yggdrasil-decision-forests

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

https://github.com/google/yggdrasil-decision-forests

Science Score: 49.0%

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1 of 35 committers (2.9%) from academic institutions
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Low similarity (15.4%) to scientific vocabulary

Keywords

cart cli cpp decision-forest decision-trees distributed-computing go gradient-boosting interpretability javascript machine-learning ml pypi python random-forest tensorflow

Keywords from Contributors

distribution deep-neural-networks jax transformers interactive gpt-3 interface prompt-engineering mot multi-agents

Last synced: 6 months ago · JSON representation

Repository

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

Basic Info

Host: GitHub
Owner: google
License: apache-2.0
Language: C++
Default Branch: main
Homepage: https://ydf.readthedocs.io/
Size: 45.2 MB

Statistics

Stars: 603
Watchers: 16
Forks: 65
Open Issues: 43
Releases: 37

Topics

cart cli cpp decision-forest decision-trees distributed-computing go gradient-boosting interpretability javascript machine-learning ml pypi python random-forest tensorflow

Created almost 5 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Citation

README.md

YDF (Yggdrasil Decision Forests) is a library to train, evaluate, interpret, and serve Random Forest, Gradient Boosted Decision Trees, CART and Isolation forest models.

See the documentation for more information on YDF.

Installation

To install YDF from PyPI, run:

shell pip install ydf -U

Usage example

```python import ydf import pandas as pd

Load dataset with Pandas

dspath = "https://raw.githubusercontent.com/google/yggdrasil-decision-forests/main/yggdrasildecisionforests/testdata/dataset/" trainds = pd.readcsv(dspath + "adulttrain.csv") testds = pd.readcsv(dspath + "adulttest.csv")

Train a Gradient Boosted Trees model

model = ydf.GradientBoostedTreesLearner(label="income").train(train_ds)

Look at a model (input features, training logs, structure, etc.)

model.describe()

Evaluate a model (e.g. roc, accuracy, confusion matrix, confidence intervals)

model.evaluate(test_ds)

Generate predictions

model.predict(test_ds)

Analyse a model (e.g. partial dependence plot, variable importance)

model.analyze(test_ds)

Benchmark the inference speed of a model

model.benchmark(test_ds)

Save the model

model.save("/tmp/my_model") ```

Example with the C++ API.

```c++ auto dataset_path = "csv:train.csv";

// List columns in training dataset DataSpecification spec; CreateDataSpec(dataset_path, false, {}, &spec);

// Create a training configuration TrainingConfig trainconfig; trainconfig.setlearner("RANDOMFOREST"); trainconfig.settask(Task::CLASSIFICATION); trainconfig.setlabel("my_label");

// Train model std::uniqueptr learner; GetLearner(trainconfig, &learner); auto model = learner->Train(dataset_path, spec);

// Export model SaveModel("my_model", model.get()); ```

(based on examples/beginner.cc)

Next steps

Check the Getting Started tutorial 🧭.

Citation

If you us Yggdrasil Decision Forests in a scientific publication, please cite the following paper: Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library.

Bibtex

@inproceedings{GBBSP23, author = {Mathieu Guillame{-}Bert and Sebastian Bruch and Richard Stotz and Jan Pfeifer}, title = {Yggdrasil Decision Forests: {A} Fast and Extensible Decision Forests Library}, booktitle = {Proceedings of the 29th {ACM} {SIGKDD} Conference on Knowledge Discovery and Data Mining, {KDD} 2023, Long Beach, CA, USA, August 6-10, 2023}, pages = {4068--4077}, year = {2023}, url = {https://doi.org/10.1145/3580305.3599933}, doi = {10.1145/3580305.3599933}, }

Raw

Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library, Guillame-Bert et al., KDD 2023: 4068-4077. doi:10.1145/3580305.3599933

Contact

You can contact the core development team at decision-forests-contact@google.com.

Credits

Yggdrasil Decision Forests and TensorFlow Decision Forests are developed by:

Mathieu Guillame-Bert (gbm AT google DOT com)
Richard Stotz (richardstotz AT google DOT com)
Jan Pfeifer (janpf AT google DOT com)
Sebastian Bruch (sebastian AT bruch DOT io)
Arvind Srinivasan (arvnd AT google DOT com)

Contributing

Contributions to TensorFlow Decision Forests and Yggdrasil Decision Forests are welcome. If you want to contribute, check the contribution guidelines.

