Python 在scikit学习中绘制数据集上的决策树
我一直在尝试将我的数据集随机划分为测试集和训练集,并在5层的决策树上进行训练,然后绘制决策树 顺便说一句,我不允许用熊猫来做这件事 以下是我试图做的:Python 在scikit学习中绘制数据集上的决策树,python,numpy,scikit-learn,decision-tree,Python,Numpy,Scikit Learn,Decision Tree,我一直在尝试将我的数据集随机划分为测试集和训练集,并在5层的决策树上进行训练,然后绘制决策树 顺便说一句,我不允许用熊猫来做这件事 以下是我试图做的: import numpy from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score from sklearn import tree from sklearn.model_selection import train_tes
import numpy
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn import tree
from sklearn.model_selection import train_test_split
filename = 'diabetes.csv'
raw_data = open(filename, 'rt')
data = numpy.loadtxt(raw_data, delimiter=",", skiprows=1)
print(data.shape)
X = data[:,0:8] #identify columns as data sets
Y = data[:, 9] #identfy last column as target
print(X)
print(Y)
X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=0.25)
treeClassifier = DecisionTreeClassifier(max_depth=5)
treeClassifier.fit(X_train, Y_train)
with open("treeClassifier.txt", "w") as f:
f = tree.export_graphviz(treeClassifier, out_file=f)
我的输出是:
(768, 10)
[[ 6. 148. 72. ... 33.6 0.627 50. ]
[ 1. 85. 66. ... 26.6 0.351 31. ]
[ 8. 183. 64. ... 23.3 0.672 32. ]
...
[ 5. 121. 72. ... 26.2 0.245 30. ]
[ 1. 126. 60. ... 30.1 0.349 47. ]
[ 1. 93. 70. ... 30.4 0.315 23. ]]
[1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 1. 1. 1. 1. 0. 1. 0. 0. 1. 1.
1. 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 1. 0. 1. 0. 0.
1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0.
1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0.
0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 1. 1. 1. 0. 0. 0.
1. 0. 0. 0. 1. 1. 0. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.
0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0.
0. 0. 1. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 0. 0.
1. 1. 0. 1. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 1. 1. 1.
1. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 1. 1. 1. 1. 0.
0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 1. 0. 0.
1. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1.
0. 0. 0. 1. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 1. 0. 0.
1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 1. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0.
0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 1. 1. 0. 1.
1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0.
0. 0. 1. 1. 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 1. 0.
1. 1. 0. 0. 0. 0. 1. 1. 0. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 1. 0. 1. 0. 0.
0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1.
0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.
1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0.
0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 1. 1. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.
0. 1. 0. 1. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 0. 1. 0.
0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 1.
1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 1. 1. 1. 0. 1. 1. 0. 0. 0. 0.
0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 0. 1.
1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1.
0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 1.
0. 0. 1. 0. 1. 1. 1. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 1. 0.]
下面是我希望生成的树的外观示例:
|--- feature_2 <= 2.45
| |--- class: 0
|--- feature_2 > 2.45
| |--- feature_3 <= 1.75
| | |--- feature_2 <= 4.95
| | | |--- feature_3 <= 1.65
| | | | |--- class: 1
| | | |--- feature_3 > 1.65
| | | | |--- class: 2
| | |--- feature_2 > 4.95
| | | |--- feature_3 <= 1.55
| | | | |--- class: 2
| | | |--- feature_3 > 1.55
| | | | |--- feature_0 <= 6.95
| | | | | |--- class: 1
| | | | |--- feature_0 > 6.95
| | | | | |--- class: 2
| |--- feature_3 > 1.75
| | |--- feature_2 <= 4.85
| | | |--- feature_1 <= 3.10
| | | | |--- class: 2
| | | |--- feature_1 > 3.10
| | | | |--- class: 1
| | |--- feature_2 > 4.85
| | | |--- class: 2
我遇到的问题是,在我的树中,我没有得到'class=0\class=1'属性。我认为问题可能出在Y=data[:,9]
部分,第9列对0或1进行分类——这是class属性,但我看不到任何方法来更改它以使其出现在树中;可能是树中的某个内容。导出图形viz
函数?我是否缺少一个参数?任何帮助都将不胜感激。如果您更换
tree.export_graphviz(treeClassifier, out_file=f)
与
你应该很好
比如说,
import graphviz
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.model_selection import train_test_split
np.random.seed(42)
X = np.random.random((100, 8))
Y = np.random.randint(2, size=100)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25)
tree_classifier = DecisionTreeClassifier(max_depth=5)
tree_classifier.fit(X_train, Y_train)
dot_data = tree.export_graphviz(tree_classifier, class_names=['0', '1'], out_file=None)
graph = graphviz.Source(dot_data)
graph
要使它看起来更像您提到的示例,您可以使用
tree.export_graphviz(treeClassifier, class_names=['0', '1'],
filled=True, rounded=True, out_file=f)
这里有另一个不用熊猫的答案。除了此处列出的其他方法外,从scikit学习版本21.0(大约2019年5月)起,现在可以使用scikit学习的matplotlib绘制决策树,而无需依赖graphviz
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
X, y = load_iris(return_X_y=True)
# Make an instance of the Model
clf = DecisionTreeClassifier(max_depth = 5)
# Train the model on the data
clf.fit(X, y)
fn=['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)']
cn=['setosa', 'versicolor', 'virginica']
# Setting dpi = 300 to make image clearer than default
fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)
tree.plot_tree(clf,
feature_names = fn,
class_names=cn,
filled = True);
fig.savefig('imagename.png')
下图是保存的内容。
代码是根据这一点改编的 我知道有4种方法可以绘制scikit学习决策树:
- 使用sklearn.tree.export_text方法打印树的文本表示形式
- 使用sklearn.tree.plot_树方法打印(需要matplotlib)
- 使用sklearn.tree.export_graphviz方法绘图(需要graphviz)
- 使用dtreeviz软件包绘图(需要dtreeviz和graphviz)
|--- feature_2 <= 2.45
| |--- class: 0
|--- feature_2 > 2.45
| |--- feature_3 <= 1.75
| | |--- feature_2 <= 4.95
| | | |--- feature_3 <= 1.65
| | | | |--- class: 1
| | | |--- feature_3 > 1.65
| | | | |--- class: 2
| | |--- feature_2 > 4.95
| | | |--- feature_3 <= 1.55
| | | | |--- class: 2
| | | |--- feature_3 > 1.55
| | | | |--- feature_0 <= 6.95
| | | | | |--- class: 1
| | | | |--- feature_0 > 6.95
| | | | | |--- class: 2
| |--- feature_3 > 1.75
| | |--- feature_2 <= 4.85
| | | |--- feature_1 <= 3.10
| | | | |--- class: 2
| | | |--- feature_1 > 3.10
| | | | |--- class: 1
| | |--- feature_2 > 4.85
| | | |--- class: 2
示例输出与您将从export_graphviz
获得的结果非常相似:
你也可以试试这个软件包。它会给你更多的信息。例如:
您可以在以下博文中找到sklearn决策树的不同可视化与代码片段的比较: