Contents:
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 PyMC4 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 PyMC4.
See :doc:`why` for a more detailed description of why pymc-learn was
created.
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 TensorFlow,
PyMC4, 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 PyMC4.
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}
}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
.. 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