License

Apache License 2.0

Owner

Name: Google
Login: google
Kind: organization
Email: opensource@google.com
Location: United States of America

Website: https://opensource.google/
Twitter: GoogleOSS
Repositories: 2,773
Profile: https://github.com/google

Google ❤️ Open Source

GitHub Events

Total

Create event: 9
Release event: 5
Issues event: 63
Watch event: 118
Delete event: 2
Issue comment event: 105
Push event: 230
Pull request review event: 11
Pull request review comment event: 13
Pull request event: 31
Fork event: 18

Last Year

Create event: 9
Release event: 5
Issues event: 63
Watch event: 118
Delete event: 2
Issue comment event: 105
Push event: 230
Pull request review event: 11
Pull request review comment event: 13
Pull request event: 31
Fork event: 18

Committers

Last synced: 6 months ago

All Time

Total Commits: 1,259
Total Committers: 35
Avg Commits per committer: 35.971
Development Distribution Score (DDS): 0.46

Past Year

Commits: 374
Committers: 20
Avg Commits per committer: 18.7
Development Distribution Score (DDS): 0.481

Top Committers

Name	Email	Commits
Mathieu Guillame-Bert	g**m@g**m	680
Richard Stotz	r**z@g**m	460
TensorFlow Decision Forests Team	n**y@g**m	58
Damiano Amatruda	d**a@g**m	9
Jan Pfeifer	j**f@g**m	8
Arvind Srinivasan	a**d@g**m	3
Ariel Lubonja	a**l@c**u	3
Dmitry Tsarkov	t**r@g**m	3
Yggdrasil Decision Forests Team	d**t@g**m	2
Jake VanderPlas	v**s@g**m	2
Alejandro Barrachina Argudo	4****2	2
Emmanuel Ferdman	e**n@g**m	2
Howard Chiam	h****m	2
Ivo Ristovski List	i**t@g**m	2
Jean-Baptiste Lespiau	j**u@g**m	2
Peter Hawkins	p**s@g**m	2
Bogdan Graur	b**r@g**m	1
Alejandro Cruzado-Ruiz	l**m@g**m	1
Alex	b****z	1
Arno Eigenwillig	a**w@g**m	1
Chris Kennelly	c**y@g**m	1
David Dunleavy	d**y@g**m	1
Florian Mayer	f**r@g**m	1
Hana Joo	h**o@g**m	1
John Cater	j**r@g**m	1
John QiangZhang	j**g@g**m	1
Laramie Leavitt	l**r@g**m	1
Matthew Soulanille	m**w@s**t	1
Mehdi Amini	a**m@g**m	1
Michelangelo Conserva	m**a@g**m	1
and 5 more...

Committer Domains (Top 20 + Academic)

google.com: 26 soulanille.net: 1 cs.jhu.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 133
Total pull requests: 55
Average time to close issues: 2 months
Average time to close pull requests: 10 days
Total issue authors: 76
Total pull request authors: 20
Average comments per issue: 3.08
Average comments per pull request: 0.93
Merged pull requests: 35
Bot issues: 0
Bot pull requests: 3

Past Year

Issues: 54
Pull requests: 30
Average time to close issues: 16 days
Average time to close pull requests: 5 days
Issue authors: 42
Pull request authors: 10
Average comments per issue: 1.41
Average comments per pull request: 1.13
Merged pull requests: 16
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

JoseAF (9)
Arnold1 (6)
CodingDoug (5)
lusis-ai (5)
andrea-cassioli-maersk (5)
TonyCongqianWang (4)
jimidle (4)
rlcauvin (4)
alpetukhov (3)
marquisthunder (3)
stephen-up (3)
salamanders (3)
omit-ai (3)
PSSF23 (3)
patrickjedlicka (3)

Pull Request Authors

achoum (18)
rstz (13)
dependabot[bot] (4)
YueWan1 (3)
ariellubonja (3)
hchiam (3)
emmanuel-ferdman (3)
ALK222 (2)
fmayer (2)
copybara-service[bot] (1)
Neutrovertido (1)
janpfeifer (1)
stephen-up (1)
Willian-Zhang (1)
LarytheLord (1)

