Skip to main content
|Build Status| |PyPI version| |Coverage Status| |PyPI pyversions|

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

Metadata

Release files for mljar 0.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mljar 0.1.0
File Size Uploaded
mljar-0.1.0.tar.gz 15.8 kB Details

Release files / mljar-0.1.0.tar.gz

Download URL mljar-0.1.0.tar.gz
Size 15.8 kB
Tags Source
SHA-256 checksum
How to use checksums
fbfd079caa8ebc9060eece7bbb5692d906b5eaa38caa535305a36eb41aa1bc96
BLAKE2b-256 checksum
How to use checksums
e8070c140aa7f0a14e85059a203d9b4a2ad206031a613c8ab274a57de19aa580
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No

Release history Release notifications | RSS feed

This release

0.1.0 This release

1 release file

0.0.9

1 release file

0.0.6

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page