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restrict Jinja2<3.1 in docs
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MichaelFu1998-create committed Apr 13, 2022
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141 changes: 19 additions & 122 deletions docs/_build/html/_modules/index.html
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<h1>All modules for which code is available</h1>
<ul><li><a href="pyexplainer/pyexplainer_pyexplainer.html">pyexplainer.pyexplainer_pyexplainer</a></li>
<li><a href="pyexplainer/rulefit.html">pyexplainer.rulefit</a></li>
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Expand Up @@ -9,6 +9,7 @@ Welcome to pyexplainer's documentation!

.. image:: https://img.shields.io/badge/badges-shields.io-brightgreen
:target: https://shields.io/
.. image:: ../img/logo_50x50.png

python ML explainer for SEA

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.. _pip: https://pip.pypa.io
.. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/
|
If you are with conda, simply run the command below to install pyexplainer under your conda environment.

.. code-block:: console
$ conda install -c conda-forge pyexplainer
From sources
------------

Expand All @@ -32,28 +37,31 @@ You can either clone the public repository:

.. code-block:: console
$ git clone git://github.com/MichaelFu1998-create/pyexplainer
$ git clone git://github.com/awsm-research/PyExplainer
Or download the `tarball`_:

.. code-block:: console
$ curl -OL https://github.com/MichaelFu1998-create/pyexplainer/tarball/main
Once you have a copy of the source, you can install it. The method of installation will depend on the packaging library being used.
$ curl -OL https://github.com/awsm-research/PyExplainer/tarball/main
For example, if `setuptools` is being used (a setup.py file is present), install pyexplainer with:
Once you have a copy of the source, cd to the main dir of pyexplainer:

.. code-block:: console
$ python setup.py install
$ cd pyexplainer
If `poetry` is being used (poetry.lock and pyproject.toml files are present), install pyexplainer with:
Then install it via `poetry <https://python-poetry.org/>`_:

.. code-block:: console
$ poetry install
If you do not have poetry installed yet, simply run the command below to install it, for more information, click `here <https://python-poetry.org/docs/>`_:

.. code-block:: console
$ (Invoke-WebRequest -Uri https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py -UseBasicParsing).Content | python -
.. _Github repo: https://github.com/MichaelFu1998-create/pyexplainer
.. _tarball: https://github.com/MichaelFu1998-create/pyexplainer/tarball/master
.. _Github repo: https://github.com/awsm-research/PyExplainer
.. _tarball: https://github.com/awsm-research/PyExplainer/tarball/master
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.. highlight:: python

=====
Usage
Tutorial
=====

To use pyexplainer in a project::
Quick start
--------------

To use pyexplainer in a project, first we need to import the required library::

from pyexplainer import pyexplainer_pyexplainer
We can obtain the default dataset and global model stored in a dictionary as below, which is necessary to initialise a PyExplainer object later ::
default_data_and_model = pyexplainer_pyexplainer.get_dflt()
"""
Important Note.
The default dictionary has the following keys:
1. X_train - Training features used to train the global model
2. y_train - Training labels used to train the global model
3. indep - The column names of features
4. dep - The column name of labels
5. blackbox_model - Trained global model (only support supervised classification models from sklearn)
6. X_explain - One row of features (independent variables) to be explained
7. y_explain - One row of label (dependent variable) to be explained
8. full_ft_names - Full column names of features
"""
Initialise PyExplainer object with X_train, y_train, indep, dep, and blackbox_model. Attention!! PyExplainer only supports `sklearn's supervised classification model <https://scikit-learn.org/stable/supervised_learning.html>`_ ::
# Note that full_ft_names is optional while other variables are necessary
py_explainer = pyexplainer_pyexplainer.PyExplainer(X_train = default_data_and_model['X_train'],
y_train = default_data_and_model['y_train'],
indep = default_data_and_model['indep'],
dep = default_data_and_model['dep'],
blackbox_model = default_data_and_model['blackbox_model']
full_ft_names = default_data_and_model['full_ft_names'])

Prepare data using default data dictionary to trigger explain function later::

# one row of features (independent variables) to be explained
X_explain = default_data_and_model['X_explain']
# one row of label (dependent variable) to be explained
y_explain = default_data_and_model['y_explain']

Trigger explain function under PyExplainer object to get rules::

created_rules = py_explainer.explain(X_explain=X_explain,
y_explain=y_explain,
search_function='crossoverinterpolation')

Visualise those rules using visualise function under PyExplainer object::

py_explainer.visualise(created_rule_obj)
Full tutorial
--------------



import pyexplainer
Simply click `here <https://github.com/awsm-research/PyExplainer/blob/master/TUTORIAL.ipynb>`_ to access our full tutorial Jupyter Notebook, in which we introduce the full workflow from data preparation to explainable prediction ::
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