PegasosQSVC¶
- class PegasosQSVC(quantum_kernel=None, C=1.0, num_steps=1000, precomputed=False, seed=None)[sorgente]¶
Implements Pegasos Quantum Support Vector Classifier algorithm. The algorithm has been developed in [1] and includes methods
fit
,predict
anddecision_function
following the signatures of sklearn.svm.SVC. This implementation is adapted to work with quantum kernels.Example
quantum_kernel = FidelityQuantumKernel() pegasos_qsvc = PegasosQSVC(quantum_kernel=quantum_kernel) pegasos_qsvc.fit(sample_train, label_train) pegasos_qsvc.predict(sample_test)
- References
- [1]: Shalev-Shwartz et al., Pegasos: Primal Estimated sub-GrAdient SOlver for SVM.
- Parametri
quantum_kernel (BaseKernel | None) – a quantum kernel to be used for classification. Has to be
None
when a precomputed kernel is used.C (float) – Positive regularization parameter. The strength of the regularization is inversely proportional to C. Smaller
C
induce smaller weights which generally helps preventing overfitting. However, due to the nature of this algorithm, some of the computation steps become trivial for largerC
. Thus, largerC
improve the performance of the algorithm drastically. If the data is linearly separable in feature space,C
should be chosen to be large. If the separation is not perfect,C
should be chosen smaller to prevent overfitting.num_steps (int) – number of steps in the Pegasos algorithm. There is no early stopping criterion. The algorithm iterates over all steps.
precomputed (bool) – a boolean flag indicating whether a precomputed kernel is used. Set it to
True
in case of precomputed kernel.seed (int | None) – a seed for the random number generator
- Solleva
if
quantum_kernel
is passed andprecomputed
is set toTrue
. To use a precomputed kernel,quantum_kernel
has to be of theNone
type.
if
quantum_kernel
neither instance ofBaseKernel
norNone
.
Attributes
Returns number of steps in the Pegasos algorithm.
Returns a boolean flag indicating whether a precomputed kernel is used.
Returns quantum kernel
Methods
Evaluate the decision function for the samples in X.
fit
(X, y[, sample_weight])Fit the model according to the given training data.
predict
(X)Perform classification on samples in X.