Contents:
pymc-learn is a library for practical probabilistic machine learning in Python.
It provides probabilistic models in a syntax that mimics
scikit-learn.
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 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 source as follows:
pip install git+https://github.com/pymc-learn/pymc-learnpymc-learn is tested on Python 2.7, 3.5 & 3.6 and depends on Theano,
PyMC3, 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) # doctest: +ELLIPSIS
0.3680...
>>> gpr.predict(X[:2,:], return_std=True) # doctest: +ELLIPSIS
(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:
Pymc-learn Developers Team (2019). pymc-learn: Practical probabilistic machine learning in Python. arXiv preprint arXiv:xxxx.xxxxx. Forthcoming.
Or using BibTex as follows:
@article{Pymc-learn,
title={pymc-learn: Practical probabilistic machine learning in {P}ython},
author={Pymc-learn Developers Team},
journal={arXiv preprint arXiv:xxxx.xxxxx},
year={2019}
}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. pymc-learn leverages the Base template provided by the PyMC3 Models
project: https://github.com/parsing-science/pymc3_models.
.. 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 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