在决策树中显示更多属性

Question

我目前正在使用以下代码查看决策树.有没有办法我们可以将一些计算字段输出为输出？

例如,是有可能在树的叶子在每个节点显示输入属性的总和,特征1的从"X"的数据数组,即总和.

from sklearn import datasets

iris = datasets.load_iris()
X = iris.data[:]  
y = iris.target
#%%
from sklearn.tree import DecisionTreeClassifier
alg=DecisionTreeClassifier( max_depth=5,min_samples_leaf=2, max_leaf_nodes = 10)
alg.fit(X,y)

#%%
## View tree
import graphviz
from sklearn import tree
dot_data = tree.export_graphviz(alg,out_file=None, node_ids = True, proportion = True, class_names = True, filled = True, rounded = True)
graph = graphviz.Source(dot_data)
graph

Answer 1

在github页面上有关于scikit-learn的决策树的讨论很多.这个SO问题和这个scikit-learn文档页面都有答案,它提供了帮助您入门的框架.通过所有链接,这里有一些功能允许用户以一般化的方式解决问题.这些功能很容易修改,因为我不知道你是指所有的叶子还是每片叶子.我的方法是后者.

第一个函数使用apply廉价的方法来查找叶节点的索引.没有必要实现你所要求的,但我把它包括在内是为了方便,因为你提到你想要研究叶节点和叶节点索引可能是先验未知的.

def find_leaves(X, clf):
    """A cheap function to find leaves of a DecisionTreeClassifier
    clf must be a fitted DecisionTreeClassifier
    """
    return set(clf.apply(X))

结果示例:

find_leaves(X, alg)
{1, 7, 8, 9, 10, 11, 12}

以下函数将返回满足条件值的数组node和feature,其中node是从要为值和树的节点的索引feature是列(或特征),您想从X.

def node_feature_values(X, clf, node=0, feature=0, require_leaf=False):
    """this function will return an array of values 
    from the input array X. Array values will be limited to
     1. samples that passed through <node> 
     2. and from the feature <feature>.

    clf must be a fitted DecisionTreeClassifier
    """
    leaf_ids = find_leaves(X, clf)
    if (require_leaf and
        node not in leaf_ids):
        print("<require_leaf> is set, "
                "select one of these nodes:\n{}".format(leaf_ids))
        return

    # a sparse array that contains node assignment by sample
    node_indicator = clf.decision_path(X)
    node_array = node_indicator.toarray()

    # which samples at least passed through the node
    samples_in_node_mask = node_array[:,node]==1

    return X[samples_in_node_mask, feature]

应用于示例:

values_arr = node_feature_values(X, alg, node=12, feature=0, require_leaf=True)

array([6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7,
       5.8, 6.4, 6.5, 7.7, 7.7, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.1, 6.4,
       7.4, 7.9, 6.4, 7.7, 6.3, 6.4, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7,
       6.3, 6.5, 6.2, 5.9])

现在,用户可以对给定特征的样本子集执行所需的任何数学运算.

即树的叶子中'X'数据数组中的特征1的总和.

print("There are {} total samples in this node, "
      "{}% of the total".format(len(values_arr), len(values_arr) / float(len(X))*100))
print("Feature Sum: {}".format(values_arr.sum()))

There are 43 total samples in this node,28.666666666666668% of the total
Feature Sum: 286.69999999999993

更新
重新阅读的问题后,这是唯一的解决方案,我可以快速地放在一起,不涉及修改scikit源代码export.py.下面的代码仍然依赖于先前定义的函数.此代码dot通过pydot和networkx修改字符串.

# Load the data from `dot_data` variable, which you defined.
import pydot
dot_graph = pydot.graph_from_dot_data(dot_data)[0]

import networkx as nx
MG = nx.nx_pydot.from_pydot(dot_graph)

# Select a `feature` and edit the `dot` string in `networkx`.
feature = 0
for n in find_leaves(X, alg):
    nfv = node_feature_values(X, alg, node=n, feature=feature)
    MG.node[str(n)]['label'] = MG.node[str(n)]['label'] + "\nfeature_{} sum: {}".format(feature, nfv.sum())

# Export the `networkx` graph then plot using `graphviz.Source()`
new_dot_data = nx.nx_pydot.to_pydot(MG)
graph = graphviz.Source(new_dot_data.create_dot())
graph

请注意,所有叶子都具有来自X特征的值的总和0.我认为,实现您所要求的最佳方法是修改tree.py和/或export.py本地支持此功能.