uncalibrated-linearsvc 0.0.2

Uncalibrated Linearsvc

uncalibrated-linearsvc

uncalibrated-linearsvc provides a small scikit-learn-compatible wrapper around sklearn.svm.LinearSVC that adds a predict_proba() method.

What It Is

The package exposes LinearSVCWithUncalibratedProbabilities, a subclass of LinearSVC. It keeps the normal LinearSVC API and behavior, then implements predict_proba() from the classifier's decision_function() output.

This is useful when downstream sklearn tools expect probability-shaped output. One motivating case in the source is multiclass ROC AUC, where roc_auc_score() requires per-class scores that sum to 1.

How It Works

predict_proba(X) calls decision_function(X) and passes the resulting margins through genetools.stats.run_sigmoid_if_binary_and_softmax_if_multiclass():

• binary classifiers use a sigmoid transform and return shape (n_samples, 2);
• multiclass classifiers use a softmax transform and return shape (n_samples, n_classes).

Rows are normalized to sum to 1, matching the shape expected from sklearn classifiers with predict_proba().

Important Limitation

The returned values are uncalibrated. They are transformed SVM decision margins, not calibrated probability estimates. Use them when you need normalized probability-like scores for sklearn APIs, not when you need well-calibrated confidence values. For calibrated probabilities, use sklearn's calibration tools instead.

Installation

pip install uncalibrated_linearsvc

The package requires Python 3.8 or newer and depends on numpy, scikit-learn, and genetools.

Usage

from uncalibrated_linearsvc import LinearSVCWithUncalibratedProbabilities

clf = LinearSVCWithUncalibratedProbabilities(
    dual=False,
    multi_class="ovr",
    random_state=0,
)

clf.fit(X_train, y_train)

predictions = clf.predict(X_test)
probability_like_scores = clf.predict_proba(X_test)

Because the class subclasses LinearSVC, constructor arguments such as dual, multi_class, and random_state are the same as in scikit-learn.

Development

pip install -r requirements_dev.txt
pip install -e .
make test
make lint

The test suite covers binary and multiclass predict_proba() shape handling and row normalization.

Changelog

0.0.1

• First release on PyPI.