Metadata-Version: 2.1
Name: argueview
Version: 0.1.5
Summary: ArgueView is a tool for generating text-based presentations for machine-learning predictions and feature-importance based explanation tools. The tool makes use of Toulmin's model of argumentation for structuring the text-based explanations.
Home-page: https://github.com/SophiaHadash/ArgueView
Author: Sophia Hadash
Author-email: s.hadash@tue.nl
License: UNKNOWN
Description: 
        <p align="center">
          <img href="https://github.com/SophiaHadash/ArgueView" alt="ArgueView" src="https://raw.githubusercontent.com/SophiaHadash/ArgueView/master/screenshots/logo.svg" width="50%" />
        <p>
        
        --- 
        [![Build Status](https://jenkins.tuneblendr.com/job/ArgueView/job/master/badge/icon?style=flat&link=https%3A%2F%2Fjenkins.tuneblendr.com%2Fblue%2Forganizations%2Fjenkins%2FTuneblendr%2Factivity "Build Status")](https://jenkins.tuneblendr.com/blue/organizations/jenkins/ArgueView/activity)
        
        ArgueView is a tool for generating text-based presentations for machine-learning predictions and feature-importance based explanation tools. The tool makes use of Toulmin's model of argumentation for structuring the text-based explanations.
        
        Example output using the visualizer:
        
        ![Example Visualization](https://github.com/sophiahadash/argueview/blob/master/screenshots/toulmin-visualizer.png?raw=true)
        
        Example ASCII output:
        ![Example output](https://github.com/sophiahadash/argueview/blob/master/screenshots/scr1.png?raw=true)
        
        
        The procedure for creating ArgueView explanations is as follows:
        1. A traditional machine-learning context is created (i.e. data, model)
        2. An explainer is employed to produce *feature importance*. This can be a white-box or black-box explainer. An example of a black-box explainer is [LIME](https://github.com/marcotcr/lime).
        3. The machine-learning context and the *feature importance* are given to ArgueView such that it can produce a textual explanation.
        
        ![Procedure visualization](https://github.com/sophiahadash/argueview/blob/master/screenshots/model.png?raw=true)
        
        ## Installation
        
        ArgueView is available as a python package on [PyPi](https://pypi.org/project/argueview/). You can install it using `pip`:
        
        ```
        pip install argueview
        ```
        
        Or, using `pipenv`:
        
        ```
        pipenv install argueview
        ```
        
        ## Usage
        
        Usage is documented in our examples. Examples are available in python and jupyter notebooks. The following examples are available:
        
        - minimal, hypothetical data and explainer: [python](https://github.com/SophiaHadash/ArgueView/blob/master/examples/fruit_plain/example.py)
        - creditg data with [LIME](https://github.com/marcotcr/lime) explainer: [python](https://github.com/SophiaHadash/ArgueView/blob/master/examples/creditg_lime/example.py), [notebook](https://github.com/SophiaHadash/ArgueView/blob/master/examples/creditg_lime/example.ipynb)
        - creditg data with [shap](https://github.com/slundberg/shap) explainer: [python](https://github.com/SophiaHadash/ArgueView/blob/master/examples/creditg_shap/example.py), [notebook](https://github.com/SophiaHadash/ArgueView/blob/master/examples/creditg_shap/example.ipynb)
        
        ## Running the examples
        
        There are two examples available to help you learn how to use ArgueView. The 'plain' examples uses hypothetical data to show a minimalistic use-case. The CreditG example uses real data and a real ML model to illustrate a real-world use case.
        
        If you would like to run the CreditG example the script needs to obtain the data. For this we use [OpenML](https://www.openml.org/). However, usage requires a valid API key and you will need to obtain one to run the example.
        
        After you have obtained your key, create a `.env` file with your [OpenML](https://www.openml.org/) API key. 
        
        ```
        echo "OML_APIKEY={my-key}" > .env
        ```
        *Note: You can skip this step if you want to run the hypothetical example.*
        
        Install all dependencies:
        
        ```
        pipenv install --dev
        ```
        
        Run an example:
        
        ```
        /path/to/python3 ./examples/{example}/example.py
        ```
        
        Additionally, there is are Jupyther Notebooks available to directly see how ArgueView works.
        
        ## Building
        
        Follow these steps to build ArgueView from source.
        
        - make sure you install the dependencies. ArgueView requires the following dependencies: `python>=3.5`, `pip3`, `pipenv`, `git`.
        - build using make
            ``` 
            make
            ```
        
        ### Using Docker
        
        Alternatively you can build ArgueView using docker.
        
        - build context dockerfile
            ```
            docker build -t argueview/context:local .
            ```
        - run `build.sh` in a container
            ```
            CID=$(docker run 
                -v /var/run/docker.sock:/var/run/docker.sock
                argueview/context
                build.sh)
            ```
        - copy results from the container
            ```
            docker cp ${CID}:/argueview/argueview.egg-info ./argueview.egg-info
            docker cp ${CID}:/argueview/build ./build
            docker cp ${CID}:/argueview/dist ./dist
            ```
        
Keywords: explanations,text,toulmin,argumentation
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Development Status :: 3 - Alpha
Requires-Python: >=3.5
Description-Content-Type: text/markdown
