diff --git a/sklearn/ensemble/forest.py b/sklearn/ensemble/forest.py
index 3272f18b0a2f4961f944014df099e30a9b7f9d20..6ef69221846dde6c272d1da16831595d25784a97 100644
--- a/sklearn/ensemble/forest.py
+++ b/sklearn/ensemble/forest.py
@@ -172,7 +172,8 @@ class BaseForest(BaseEnsemble, SelectorMixin):
oob_score=False,
n_jobs=1,
random_state=None,
- verbose=0):
+ verbose=0,
+ pre_dispatch="2*n_jobs"):
super(BaseForest, self).__init__(
base_estimator=base_estimator,
n_estimators=n_estimators,
@@ -182,6 +183,7 @@ class BaseForest(BaseEnsemble, SelectorMixin):
self.compute_importances = compute_importances
self.oob_score = oob_score
self.n_jobs = n_jobs
+ self.pre_dispatch = pre_dispatch
self.random_state = check_random_state(random_state)
self.feature_importances_ = None
@@ -237,7 +239,8 @@ class BaseForest(BaseEnsemble, SelectorMixin):
n_jobs, n_trees, _ = _partition_trees(self)
# Parallel loop
- all_trees = Parallel(n_jobs=n_jobs, verbose=self.verbose)(
+ all_trees = Parallel(n_jobs=n_jobs, verbose=self.verbose,
+ pre_dispatch=self.pre_dispatch)(
delayed(_parallel_build_trees)(
n_trees[i],
self,
@@ -302,7 +305,8 @@ class ForestClassifier(BaseForest, ClassifierMixin):
oob_score=False,
n_jobs=1,
random_state=None,
- verbose=0):
+ verbose=0,
+ pre_dispatch="2*n_jobs"):
super(ForestClassifier, self).__init__(
base_estimator,
n_estimators=n_estimators,
@@ -312,7 +316,8 @@ class ForestClassifier(BaseForest, ClassifierMixin):
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
- verbose=verbose)
+ verbose=verbose,
+ pre_dispatch=pre_dispatch)
def predict(self, X):
"""Predict class for X.
@@ -402,7 +407,8 @@ class ForestRegressor(BaseForest, RegressorMixin):
oob_score=False,
n_jobs=1,
random_state=None,
- verbose=0):
+ verbose=0,
+ pre_dispatch="2*n_jobs"):
super(ForestRegressor, self).__init__(
base_estimator,
n_estimators=n_estimators,
@@ -412,7 +418,8 @@ class ForestRegressor(BaseForest, RegressorMixin):
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
- verbose=verbose)
+ verbose=verbose,
+ pre_dispatch=pre_dispatch)
def predict(self, X):
"""Predict regression target for X.
@@ -527,6 +534,23 @@ class RandomForestClassifier(ForestClassifier):
verbose : int, optional (default=0)
Controlls the verbosity of the tree building process.
+ pre_dispatch: int, or string, optional
+ Controls the number of jobs that get dispatched during parallel
+ execution. Reducing this number can be useful to avoid an
+ explosion of memory consumption when more jobs get dispatched
+ than CPUs can process. This parameter can be:
+
+ - None, in which case all the jobs are immediatly
+ created and spawned. Use this for lightweight and
+ fast-running jobs, to avoid delays due to on-demand
+ spawning of the jobs
+
+ - An int, giving the exact number of total jobs that are
+ spawned
+
+ - A string, giving an expression as a function of n_jobs,
+ as in '2*n_jobs'
+
Attributes
----------
`feature_importances_` : array, shape = [n_features]
@@ -561,7 +585,8 @@ class RandomForestClassifier(ForestClassifier):
oob_score=False,
n_jobs=1,
random_state=None,
- verbose=0):
+ verbose=0,
+ pre_dispatch="2*n_jobs"):
super(RandomForestClassifier, self).__init__(
base_estimator=DecisionTreeClassifier(),
n_estimators=n_estimators,
@@ -573,7 +598,8 @@ class RandomForestClassifier(ForestClassifier):
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
- verbose=verbose)
+ verbose=verbose,
+ pre_dispatch=pre_dispatch)
self.criterion = criterion
self.max_depth = max_depth
@@ -662,6 +688,23 @@ class RandomForestRegressor(ForestRegressor):
verbose : int, optional (default=0)
Controlls the verbosity of the tree building process.
