PegasosQSVC

class PegasosQSVC(quantum_kernel=None, C=1.0, num_steps=1000, precomputed=False, seed=None)

Bases : SVC, SerializableModelMixin

This class implements Pegasos Quantum Support Vector Classifier algorithm developed in [1] and includes overridden methods fit and predict from the SVC super-class. This implementation is adapted to work with quantum kernels.

Example

quantum_kernel = QuantumKernel()

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.

Pegasos for SVM

Paramètres

• quantum_kernel (Optional[QuantumKernel]) – QuantumKernel 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 larger C. Thus, larger C 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 (Optional[int]) – a seed for the random number generator

Lève

• ValueError –

  • if quantum_kernel is passed and precomputed is set to True. To use a precomputed kernel, quantum_kernel has to be of the None type.

• TypeError –

  • if quantum_instance neither instance of QuantumKernel nor None.

Attributes

FITTED
UNFITTED
num_steps	Returns number of steps in the Pegasos algorithm.
precomputed	Returns a boolean flag indicating whether a precomputed kernel is used.
quantum_kernel	Returns quantum kernel

Methods

fit(X, y[, sample_weight])	Fit the model according to the given training data.
predict(X)	Perform classification on samples in X.