我一直在尝试分析在DecisionTreeRegressor
中受过训练的sklearn
。我发现http://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html对于确定拆分树中每个分支的属性很有用,尤其是以下代码片段:
n_nodes = estimator.tree_.node_count
children_left = estimator.tree_.children_left
children_right = estimator.tree_.children_right
feature = estimator.tree_.feature
threshold = estimator.tree_.threshold
# The tree structure can be traversed to compute various properties such
# as the depth of each node and whether or not it is a leaf.
node_depth = np.zeros(shape=n_nodes, dtype=np.int64)
is_leaves = np.zeros(shape=n_nodes, dtype=bool)
stack = [(0, -1)] # seed is the root node id and its parent depth
while len(stack) > 0:
node_id, parent_depth = stack.pop()
node_depth[node_id] = parent_depth + 1
# If we have a test node
if (children_left[node_id] != children_right[node_id]):
stack.append((children_left[node_id], parent_depth + 1))
stack.append((children_right[node_id], parent_depth + 1))
else:
is_leaves[node_id] = True
print("The binary tree structure has %s nodes and has "
"the following tree structure:"
% n_nodes)
for i in range(n_nodes):
if is_leaves[i]:
print("%snode=%s leaf node." % (node_depth[i] * "\t", i))
else:
print("%snode=%s test node: go to node %s if X[:, %s] <= %s else to "
"node %s."
% (node_depth[i] * "\t",
i,
children_left[i],
feature[i],
threshold[i],
children_right[i],
))
但是,这并不能告诉我每个叶节点的值。如果以上内容打印出如下内容:
The binary tree structure has 7 nodes and has the following tree structure:
node=0 test node: go to node 1 if X[:, 2] <= 1.00764083862 else to node 4.
node=1 test node: go to node 2 if X[:, 2] <= 0.974808812141 else to node 3.
node=2 leaf node.
node=3 leaf node.
node=4 test node: go to node 5 if X[:, 0] <= -2.90554761887 else to node 6.
node=5 leaf node.
node=6 leaf node.
例如,我怎么知道节点2代表的值?
答案 0 :(得分:0)
您要寻找的方法是estimator.tree_.value
让我们举一个可复制的示例,因为您从文档链接到的是分类而不是回归:
import numpy as np
from sklearn.tree import DecisionTreeRegressor
# dummy data
rng = np.random.RandomState(1)
X = np.sort(5 * rng.rand(80, 1), axis=0)
y = np.sin(X).ravel()
y[::5] += 3 * (0.5 - rng.rand(16))
estimator = DecisionTreeRegressor(max_depth=3)
estimator.fit(X, y)
然后,使用您的代码按原样获得:
The binary tree structure has 15 nodes and has the following tree structure:
node=0 test node: go to node 1 if X[:, 0] <= 3.13275051117 else to node 8.
node=1 test node: go to node 2 if X[:, 0] <= 0.513901114464 else to node 5.
node=2 test node: go to node 3 if X[:, 0] <= 0.0460066311061 else to node 4.
node=3 leaf node.
node=4 leaf node.
node=5 test node: go to node 6 if X[:, 0] <= 2.02933192253 else to node 7.
node=6 leaf node.
node=7 leaf node.
node=8 test node: go to node 9 if X[:, 0] <= 3.85022854805 else to node 12.
node=9 test node: go to node 10 if X[:, 0] <= 3.42930102348 else to node 11.
node=10 leaf node.
node=11 leaf node.
node=12 test node: go to node 13 if X[:, 0] <= 4.68025827408 else to node 14.
node=13 leaf node.
node=14 leaf node.
现在,estimator.tree_.value
包含所有树节点(此处为15)的值:
len(estimator.tree_.value)
# 15
例如,我们要获取节点#3的值
estimator.tree_.value[3]
# array([[-1.1493464]])
有关value
内容(包括非终端节点)的详细说明,请参见