SVMLearner

class pyprophet.scoring.classifiers.SVMLearner(C, max_iter=1000, autotune=False)

Bases: LinearLearner

Implements a Support Vector Linear Classification (SVM) learner.

- tune

Tune hyperparameters (C, max_iter) using GridSearchCV.

- learn

Train the SVM model using decoy and target peaks.

- score

Score the given peaks using the SVM model.

- get_parameters

Retrieve the parameters of the SVM model.

- set_parameters

Set the parameters of the SVM model.

__init__(C, max_iter=1000, autotune=False)

get_parameters()

Retrieve the parameters of the SVM model.

learn(decoy_peaks, target_peaks, use_main_score=True)

Train the SVM model using decoy and target peaks.

score(peaks, use_main_score)

Score the given peaks using the SVM model.

set_parameters(classifier)

Set the parameters of the SVM model.

tune(decoy_peaks, target_peaks, use_main_score=True, cv_splits=3, n_jobs=-1)

Tune hyperparameters (C, max_iter) using GridSearchCV.

Parameters:

• decoy_peaks (Experiment) – Decoy peaks data.

• target_peaks (Experiment) – Target peaks data.

• use_main_score (bool) – Whether to use the main score.

• cv_splits (int) – Number of cross-validation splits.

• n_jobs (int) – Number of parallel jobs.

Returns:

The tuned learner instance.

Return type:

SVMLearner