# py-causal

**Repository Path**: zhangbeibei_page/py-causal

## Basic Information

- **Project Name**: py-causal
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-07-04
- **Last Updated**: 2024-12-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

py-causal
========
Python APIs for causal modeling algorithms developed by the University of Pittsburgh/Carnegie Mellon University [Center for Causal Discovery](http://www.ccd.pitt.edu). 


This code is distributed under the LGPL 2.1 license.

Requirements:
============

Python 2.7 and 3.6
* javabridge>=1.0.11
* pandas
* numpy 
* JDK 1.8
* pydot (Optional)
* GraphViz (Optional)

Installation overview:
======================
We have found two approaches to be useful:
* Direct python installation with pip, possibly including use of [Jupyter](http://jupyter.org/). This approach is likely best for users who have Python installed and are familiar with installing Python modules.
* Installation via [Anaconda](https://www.continuum.io/downloads), which  installs Python and related utilities.

Directions for both approaches are given below...

Installation with pip
=====================

First install Java 8 or higher and Python 2.7 or higher.

If you do not have pip installed already, try [these instructions](https://pip.pypa.io/en/stable/installing/).

Once pip is installed, execute these commands

    pip install -U numpy
    pip install -U pandas
    pip install -U javabridge
    pip install -U pydot # optional
    pip install -U GraphViz # optional

Note: you also need to install the GraphViz engine by following [these instructions](http://www.graphviz.org/download/).

We have observed that on some OS X installations, pydot may provide the following response
    Couldn't import dot_parser, loading of dot files will not be possible.

If you see this, try the following

     pip uninstall pydot
     pip install pyparsing==1.5.7
     pip install pydot


Then, from within the py-causal directory, run the following command:

    python setup.py install
    
or use the pip command:

    pip install git+git://github.com/bd2kccd/py-causal
    
After running this command, enter a python shell and attempt the following imports:
    
    import pandas as pd
    import pydot
    from pycausal import search as s

Finally, try to run the python example

    python py-causal-fges-continuous-example.py

Be sure to run this from within the py-causal directory.

This program will create a file named `tetrad.svg`, which should be viewable in any SVG capable program. If you see a causal graph, everything is working correctly.

Running Jupyter/IPython
-----------------------

We have found [Jupyter](http://jupyter.org/) notebooks to be helpful. (Those who have run IPython in the past should know that Jupyter is simply a new name for IPython). To add Jupyter to your completed python install, simply run

    pip -U jupyter
    jupyter notebook
 
 
 and then load one of the Jupyter notebooks found in this installation. 

Anaconda/Jupyter
================

First install Java 8 or higher and Python 2.7 or higher.

Installing Python with Anaconda and Jupyter may be easier for some users:

* [Download and install Anaconda](https://www.continuum.io/downloads) 

Then run the following to configure anaconda

    conda install javabridge
    conda install pandas  
    conda install numpy
    conda install pydot
    conda install graphviz 
    conda install -c https://conda.anaconda.org/chirayu pycausal 
    jupyter notebook

and then load one of the Jupyter notebooks.

Docker Image
============

The pre-installed py-causal Docker image is also available at [Docker Hub](https://hub.docker.com/r/chirayukong/py-causal-notebook/)