Metadata-Version: 2.1
Name: dtreeplt
Version: 0.1.43
Summary: Visualize Decision Tree without Graphviz.
Home-page: https://github.com/nekoumei/dtreeplt
Author: nekoumei
Author-email: nekoumei@gmail.com
Maintainer: nekoumei
Maintainer-email: nekoumei@gmail.com
License: MIT
Description: # dtreeplt
        it draws Decision Tree not using Graphviz, but only matplotlib.  
        If `interactive == True`, it draws Interactive Decision Tree on Notebook.
        
        ## Output Image using proposed method: dtreeplt (using only matplotlib)
        ![graphviz](output/result.png)
        
        ## Output Image using conventional method: export_graphviz (Using Graphviz)
        ![graphviz](output/using_graphviz.png)
        
        ## Output Image using dtreeplt Interactive Decision Tree  
          
        ![graphviz](output/idt_demo.gif)
        
        ## 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
        ```python
        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
        ```python
        # 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')
        ```
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: MacOS X
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
