Metadata-Version: 2.1
Name: treeplot
Version: 0.1.2
Summary: Python package treeplot vizualizes a tree based on a randomforest or xgboost model.
Home-page: https://github.com/erdogant/treeplot
Author: Erdogan Taskesen
Author-email: erdogant@gmail.com
License: UNKNOWN
Download-URL: https://github.com/erdogant/treeplot/archive/0.1.2.tar.gz
Description: # treeplot
        
        [![Python](https://img.shields.io/pypi/pyversions/treeplot)](https://img.shields.io/pypi/pyversions/treeplot)
        [![PyPI Version](https://img.shields.io/pypi/v/treeplot)](https://pypi.org/project/treeplot/)
        [![License](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/erdogant/treeplot/blob/master/LICENSE)
        [![Downloads](https://pepy.tech/badge/treeplot/week)](https://pepy.tech/project/treeplot/week)
        [![Donate](https://img.shields.io/badge/donate-grey.svg)](https://erdogant.github.io/donate/?currency=USD&amount=5)
        
        * treeplot is Python package
        
        ### Contents
        - [Installation](#-installation)
        - [Quick Start](#-quick-start)
        - [Contribute](#-contribute)
        - [Maintainers](#-maintainers)
        - [License](#-copyright)
        
        ### Installation
        * Install treeplot from PyPI (recommended). treeplot is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows. 
        * It is distributed under the MIT license.
        
        #### Quick Start
        ```
        pip install treeplot
        ```
        
        * Alternatively, install treeplot from the GitHub source:
        ```bash
        git clone https://github.com/erdogant/treeplot.git
        cd treeplot
        python setup.py install
        ```  
        
        #### Import treeplot package
        ```python
        import treeplot
        ```
        
        #### Example RandomForest:
        ```python
        # Load example dataset
        X,y = treeplot.import_example()
        
        # Learn model
        from sklearn.ensemble import RandomForestClassifier
        model = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0).fit(X, y)
        
        # Make plot
        ax = treeplot.plot(model)
        
        # or alternatively if you have more parameters to specify:
        ax = treeplot.randomforest(model, export='pdf')
        ```
        
        #### Example XGboost:
        ```python
        # Load example dataset
        X,y = treeplot.import_example()
        
        # Learn model
        from xgboost import XGBClassifier
        model = XGBClassifier(n_estimators=100, max_depth=2, random_state=0).fit(X, y)
        
        # Make plot
        ax = treeplot.plot(model)
        
        # or alternatively if you have more parameters to specify:
        ax = treeplot.xgboost(model, plottype='vertical')
        ```
        
        
        #### Maintainers
        * Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)
        
        #### Contribute
        * Contributions are welcome.
        
        #### Licence
        See [LICENSE](LICENSE) for details.
        
        #### Donation
        * This work is created and maintained in my free time. Contributions of any kind are very appreciated. <a href="https://erdogant.github.io/donate/?currency=USD&amount=5">Sponsering</a> is also possible.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
