Визуализировать дерево решений python

sklearn.tree .plot_tree¶

sklearn.tree. plot_tree ( decision_tree , * , max_depth = None , feature_names = None , class_names = None , label = ‘all’ , filled = False , impurity = True , node_ids = False , proportion = False , rounded = False , precision = 3 , ax = None , fontsize = None ) [source] ¶

The sample counts that are shown are weighted with any sample_weights that might be present.

The visualization is fit automatically to the size of the axis. Use the figsize or dpi arguments of plt.figure to control the size of the rendering.

The decision tree to be plotted.

max_depth int, default=None

The maximum depth of the representation. If None, the tree is fully generated.

feature_names list of str, default=None

Names of each of the features. If None, generic names will be used (“x[0]”, “x[1]”, …).

class_names list of str or bool, default=None

Names of each of the target classes in ascending numerical order. Only relevant for classification and not supported for multi-output. If True , shows a symbolic representation of the class name.

label , default=’all’

Whether to show informative labels for impurity, etc. Options include ‘all’ to show at every node, ‘root’ to show only at the top root node, or ‘none’ to not show at any node.

filled bool, default=False

When set to True , paint nodes to indicate majority class for classification, extremity of values for regression, or purity of node for multi-output.

impurity bool, default=True

When set to True , show the impurity at each node.

node_ids bool, default=False

When set to True , show the ID number on each node.

proportion bool, default=False

When set to True , change the display of ‘values’ and/or ‘samples’ to be proportions and percentages respectively.

rounded bool, default=False

When set to True , draw node boxes with rounded corners and use Helvetica fonts instead of Times-Roman.

precision int, default=3

Number of digits of precision for floating point in the values of impurity, threshold and value attributes of each node.

ax matplotlib axis, default=None

Axes to plot to. If None, use current axis. Any previous content is cleared.

fontsize int, default=None

Size of text font. If None, determined automatically to fit figure.

Returns : annotations list of artists

List containing the artists for the annotation boxes making up the tree.

>>> from sklearn.datasets import load_iris >>> from sklearn import tree 

>>> clf = tree.DecisionTreeClassifier(random_state=0) >>> iris = load_iris() 

>>> clf = clf.fit(iris.data, iris.target) >>> tree.plot_tree(clf) [. ] 

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Visualize a Decision Tree in 4 Ways with Scikit-Learn and Python

A Decision Tree is a supervised algorithm used in machine learning. It is using a binary tree graph (each node has two children) to assign for each data sample a target value. The target values are presented in the tree leaves. To reach to the leaf, the sample is propagated through nodes, starting at the root node. In each node a decision is made, to which descendant node it should go. A decision is made based on the selected sample’s feature. Decision Tree learning is a process of finding the optimal rules in each internal tree node according to the selected metric.

The decision trees can be divided, with respect to the target values, into:

• Classification trees used to classify samples, assign to a limited set of values — classes. In scikit-learn it is DecisionTreeClassifier .
• Regression trees used to assign samples into numerical values within the range. In scikit-learn it is DecisionTreeRegressor .

Decision trees are a popular tool in decision analysis. They can support decisions thanks to the visual representation of each decision.

Below I show 4 ways to visualize Decision Tree in Python:

• print text representation of the tree with sklearn.tree.export_text method
• plot with sklearn.tree.plot_tree method (matplotlib needed)
• plot with sklearn.tree.export_graphviz method (graphviz needed)
• plot with dtreeviz package (dtreeviz and graphviz needed)

I will show how to visualize trees on classification and regression tasks.

Train Decision Tree on Classification Task

I will train a DecisionTreeClassifier on iris dataset. I will use default hyper-parameters for the classifier.

from matplotlib import pyplot as plt from sklearn import datasets from sklearn.tree import DecisionTreeClassifier from sklearn import tree 

# Prepare the data data iris = datasets.load_iris() X = iris.data y = iris.target 

# Fit the classifier with default hyper-parameters clf = DecisionTreeClassifier(random_state=1234) model = clf.fit(X, y) 

Print Text Representation

Exporting Decision Tree to the text representation can be useful when working on applications whitout user interface or when we want to log information about the model into the text file. You can check details about export_text in the sklearn docs.

