mcfly

A deep learning tool for time series classification and regression

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 6 DOI reference(s) in README
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Academic publication links
Links to: zenodo.org
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Committers with academic emails
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (19.8%) to scientific vocabulary

Keywords

auto-ml deep-learning time-series

Keywords from Contributors

research-software fair action python-template copier-template copier copier-python point-cloud similarity-measures metabolomics

Last synced: 6 months ago · JSON representation ·

Repository

A deep learning tool for time series classification and regression

Basic Info

Host: GitHub
Owner: NLeSC
License: apache-2.0
Language: JavaScript
Default Branch: main
Homepage:
Size: 10.9 MB

Statistics

Stars: 366
Watchers: 30
Forks: 83
Open Issues: 62
Releases: 17

Topics

auto-ml deep-learning time-series

Created almost 10 years ago · Last pushed 7 months ago

Metadata Files

Readme License Citation Roadmap Zenodo

README.md

The goal of mcfly is to ease the use of deep learning technology for time series classification and regression. The advantage of deep learning is that it can handle raw data directly, without the need to compute signal features. Deep learning does not require expert domain knowledge about the data, and has been shown to be competitive with conventional machine learning techniques. As an example, you can apply mcfly on accelerometer data for activity classification, as shown in the tutorial.

If you use mcfly in your research, please cite the following software paper:

D. van Kuppevelt, C. Meijer, F. Huber, A. van der Ploeg, S. Georgievska, V.T. van Hees. Mcfly: Automated deep learning on time series. SoftwareX, Volume 12, 2020. doi: 10.1016/j.softx.2020.100548

Installation

Prerequisites: - Python 3.10, 3.11 - pip - Tensorflow 2, PyTorch or JAX

Installing all dependencies in separate conda environment: ```sh conda env create -f environment.yml

activate this new environment

source activate mcfly ```

To install the package, run one of the following commands in the project directory:

pip install mcfly[tensorflow]
pip install mcfly[torch]
pip install mcfly[jax]

Please note: If you are not using tensorflow, you have to set the environment variable KERAS_BACKEND accordingly to your chosen backend.

For GPU support take a look at the latest version of the requirements section "most stable GPU environment" inside the Keras documentation or directly in their GitHub repository.

Visualization

We build a tool to visualize the configuration and performance of the models. The tool can be found on http://nlesc.github.io/mcfly/. To run the model visualization on your own computer, cd to the html directory and start up a python web server:

python -m http.server 8888 &

Navigate to http://localhost:8888/ in your browser to open the visualization. For a more elaborate description of the visualization see user manual.

User documentation

User and code documentation.

Contributing

You are welcome to contribute to the code via pull requests. Please have a look at the NLeSC guide for guidelines about software development.

We use numpy-style docstrings for code documentation.

Licensing

Source code and data of mcfly are licensed under the Apache License, version 2.0.

Owner

Name: Netherlands eScience Center
Login: NLeSC
Kind: organization
Location: Amsterdam, The Netherlands

Website: https://esciencecenter.nl
Repositories: 206
Profile: https://github.com/NLeSC

Citation (CITATION.cff)

# YAML 1.2
---
abstract: "The goal of mcfly is to ease the use of deep learning technology for time series classification. The advantage of deep learning is that it can handle raw data directly, without the need to compute signal features. Deep learning does not require expert domain knowledge about the data, and has been shown to be competitive with conventional machine learning techniques. As an example, you can apply mcfly on accelerometer data for activity classification."
authors:
  -
    affiliation: "Netherlands eScience Center"
    family-names: Kuppevelt
    given-names: Dafne
    name-particle: van
  -
    affiliation: "Netherlands eScience Center"
    family-names: Meijer
    given-names: Christiaan
    orcid: "https://orcid.org/0000-0002-5529-5761"
  -
    affiliation: "Netherlands eScience Center"
    family-names: Huber
    given-names: Florian
    orcid: "https://orcid.org/0000-0002-3535-9406"
  -
    affiliation: "Netherlands eScience Center"
    family-names: Hees
    given-names: Vincent
    name-particle: van
    orcid: "https://orcid.org/0000-0003-0182-9008"
  -
    affiliation: "Netherlands eScience Center"
    family-names: Solino Fernandez
    given-names: Breixo
  -
    affiliation: "Netherlands eScience Center"
    family-names: Bos
    given-names: Patrick
  -
    affiliation: "Netherlands eScience Center"
    family-names: Spaaks
    given-names: Jurriaan
  -
    affiliation: "Netherlands eScience Center"
    family-names: Kuzak
    given-names: Mateusz
    orcid: "https://orcid.org/0000-0003-0087-6021"
  -
    affiliation: "Netherlands eScience Center"
    family-names: Hidding
    given-names: Johan
  -
    affiliation: "Netherlands eScience Center"
    family-names: Ploeg
    given-names: Atze
    name-particle: "van der"
  -
    affiliation: "Netherlands eScience Center"
    family-names: Lüken
    given-names: Malte
    orcid: "https://orcid.org/0000-0001-7095-203X"
  -
    affiliation: "Netherlands eScience Center"
    family-names: Lyashevska
    given-names: Olga
    orcid: "https://orcid.org/0000-0002-8686-8550"
cff-version: "1.2.0"
date-released: 2022-12-21
doi: "10.5281/zenodo.596127"
keywords:
  - "machine learning"
  - "deep learning"
  - "time series"
  - "automated machine learning"
license: "Apache-2.0"
message: "If you use this software, please cite it using these metadata."
title: "mcfly: deep learning for time series"
version: "4.1.0"
references:
  - type: article
    title: "Mcfly: Automated deep learning on time series"
    doi: 10.1016/j.softx.2020.100548
    authors:
    -
      affiliation: "Netherlands eScience Center"
      family-names: Kuppevelt
      given-names: Dafne
      name-particle: van
    -
      affiliation: "Netherlands eScience Center"
      family-names: Meijer
      given-names: Christiaan
    -
      affiliation: "Netherlands eScience Center"
      family-names: Huber
      given-names: Florian
      orcid: "https://orcid.org/0000-0002-3535-9406"
    -
      affiliation: "Netherlands eScience Center"
      family-names: Ploeg
      given-names: Atze
      name-particle: "van der"
    -
      affiliation: "Netherlands eScience Center"
      family-names: Sonja
      given-names: Georgievska
    -
      affiliation: "Netherlands eScience Center"
      family-names: Hees
      given-names: Vincent
      name-particle: van
      orcid: "https://orcid.org/0000-0003-0182-9008"

