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gmr

gmr: Gaussian Mixture Regression - Published in JOSS (2021)

https://github.com/alexanderfabisch/gmr

Science Score: 100.0%

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    Found 15 DOI reference(s) in README and JOSS metadata
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    1 of 4 committers (25.0%) from academic institutions
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    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

gaussian-mixture-models machine-learning python regression

Scientific Fields

Engineering Computer Science - 40% confidence
Last synced: 4 months ago · JSON representation ·

Repository

Gaussian Mixture Regression

Basic Info
Statistics
  • Stars: 197
  • Watchers: 5
  • Forks: 52
  • Open Issues: 4
  • Releases: 8
Topics
gaussian-mixture-models machine-learning python regression
Created almost 12 years ago · Last pushed 4 months ago
Metadata Files
Readme License Citation

README.md

gmr

Gaussian Mixture Models (GMMs) for clustering and regression in Python.

Coverage DOI (JOSS) DOI (Zenodo)

Example

(Source code of example)

Documentation

Installation

Install from PyPI:

bash pip install gmr

If you want to be able to run all examples, pip can install all necessary examples with

bash pip install gmr[all]

You can also install gmr from source:

bash pip install -e .

Example

Estimate GMM from samples, sample from GMM, and make predictions:

```python import numpy as np from gmr import GMM

Your dataset as a NumPy array of shape (nsamples, nfeatures):

X = np.random.randn(100, 2)

gmm = GMM(ncomponents=3, randomstate=0) gmm.from_samples(X)

Estimate GMM with expectation maximization:

X_sampled = gmm.sample(100)

Make predictions with known values for the first feature:

x1 = np.random.randn(20, 1) x1index = [0] x2predictedmean = gmm.predict(x1index, x1) ```

For more details, see:

python help(gmr)

or have a look at the API documentation

You can see the results of all the examples here.

You can find worked examples in this Google Colab notebook.

How Does It Compare to scikit-learn?

There is an implementation of Gaussian Mixture Models for clustering in scikit-learn as well. Regression could not be easily integrated in the interface of sklearn. That is the reason why I put the code in a separate repository. It is possible to initialize GMR from sklearn though:

python from sklearn.mixture import GaussianMixture from gmr import GMM gmm_sklearn = GaussianMixture(n_components=3, covariance_type="diag") gmm_sklearn.fit(X) gmm = GMM( n_components=3, priors=gmm_sklearn.weights_, means=gmm_sklearn.means_, covariances=np.array([np.diag(c) for c in gmm_sklearn.covariances_]))

For model selection with sklearn we furthermore provide an optional regressor interface.

Gallery

Diagonal covariances

Sample from confidence interval

Generate trajectories

Sample time-invariant trajectories

You can find all examples here.

Saving a Model

This library does not directly offer a function to store fitted models. Since the implementation is pure Python, it is possible, however, to use standard Python tools to store Python objects. For example, you can use pickle to temporarily store a GMM:

```python import numpy as np import pickle import gmr gmm = gmr.GMM(ncomponents=2) gmm.fromsamples(X=np.random.randn(1000, 3))

Save object gmm to file 'file'

pickle.dump(gmm, open("file", "wb"))

Load object from file 'file'

gmm2 = pickle.load(open("file", "rb")) ```

It might be required to store models more permanently than in a pickle file, which might break with a change of the library or with the Python version. In this case you can choose a storage format that you like and store the attributes gmm.priors, gmm.means, and gmm.covariances. These can be used in the constructor of the GMM class to recreate the object and they can also be used in other libraries that provide a GMM implementation. The MVN class only needs the attributes mean and covariance to define the model.

API Documentation

API documentation is available here.

Citation

If you use the library gmr in a scientific publication, I would appreciate citation of the following paper:

Fabisch, A., (2021). gmr: Gaussian Mixture Regression. Journal of Open Source Software, 6(62), 3054, https://doi.org/10.21105/joss.03054

Bibtex entry:

bibtex @article{Fabisch2021, doi = {10.21105/joss.03054}, url = {https://doi.org/10.21105/joss.03054}, year = {2021}, publisher = {The Open Journal}, volume = {6}, number = {62}, pages = {3054}, author = {Alexander Fabisch}, title = {gmr: Gaussian Mixture Regression}, journal = {Journal of Open Source Software} }

Contributing

How can I contribute?

If you discover bugs, have feature requests, or want to improve the documentation, you can open an issue at the issue tracker of the project.

If you want to contribute code, please open a pull request via GitHub by forking the project, committing changes to your fork, and then opening a pull request from your forked branch to the main branch of gmr.

Development Environment

I would recommend to install gmr from source in editable mode with pip and install all dependencies:

bash pip install -e .[all,test,doc]

You can now run tests with

bash pytest

This will also generate a coverage report and output an HTML overview to the folder htmlcov/.

Generate Documentation

The API documentation is generated with pdoc3. If you want to regenerate it, you can run

bash pdoc gmr --html --skip-errors

Related Publications

The first publication that presents the GMR algorithm is

[1] Z. Ghahramani, M. I. Jordan, "Supervised learning from incomplete data via an EM approach," Advances in Neural Information Processing Systems 6, 1994, pp. 120-127, https://proceedings.neurips.cc/paper/1993/hash/f2201f5191c4e92cc5af043eebfd0946-Abstract.html

but it does not use the term Gaussian Mixture Regression, which to my knowledge occurs first in

[2] S. Calinon, F. Guenter and A. Billard, "On Learning, Representing, and Generalizing a Task in a Humanoid Robot," in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 37, no. 2, 2007, pp. 286-298, doi: 10.1109/TSMCB.2006.886952.

