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。有没有一种方法可以将一些计算字段也导出为输出？

例如，是否可以在每个节点显示输入属性的总和，即树叶中 'X' 数据数组的特征 1 的总和。

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

github page. There are answers on this SO question and this scikit-learn documentation page 上的 scikit-learn 中有很多关于决策树的讨论，它们提供了让您入门的框架。排除所有链接后，这里有一些功能允许用户以通用的方式解决问题。这些功能可以很容易地修改，因为我不知道你的意思是 所有叶子 还是每个叶子单独。我的做法是后者。

第一个函数使用 apply 作为查找叶节点索引的廉价方法。没有必要实现你的要求，但我把它包括在内是为了方便，因为你提到你想调查叶节点，而叶节点索引可能是未知的 a priori.

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))

示例结果：

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

下面的函数将return一个满足node和feature条件的值数组，其中node是树中节点的索引您想要的值和 feature 是您想要来自 X.

的列（或特征）

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，用户可以针对给定特征对样本子集执行所需的任何数学运算。

i.e. sum of feature 1 from 'X' data array in the leafs of the tree.

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
重新阅读问题后，这是我可以快速组合的唯一解决方案，不涉及修改 export.py. Code below still relies on previously defined functions. This code modifies the dotstring via pydot and networkx 的 scikit 源代码。

# 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，所有叶子都具有特征 0 的 X 值的总和。我认为完成您所要求的最好方法是修改 tree.py and/or export.py 以原生支持此功能。

在决策树中显示更多属性

Display more attributes in the decision tree

python

scikit-learn

decision-tree

pygraphviz