Package: pymc-learn
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 documentationand/or other materials provided with the distribution.3. Neither the name of Pymc-learn nor the names of any contributors may be used toendorse or promote products derived from this software without specific priorwritten permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THEIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSEARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BELIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, ORCONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OFSUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESSINTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER INCONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OFTHE 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|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 probabilisticmachine learning in Python*.It provides a variety of state-of-the art probabilistic models for supervisedand unsupervised machine learning. **It is inspired by**`scikit-learn <http://scikit-learn.org>`_ **and focuses on bringing probabilisticmachine 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-learnand `PyMC3 <https://docs.pymc.io/>`_ 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 modelsusing 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`` wascreated... NOTE::``pymc-learn`` leverages and extends the Base template provided by thePyMC3 Models project: https://github.com/parsing-science/pymc3_modelsTransitioning from PyMC3 to PyMC4.................................... raw:: html<embed><blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">.<a href="https://twitter.com/pymc_learn?ref_src=twsrc%5Etfw">@pymc_learn</a> has been following closely the development of <a href="https://twitter.com/hashtag/PyMC4?src=hash&amp;ref_src=twsrc%5Etfw">#PyMC4</a> with the aim of switching its backend from <a href="https://twitter.com/hashtag/PyMC3?src=hash&amp;ref_src=twsrc%5Etfw">#PyMC3</a> to PyMC4 as the latter grows to maturity. Core devs are invited. Here&#39;s the tentative roadmap for PyMC4: <a href="https://t.co/Kwjkykqzup">https://t.co/Kwjkykqzup</a> cc <a href="https://twitter.com/pymc_devs?ref_src=twsrc%5Etfw">@pymc_devs</a> <a href="https://t.co/Ze0tyPsIGH">https://t.co/Ze0tyPsIGH</a></p>&mdash; pymc-learn (@pymc_learn) <a href="https://twitter.com/pymc_learn/status/1059474316801249280?ref_src=twsrc%5Etfw">November 5, 2018</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script></embed>----Familiar user interface-----------------------``pymc-learn`` mimics scikit-learn. You don't have to completely rewriteyour scikit-learn ML code... code-block:: pythonfrom sklearn.linear_model \                         from pmlearn.linear_model \import LinearRegression                             import LinearRegressionlr = LinearRegression()                             lr = LinearRegression()lr.fit(X, y)                                        lr.fit(X, y)The difference between the two models is that ``pymc-learn`` estimates modelparameters using Bayesian inference algorithms such as MCMC or variationalinference. This produces calibrated quantities of uncertainty for modelparameters 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 <https://www.anaconda.com/download/>`_,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:: bashpip install pymc-learnOr from source as follows:.. code-block:: bashpip 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 itprovides `Math Kernel Library <https://anaconda.org/anaconda/mkl-service>`_ (MKL)routines to accelerate math functions. If you are having trouble, try usinga distribution of Python that includes these packages like`Anaconda <https://www.anaconda.com/download/>`_.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 algorithmsthat are fast and almost as flexible as MCMC. For instance AutomaticDifferentation Variational Inference (ADVI) is illustrated in the code below... code-block:: pythonfrom pmlearn.neural_network import MLPClassifiermodel = MLPClassifier()model.fit(X_train, y_train, inference_type="advi")Instead of drawing samples from the posterior, these algorithms fita distribution (e.g. normal) to the posterior turning a sampling problem intoan 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 machinelearning 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 considerthis reference::Carlson, Nicole (2018). Custom PyMC3 models built on top of the scikit-learnAPI. https://github.com/parsing-science/pymc3_modelsOr 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 Startedinstall.rstsupport.rstwhy.rst----**User Guide**The main documentation. This contains an in-depth description of all modelsand how to apply them.* :doc:`user_guide`.. toctree:::maxdepth: 1:hidden::caption: User Guideuser_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: Examplesregression.rstclassification.rstmixture.rstneural_networks.rst----**API Reference**``pymc-learn`` leverages and extends the Base template provided by the PyMC3Models project: https://github.com/parsing-science/pymc3_models.* :doc:`api`.. toctree:::maxdepth: 1:hidden::caption: API Referenceapi.rst----**Help & reference*** :doc:`develop`* :doc:`support`* :doc:`changelog`* :doc:`cite`.. toctree:::maxdepth: 1:hidden::caption: Help & referencedevelop.rstsupport.rstchangelog.rstcite.rst.. |Binder| image:: https://img.shields.io/badge/try-online-579ACA.svg?logo=data:image/png;base64,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: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.. |status| image:: https://img.shields.io/badge/Status-Beta-blue.svgPlatform: UNKNOWNClassifier: Programming Language :: PythonClassifier: Programming Language :: Python :: 2Classifier: Programming Language :: Python :: 3Classifier: Programming Language :: Python :: 2.7Classifier: Programming Language :: Python :: 3.4Classifier: Programming Language :: Python :: 3.5Classifier: Programming Language :: Python :: 3.6Classifier: Topic :: Scientific/EngineeringClassifier: Topic :: Scientific/Engineering :: MathematicsClassifier: Operating System :: OS Independent