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

Probabilistic Scoring System – a probabilistic & incremental extension to Scoring Systems
https://github.com/trr318/scikit-psl

classifier decision-making decision-support-system probabilistic-classifiers scoring-system sklearn

Last synced: 18 days ago
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Probabilistic Scoring System – a probabilistic & incremental extension to Scoring Systems

• Host: GitHub
• URL: https://github.com/trr318/scikit-psl
• Owner: TRR318
• License: mit
• Created: 2023-08-08T08:30:29.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-10-11T14:21:57.000Z (about 1 month ago)
• Last Synced: 2024-10-19T16:45:18.182Z (about 1 month ago)
• Topics: classifier, decision-making, decision-support-system, probabilistic-classifiers, scoring-system, sklearn
• Language: Python
• Homepage:
• Size: 275 KB
• Stars: 13
• Watchers: 2
• Forks: 2
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE
  • Citation: CITATION.cff

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# 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](https://scikit-learn.org/stable/glossary.html#glossary-estimator-types).

# 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 make_classification

from sklearn.model_selection import train_test_split

from skpsl import ProbabilisticScoringList

# Generating synthetic data with continuous features and a binary target variable

X, y = make_classification(n_informative=10, random_state=42)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=42)

psl = ProbabilisticScoringList({-1, 1, 2})

psl.fit(X_train, y_train)

print(f"Brier score: {psl.score(X_test, y_test, -1):.4f}")

"""

Brier score: 0.2438  (lower is better)

"""

df = psl.inspect(5)

print(df.to_string(index=False, na_rep="-", 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 

"""

```