Data

Tools

Indexes

Applications

Experiments

Open Source Science

scikit-psl

Scoring Lists – a probabilistic & incremental extension to Scoring Systems

https://github.com/trr318/scikit-psl

Science Score: 65.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ✓
    Institutional organization owner
    Organization trr318 has institutional domain (trr318.uni-paderborn.de)
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.7%) to scientific vocabulary

Keywords

classifier decision-making decision-support-system probabilistic-classifiers scoring-system sklearn
Last synced: 6 months ago · JSON representation ·

Repository

Scoring Lists – a probabilistic & incremental extension to Scoring Systems

Basic Info
  • Host: GitHub
  • Owner: TRR318
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 2.72 MB
Statistics
  • Stars: 18
  • Watchers: 2
  • Forks: 4
  • Open Issues: 10
  • Releases: 0
Topics
classifier decision-making decision-support-system probabilistic-classifiers scoring-system sklearn
Created over 2 years ago · Last pushed 8 months ago
Metadata Files
Readme Changelog License Citation

README.md

License Pip Paper

Probabilistic Scoring Lists

Probabilistic scoring lists are incremental models that evaluate one feature of the dataset at a time. PSLs can be seen as a extension to scoring systems in two ways: - they can be evaluated at any stage allowing to trade of model complexity and prediction speed. - they provide probablistic predictions instead of deterministic decisions for each possible score.

Scoring systems are used as decision support systems for human experts e.g. in medical or judical decision making.

This implementation adheres to the sklearn-api.

Install

bash pip install scikit-psl

Usage

For examples have a look at the examples folder, but here is a simple example

```python from sklearn.datasets import makeclassification from sklearn.modelselection import traintestsplit

from skpsl import ProbabilisticScoringList

Generating synthetic data with continuous features and a binary target variable

X, y = makeclassification(ninformative=10, randomstate=42) Xtrain, Xtest, ytrain, ytest = traintestsplit(X, y, testsize=.2, random_state=42)

psl = ProbabilisticScoringList({-1, 1, 2}) psl.fit(Xtrain, ytrain) print(f"Brier score: {psl.score(Xtest, ytest, -1):.4f}") """ Brier score: 0.2438 (lower is better) """

df = psl.inspect(5) print(df.tostring(index=False, narep="-", justify="center", float_format=lambda x: f"{x:.2f}")) """ Stage Threshold Score T = -2 T = -1 T = 0 T = 1 T = 2 T = 3 T = 4 T = 5 0 - - - - 0.51 - - - - - 1 >-2.4245 2.00 - - 0.00 - 0.63 - - - 2 >-0.9625 -1.00 - 0.00 0.00 0.48 1.00 - - - 3 >0.4368 -1.00 0.00 0.00 0.12 0.79 1.00 - - - 4 >-0.9133 1.00 0.00 0.00 0.12 0.12 0.93 1.00 - - 5 >2.4648 2.00 0.00 0.00 0.07 0.07 0.92 1.00 1.00 1.00 """ ```

Owner

  • Name: TRR 318 – Constructing Explainability
  • Login: TRR318
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
license: "MIT"
authors:
- family-names: "Stefan"
  given-names: "Heid"
  orcid: "https://orcid.org/0000-0002-9461-7372"
- family-names: "Jonas"
  given-names: "Hanselle"
  orcid: "https://orcid.org/0000-0002-1231-4985"
title: "scikit-psl"
version: 0.7.2
date-released: 2024-10-11
url: "https://github.com/trr318/scikit-psl"

GitHub Events

Total
  • Issues event: 2
  • Watch event: 7
  • Delete event: 2
  • Push event: 1
  • Fork event: 3
  • Create event: 3
Last Year
  • Issues event: 2
  • Watch event: 7
  • Delete event: 2
  • Push event: 1
  • Fork event: 3
  • Create event: 3

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 72
  • Total Committers: 4
  • Avg Commits per committer: 18.0
  • Development Distribution Score (DDS): 0.556
Past Year
  • Commits: 46
  • Committers: 4
  • Avg Commits per committer: 11.5
  • Development Distribution Score (DDS): 0.674
Top Committers
Name Email Commits
Stefan Heid s****d@u****e 32
Stefan Heid s****d@m****g 18
Stefan s****d@l****e 15
Jonas Hanselle j****e@i****e 7
Committer Domains (Top 20 + Academic)
ifi.lmu.de: 1 lmu.de: 1 mailbox.org: 1 upb.de: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 31
  • Total pull requests: 13
  • Average time to close issues: 14 days
  • Average time to close pull requests: 1 day
  • Total issue authors: 3
  • Total pull request authors: 2
  • Average comments per issue: 0.58
  • Average comments per pull request: 0.15
  • Merged pull requests: 12
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 4
  • Pull requests: 1
  • Average time to close issues: about 1 month
  • Average time to close pull requests: less than a minute
  • Issue authors: 2
  • Pull request authors: 1
  • Average comments per issue: 0.5
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • stheid (21)
  • JonasHanselle (5)
  • jk31 (1)
Pull Request Authors
  • stheid (11)
  • JonasHanselle (2)
Top Labels
Issue Labels
enhancement (5) bug (3) question (1) wontfix (1)
Pull Request Labels
bug (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 36 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 16
  • Total maintainers: 2
pypi.org: scikit-psl

Probabilistic Scoring List classifier

  • Versions: 16
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 36 Last month
Rankings
Dependent packages count: 7.5%
Downloads: 12.3%
Forks count: 30.2%
Average: 30.4%
Stargazers count: 32.2%
Dependent repos count: 69.8%
Maintainers (2)
jmhansel stefan.heid
Last synced: 6 months ago

Dependencies

pyproject.toml pypi
  • numpy ^1.25.2
  • python >=3.9,<3.13
  • scikit-learn ^1.3.0
  • scipy ^1.11.1
  • sphinx ^7.1
  • sphinx_rtd_theme ^0.4.3