|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
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)
| 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 |
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