graph.write_pdf("iris.pdf") AttributeError: 'list' object has no attribute 'write_pdf'

Question

我的代码是遵循 google.The 机器学习的 class 两个代码是 same.I 不知道为什么它显示 error.May 是变量的类型是error.But google的代码和me.Who一样有没有遇到过这个问题？

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

我认为您使用的是更新版本的 python。请尝试使用 pydotplus。

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

这应该可以做到。

Answer 2

pydot.graph_from_dot_data() returns 一个列表，所以尝试：

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

Answer 3

我通过 conda 安装 scikit-learn，但所有这些都不起作用。首先，我必须安装 libtool

brew install libtool --universal

那我关注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')

最终在终端中转换为 png

dot -Tpng tree.dot -o tree.png

Answer 4

@Alex Sokolov，对于 window 中的案例，我下载并安装/解压缩 the following to a folder then setup the PATH in Windows environment variables。重新运行 py 代码对我有用。希望对你有帮助。

Answer 5

我遇到了完全相同的问题。原来我没有安装graphviz。一旦我这样做了，它就开始工作了。

Answer 6

我尝试了前面的答案，但在运行脚本时仍然出现错误因此，我刚刚使用了 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")

感谢之前的贡献者。

Answer 7

希望对您有所帮助，我也遇到了类似的问题。我决定不使用 pydot / pydotplus，而是 graphviz。我（几乎）修改了代码，它创造了奇迹！ :)

# 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)

Answer 8

它在 Python3.7 上的工作方式如下，但不要忘记使用 Anaconda 提示安装 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 9

我使用 Anaconda。这是对我有用的：运行来自终端：

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：

dot -Tpng tree.dot -o tree.png

Answer 10

要添加您的 n_estimators 号码的所有图表，您可以执行以下操作：

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)

你也可以换行

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

这个

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

它仍然有效。

graph.write_pdf("iris.pdf") AttributeError: 'list' object has no attribute 'write_pdf'

graph.write_pdf("iris.pdf") AttributeError: 'list' object has no attribute 'write_pdf'

python

machine-learning

graphviz

pydot

scikit-learn