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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
.@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
pymc-learn mimics scikit-learn. You don't have to completely rewrite
your scikit-learn ML code.
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.
You can install pymc-learn from PyPi using pip as follows:
pip install pymc-learnOr from source as follows:
pip install git+https://github.com/pymc-learn/pymc-learnCaution!
pymc-learn is under heavy development.
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).
# 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...]))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.
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.
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:
@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:
@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}
}Getting Started
.. 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.
.. 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.
.. 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.
.. toctree:: :maxdepth: 1 :hidden: :caption: API Reference api.rst
Help & reference
.. toctree:: :maxdepth: 1 :hidden: :caption: Help & reference develop.rst support.rst changelog.rst cite.rst