dtreeplt

it draws Decision Tree not using Graphviz, but only matplotlib.
If interactive == True, it draws Interactive Decision Tree on Notebook.

Note

On sklearn newer than 0.21, sklearn.tree.plot_tree is implemented.
This also draws trees not using Graphviz.

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
from sklearn.tree import plot_tree
%matplotlib inline

iris = load_iris()
model = DecisionTreeClassifier()
model.fit(iris.data, iris.target)

fig = plt.figure(figsize=(15, 8))
ax = fig.add_subplot()
plot_tree(model, feature_names=iris.feature_names, ax=ax, class_names=iris.target_names, filled=True);

Output Image using proposed method: dtreeplt (using only matplotlib)

Output Image using conventional method: export_graphviz (Using Graphviz)

Output Image using dtreeplt Interactive Decision Tree

Installation

If you want to use the latest version, please use them on git.

pip install git+https://github.com/nekoumei/dtreeplt.git

when it comes to update, command like below.

pip install git+https://github.com/nekoumei/dtreeplt.git -U

Requirements: see requirements.txt
Python 3.6.X.

Usage

Quick Start

from dtreeplt import dtreeplt
dtree = dtreeplt()
dtree.view()
# If you want to use interactive mode, set the parameter like below.
# dtree.view(interactive=True)

Using trained DecisionTreeClassifier

# You should prepare trained model,feature_names, target_names.
# in this example, use iris datasets.
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from dtreeplt import dtreeplt

iris = load_iris()
model = DecisionTreeClassifier()
model.fit(iris.data, iris.target)

dtree = dtreeplt(
    model=model,
    feature_names=iris.feature_names,
    target_names=iris.target_names
)
fig = dtree.view()
#if you want save figure, use savefig method in returned figure object.
#fig.savefig('output.png')