Metadata-Version: 1.1
Name: pymc-learn
Version: 0.0.1rc2
Summary: Practical Probabilistic Machine Learning in Python
Home-page: https://github.com/pymc-learn/pymc-learn
Author: Pymc-Learn Team
Author-email: daniel.emaasit@gmail.com
License: Copyright (c) 2019 Pymc-learn Developers
All rights reserved.

Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:

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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.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 a variety of state-of-the art probabilistic models for supervised
        and unsupervised machine learning. **It is inspired by**
        `scikit-learn <http://scikit-learn.org>`_ **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 <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 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
        
        
        Transitioning 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 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 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.
        
        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 <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.
        
        * :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
        
        .. |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
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        .. |Docs| image:: https://readthedocs.org/projects/pymc-learn/badge/?version=latest
           :target: https://pymc-learn.readthedocs.io/en/latest/?badge=latest
           :alt: Documentation Status
        
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           :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
