README.rst

pymc-learn: Practical Probabilistic Machine Learning in Python

Contents:

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

---

What is pymc-learn?

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 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

---

Familiar user interface

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.

---

Quick Install

You can install pymc-learn from source as follows:

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

Dependencies

pymc-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).

---

Quick Start

# 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...]))

---

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.

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:

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}
}

License

New BSD-3 license

---

Index

Getting Started

• install
• support
• why

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.

• user_guide

user_guide.rst

---

Examples

Pymc-learn provides probabilistic models for machine learning, in a familiar scikit-learn syntax.

• regression
• classification
• mixture
• neural_networks
• api

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.

• api

api.rst

---

Help & reference

• develop
• support
• changelog
• cite

develop.rst support.rst changelog.rst cite.rst