id]
if inst in inst_seed_config:
if seed in inst_seed_config[inst]:
inst_seed_config[inst][seed].append(config)
else:
inst_seed_config[inst][seed] = [config]
else:
inst_seed_config[inst] = {seed: [config]}
inst_seed_config = {
i: sorted([(seed, list(inst_seed_config[i][seed]))
for seed in inst_seed_config[i]],
key=lambda x: len(x[1]))
for i in inst_seed_config
}
return inst_seed_config
def _get_configs(self, mode: str) -> typing.List[str]:
"""
Return desired configs
Parameters
----------
mode: str
str from [def, inc, def+inc, wallclock_time, cpu_time, all]
time evaluates at cpu- or wallclock-timesteps of:
[max_time/2^0, max_time/2^1, max_time/2^3, ..., default]
with max_time being the highest recorded time
Returns
-------
configs: list<Configuration>
list with desired configurations
"""
# Add desired configs
configs = []
mode = mode.lower()
if mode not in [
'def', 'inc', 'def+inc', 'wallclock_time', 'cpu_time', 'all'
]:
raise ValueError(
"%s not a valid option for config_mode in validation." % mode)
if mode == "def" or mode == "def+inc":
configs.append(self.scen.cs.get_default_configuration())
if mode == "inc" or mode == "def+inc":
configs.append(self.traj[-1]["incumbent"])
if mode in ["wallclock_time", "cpu_time"]:
# get highest time-entry and add entries from there
# not using wallclock_limit in case it's inf
if (mode == "wallclock_time"
and np.isfinite(self.scen.wallclock_limit)):
max_time = self.scen.wallclock_limit
elif (mode == "cpu_time"
and np.isfinite(self.scen.algo_runs_timelimit)):
max_time = self.scen.algo_runs_timelimit
else:
max_time = self.traj[-1][mode]
counter = 2**0
for entry in self.traj[::-1]:
if (entry[mode] <= max_time / counter
and entry["incumbent"] not in configs):
configs.append(entry["incumbent"])
counter *= 2
if not self.traj[0]["incumbent"] in configs:
configs.append(traj[0]["incumbent"]) # add first
if mode == "all":
for entry in self.traj:
if not entry["incumbent"] in configs:
configs.append(entry["incumbent"])
self.logger.debug("Gathered %d configurations for mode %s.",
len(configs), mode)
return configs
def _get_instances(self, mode: str) -> typing.List[str]:
"""
Get desired instances
Parameters
----------
mode: str
what instances to use for validation, from [train, test, train+test]
Returns
-------
instances: list<str>
instances to be used
"""
instance_mode = mode.lower()
if mode not in ['train', 'test', 'train+test']:
raise ValueError(
"%s not a valid option for instance_mode in validation." %
mode)
# Make sure if instances matter, than instances should be passed
if ((instance_mode == 'train' and self.scen.train_insts == [None]) or
(instance_mode == 'test' and self.scen.test_insts == [None])):
self.logger.warning(
"Instance mode is set to %s, but there are no "
"%s-instances specified in the scenario. Setting instance mode to"
"\"train+test\"!", instance_mode, instance_mode)
instance_mode = 'train+test'
instances = []
if ((instance_mode == 'train' or instance_mode == 'train+test')
and not self.scen.train_insts == [None]):
instances.extend(self.scen.train_insts)
if ((instance_mode == 'test' or instance_mode == 'train+test')
and not self.scen.test_insts == [None]):
instances.extend(self.scen.test_insts)
return instances
else:
# Do Blended BO for 250 iterations
observed_X = []
observed_y = []
observed_i = []
surpassed = None
for iteration in range(0, 250):
# We need to have observed at least 3 items for the model to be able to predict
surr_predictions = np.zeros_like(test_index)
if iteration > 2 and alpha < 1:
surr_estimator.train(
np.array(observed_X).astype(float),
np.array(observed_y))
mu, var = surr_estimator.predict(
np.array(surr_X.iloc[test_index]).astype(float))
mu = mu.reshape(-1)
var = var.reshape(-1)
sigma = np.sqrt(var)
diff = mu - np.max(observed_y)
Z = diff / sigma
ei = diff * norm.cdf(Z) + sigma * norm.pdf(Z)
surr_predictions = ei
# surr_predictions = surr_estimator.predict(np.array(surr_X.iloc[test_index]).astype(float))
# print(iteration, "\t", np.std(surr_predictions), "\t", np.std(meta_predictions))
m_corr, m_pvalue = kendalltau(meta_predictions, y[test_index])
s_corr, s_pvalue = kendalltau(surr_predictions, y[test_index])
if s_corr > m_corr:
