使用 Scikit-Learn 在 Python 中为随机森林绘制树
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Plot trees for a Random Forest in Python with Scikit-Learn
提问by Zoya
I want to plot a decision tree of a random forest. So, i create the following code:
我想绘制随机森林的决策树。所以，我创建了以下代码：
clf = RandomForestClassifier(n_estimators=100)
import pydotplus
import six
from sklearn import tree
dotfile = six.StringIO()
i_tree = 0
for tree_in_forest in clf.estimators_:
if (i_tree <1):
tree.export_graphviz(tree_in_forest, out_file=dotfile)
pydotplus.graph_from_dot_data(dotfile.getvalue()).write_png('dtree'+ str(i_tree) +'.png')
i_tree = i_tree + 1
But it doesn't generate anything.. Have you an idea how to plot a decision tree from random forest ?
但它不会产生任何东西......你知道如何从随机森林中绘制决策树吗？
Thank you,
谢谢，
回答by user6903745
Assuming your Random Forest model is already fitted,
first you should first import the export_graphvizfunction:
假设您的随机森林模型已经拟合，首先您应该首先导入export_graphviz函数：
from sklearn.tree import export_graphviz
In your for cycle you could do the following to generate the dotfile
在您的 for 循环中，您可以执行以下操作来生成dot文件
export_graphviz(tree_in_forest,
feature_names=X.columns,
filled=True,
rounded=True)
The next line generates a png file
下一行生成一个png文件
os.system('dot -Tpng tree.dot -o tree.png')
回答by Michael James Kali Galarnyk
After you fit a random forest model in scikit-learn, you can visualize individual decision trees from a random forest. The code below first fits a random forest model.
在 scikit-learn 中拟合随机森林模型后，您可以可视化随机森林中的各个决策树。下面的代码首先适合随机森林模型。
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn import tree
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
# Load the Breast Cancer Dataset
data = load_breast_cancer()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['target'] = data.target
# Arrange Data into Features Matrix and Target Vector
X = df.loc[:, df.columns != 'target']
y = df.loc[:, 'target'].values
# Split the data into training and testing sets
X_train, X_test, Y_train, Y_test = train_test_split(X, y, random_state=0)
# Random Forests in `scikit-learn` (with N = 100)
rf = RandomForestClassifier(n_estimators=100,
random_state=0)
rf.fit(X_train, Y_train)
You can now visualize individual trees. The code below visualizes the first decision tree.
您现在可以可视化单个树。下面的代码可视化了第一个决策树。
fn=data.feature_names
cn=data.target_names
fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=800)
tree.plot_tree(rf.estimators_[0],
feature_names = fn,
class_names=cn,
filled = True);
fig.savefig('rf_individualtree.png')
The image below is what is saved.
下图是保存的内容。
Because this question asked for trees, you can visualize all the estimators (decision trees) from a random forest if you like. The code below visualizes the first 5 from the random forest model fit above.
因为这个问题要求树，如果你愿意，你可以从随机森林中可视化所有的估计器（决策树）。下面的代码可视化了上面拟合的随机森林模型中的前 5 个。
# This may not the best way to view each estimator as it is small
fn=data.feature_names
cn=data.target_names
fig, axes = plt.subplots(nrows = 1,ncols = 5,figsize = (10,2), dpi=900)
for index in range(0, 5):
tree.plot_tree(rf.estimators_[index],
feature_names = fn,
class_names=cn,
filled = True,
ax = axes[index]);
axes[index].set_title('Estimator: ' + str(index), fontsize = 11)
fig.savefig('rf_5trees.png')
The image below is what is saved.
下图是保存的内容。
The code was adapted from this post.
代码改编自这篇文章。
回答by Samiran Bera
you can view each tree like this,
你可以像这样查看每棵树，
i_tree = 0
for tree_in_forest in FT_cls_gini.estimators_:
if (i_tree ==3):
tree.export_graphviz(tree_in_forest, out_file=dotfile)
graph = pydotplus.graph_from_dot_data(dotfile.getvalue())
i_tree = i_tree + 1
Image(graph.create_png())
回答by Mirodil
You can draw a single tree:
您可以绘制一棵树：
from sklearn.tree import export_graphviz
from IPython import display
from sklearn.ensemble import RandomForestRegressor
m = RandomForestRegressor(n_estimators=1, max_depth=3, bootstrap=False, n_jobs=-1)
m.fit(X_train, y_train)
str_tree = export_graphviz(m,
out_file=None,
feature_names=X_train.columns, # column names
filled=True,
special_characters=True,
rotate=True,
precision=0.6)
display.display(str_tree)
