qiskit.aqua.algorithms.SklearnSVM¶
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class
SklearnSVM(training_dataset, test_dataset=None, datapoints=None, gamma=None, multiclass_extension=None)[source]¶ 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.- Parameters
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.
- Raises
AquaError – Multiclass extension not supplied when number of classes > 2
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__init__(training_dataset, test_dataset=None, datapoints=None, gamma=None, multiclass_extension=None)[source]¶ - Parameters
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.
- Raises
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
returns class to label
returns label to class
Return a numpy random.
returns result
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property
class_to_label¶ returns class to label
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property
label_to_class¶ returns label to class
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load_model(file_path)[source]¶ Load a model from a file path.
- Parameters
file_path (str) – the path of the saved model.
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predict(data)[source]¶ Predict using the SVM
- Parameters
data (numpy.ndarray) – NxD array, where N is the number of data, D is the feature dimension.
- Returns
predicted labels, Nx1 array
- Return type
numpy.ndarray
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property
random¶ Return a numpy random.
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property
ret¶ returns result
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run()¶ Execute the classical algorithm.
- Returns
results of an algorithm.
- Return type
dict
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save_model(file_path)[source]¶ Save the model to a file path.
- Parameters
file_path (str) – a path to save the model.