Practical Probabilistic Machine Learning in Python
Project description
All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of Pymc-learn nor the names of any contributors may be used to
endorse or promote products derived from this software without specific prior
written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
THE POSSIBILITY OF SUCH DAMAGE.
Description: 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
|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 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
.. |Binder| image:: https://mybinder.org/badge.svg
:target: https://mybinder.org/v2/gh/pymc-learn/pymc-learn/master?filepath=%2Fdocs%2Fnotebooks?urlpath=lab
.. |Travis| image:: https://travis-ci.com/pymc-learn/pymc-learn.svg?branch=master
:target: https://travis-ci.com/pymc-learn/pymc-learn
.. |Coverage| image:: https://coveralls.io/repos/github/pymc-learn/pymc-learn/badge.svg?branch=master
:target: https://coveralls.io/github/pymc-learn/pymc-learn?branch=master
.. |Python27| image:: https://img.shields.io/badge/python-2.7-blue.svg
:target: https://badge.fury.io/py/pymc-learn
.. |Python36| image:: https://img.shields.io/badge/python-3.6-blue.svg
:target: https://badge.fury.io/py/pymc-learn
.. |Docs| image:: https://readthedocs.org/projects/pymc-learn/badge/?version=latest
:target: https://pymc-learn.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. |License| image:: https://img.shields.io/badge/license-BSD-blue.svg
:alt: Hex.pm
:target: https://github.com/pymc-learn/pymc-learn/blob/master/LICENSE
.. |Pypi| image:: https://badge.fury.io/py/pymc-learn.svg
:target: https://badge.fury.io/py/pymc-learn
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Operating System :: OS Independent
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of Pymc-learn nor the names of any contributors may be used to
endorse or promote products derived from this software without specific prior
written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
THE POSSIBILITY OF SUCH DAMAGE.
Description: 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
|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 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
.. |Binder| image:: https://mybinder.org/badge.svg
:target: https://mybinder.org/v2/gh/pymc-learn/pymc-learn/master?filepath=%2Fdocs%2Fnotebooks?urlpath=lab
.. |Travis| image:: https://travis-ci.com/pymc-learn/pymc-learn.svg?branch=master
:target: https://travis-ci.com/pymc-learn/pymc-learn
.. |Coverage| image:: https://coveralls.io/repos/github/pymc-learn/pymc-learn/badge.svg?branch=master
:target: https://coveralls.io/github/pymc-learn/pymc-learn?branch=master
.. |Python27| image:: https://img.shields.io/badge/python-2.7-blue.svg
:target: https://badge.fury.io/py/pymc-learn
.. |Python36| image:: https://img.shields.io/badge/python-3.6-blue.svg
:target: https://badge.fury.io/py/pymc-learn
.. |Docs| image:: https://readthedocs.org/projects/pymc-learn/badge/?version=latest
:target: https://pymc-learn.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. |License| image:: https://img.shields.io/badge/license-BSD-blue.svg
:alt: Hex.pm
:target: https://github.com/pymc-learn/pymc-learn/blob/master/LICENSE
.. |Pypi| image:: https://badge.fury.io/py/pymc-learn.svg
:target: https://badge.fury.io/py/pymc-learn
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Operating System :: OS Independent
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