GitHub Events

Total

Create event: 4
Release event: 4
Issues event: 2
Watch event: 3
Issue comment event: 2
Push event: 9
Pull request review event: 2
Pull request review comment event: 2
Pull request event: 3
Fork event: 1

Last Year

Create event: 4
Release event: 4
Issues event: 2
Watch event: 3
Issue comment event: 2
Push event: 9
Pull request review event: 2
Pull request review comment event: 2
Pull request event: 3
Fork event: 1

Committers

Last synced: 8 months ago

All Time

Total Commits: 764
Total Committers: 16
Avg Commits per committer: 47.75
Development Distribution Score (DDS): 0.709

Past Year

Commits: 7
Committers: 1
Avg Commits per committer: 7.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Christiaan Meijer	c**r@e**l	222
dafnevk	d**t@e**l	178
florian-huber	3****r	113
vincent	v**s@g**m	98
breixo	b**o@g**m	76
maltelueken	m**n@a**e	24
E. G. Patrick Bos	e**s@g**m	19
Jurriaan H. Spaaks	j**s@e**l	7
Olga Lyashevska	o**a@h**l	7
Olga Lyashevska	o**a@g**m	6
Mateusz Kuzak	m**k@g**m	6
Johan Hidding	j**g@g**m	3
atzeus	a**s@g**m	2
github-actions[bot]	g****]	1
Faruk D	1****n	1
Abel Soares Siqueira	a**a@g**m	1

Committer Domains (Top 20 + Academic)

esciencecenter.nl: 3 herenstraat.nl: 1 arcor.de: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time

Total issues: 69
Total pull requests: 34
Average time to close issues: 4 months
Average time to close pull requests: 2 months
Total issue authors: 13
Total pull request authors: 11
Average comments per issue: 0.9
Average comments per pull request: 0.68
Merged pull requests: 28
Bot issues: 0
Bot pull requests: 1

Past Year

Issues: 0
Pull requests: 2
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 2
Average comments per issue: 0
Average comments per pull request: 0.0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 1

View more stats

Top Authors

Issue Authors

cwmeijer (28)
dafnevk (11)
bsolino (10)
florian-huber (7)
maltelueken (4)
jmrichardson (2)
rmndrs89 (1)
machielg (1)
wmotte (1)
ghost (1)
vincentvanhees (1)
shamilnabiyev (1)
kivicode (1)

Pull Request Authors

maltelueken (9)
cwmeijer (6)
bsolino (4)
florian-huber (4)
lyashevska (3)
dafnevk (3)
dependabot[bot] (2)
HeinrichAD (2)
jhidding (1)
fdiblen (1)
abelsiqueira (1)

Top Labels

Issue Labels

code quality (10) documentation (7) must-have (3) Research (2) classification (2) bug (2) enhancement (2) tutorial (1)

Pull Request Labels

dependencies (2) github_actions (2) enhancement (1)

Packages

Total packages: 1
Total downloads:
- pypi 102 last-month

Total dependent packages: 0
Total dependent repositories: 4
Total versions: 14
Total maintainers: 1

pypi.org: mcfly

Deep learning for time series data

Homepage: https://github.com/NLeSC/mcfly
Documentation: https://mcfly.readthedocs.io/
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Latest release: 4.1.0
published 7 months ago

Versions: 14
Dependent Packages: 0
Dependent Repositories: 4
Downloads: 102 Last month

Rankings

Stargazers count: 3.4%

Forks count: 4.9%

Dependent repos count: 7.5%

Dependent packages count: 10.1%

Average: 11.8%

Downloads: 33.0%

Maintainers (1)

nlesc

Last synced: 6 months ago

Dependencies

setup.py pypi

h5py *
numpy *
scikit-learn >=0.15.0
scipy >=0.11
tensorflow >=2.0.0

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