qiskit.aqua.components.multiclass_extensions.AllPairs
class AllPairs
The All-Pairs multiclass extension.
In the all-pairs reduction, one trains binary classifiers for a -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 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__
__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
predict(x)
Applying multiple estimators for prediction.
Parameters
x (numpy.ndarray) – NxD array
Returns
predicted labels, Nx1 array
Return type
numpy.ndarray
set_estimator
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
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
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.