绘制所有测试样本的决策路径

Question

有这段代码来自：

How to display the path of a Decision Tree for test samples?

基本上，它在决策树图上绘制样本的决策路径，以了解特定预测是如何做出的

import pydotplus
from sklearn.datasets import load_iris
from sklearn import tree

clf = tree.DecisionTreeClassifier(random_state=42)
iris = load_iris()

clf = clf.fit(iris.data, iris.target)

dot_data = tree.export_graphviz(clf, out_file=None,
                                feature_names=iris.feature_names,
                                class_names=iris.target_names,
                                filled=True, rounded=True,
                                special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data)

# empty all nodes, i.e.set color to white and number of samples to zero
for node in graph.get_node_list():
    if node.get_attributes().get('label') is None:
        continue
    if 'samples = ' in node.get_attributes()['label']:
        labels = node.get_attributes()['label'].split('<br/>')
        for i, label in enumerate(labels):
            if label.startswith('samples = '):
                labels[i] = 'samples = 0'
        node.set('label', '<br/>'.join(labels))
        node.set_fillcolor('white')

samples = iris.data[129:130]
decision_paths = clf.decision_path(samples)

for decision_path in decision_paths:
    for n, node_value in enumerate(decision_path.toarray()[0]):
        if node_value == 0:
            continue
        node = graph.get_node(str(n))[0]            
        node.set_fillcolor('green')
        labels = node.get_attributes()['label'].split('<br/>')
        for i, label in enumerate(labels):
            if label.startswith('samples = '):
                labels[i] = 'samples = {}'.format(int(label.split('=')[1]) + 1)

        node.set('label', '<br/>'.join(labels))

filename = 'tree.png'
graph.write_png(filename)

我想做的是在我的 Jupiter notebook 中的不同图中绘制所有样本决策路径。我应该在代码中添加什么？

Answer 1

由于问题没有提供具体的预期结果，我假设您想要绘制分类结果的决策路径部分。

其中一种解决方案是添加一层 for 循环来实现它。但是，它可能会影响程序的性能。因此，请谨慎使用。

import pydotplus
from sklearn.datasets import load_iris
from sklearn import tree

clf = tree.DecisionTreeClassifier(random_state=42)
iris = load_iris()

clf = clf.fit(iris.data, iris.target)

dot_data = tree.export_graphviz(clf, out_file=None,
                                feature_names=iris.feature_names,
                                class_names=iris.target_names,
                                filled=True, rounded=True,
                                special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data)

# empty all nodes, i.e.set color to white and number of samples to zero
for node in graph.get_node_list():
    if node.get_attributes().get('label') is None:
        continue
    if 'samples = ' in node.get_attributes()['label']:
        labels = node.get_attributes()['label'].split('<br/>')
        for i, label in enumerate(labels):
            if label.startswith('samples = '):
                labels[i] = 'samples = 0'
        node.set('label', '<br/>'.join(labels))
        node.set_fillcolor('white')

# samples = iris.data[129:130]
samples = iris.data[120:130] # <-- Classifying 10 data
decision_paths = clf.decision_path(samples)

for decision_path in decision_paths:
    for path in decision_path.toarray(): # <-- Adding one more layer of for loop to loop each path
    # for n, node_value in enumerate(decision_path.toarray()[0]):
        for n, node_value in enumerate(path):
            if node_value == 0:
                continue
            node = graph.get_node(str(n))[0]
            node.set_fillcolor('green')
            labels = node.get_attributes()['label'].split('<br/>')
            for i, label in enumerate(labels):
                if label.startswith('samples = '):
                    labels[i] = 'samples = {}'.format(int(label.split('=')[1]) + 1)

            node.set('label', '<br/>'.join(labels))

# display it inline
display_png(Image(graph.create_png()))

# or save it as png
filename = 'tree.png'
graph.write_png(filename)

结果：