random-survival-forest
A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.
Science Score: 64.0%
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✓CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
1 of 3 committers (33.3%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (9.6%) to scientific vocabulary
Keywords
Repository
A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.
Basic Info
Statistics
- Stars: 60
- Watchers: 2
- Forks: 9
- Open Issues: 0
- Releases: 14
Topics
Metadata Files
README.md
Random Survival Forest
The Random Survival Forest package provides a python implementation of the survival prediction method originally published by Ishwaran et al. (2008).
Reference: Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. The annals of applied statistics, 2(3), 841-860.
Installation
sh
$ pip install random-survival-forest
Contribute
- Source Code: https://github.com/julianspaeth/random-survival-forest
Performance
This implemention is not optimized for being highly performant. It is programmed in pure python. If you have large datasets (large sample size) or use a very high number of trees, I suggest using the scikit-survival package.
Getting Started
```python import time
from lifelines import datasets from sklearn.modelselection import traintest_split
from randomsurvivalforest.models import RandomSurvivalForest from randomsurvivalforest.scoring import concordance_index
rossi = datasets.load_rossi()
Attention: duration column (time until event occurs) must be index 1, event column index 0 in y
y = rossi.loc[:, ["arrest", "week"]] X = rossi.drop(["arrest", "week"], axis=1) X, Xtest, y, ytest = traintestsplit(X, y, testsize=0.33, randomstate=10)
print("Start training...") starttime = time.time() rsf = RandomSurvivalForest(nestimators=10, njobs=-1, randomstate=10) rsf = rsf.fit(X, y) print(f'--- {round(time.time() - starttime, 3)} seconds ---') ypred = rsf.predict(Xtest) cval = concordanceindex(ytime=ytest["week"], ypred=ypred, yevent=ytest["arrest"]) print(f'C-index {round(cval, 3)}') ```
Feedback
If you are having issues or feedback, please let me know. I am happy to fix some bug or implement feature requests.
julian.alexander.spaeth@uni-hamburg..de
This package is open-source. If it helped you or you even use it comercially, I would be happy about a little support:
License
MIT
Owner
- Name: Julian Späth
- Login: julianspaeth
- Kind: user
- Location: Germany
- Company: @FeatureCloud
- Website: https://read.cv/spaethju
- Twitter: julian_spaeth
- Repositories: 10
- Profile: https://github.com/julianspaeth
PHD Student @CosyBio @UHH
Citation (CITATION.cff)
cff-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Späth
given-names: Julian
orcid: https://orcid.org/0000-0003-4562-5816
title: Random Survival Forest
doi: 10.5281/zenodo.5146376
version: v0.1.2-beta
GitHub Events
Total
- Watch event: 5
- Push event: 1
- Fork event: 1
Last Year
- Watch event: 5
- Push event: 1
- Fork event: 1
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 61
- Total Committers: 3
- Avg Commits per committer: 20.333
- Development Distribution Score (DDS): 0.525
Top Committers
| Name | Commits | |
|---|---|---|
| Julian Späth | s****u@p****e | 29 |
| Julian Späth | S****u@p****e | 25 |
| Julian Späth | j****h@w****e | 7 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 8
- Total pull requests: 6
- Average time to close issues: about 1 year
- Average time to close pull requests: 2 days
- Total issue authors: 8
- Total pull request authors: 1
- Average comments per issue: 1.0
- Average comments per pull request: 0.0
- Merged pull requests: 6
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- willthd (1)
- AtlasGao (1)
- mathewrosssmith (1)
- aaby4373 (1)
- mufassir-khan (1)
- FabioRovai (1)
- pancratm (1)
- heavycello (1)
Pull Request Authors
- julianspaeth (6)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 139 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 14
- Total maintainers: 1
pypi.org: random-survival-forest
A Random Survival Forest implementation inspired by Ishwaran et al.
- Homepage: https://github.com/julianspaeth/random-survival-forest
- Documentation: https://random-survival-forest.readthedocs.io/
- License: MIT License
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Latest release: 0.8.2
published almost 2 years ago
Rankings
Maintainers (1)
Dependencies
- joblib *
- lifelines *
- multiprocess *
- numpy *
- pandas *
- scikit-learn *
- get *
- joblib *
- lifelines *
- multiprocess *
- numpy *
- pandas *
- scikit-learn *
- actions/checkout v3 composite
- actions/setup-python v3 composite