python – 为决策树中的每个数据点查找相应的叶节点(scikit-learn)

例如,我的输入可能如下所示：

array([[ 5.1,  3.5,  1.4,  0.2],
       [ 4.9,  3. ,  1.4,  0.2],
       [ 4.7,  3.2,  1.3,  0.2]])

我们假设相应的叶节点分别为16,5和45.我希望我的输出是：

leaf_node_id = array([16, 5, 45])

我已经阅读了关于SF的scikit-learn邮件列表和相关问题,但我仍然无法使其工作.这是我在邮件列表中找到的一些提示,但仍然无效.

http://sourceforge.net/p/scikit-learn/mailman/message/31728624/

在一天结束时,我只想要一个函数Ge​​tLeafNode(clf,X_valida),使其输出是相应叶节点的列表.下面是重现我收到的错误的代码.所以,任何建议都将非常感激.

from sklearn.datasets import load_iris
from sklearn import tree

# load data and divide it to train and validation
iris = load_iris()

num_train = 100
X_train = iris.data[:num_train,:]
X_valida = iris.data[num_train:,:]

y_train = iris.target[:num_train]
y_valida = iris.target[num_train:]

# fit the decision tree using the train data set
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train, y_train)

# Now I want to know the corresponding leaf node id for each of my training data point
clf.tree_.apply(X_train)

# This gives the error message below:
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-17-2ecc95213752> in <module>()
----> 1 clf.tree_.apply(X_train)

_tree.pyx in sklearn.tree._tree.Tree.apply (sklearn/tree/_tree.c:19595)()

ValueError: Buffer dtype mismatch, expected 'DTYPE_t' but got 'double'

from sklearn.datasets import load_iris
from sklearn import tree

# load data and divide it to train and validation
iris = load_iris()

num_train = 100
X_train = iris.data[:num_train,:]
X_valida = iris.data[num_train:,:]

y_train = iris.target[:num_train]
y_valida = iris.target[num_train:]

# fit the decision tree using the train data set
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train, y_train)

# Compute the leaf node id for each of my training data points
clf.apply(X_train)

产生输出

array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2])