Metadata-Version: 2.1
Name: mlsquare
Version: 0.2.2.1
Summary: Machine Learning for Machine Learning
Home-page: https://mlsquare.org
Author: MLSquare
Author-email: info@mlsquare.org
License: MIT
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
Description-Content-Type: text/x-rst; charset=UTF-8
Provides-Extra: testing
License-File: LICENSE.txt
License-File: AUTHORS.rst

==============================================
[ML]² :  Machine Learning for Machine Learning
==============================================

.. image:: https://circleci.com/gh/mlsquare/mlsquare/tree/dev.svg?style=svg
    :target: https://circleci.com/gh/mlsquare/mlsquare/tree/dev

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ML Square is a python library that utilises deep learning techniques to  

- Enable interoperability between existing standard machine learning frameworks.  
- Provide explainability as a first-class function.  
- Make ML self learnable.  

================
Getting Started!
================

Setting up ``mlsquare`` is simple and easy

    1. Create a Virtual Environment(optional)

    .. code-block:: bash

        virtualenv ~/venv
        source ~/venv/bin/activate

    2. Install ``mlsquare`` package

    .. code-block:: bash

        pip install mlsquare

    3. Import ``dope`` function from ``mlsquare`` and pass the ``sklearn`` model object

    .. code-block:: python

        >>> from mlsquare import dope
        >>> from sklearn.linear_model import LinearRegression
        >>> from sklearn.preprocessing import StandardScaler
        >>> from sklearn.model_selection import train_test_split
        >>> import pandas as pd
        >>> from sklearn.datasets import load_diabetes

        >>> model = LinearRegression()
        >>> diabetes = load_diabetes()

        >>> X = diabetes.data
        >>> sc = StandardScaler()
        >>> X = sc.fit_transform(X)
        >>> Y = diabetes.target
        >>> x_train, x_test, y_train, y_test =
            train_test_split(X, Y, test_size=0.60, random_state=0)

        >>> m = dope(model)

        >>> # All sklearn operations can be performed on m, except that the underlying implementation uses DNN
        >>> m.fit(x_train, y_train)
        >>> m.score(x_test, y_test)

========
Tutorial
========

For a comprehensive tutorial please do checkout this `link <https://github.com/mlsquare/mlsquare/blob/master/examples/dope.ipynb>`__

==========
Contribute
==========

To get started with contributing, refer our devoloper guide `here <https://github.com/mlsquare/mlsquare/blob/master/docs/developer.rst>`__


For detailed documentation refer `documentation <http://mlsquare.readthedocs.io>`__


We would love to hear your feedback. Drop us a mail at *info@mlsquare.org*


