Metadata-Version: 1.1
Name: mljar
Version: 0.0.9
Summary: Python wrapper over MLJAR API
Home-page: https://github.com/mljar/mljar-api-python
Author: Piotr Plonski
Author-email: contact@mljar.com
License: Apache-2.0
Description: |Build Status| |PyPI version| |Coverage Status|
        
        mljar-api-python
        ================
        
        A simple python wrapper over mljar API. It allows MLJAR users to create
        Machine Learning models with few lines of code:
        
        .. code:: python
        
            from mljar import Mljar
        
            model = Mljar(project='My awesome project', experiment='First experiment')
            model.fit(X,y)
        
            model.predict(X)
        
        That's all folks! Yeah, I know, this makes Machine Learning super easy!
        You can use this code for following Machine Learning tasks: \* Binary
        classification (your target has only two unique values) \* Regression
        (your target value is continuous) \* More is coming soon!
        
        How to install
        --------------
        
        You can install mljar with **pip**:
        
        ::
        
            pip install -U mljar
        
        or from source code:
        
        ::
        
            python setup.py install
        
        How to use it
        -------------
        
        1. Create an account at mljar.com and login.
        2. Please go to your users settings (top, right corner).
        3. Get your token, for example 'exampleexampleexample'.
        4. Set environment variable ``MLJAR_TOKEN`` with your token value:
        
           ::
        
               export MLJAR_TOKEN=exampleexampleexample
        
        5. That's all, you are ready to use MLJAR in your python code!
        
        What's going on?
        ----------------
        
        -  This wrapper allows you to search through different Machine Learning
           algorithms and tune each of the algorithm.
        -  By searching and tuning ML algorithm to your data you will get very
           accurate model.
        -  By calling method ``fit`` from ``Mljar class`` you create new project
           and start experiment with models training. All your results will be
           accessible from your mljar.com account - this makes Machine Learning
           super easy and keeps all your models and results in beautiful order.
           So, you will never miss anything.
        -  All computations are done in MLJAR Cloud, they are executed in
           parallel. So after calling ``fit`` method you can switch your
           computer off and MLJAR will do the job for you!
        -  I think this is really amazing! What do you think? Please let us know
           at ``contact@mljar.com``.
        
        Examples
        --------
        
        The examples are `here! <https://github.com/mljar/mljar-examples>`__.
        
        Testing
        -------
        
        To run tests with command:
        
        ::
        
            python -m tests.run
        
        .. |Build Status| image:: https://travis-ci.org/mljar/mljar-api-python.svg?branch=master
           :target: https://travis-ci.org/mljar/mljar-api-python
        .. |PyPI version| image:: https://badge.fury.io/py/mljar.svg
           :target: https://badge.fury.io/py/mljar
        .. |Coverage Status| image:: https://coveralls.io/repos/github/mljar/mljar-api-python/badge.svg?branch=master
           :target: https://coveralls.io/github/mljar/mljar-api-python?branch=master
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