text_representation = tree.export_text(clf) print(text_representation) 

|--- feature_2 2.45 | |--- feature_3 1.65 | | | | |--- class: 2 | | |--- feature_2 > 4.95 | | | |--- feature_3 1.55 | | | | |--- feature_0 6.95 | | | | | |--- class: 2 | |--- feature_3 > 1.75 | | |--- feature_2 3.10 | | | | |--- class: 1 | | |--- feature_2 > 4.85 | | | |--- class: 2 

If you want to save it to the file, it can be done with following code:

with open("decistion_tree.log", "w") as fout: fout.write(text_representation) 

Plot Tree with plot_tree

The plot_tree method was added to sklearn in version 0.21 . It requires matplotlib to be installed. It allows us to easily produce figure of the tree (without intermediate exporting to graphviz) The more information about plot_tree arguments are in the docs.

fig = plt.figure(figsize=(25,20)) _ = tree.plot_tree(clf, feature_names=iris.feature_names, class_names=iris.target_names, filled=True) 

(The plot_tree returns annotations for the plot, to not show them in the notebook I assigned returned value to _ .)

To save the figure to the .png file:

fig.savefig("decistion_tree.png") 

Please notice that I’m using filled=True in the plot_tree . When this parameter is set to True the method uses color to indicate the majority of the class. (It will be nice if there will be some legend with class and color matching.)

Visualize Decision Tree with graphviz

Please make sure that you have graphviz installed ( pip install graphviz ). To plot the tree first we need to export it to DOT format with export_graphviz method (link to docs). Then we can plot it in the notebook or save to the file.

import graphviz # DOT data dot_data = tree.export_graphviz(clf, out_file=None, feature_names=iris.feature_names, class_names=iris.target_names, filled=True) # Draw graph graph = graphviz.Source(dot_data, format="png") graph 

graph.render("decision_tree_graphivz") 

'decision_tree_graphivz.png' 

Plot Decision Tree with dtreeviz Package

The dtreeviz package is available in github. It can be installed with pip install dtreeviz . It requires graphviz to be installed (but you dont need to manually convert between DOT files and images). To plot the tree just run:

from dtreeviz.trees import dtreeviz # remember to load the package viz = dtreeviz(clf, X, y, target_name="target", feature_names=iris.feature_names, class_names=list(iris.target_names)) viz 

Save visualization to the file:

Visualizing the Decision Tree in Regression Task

Below, I present all 4 methods for DecisionTreeRegressor from scikit-learn package (in python of course).

from sklearn import datasets from sklearn.tree import DecisionTreeRegressor from sklearn import tree 

# Prepare the data data boston = datasets.load_boston() X = boston.data y = boston.target 

To keep the size of the tree small, I set max_depth = 3 .

# Fit the regressor, set max_depth = 3 regr = DecisionTreeRegressor(max_depth=3, random_state=1234) model = regr.fit(X, y) 

text_representation = tree.export_text(regr) print(text_representation) 

|--- feature_5 1.38 | | | |--- value: [22.91] | |--- feature_12 > 14.40 | | |--- feature_0 6.99 | | | |--- value: [11.98] |--- feature_5 > 6.94 | |--- feature_5 0.66 | | | |--- value: [14.40] | |--- feature_5 > 7.44 | | |--- feature_10 19.65 | | | |--- value: [21.90] 

fig = plt.figure(figsize=(25,20)) _ = tree.plot_tree(regr, feature_names=boston.feature_names, filled=True) 

Please notice, that the color of the leaf is coresponding to the predicted value.

dot_data = tree.export_graphviz(regr, out_file=None, feature_names=boston.feature_names, filled=True) graphviz.Source(dot_data, format="png") 

from dtreeviz.trees import dtreeviz # remember to load the package viz = dtreeviz(regr, X, y, target_name="target", feature_names=boston.feature_names) viz 

From above methods my favourite is visualizing with dtreeviz package. I like it becuause:

• it shows the distribution of decision feature in the each node (nice!)
• it shows the class-color matching legend
• it shows the distribution of the class in the leaf in case of classification tasks, and mean of the leaf’s reponse in the case of regression tasks

It would be great to have dtreeviz visualization in the interactive mode, so the user can dynamically change the depth of the tree. I’m using dtreeviz package in my Automated Machine Learning (autoML) Python package mljar-supervised . You can check the details of the implementation in the github repository. One important thing is, that in my AutoML package I’m not using decision trees with max_depth greater than 4 . I add this limit to not have too large trees, which in my opinion loose the ability of clear understanding what’s going on in the model. Below is the example of the markdown report for Decision Tree generated by mljar-supervised .

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