The instance should either be a .txt at format (feature1=...,feature2=...) or a json file
The project consists in deploying the explicability algorithms from Joao Marques-Silva’s team at ANITI on a web platform.
Indeed they developed multiple algorithms to explain Decision Tree, Random Forest, Naive Bayes, Neural Network … But these algorithms are not accessible to other researchers so the team wants a “showcase site” .
The tools are described in recent papers by ANITI’s DeepLever Chair, with prototypes already available for most of the papers.
### Requirements
Please visit Joao Marques-Silva's work on : [Github Repo](https://github.com/jpmarquessilva/)
You will see some repos integrated here.
## Requirements
Python-3.8.10
`pip install -r requirements.txt`
```commandline
$ pip install dash
$ pip install pandas
$ pip install -r requirements.txt
```
Import graphviz manually
### Running
## Structure
The structure is adapted for deployment on Heroku, if you don't wish to deploy, you can delete Procfile and runtime.txt.
## Running locally
Run app.py then visit localhost
Set the parameters and upload the models (you should upload data for random forest).
The instance should either be a .txt at format (feature1=...,feature2=...) or a json file
## Deployed app
Visit :
[App web](https://aniti-fxtools.herokuapp.com/)
Set the parameters and upload the models (you should upload data for random forest).
The instance should either be a .txt at format (feature1=...,feature2=...) or a json file
To deploy new changes, please execute this code :
```commandline
$ git status # view the changes
$ git add . # add all the changes
$ git commit -m 'a description of the changes'
$ git push heroku master
```
However, for this free deployment, you have a limited slug size. It might need to be deployed somewhere else.