graph.write_pdf（“iris.pdf”）AttributeError：'list'对象没有属性'write_pdf'

Question

我的代码是按照谷歌的机器学习类。两个代码是相同的。我不知道为什么它显示错误。可能是变量的类型是错误。但谷歌的代码对我来说是相同的。谁有有这个问题吗？

这是错误

[0 1 2]
[0 1 2]
Traceback (most recent call last):
  File "/media/joyce/oreo/python/machine_learn/VisualizingADecisionTree.py", line 34, in <module>
    graph.write_pdf("iris.pdf")
AttributeError: 'list' object has no attribute 'write_pdf'
[Finished in 0.4s with exit code 1]
[shell_cmd: python -u "/media/joyce/oreo/python/machine_learn/VisualizingADecisionTree.py"]
[dir: /media/joyce/oreo/python/machine_learn]
[path: /usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games]

这是代码

import numpy as np
from sklearn.datasets import load_iris
from sklearn import tree

iris = load_iris()
test_idx = [0, 50, 100]

# training data
train_target = np.delete(iris.target, test_idx)
train_data = np.delete(iris.data, test_idx, axis=0)

# testing data
test_target = iris.target[test_idx]
test_data = iris.data[test_idx]

clf = tree.DecisionTreeClassifier()
clf.fit(train_data, train_target)

print test_target
print clf.predict(test_data) 

# viz code
from sklearn.externals.six import StringIO
import pydot
dot_data = StringIO()
tree.export_graphviz(clf,
        out_file=dot_data,
        feature_names=iris.feature_names,
        class_names=iris.target_names,
        filled=True, rounded=True,
        impurity=False)

graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("iris.pdf")

Answer 1

我认为您使用的是较新版本的python。请尝试使用pydotplus。

import pydotplus
...
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("iris.pdf")

这应该这样做。

Answer 2

pydot.graph_from_dot_data()会返回一个列表，请尝试：

graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph[0].write_pdf("iris.pdf") 

Answer 3

我有完全相同的问题。原来我没有安装graphviz。一旦我这样做，它开始工作。

Answer 4

@Alex Sokolov，对于我在窗口中的情况，我下载并安装/解压缩the following到一个文件夹，然后设置PATH in Windows environment variables。重新运行py代码对我有用。希望对你有所帮助。

Answer 5

我通过conda安装scikit-learn，所有这些都无法正常工作。 首先，我必须安装libtool

brew install libtool --universal

然后我关注this sklearn guide 然后将python文件更改为此代码

clf = clf.fit(train_data, train_target)
tree.export_graphviz(clf,out_file='tree.dot') 

最后转换为终端

中的png

dot -Tpng tree.dot -o tree.png

Answer 6

我尝试了以前的答案，但在运行脚本时仍然出错，因此， 我刚用 pydotplus

import pydotplus

并使用以下命令安装“ graphviz ”：

sudo apt-get install graphviz

然后它对我有用，我添加了

graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("iris.pdf")

感谢以前的贡献者。

Answer 7

它在Python3.7上的工作方式如下，但不要忘记使用Anaconda提示符安装pydot：

   from sklearn.externals.six import StringIO
   import pydot

   # viz code
   dot_data = StringIO()
   tree.export_graphviz(clf, out_file=dot_data, feature_names=iris.feature_names,
                 class_names=iris.target_names, filled=True, rounded=True,
                 impurity=False)
   graph = pydot.graph_from_dot_data(dot_data.getvalue())
   graph[0].write_pdf('iris.pdf')

Answer 8

我使用水蟒。这对我有用： 从终端运行：

conda install python-graphviz
conda install pydot     ## don't forget this <-----------------

然后运行

clf = clf.fit(train_data, train_target)
tree.export_graphviz(clf,out_file='tree.dot')

然后从终端上

dot -Tpng tree.dot -o tree.png

Answer 9

要为n_estimator的数量添加所有图形，可以执行以下操作：

for i in range(0, n):  #n is your n_estimators number
    dot_data = StringIO()
    tree.export_graphviz(clf.estimators_[i], out_file=dot_data, feature_names=iris.feature_names,
                        class_names=iris.target_names, filled=True, rounded=True,
                        impurity=False)
    graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
    graph.write_pdf("iris%s.pdf"%i)

您也可以切换线路

graph = pydotplus.graph_from_dot_data(dot_data.getvalue())

为此

(graph,) = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("iris.pdf")

它仍然可以工作。

Answer 10

我希望这会有所帮助，我遇到了类似的问题。我决定不使用pydot / pydotplus，而是使用 graphviz 。我修改了（几乎没有）代码，它可以创造奇迹！ ：）

# 2. Train classifier
# Testing Data
# Examples used to "test" the classifier's accuracy
# Not part of the training data
import numpy as np
from sklearn.datasets import load_iris
from sklearn import tree
iris = load_iris()
test_idx = [0, 50, 100] # Grabs one example of each flower for testing data (in the data set it so happens to be that
                        # each flower begins at 0, 50, and 100

# training data
train_target = np.delete(iris.target, test_idx)     # Delete all but 3 for training target data
train_data = np.delete(iris.data, test_idx, axis=0) # Delete all but 3 for training data

# testing data
test_target = iris.target[test_idx] # Get testing target data
test_data = iris.data[test_idx]     # Get testing data

# create decision tree classifier and train in it on the testing data
clf = tree.DecisionTreeClassifier()
clf.fit(train_data, train_target)

# Predict label for new flower
print(test_target)
print(clf.predict(test_data))

# Visualize the tree
from sklearn.externals.six import StringIO
import graphviz
dot_data = StringIO()
tree.export_graphviz(clf,
        out_file=dot_data,
        feature_names=iris.feature_names,
        class_names=iris.target_names,
        filled=True, rounded=True,
        impurity=False)
graph = graphviz.Source(dot_data.getvalue())
graph.render("iris.pdf", view=True)