forest: add min_weight_fraction_leaf

Author: ndawe

sklearn/ensemble/forest.py

@@ -669,6 +669,11 @@ class RandomForestClassifier(ForestClassifier):
         ``min_samples_leaf`` samples.
         Note: this parameter is tree-specific.
 
+    min_weight_fraction_leaf : float, optional (default=0.)
+        The minimum weighted fraction of the input samples required to be at a
+        leaf node.
+        Note: this parameter is tree-specific.
+
     max_leaf_nodes : int or None, optional (default=None)
         Grow trees with ``max_leaf_nodes`` in best-first fashion.
         Best nodes are defined as relative reduction in impurity.
@@ -736,6 +741,7 @@ def __init__(self,
                  max_depth=None,
                  min_samples_split=2,
                  min_samples_leaf=1,
+                 min_weight_fraction_leaf=0.,
                  max_features="auto",
                  max_leaf_nodes=None,
                  bootstrap=True,
@@ -749,8 +755,9 @@ def __init__(self,
             base_estimator=DecisionTreeClassifier(),
             n_estimators=n_estimators,
             estimator_params=("criterion", "max_depth", "min_samples_split",
-                              "min_samples_leaf", "max_features",
-                              "max_leaf_nodes", "random_state"),
+                              "min_samples_leaf", "min_weight_fraction_leaf",
+                              "max_features", "max_leaf_nodes",
+                              "random_state"),
             bootstrap=bootstrap,
             oob_score=oob_score,
             n_jobs=n_jobs,
@@ -761,6 +768,7 @@ def __init__(self,
         self.max_depth = max_depth
         self.min_samples_split = min_samples_split
         self.min_samples_leaf = min_samples_leaf
+        self.min_weight_fraction_leaf = min_weight_fraction_leaf
         self.max_features = max_features
         self.max_leaf_nodes = max_leaf_nodes
 
@@ -827,6 +835,11 @@ class RandomForestRegressor(ForestRegressor):
         ``min_samples_leaf`` samples.
         Note: this parameter is tree-specific.
 
+    min_weight_fraction_leaf : float, optional (default=0.)
+        The minimum weighted fraction of the input samples required to be at a
+        leaf node.
+        Note: this parameter is tree-specific.
+
     max_leaf_nodes : int or None, optional (default=None)
         Grow trees with ``max_leaf_nodes`` in best-first fashion.
         Best nodes are defined as relative reduction in impurity.
@@ -883,6 +896,7 @@ def __init__(self,
                  max_depth=None,
                  min_samples_split=2,
                  min_samples_leaf=1,
+                 min_weight_fraction_leaf=0.,
                  max_features="auto",
                  max_leaf_nodes=None,
                  bootstrap=True,
@@ -896,8 +910,9 @@ def __init__(self,
             base_estimator=DecisionTreeRegressor(),
             n_estimators=n_estimators,
             estimator_params=("criterion", "max_depth", "min_samples_split",
-                              "min_samples_leaf", "max_features",
-                              "max_leaf_nodes", "random_state"),
+                              "min_samples_leaf", "min_weight_fraction_leaf",
+                              "max_features", "max_leaf_nodes",
+                              "random_state"),
             bootstrap=bootstrap,
             oob_score=oob_score,
             n_jobs=n_jobs,
@@ -908,6 +923,7 @@ def __init__(self,
         self.max_depth = max_depth
         self.min_samples_split = min_samples_split
         self.min_samples_leaf = min_samples_leaf
+        self.min_weight_fraction_leaf = min_weight_fraction_leaf
         self.max_features = max_features
         self.max_leaf_nodes = max_leaf_nodes
 
@@ -975,6 +991,11 @@ class ExtraTreesClassifier(ForestClassifier):
         ``min_samples_leaf`` samples.
         Note: this parameter is tree-specific.
 
