fuzzy-rough-learn 0.2.2

fuzzy-rough-learn

fuzzy-rough-learn is a library of machine learning algorithms involving fuzzy rough sets, as well as data descriptors that can be used for one-class classification / novelty detection. It builds on scikit-learn, but uses a slightly different api, best illustrated with a concrete example:

from sklearn import datasets
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.model_selection import train_test_split

from frlearn.base import probabilities_from_scores, select_class
from frlearn.classifiers import FRNN
from frlearn.feature_preprocessors import RangeNormaliser

# Import example data.
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Split into train and test sets.
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)

# Create an instance of the FRNN classifier, construct the model, and query on the test set.
clf = FRNN(preprocessors=(RangeNormaliser(), ))
model = clf(X_train, y_train)
scores = model(X_test)

# Convert scores to probabilities and calculate the AUROC.
probabilities = probabilities_from_scores(scores)
auroc = roc_auc_score(y_test, probabilities, multi_class='ovo')
print('AUROC:', auroc)

# Select classes with the highest scores and calculate the accuracy.
classes = select_class(scores)
accuracy = accuracy_score(y_test, classes)
print('accuracy:', accuracy)

Both classifiers and feature preprocessors are functions that take training data and output a model. Models are functions that take data and output something else. Classifier models output class scores, preprocessor models output a transformation of the data. Preprocessors can be added as a keyword argument when initialising a classifier, which automatically creates a preprocessor model on the basis of the training data and applies it to the training and the test data.

Lenz OU, Peralta D, Cornelis C (2020).
fuzzy-rough-learn 0.1: a Python library for machine learning with fuzzy rough sets.
IJCRS 2020: Proceedings of the International Joint Conference on Rough Sets, pp 491–499.
Lecture Notes in Artificial Intelligence, vol 12179, Springer.
doi: 10.1007/978-3-030-52705-1_36

@inproceedings{lenz20fuzzyroughlearn,
  title={{f}uzzy-rough-learn 0.1: a {P}ython library for machine learning with fuzzy rough sets},
  author={Lenz, Oliver Urs and Peralta, Daniel and Cornelis, Chris},
  booktitle={{IJCRS} 2020: Proceedings of the International Joint Conference on Rough Sets},
  pages={491--499},
  year={2020},
  series={Lecture Notes in Artificial Intelligence},
  volume={12179},
  publisher={Springer}
}