Top Labels

Issue Labels

enhancement (5) bug (3) solved-in-next-release (3) help wanted (2) documentation (1) good first issue (1) question (1)

Pull Request Labels

dependencies (4) javascript (2) cla: yes (1)

Packages

Total packages: 5
Total downloads:
- npm 8,171 last-month
- pypi 91,609 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 1
(may contain duplicates)
Total versions: 84
Total maintainers: 4

proxy.golang.org: github.com/google/yggdrasil-decision-forests/yggdrasil_decision_forests/port/go

Homepage: https://github.com/google/yggdrasil-decision-forests
Documentation: https://pkg.go.dev/github.com/google/yggdrasil-decision-forests/yggdrasil_decision_forests/port/go#section-documentation
License: Apache-2.0, MIT
Latest release: v1.5.0
published over 2 years ago

Versions: 54
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Stargazers count: 2.2%

Forks count: 3.1%

Average: 5.4%

Dependent packages count: 7.0%

Dependent repos count: 9.3%

Last synced: 6 months ago

pypi.org: ydf

YDF (short for Yggdrasil Decision Forests) is a library for training, serving, evaluating and analyzing decision forest models such as Random Forest and Gradient Boosted Trees.

Homepage: https://github.com/google/yggdrasil-decision-forests
Documentation: https://ydf.readthedocs.io/
License: Apache 2.0
Latest release: 0.13.0
published 7 months ago

Versions: 25
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 91,609 Last month

Rankings

Stargazers count: 3.3%

Forks count: 6.0%

Downloads: 7.6%

Average: 9.7%

Dependent packages count: 10.1%

Dependent repos count: 21.5%

Maintainers (2)

gbm richardstotz

Last synced: 6 months ago

npmjs.org: ydf-training

Training YDF models in Javascript.

Homepage: https://ydf.readthedocs.io
License: Apache-2.0
Latest release: 0.0.1
published over 1 year ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 11 Last month

Rankings

Stargazers count: 2.5%

Forks count: 3.3%

Average: 17.2%

Dependent repos count: 25.7%

Dependent packages count: 37.2%

Maintainers (2)

achoum rstz1

Last synced: 6 months ago

npmjs.org: yggdrasil-decision-forests

With this package, you can generate predictions of machine learning models trained with YDF in browser and with NodeJS.

Homepage: https://ydf.readthedocs.io
License: Apache-2.0
Latest release: 0.0.3
published over 1 year ago

Versions: 3
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 8,113 Last month

Rankings

Stargazers count: 2.8%

Forks count: 3.5%

Average: 19.0%

Dependent repos count: 28.4%

Dependent packages count: 41.3%

Maintainers (1)

achoum

Last synced: 6 months ago

npmjs.org: ydf-inference

With this package, you can generate predictions of machine learning models trained with YDF in browser and with NodeJS.

Homepage: https://ydf.readthedocs.io
License: Apache-2.0
Latest release: 0.0.4
published over 1 year ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 47 Last month

Rankings

Dependent repos count: 25.7%

Average: 31.5%

Dependent packages count: 37.2%

Maintainers (1)

rstz1

Last synced: 6 months ago

Dependencies

yggdrasil_decision_forests/port/go/go.mod go

github.com/google/go-cmp v0.5.8
google.golang.org/protobuf v1.28.1

yggdrasil_decision_forests/port/go/go.sum go

github.com/golang/protobuf v1.5.0
github.com/google/go-cmp v0.5.5
github.com/google/go-cmp v0.5.8
golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543
google.golang.org/protobuf v1.26.0-rc.1
google.golang.org/protobuf v1.28.1

documentation/rtd/requirements.txt pypi

myst-parser *
readthedocs-sphinx-search ==0.1.1
sphinx ==4.2.0
sphinx-autoapi *
sphinx-autodoc-typehints *
sphinx-book-theme >=0.3.3
sphinx-copybutton >=0.5.0
sphinx-remove-toctrees *
sphinx-sitemap *
sphinx_design *
sphinx_rtd_theme ==1.0.0