mlsquare 0.1.1

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

|contributors| |activity|

.. |contributors| image:: https://img.shields.io/github/contributors/mlsquare/mlsquare.svg
:alt: contributors
:target: https://github.com/mlsquare/mlsquare/graphs/contributors

.. |activity| image:: https://img.shields.io/github/commit-activity/m/mlsquare/mlsquare.svg
:alt: activity
:target: https://github.com/mlsquare/mlsquare/pulse

.. |last_commit| image:: https://img.shields.io/github/last-commit/mlsquare/mlsquare.svg
:alt: last_commit
:target: https://github.com/mlsquare/mlsquare/commits/master

.. |size| image:: https://img.shields.io/github/repo-size/mlsquare/mlsquare.svg
:alt: size


MLSquare is an open source developer-friendly Python library, designed to make use of Deep Learning for Machine Learning developers.


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

Setting up ``mlsquare`` is simple and easy

1. Create a Virtual Environment

.. 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.imly 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

>>> model = LinearRegression()
>>> data = pd.read_csv('./datasets/diabetes.csv', delimiter=",",
header=None, index_col=False)
>>> sc = StandardScaler()
>>> data = sc.fit_transform(data)
>>> data = pd.DataFrame(data)

>>> X = data.iloc[:, :-1]
>>> Y = data.iloc[:, -1]
>>> 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/imly.ipynb



For detailed documentation refer `documentation`__

__ http://mlsquare.readthedocs.io


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