qiskit.aqua.components.multiclass_extensions.AllPairs

class AllPairs

The All-Pairs multiclass extension.

In the all-pairs reduction, one trains \(k(k−1)/2\) binary classifiers for a \(k\)-way multiclass problem; each receives the samples of a pair of classes from the original training set, and must learn to distinguish these two classes. At prediction time, a weighted voting scheme is used: all \(k(k−1)/2\) classifiers are applied to an unseen sample, and each class gets assigned the sum of all the scores obtained by the various classifiers. The combined classifier returns as a result the class getting the highest value.

__init__()

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__()	Initialize self.
predict(x)	Applying multiple estimators for prediction.
set_estimator(estimator_cls[, params])	Called internally to set Estimator and parameters :type estimator_cls: Callable[[List], Estimator] :param estimator_cls: An Estimator class :type params: Optional[List] :param params: Parameters for the estimator
test(x, y)	Testing multiple estimators each for distinguishing a pair of classes.
train(x, y)	Training multiple estimators each for distinguishing a pair of classes.

predict(x)

Applying multiple estimators for prediction.

Parameters

x (numpy.ndarray) – NxD array

Returns

predicted labels, Nx1 array

Return type

numpy.ndarray

set_estimator(estimator_cls, params=None)

Called internally to set Estimator and parameters :type estimator_cls: Callable[[List], Estimator] :param estimator_cls: An Estimator class :type params: Optional[List] :param params: Parameters for the estimator

Return type

None

test(x, y)

Testing multiple estimators each for distinguishing a pair of classes.

Parameters

• x (numpy.ndarray) – input points

• y (numpy.ndarray) – input labels

Returns

accuracy

Return type

float

train(x, y)

Training multiple estimators each for distinguishing a pair of classes.

Parameters

• x (numpy.ndarray) – input points

• y (numpy.ndarray) – input labels

Raises

ValueError – can not be fit when only one class is present.