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Contents:

1. Github repo
2. What is pymc-learn?
3. Quick Install
4. Quick Start
5. Index

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 byscikit-learnand 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-learnandPyMC3 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 byPyMC3.See:doc:`why` for a more detailed description of whypymc-learn wascreated.

.@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_devshttps://t.co/Ze0tyPsIGH

— pymc-learn (@pymc_learn)November 5, 2018

pymc-learn mimics scikit-learn. You don't have to completely rewriteyour scikit-learn ML code.

fromsklearn.linear_model \frompmlearn.linear_model \importLinearRegressionimportLinearRegressionlr=LinearRegression()lr=LinearRegression()lr.fit(X,y)lr.fit(X,y)

The difference between the two models is thatpymc-learn estimates modelparameters using Bayesian inference algorithms such as MCMC or variationalinference. This produces calibrated quantities of uncertainty for modelparameters and predictions.

pymc-learn requires a working Python interpreter (2.7 or 3.5+).It is recommend installing Python and key numerical libraries using theAnaconda 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 installpymc-learn from PyPi using pip as follows:

pip install pymc-learn

Or from source as follows:

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

pymc-learn is tested on Python 2.7, 3.5 & 3.6 and depends on Theano,PyMC3, Scikit-learn, NumPy, SciPy, and Matplotlib (seerequirements.txtfor version information).

# For regression using Bayesian Nonparametrics>>>fromsklearn.datasetsimportmake_friedman2>>>frompmlearn.gaussian_processimportGaussianProcessRegressor>>>frompmlearn.gaussian_process.kernelsimportDotProduct,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...]))

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.

frompmlearn.neural_networkimportMLPClassifiermodel=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.

To citepymc-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:

@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 citepymc-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_models

Or using BibTex as follows:

@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}}

New BSD-3 license

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 modelsand 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 PyMC3Models 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