qiskit.aqua.algorithms.SklearnSVM

class SklearnSVM(training_dataset, test_dataset=None, datapoints=None, gamma=None, multiclass_extension=None)

The Sklearn SVM algorithm (classical).

This scikit-learn based SVM algorithm uses a classical approach to experiment with feature map classification problems. See also the quantum classifier QSVM.

Internally, this algorithm will run the binary classification or multiclass classification based on how many classes the data has. If the data has more than 2 classes then a multiclass_extension is required to be supplied. Aqua provides several multiclass_extensions.

Paramètres

• training_dataset (Dict[str, ndarray]) – Training dataset.

• test_dataset (Optional[Dict[str, ndarray]]) – Testing dataset.

• datapoints (Optional[ndarray]) – Prediction dataset.

• gamma (Optional[int]) – Used as input for sklearn rbf_kernel which is used internally. See sklearn.metrics.pairwise.rbf_kernel for more information about gamma.

• multiclass_extension (Optional[MulticlassExtension]) – If number of classes is greater than 2 then a multiclass scheme must be supplied, in the form of a multiclass extension.

Lève

AquaError – Multiclass extension not supplied when number of classes > 2

__init__(training_dataset, test_dataset=None, datapoints=None, gamma=None, multiclass_extension=None)

Paramètres

• training_dataset (Dict[str, ndarray]) – Training dataset.

• test_dataset (Optional[Dict[str, ndarray]]) – Testing dataset.

• datapoints (Optional[ndarray]) – Prediction dataset.

• gamma (Optional[int]) –

  Used as input for sklearn rbf_kernel which is used internally. See sklearn.metrics.pairwise.rbf_kernel for more information about gamma.

• multiclass_extension (Optional[MulticlassExtension]) – If number of classes is greater than 2 then a multiclass scheme must be supplied, in the form of a multiclass extension.

Lève

AquaError – Multiclass extension not supplied when number of classes > 2

Methods

__init__(training_dataset[, test_dataset, …])	type training_dataset Dict[str, ndarray]
load_model(file_path)	Load a model from a file path.
predict(data)	Predict using the SVM
run()	Execute the classical algorithm.
save_model(file_path)	Save the model to a file path.
test(data, labels)	Test the SVM
train(data, labels)	Train the SVM

Attributes

class_to_label	returns class to label
label_to_class	returns label to class
random	Return a numpy random.
ret	returns result

property class_to_label

returns class to label

property label_to_class

returns label to class

load_model(file_path)

Load a model from a file path.

Paramètres

file_path (str) – the path of the saved model.

predict(data)

Predict using the SVM

Paramètres

data (numpy.ndarray) – NxD array, where N is the number of data, D is the feature dimension.

Renvoie

predicted labels, Nx1 array

Type renvoyé

numpy.ndarray

property random

Return a numpy random.

property ret

returns result

run()

Execute the classical algorithm.

Renvoie

results of an algorithm.

Type renvoyé

dict

save_model(file_path)

Save the model to a file path.

Paramètres

file_path (str) – a path to save the model.

test(data, labels)

Test the SVM

Paramètres

• data (numpy.ndarray) – NxD array, where N is the number of data, D is the feature dimension.

• labels (numpy.ndarray) – Nx1 array, where N is the number of data

Renvoie

accuracy

Type renvoyé

float

train(data, labels)

Train the SVM

Paramètres

• data (numpy.ndarray) – NxD array, where N is the number of data, D is the feature dimension.

• labels (numpy.ndarray) – Nx1 array, where N is the number of data