A recent survey on various regression models including GMR is the following:

[3] F. Stulp, O. Sigaud, "Many regression algorithms, one unified model: A review," in Neural Networks, vol. 69, 2015, pp. 60-79, doi: 10.1016/j.neunet.2015.05.005.

Sylvain Calinon has a good introduction in his slides on nonlinear regression for his machine learning course.

Owner

  • Name: Alexander Fabisch
  • Login: AlexanderFabisch
  • Kind: user
  • Location: Bremen
  • Company: German Research Center for Artificial Intelligence (DFKI GmbH, @dfki), Robotics Innovation Center (@dfki-ric)

researcher, software developer, in robotics and machine learning

JOSS Publication

gmr: Gaussian Mixture Regression
Published
June 06, 2021
DOI
Volume 6, Issue 62, Page 3054
Authors
Alexander Fabisch ORCID
Robotics Innovation Center, DFKI GmbH, Bremen, Germany
Editor
Mikkel Meyer Andersen ORCID
Tags
machine learning regression
View PDF Review Thread Software Archive

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Fabisch
  given-names: Alexander
  orcid: https://orcid.org/0000-0003-2824-7956
title: "gmr"
url: https://github.com/AlexanderFabisch/gmr
doi: 10.5281/zenodo.4449631
preferred-citation:
    type: article
    authors:
    - family-names: Fabisch
      given-names: Alexander
      orcid: https://orcid.org/0000-0003-2824-7956
    title: "gmr: Gaussian Mixture Regression"
    doi: 10.21105/joss.03054
    journal: Journal of Open Source Software
    start: 3054
    issue: 62
    volume: 6
    month: 6
    year: 2021

Papers & Mentions

Total mentions: 1

Drosophila melanogaster as a Model Organism of Brain Diseases
Last synced: 2 months ago

GitHub Events

Total
  • Create event: 2
  • Release event: 2
  • Issues event: 2
  • Watch event: 25
  • Issue comment event: 13
  • Push event: 13
  • Pull request review event: 3
  • Pull request review comment event: 1
  • Pull request event: 3
  • Fork event: 3
Last Year
  • Create event: 2
  • Release event: 2
  • Issues event: 2
  • Watch event: 25
  • Issue comment event: 13
  • Push event: 13
  • Pull request review event: 3
  • Pull request review comment event: 1
  • Pull request event: 3
  • Fork event: 3

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 259
  • Total Committers: 4
  • Avg Commits per committer: 64.75
  • Development Distribution Score (DDS): 0.042
Past Year
  • Commits: 22
  • Committers: 2
  • Avg Commits per committer: 11.0
  • Development Distribution Score (DDS): 0.227
Top Committers
Name Email Commits
Alexander Fabisch a****h@g****m 248
John Kitchin j****n@a****u 5
Marcelo R. Albuquerque m****e@g****m 4
Joao Felipe Santos j****l@g****m 2
Committer Domains (Top 20 + Academic)
andrew.cmu.edu: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 31
  • Total pull requests: 21
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 18 days
  • Total issue authors: 21
  • Total pull request authors: 4
  • Average comments per issue: 3.48
  • Average comments per pull request: 1.71
  • Merged pull requests: 18
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 3
  • Average time to close issues: N/A
  • Average time to close pull requests: about 12 hours
  • Issue authors: 0
  • Pull request authors: 2
  • Average comments per issue: 0
  • Average comments per pull request: 3.33
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • AlexanderFabisch (10)
  • nardi (2)
  • mralbu (1)
  • MetaDev (1)
  • inakleinbottle (1)
  • nikolai-neustroev (1)
  • ostwalprasad (1)
  • wout-konings (1)
  • rocreguant (1)
  • dmronga (1)
  • Labulitiolle (1)
  • xlzhu0317 (1)
  • show0k (1)
  • meshiguge (1)
  • sdodingnan (1)
Pull Request Authors
  • AlexanderFabisch (17)
  • jkitchin (4)
  • mralbu (1)
  • jfsantos (1)
  • ARoefer (1)
Top Labels
Issue Labels
enhancement (2) question (1) bug (1)
Pull Request Labels
enhancement (4) wontfix (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 103,218 last-month
  • Total dependent packages: 6
  • Total dependent repositories: 5
  • Total versions: 11
  • Total maintainers: 3
pypi.org: gmr

Gaussian Mixture Regression

  • Versions: 11
  • Dependent Packages: 6
  • Dependent Repositories: 5
  • Downloads: 103,218 Last month
Rankings
Dependent packages count: 3.3%
Forks count: 6.0%
Stargazers count: 6.1%
Average: 6.5%
Dependent repos count: 6.8%
Downloads: 10.2%
Maintainers (3)
Alexander.Fabisch afabisch afabisch-dfki
Last synced: 4 months ago

Dependencies

setup.py pypi
  • numpy *
.github/workflows/python-package.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • codecov/codecov-action v1.3.2 composite