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

pymc-learn: Practical probabilistic machine learning in Python
https://github.com/pymc-learn/pymc-learn

pymc3 pymc4 scikit-learn

Last synced: 1 day ago
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pymc-learn: Practical probabilistic machine learning in Python

• Host: GitHub
• URL: https://github.com/pymc-learn/pymc-learn
• Owner: pymc-learn
• License: bsd-3-clause
• Created: 2018-10-19T21:15:03.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2021-01-24T03:00:07.000Z (almost 4 years ago)
• Last Synced: 2024-12-17T15:08:33.685Z (8 days ago)
• Topics: pymc3, pymc4, scikit-learn
• Language: Python
• Homepage: http://www.pymc-learn.org
• Size: 6.3 MB
• Stars: 224
• Watchers: 15
• Forks: 22
• Open Issues: 12
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
pymc-learn: Practical Probabilistic Machine Learning in Python

===============================================================

.. image:: https://github.com/pymc-learn/pymc-learn/blob/master/docs/logos/pymc-learn-logo.jpg?raw=true

    :width: 350px

    :alt: Pymc-Learn logo

    :align: center

|status| |Travis| |Coverage| |Docs| |License| |Pypi| |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 a variety of state-of-the art probabilistic models for supervised

and unsupervised machine learning. **It is inspired by**

`scikit-learn `_ **and focuses on bringing probabilistic

machine learning to non-specialists**. It uses a syntax that mimics scikit-learn.

Emphasis is put on ease of use, productivity, flexibility, performance,

documentation, and an API consistent with scikit-learn. It depends on scikit-learn

and `PyMC3 `_ and is distributed under the new BSD-3 license,

encouraging its use in both academia and industry.

Users can now have calibrated quantities of uncertainty in their models

using powerful inference algorithms -- such as MCMC or Variational inference --

provided by `PyMC3 `_.

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

Transitioning from PyMC3 to PyMC4

..................................

.. raw:: html

    

        


.@pymc_learn has been following closely the development of #PyMC4 with the aim of switching its backend from #PyMC3 to PyMC4 as the latter grows to maturity. Core devs are invited. Here's the tentative roadmap for PyMC4: https://t.co/Kwjkykqzup cc @pymc_devs https://t.co/Ze0tyPsIGH
— pymc-learn (@pymc_learn) November 5, 2018

 

    


----

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

-----------------

``pymc-learn`` requires a working Python interpreter (2.7 or 3.5+).

It is recommend installing Python and key numerical libraries using the `Anaconda Distribution `_,

which has one-click installers available on all major platforms.

Assuming a standard Python environment is installed on your machine

(including pip), ``pymc-learn`` itself can be installed in one line using pip:

You can install ``pymc-learn`` from PyPi using pip as follows:

.. code-block:: bash

   pip install pymc-learn

Or from source as follows:

.. code-block:: bash

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

.. CAUTION::

   ``pymc-learn`` is under heavy development.

   It is recommended installing ``pymc-learn`` in a Conda environment because it

   provides `Math Kernel Library `_ (MKL)

   routines to accelerate math functions. If you are having trouble, try using

   a distribution of Python that includes these packages like

   `Anaconda `_.

Dependencies

................

``pymc-learn`` is tested on Python 2.7, 3.5 & 3.6 and depends on Theano,

PyMC3, Scikit-learn, 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)

    0.3680...

    >>> gpr.predict(X[:2,:], return_std=True)

    (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::

   Emaasit, Daniel (2018). Pymc-learn: Practical probabilistic machine

   learning in Python. arXiv preprint arXiv:1811.00542.

Or using BibTex as follows:

.. code-block:: latex

    @article{emaasit2018pymc,

      title={Pymc-learn: Practical probabilistic machine learning in {P}ython},

      author={Emaasit, Daniel and others},

      journal={arXiv preprint arXiv:1811.00542},

      year={2018}

    }

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 `__

----

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.

* :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 and extends 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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