+ pre_dispatch: int, or string, optional
+ Controls the number of jobs that get dispatched during parallel
+ execution. Reducing this number can be useful to avoid an
+ explosion of memory consumption when more jobs get dispatched
+ than CPUs can process. This parameter can be:
+
+ - None, in which case all the jobs are immediatly
+ created and spawned. Use this for lightweight and
+ fast-running jobs, to avoid delays due to on-demand
+ spawning of the jobs
+
+ - An int, giving the exact number of total jobs that are
+ spawned
+
+ - A string, giving an expression as a function of n_jobs,
+ as in '2*n_jobs'
+
Attributes
----------
`feature_importances_` : array of shape = [n_features]
@@ -696,7 +739,8 @@ class RandomForestRegressor(ForestRegressor):
oob_score=False,
n_jobs=1,
random_state=None,
- verbose=0):
+ verbose=0,
+ pre_dispatch="2*n_jobs"):
super(RandomForestRegressor, self).__init__(
base_estimator=DecisionTreeRegressor(),
n_estimators=n_estimators,
@@ -708,7 +752,8 @@ class RandomForestRegressor(ForestRegressor):
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
- verbose=verbose)
+ verbose=verbose,
+ pre_dispatch=pre_dispatch)
self.criterion = criterion
self.max_depth = max_depth
@@ -798,6 +843,23 @@ class ExtraTreesClassifier(ForestClassifier):
verbose : int, optional (default=0)
Controlls the verbosity of the tree building process.
+ pre_dispatch: int, or string, optional
+ Controls the number of jobs that get dispatched during parallel
+ execution. Reducing this number can be useful to avoid an
+ explosion of memory consumption when more jobs get dispatched
+ than CPUs can process. This parameter can be:
+
+ - None, in which case all the jobs are immediatly
+ created and spawned. Use this for lightweight and
+ fast-running jobs, to avoid delays due to on-demand
+ spawning of the jobs
+
+ - An int, giving the exact number of total jobs that are
+ spawned
+
+ - A string, giving an expression as a function of n_jobs,
+ as in '2*n_jobs'
+
Attributes
----------
`feature_importances_` : array of shape = [n_features]
@@ -834,7 +896,8 @@ class ExtraTreesClassifier(ForestClassifier):
oob_score=False,
n_jobs=1,
random_state=None,
- verbose=0):
+ verbose=0,
+ pre_dispatch="2*n_jobs"):
super(ExtraTreesClassifier, self).__init__(
base_estimator=ExtraTreeClassifier(),
n_estimators=n_estimators,
@@ -846,7 +909,8 @@ class ExtraTreesClassifier(ForestClassifier):
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
- verbose=verbose)
+ verbose=verbose,
+ pre_dispatch=pre_dispatch)
self.criterion = criterion
self.max_depth = max_depth
@@ -937,6 +1001,23 @@ class ExtraTreesRegressor(ForestRegressor):
verbose : int, optional (default=0)
Controlls the verbosity of the tree building process.
+ pre_dispatch: int, or string, optional
+ Controls the number of jobs that get dispatched during parallel
+ execution. Reducing this number can be useful to avoid an
+ explosion of memory consumption when more jobs get dispatched
+ than CPUs can process. This parameter can be:
+
+ - None, in which case all the jobs are immediatly
+ created and spawned. Use this for lightweight and
+ fast-running jobs, to avoid delays due to on-demand
+ spawning of the jobs
+
+ - An int, giving the exact number of total jobs that are
+ spawned
+
+ - A string, giving an expression as a function of n_jobs,
+ as in '2*n_jobs'
+
Attributes
----------
`feature_importances_` : array of shape = [n_features]
@@ -971,7 +1052,8 @@ class ExtraTreesRegressor(ForestRegressor):
oob_score=False,
n_jobs=1,
random_state=None,
- verbose=0):
+ verbose=0,
+ pre_dispatch="2*n_jobs"):
super(ExtraTreesRegressor, self).__init__(
base_estimator=ExtraTreeRegressor(),
n_estimators=n_estimators,
@@ -983,7 +1065,8 @@ class ExtraTreesRegressor(ForestRegressor):
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
- verbose=verbose)
+ verbose=verbose,
+ pre_dispatch=pre_dispatch)
self.criterion = criterion
self.max_depth = max_depth