+    min_weight_fraction_leaf : float, optional (default=0.)
+        The minimum weighted fraction of the input samples required to be at a
+        leaf node.
+        Note: this parameter is tree-specific.
+
     max_leaf_nodes : int or None, optional (default=None)
         Grow trees with ``max_leaf_nodes`` in best-first fashion.
         Best nodes are defined as relative reduction in impurity.
@@ -1045,6 +1066,7 @@ def __init__(self,
                  max_depth=None,
                  min_samples_split=2,
                  min_samples_leaf=1,
+                 min_weight_fraction_leaf=0.,
                  max_features="auto",
                  max_leaf_nodes=None,
                  bootstrap=False,
@@ -1058,8 +1080,8 @@ def __init__(self,
             base_estimator=ExtraTreeClassifier(),
             n_estimators=n_estimators,
             estimator_params=("criterion", "max_depth", "min_samples_split",
-                              "min_samples_leaf", "max_features",
-                              "max_leaf_nodes", "random_state"),
+                              "min_samples_leaf", "min_weight_fraction_leaf",
+                              "max_features", "max_leaf_nodes", "random_state"),
             bootstrap=bootstrap,
             oob_score=oob_score,
             n_jobs=n_jobs,
@@ -1070,6 +1092,7 @@ def __init__(self,
         self.max_depth = max_depth
         self.min_samples_split = min_samples_split
         self.min_samples_leaf = min_samples_leaf
+        self.min_weight_fraction_leaf = min_weight_fraction_leaf
         self.max_features = max_features
         self.max_leaf_nodes = max_leaf_nodes
 
@@ -1137,6 +1160,11 @@ class ExtraTreesRegressor(ForestRegressor):
         ``min_samples_leaf`` samples.
         Note: this parameter is tree-specific.
 
+    min_weight_fraction_leaf : float, optional (default=0.)
+        The minimum weighted fraction of the input samples required to be at a
+        leaf node.
+        Note: this parameter is tree-specific.
+
     max_leaf_nodes : int or None, optional (default=None)
         Grow trees with ``max_leaf_nodes`` in best-first fashion.
         Best nodes are defined as relative reduction in impurity.
@@ -1196,6 +1224,7 @@ def __init__(self,
                  max_depth=None,
                  min_samples_split=2,
                  min_samples_leaf=1,
+                 min_weight_fraction_leaf=0.,
                  max_features="auto",
                  max_leaf_nodes=None,
                  bootstrap=False,
@@ -1209,8 +1238,9 @@ def __init__(self,
             base_estimator=ExtraTreeRegressor(),
             n_estimators=n_estimators,
             estimator_params=("criterion", "max_depth", "min_samples_split",
-                              "min_samples_leaf", "max_features",
-                              "max_leaf_nodes", "random_state"),
+                              "min_samples_leaf", "min_weight_fraction_leaf",
+                              "max_features", "max_leaf_nodes",
+                              "random_state"),
             bootstrap=bootstrap,
             oob_score=oob_score,
             n_jobs=n_jobs,
@@ -1221,6 +1251,7 @@ def __init__(self,
         self.max_depth = max_depth
         self.min_samples_split = min_samples_split
         self.min_samples_leaf = min_samples_leaf
+        self.min_weight_fraction_leaf = min_weight_fraction_leaf
         self.max_features = max_features
         self.max_leaf_nodes = max_leaf_nodes
 
@@ -1268,6 +1299,11 @@ class RandomTreesEmbedding(BaseForest):
         ``min_samples_leaf`` samples.
         Note: this parameter is tree-specific.
 
+    min_weight_fraction_leaf : float, optional (default=0.)
+        The minimum weighted fraction of the input samples required to be at a
+        leaf node.
+        Note: this parameter is tree-specific.
+
     max_leaf_nodes : int or None, optional (default=None)
         Grow trees with ``max_leaf_nodes`` in best-first fashion.
         Best nodes are defined as relative reduction in impurity.
@@ -1312,6 +1348,7 @@ def __init__(self,
                  max_depth=5,
                  min_samples_split=2,
                  min_samples_leaf=1,
+                 min_weight_fraction_leaf=0.,
                  max_leaf_nodes=None,
                  sparse_output=True,
                  n_jobs=1,
@@ -1322,8 +1359,9 @@ def __init__(self,
             base_estimator=ExtraTreeRegressor(),
             n_estimators=n_estimators,
             estimator_params=("criterion", "max_depth", "min_samples_split",
-                              "min_samples_leaf", "max_features",
-                              "max_leaf_nodes", "random_state"),
+                              "min_samples_leaf", "min_weight_fraction_leaf",
+                              "max_features", "max_leaf_nodes",
+                              "random_state"),
             bootstrap=False,
             oob_score=False,
             n_jobs=n_jobs,
@@ -1334,6 +1372,7 @@ def __init__(self,
         self.max_depth = max_depth
         self.min_samples_split = min_samples_split
         self.min_samples_leaf = min_samples_leaf
+        self.min_weight_fraction_leaf = min_weight_fraction_leaf
         self.max_features = 1
         self.max_leaf_nodes = max_leaf_nodes
         self.sparse_output = sparse_output