pymc-learn 0.0.1rc0

Practical Probabilistic Machine Learning in Python

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Description: pymc-learn: Practical Probabilistic Machine Learning in Python
===============================================================

.. image:: https://github.com/pymc-learn/pymc-learn/blob/master/docs/logos/pymc_learn_logo.png?raw=true
:width: 250px
:alt: Pymc-Learn logo
:align: center

|Travis| |Coverage| |Docs| |License| |Binder|

**Contents:**

#. `Github repo`_
#. `What is pymc-learn?`_
#. `Quick Install`_
#. `Quick Start`_
#. `Index`_


.. _Github repo: https://github.com/pymc-learn/pymc-learn

----

What is pymc-learn?
------------------------

*pymc-learn is a library for practical probabilistic
machine learning in Python*.

It provides probabilistic models in a syntax that mimics
`scikit-learn <http://scikit-learn.org>`_.
Users can now have calibrated quantities of uncertainty in their models
using powerful inference algorithms -- such as MCMC or Variational inference --
provided by `PyMC3 <https://docs.pymc.io/>`_.
See :doc:`why` for a more detailed description of why ``pymc-learn`` was
created.

.. NOTE::
``pymc-learn`` leverages and extends the Base template provided by the
PyMC3 Models project: https://github.com/parsing-science/pymc3_models

----

Familiar user interface
-----------------------
``pymc-learn`` mimics scikit-learn. You don't have to completely rewrite
your scikit-learn ML code.

.. code-block:: python

from sklearn.linear_model \ from pmlearn.linear_model \
import LinearRegression import LinearRegression
lr = LinearRegression() lr = LinearRegression()
lr.fit(X, y) lr.fit(X, y)

The difference between the two models is that ``pymc-learn`` estimates model
parameters using Bayesian inference algorithms such as MCMC or variational
inference. This produces calibrated quantities of uncertainty for model
parameters and predictions.

----

Quick Install
-----------------

You can install ``pymc-learn`` from source as follows:

.. code-block:: bash

pip install git+https://github.com/pymc-learn/pymc-learn


Dependencies
................

``pymc-learn`` is tested on Python 2.7, 3.5 & 3.6 and depends on Theano,
PyMC3, NumPy, SciPy, and Matplotlib (see ``requirements.txt`` for version
information).

----


Quick Start
------------------

.. code-block:: python

# For regression using Bayesian Nonparametrics
>>> from sklearn.datasets import make_friedman2
>>> from pmlearn.gaussian_process import GaussianProcessRegressor
>>> from pmlearn.gaussian_process.kernels import DotProduct, WhiteKernel
>>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0)
>>> kernel = DotProduct() + WhiteKernel()
>>> gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
>>> gpr.score(X, y) # doctest: +ELLIPSIS
0.3680...
>>> gpr.predict(X[:2,:], return_std=True) # doctest: +ELLIPSIS
(array([653.0..., 592.1...]), array([316.6..., 316.6...]))

----

Scales to Big Data & Complex Models
-----------------------------------

Recent research has led to the development of variational inference algorithms
that are fast and almost as flexible as MCMC. For instance Automatic
Differentation Variational Inference (ADVI) is illustrated in the code below.

.. code-block:: python

from pmlearn.neural_network import MLPClassifier
model = MLPClassifier()
model.fit(X_train, y_train, inference_type="advi")


Instead of drawing samples from the posterior, these algorithms fit
a distribution (e.g. normal) to the posterior turning a sampling problem into
an optimization problem. ADVI is provided PyMC3.

----

Citing pymc-learn
------------------

To cite ``pymc-learn`` in publications, please use the following::

Pymc-learn Developers Team (2019). pymc-learn: Practical probabilistic machine
learning in Python. arXiv preprint arXiv:xxxx.xxxxx. Forthcoming.

Or using BibTex as follows:

.. code-block:: latex

@article{Pymc-learn,
title={pymc-learn: Practical probabilistic machine learning in {P}ython},
author={Pymc-learn Developers Team},
journal={arXiv preprint arXiv:xxxx.xxxxx},
year={2019}
}

If you want to cite ``pymc-learn`` for its API, you may also want to consider
this reference::

Carlson, Nicole (2018). Custom PyMC3 models built on top of the scikit-learn
API. https://github.com/parsing-science/pymc3_models

Or using BibTex as follows:

.. code-block:: latex

@article{Pymc3_models,
title={pymc3_models: Custom PyMC3 models built on top of the scikit-learn API,
author={Carlson, Nicole},
journal={},
url={https://github.com/parsing-science/pymc3_models}
year={2018}
}

License
..............

`New BSD-3 license <https://github.com/pymc-learn/pymc-learn/blob/master/LICENSE>`__

----

Index
-----

**Getting Started**

* :doc:`install`
* :doc:`support`
* :doc:`why`

.. toctree::
:maxdepth: 1
:hidden:
:caption: Getting Started

install.rst
support.rst
why.rst

----

**User Guide**

The main documentation. This contains an in-depth description of all models
and how to apply them. ``pymc-learn`` leverages the Base template provided by the PyMC3 Models
project: https://github.com/parsing-science/pymc3_models.

* :doc:`user_guide`

.. toctree::
:maxdepth: 1
:hidden:
:caption: User Guide

user_guide.rst

----

**Examples**

Pymc-learn provides probabilistic models for machine learning,
in a familiar scikit-learn syntax.

* :doc:`regression`
* :doc:`classification`
* :doc:`mixture`
* :doc:`neural_networks`
* :doc:`api`

.. toctree::
:maxdepth: 1
:hidden:
:caption: Examples

regression.rst
classification.rst
mixture.rst
neural_networks.rst

----

**API Reference**

``pymc-learn`` leverages the Base template provided by the PyMC3 Models
project: https://github.com/parsing-science/pymc3_models.

* :doc:`api`

.. toctree::
:maxdepth: 1
:hidden:
:caption: API Reference

api.rst

----

**Help & reference**

* :doc:`develop`
* :doc:`support`
* :doc:`changelog`
* :doc:`cite`

.. toctree::
:maxdepth: 1
:hidden:
:caption: Help & reference

develop.rst
support.rst
changelog.rst
cite.rst

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Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2.7
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