Pairwise difference learning library (pdll)

Pairwise Difference Learning (PDL) library is a python module. It contains a scikit-learn compatible implementation of PDL Classifier, as described in Belaid et al. 2024

PDL Classifier or PDC is a meta learner that can reduce multiclass classification problem into a binary classification problem (similar/different).

Installation

To install the package, run the following command: shell pip install -U pdll

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Usage

```python from pdll import PairwiseDifferenceClassifier

from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_blobs

Generate random data with 2 features, 10 points, and 3 classes

X, y = makeblobs(nsamples=10, nfeatures=2, centers=3, randomstate=0)

pdc = PairwiseDifferenceClassifier(estimator=RandomForestClassifier()) pdc.fit(X, y) print('score:', pdc.score(X, y))

ypred = pdc.predict(X) probapred = pdc.predict_proba(X) `` Please consultexamples/` directory for more examples.

How does it work?

The PDL algorithm works by transforming the multiclass classification problem into a binary classification problem. The algorithm works as follows:

Example 1: Graphical abstract

Example 2: PDC trained on the Iris dataset

Evaluation

To reproduce the experiment of the paper, please run run_benchmark.py with a base learner and a dataset number, between 0 and 99. Example:

python run_benchmark.py --model DecisionTreeClassifier --data 0

Scores will be stored in ./results/tmp/ directory.

Experiment

We use 99 datasets from the OpenML repository. We compare the performance of the PDC algorithm with 7 base learners. We use the macro F1 score as a metric. The search space is inspired from TPOT a state-of-the-art library in optimizing Sklearn pipelines

Score comparison

2D datasets Examples

Here we see the difference in the learned patterns between PDL and the base learner. In case PDL is compatible with the base learner (DecisionTree, RandomForest) then the scores improves. In case the base learner is not compatible with PDL (SVC, AdaBoost, ...) then the scores gets lower.

Reference

Please cite us if you use this library in your research: @article{belaid2024pairwise, title={Pairwise Difference Learning for Classification}, author={Belaid, Mohamed Karim and Rabus, Maximilian and H{\"u}llermeier, Eyke}, journal={Discovery Science}, year={2024} } The first commit correspond to the original implementation of the PDC algorithm

Acknowledgments: We would like to thank Tim Wibiral, Dorra ElMekki, Viktor Bengs, Muhammad Zeeshan Anwer, Muhammad Hossein Shaker, Alireza Javanmardi, Patrick Kolpaczki, and Maximilian Muschalik for their early comments on this work. We also acknowledge LRZ and IDIADA for computational resources.