74 sklearn/tree/tree.py
View file
@@ -268,7 +268,7 @@ def predict(self, X):


def _build_tree(X, y, is_classification, criterion,
max_depth, min_split, min_leaf,
max_depth, min_samples_split, min_samples_leaf,
min_density, max_features, random_state, n_classes, find_split,
sample_mask=None, X_argsorted=None):
"""Build a tree by recursively partitioning the data."""
@@ -291,10 +291,10 @@ def recursive_partition(X, X_argsorted, y, sample_mask, depth,
"with an empty sample_mask")

# Split samples
if depth < max_depth and n_node_samples >= min_split \
and n_node_samples >= 2 * min_leaf:
if depth < max_depth and n_node_samples >= min_samples_split \
and n_node_samples >= 2 * min_samples_leaf:
feature, threshold, best_error, init_error = find_split(X, y,
X_argsorted, sample_mask, n_node_samples, min_leaf,
X_argsorted, sample_mask, n_node_samples, min_samples_leaf,
max_features, criterion, random_state)

else:
@@ -373,16 +373,16 @@ class BaseDecisionTree(BaseEstimator, SelectorMixin):
"""
def __init__(self, criterion,
max_depth,
min_split,
min_leaf,
min_samples_split,
min_samples_leaf,
min_density,
max_features,
compute_importances,
random_state):
self.criterion = criterion
self.max_depth = max_depth
self.min_split = min_split
self.min_leaf = min_leaf
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_density = min_density
self.max_features = max_features
self.compute_importances = compute_importances
@@ -413,8 +413,8 @@ def fit(self, X, y, sample_mask=None, X_argsorted=None):
self : object
Returns self.
"""
# set min_split sensibly
self.min_split = max(self.min_split, 2 * self.min_leaf)
# set min_samples_split sensibly
self.min_samples_split = max(self.min_samples_split, 2 * self.min_samples_leaf)

# Convert data
X = np.asarray(X, dtype=DTYPE, order='F')
@@ -466,10 +466,10 @@ def fit(self, X, y, sample_mask=None, X_argsorted=None):
if len(y) != n_samples:
raise ValueError("Number of labels=%d does not match "
"number of samples=%d" % (len(y), n_samples))
if self.min_split <= 0:
raise ValueError("min_split must be greater than zero.")
if self.min_leaf <= 0:
raise ValueError("min_leaf must be greater than zero.")
if self.min_samples_split <= 0:
raise ValueError("min_samples_split must be greater than zero.")
if self.min_samples_leaf <= 0:
raise ValueError("min_samples_leaf must be greater than zero.")
if max_depth <= 0:
raise ValueError("max_depth must be greater than zero. ")
if self.min_density < 0.0 or self.min_density > 1.0:
@@ -479,7 +479,7 @@ def fit(self, X, y, sample_mask=None, X_argsorted=None):

# Build tree
self.tree_ = _build_tree(X, y, is_classification, criterion, max_depth,
self.min_split, self.min_leaf, self.min_density, max_features,
self.min_samples_split, self.min_samples_leaf, self.min_density, max_features,
self.random_state, self.n_classes_, self.find_split_,
sample_mask=sample_mask, X_argsorted=X_argsorted)

@@ -553,13 +553,13 @@ class DecisionTreeClassifier(BaseDecisionTree, ClassifierMixin):
max_depth : integer or None, optional (default=None)
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than min_split
all leaves are pure or until all leaves contain less than min_samples_split
samples.
min_split : integer, optional (default=1)
min_samples_split : integer, optional (default=1)
The minimum number of samples required to split an internal node.
min_leaf : integer, optional (default=1)
min_samples_leaf : integer, optional (default=1)
The minimum number of samples required to be at a leaf node.
min_density : float, optional (default=0.1)
@@ -641,16 +641,16 @@ class DecisionTreeClassifier(BaseDecisionTree, ClassifierMixin):
"""
def __init__(self, criterion="gini",
max_depth=None,
min_split=1,
min_leaf=1,
min_samples_split=1,
min_samples_leaf=1,
min_density=0.1,
max_features=None,
compute_importances=False,
random_state=None):
super(DecisionTreeClassifier, self).__init__(criterion,
max_depth,
min_split,
min_leaf,
min_samples_split,
min_samples_leaf,
min_density,
max_features,
compute_importances,
@@ -714,13 +714,13 @@ class DecisionTreeRegressor(BaseDecisionTree, RegressorMixin):
max_depth : integer or None, optional (default=None)
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than min_split
all leaves are pure or until all leaves contain less than min_samples_split
samples.
min_split : integer, optional (default=1)
min_samples_split : integer, optional (default=1)
The minimum number of samples required to split an internal node.
min_leaf : integer, optional (default=1)
min_samples_leaf : integer, optional (default=1)
The minimum number of samples required to be at a leaf node.
min_density : float, optional (default=0.1)
@@ -804,16 +804,16 @@ class DecisionTreeRegressor(BaseDecisionTree, RegressorMixin):
"""
def __init__(self, criterion="mse",
max_depth=None,
min_split=1,
min_leaf=1,
min_samples_split=1,
min_samples_leaf=1,
min_density=0.1,
max_features=None,
compute_importances=False,
random_state=None):
super(DecisionTreeRegressor, self).__init__(criterion,
max_depth,
min_split,
min_leaf,
min_samples_split,
min_samples_leaf,
min_density,
max_features,
compute_importances,
@@ -845,16 +845,16 @@ class ExtraTreeClassifier(DecisionTreeClassifier):
"""
def __init__(self, criterion="gini",
max_depth=None,
min_split=1,
min_leaf=1,
min_samples_split=1,
min_samples_leaf=1,
min_density=0.1,
max_features="auto",
compute_importances=False,
random_state=None):
super(ExtraTreeClassifier, self).__init__(criterion,
max_depth,
min_split,
min_leaf,
min_samples_split,
min_samples_leaf,
min_density,
max_features,
compute_importances,
@@ -892,16 +892,16 @@ class ExtraTreeRegressor(DecisionTreeRegressor):
"""
def __init__(self, criterion="mse",
max_depth=None,
min_split=1,
min_leaf=1,
min_samples_split=1,
min_samples_leaf=1,
min_density=0.1,
max_features="auto",
compute_importances=False,
random_state=None):
super(ExtraTreeRegressor, self).__init__(criterion,
max_depth,
min_split,
min_leaf,
min_samples_split,
min_samples_leaf,
min_density,
max_features,
compute_importances,