def random_forest(name,
n_estimators=None,
criterion=None,
max_features=None,
max_depth=None,
min_samples_split=None,
min_samples_leaf=None,
bootstrap=None,
oob_score=None,
n_jobs=1,
random_state=None,
verbose=False):
def _name(msg):
return '%s.%s_%s' % (name, 'random_forest', msg)
"""
Out of bag estimation only available if bootstrap=True
"""
bootstrap_oob = hp.choice(_name('bootstrap_oob'),
[(True, True),
(True, False),
(False, False)])
rval = scope.sklearn_RandomForestClassifier(
n_estimators=scope.int(hp.quniform(
_name('n_estimators'),
1, 50, 1)) if n_estimators is None else n_estimators,
criterion=hp.choice(
_name('criterion'),
['gini', 'entropy']) if criterion is None else criterion,
max_features=hp.choice(
_name('max_features'),
['sqrt', 'log2',
None]) if max_features is None else max_features,
max_depth=max_depth,
min_samples_split=hp.quniform(
_name('min_samples_split'),
1, 10, 1) if min_samples_split is None else min_samples_split,
min_samples_leaf=hp.quniform(
_name('min_samples_leaf'),
1, 5, 1) if min_samples_leaf is None else min_samples_leaf,
bootstrap=bootstrap_oob[0] if bootstrap is None else bootstrap,
oob_score=bootstrap_oob[1] if oob_score is None else oob_score,
#bootstrap=hp.choice(
# _name('bootstrap'),
# [ True, False ] ) if bootstrap is None else bootstrap,
#oob_score=hp.choice(
# _name('oob_score'),
# [ True, False ] ) if oob_score is None else oob_score,
n_jobs=n_jobs,
random_state=_random_state(_name('rstate'), random_state),
verbose=verbose,
)
return rval
def random_forest(name, criterion=None, **kwargs):
'''
Return a pyll graph with hyperparamters that will construct
a sklearn.ensemble.RandomForestClassifier model.
Args:
criterion([str]): choose 'gini' or 'entropy'.
See help(hpsklearn.components._trees_hp_space) for info on additional
available random forest/extra trees arguments.
'''
def _name(msg):
return '%s.%s_%s' % (name, 'rfc', msg)
hp_space = _trees_hp_space(_name, **kwargs)
hp_space['criterion'] = (_trees_criterion(_name('criterion'))
if criterion is None else criterion)
return scope.sklearn_RandomForestClassifier(**hp_space)
def random_forest(name, criterion=None, **kwargs):
'''
Return a pyll graph with hyperparamters that will construct
a sklearn.ensemble.RandomForestClassifier model.
Args:
criterion([str]): choose 'gini' or 'entropy'.
See help(hpsklearn.components._trees_hp_space) for info on additional
available random forest/extra trees arguments.
'''
def _name(msg):
return '%s.%s_%s' % (name, 'rfc', msg)
hp_space = _trees_hp_space(_name, **kwargs)
hp_space['criterion'] = (_trees_criterion(_name('criterion'))
if criterion is None else criterion)
return scope.sklearn_RandomForestClassifier(**hp_space)
def random_forest(name,
n_estimators=None,
criterion=None,
max_features=None,
max_depth=None,
min_samples_split=None,
min_samples_leaf=None,
bootstrap=None,
oob_score=None,
n_jobs=1,
random_state=None,
verbose=False):
def _name(msg):
return '%s.%s_%s' % (name, 'random_forest', msg)
"""
Out of bag estimation only available if bootstrap=True
"""
bootstrap_oob = hp.choice(_name('bootstrap_oob'), [(True, True),
(True, False),
(False, False)])
rval = scope.sklearn_RandomForestClassifier(
n_estimators=scope.int(hp.quniform(_name('n_estimators'), 1, 50, 1))
if n_estimators is None else n_estimators,
criterion=hp.choice(_name('criterion'), ['gini', 'entropy'])
if criterion is None else criterion,
max_features=hp.choice(_name('max_features'), ['sqrt', 'log2', None])
if max_features is None else max_features,
max_depth=max_depth,
min_samples_split=hp.quniform(_name('min_samples_split'), 1, 10, 1)
if min_samples_split is None else min_samples_split,
min_samples_leaf=hp.quniform(_name('min_samples_leaf'), 1, 5, 1)
if min_samples_leaf is None else min_samples_leaf,
bootstrap=bootstrap_oob[0] if bootstrap is None else bootstrap,
oob_score=bootstrap_oob[1] if oob_score is None else oob_score,
n_jobs=n_jobs,
random_state=_random_state(_name('rstate'), random_state),
verbose=verbose,
)
return rval
