scikit-psl 0.4.1

Probabilistic Scoring List classifier

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 a probability distribution over scores instead of hard thresholds.

Scoring Systems are used as decision support for human experts in medical or law domains.

This implementation adheres to the sklearn-api.

Install

pip install scikit-psl

Usage

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

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):.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 
"""

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

0.4.1 - 2023-10-19

Fixed

• Small import error

0.4.0 - 2023-10-17

Added

• Add brute force threshold optimization method to find the global optimum, bisect optimizer remains default method

Changed

• Restructured source files

0.3.1 - 2023-09-12

Fixed

• PSL is now correctly handles when all instances belong to the negative class
• #1 if the first feature is assigned a negative score, it is now assigned the most negative score

0.3.0 - 2023-08-10

Added

• PSL classifier can now run with continuous data and optimally (wrt. expected entropy) select thresholds to binarize the data

Changed

• Significantly improved optimum calculation for MinEntropyBinarizer (the same optimization algorithm is shared with the psls internal binarization algorithm)

0.2.0 - 2023-08-10

Added

• PSL classifier
  • introduced parallelization
  • implemented l-step lookahead
  • simple inspect(·) method that creates a tabular representation of the model

0.1.0 - 2023-08-08

Added

• Initial implementation of the PSL algorithm