Metadata-Version: 2.1
Name: mlsquare
Version: 0.1.1
Summary: Machine Learning for Machine Learning
Home-page: https://mlsquare.org
Author: MLSquare
Author-email: info@mlsquare.org
License: MIT
Description: ========
        [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*
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
