SklearnSVM
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.
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(opens in a new tab) 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
Attributes
class_to_label
returns class to label
label_to_class
returns label to class
random
Return a numpy random.
ret
returns result
Methods
load_model
SklearnSVM.load_model(file_path)
Load a model from a file path.
Parameters
file_path (str) – the path of the saved model.
predict
SklearnSVM.predict(data)
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
run
SklearnSVM.run()
Execute the classical algorithm.
Returns
results of an algorithm.
Return type
dict
save_model
SklearnSVM.save_model(file_path)
Save the model to a file path.
Parameters
file_path (str) – a path to save the model.
test
SklearnSVM.test(data, labels)
Test the SVM
Parameters
- 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
Returns
accuracy
Return type
float
train
SklearnSVM.train(data, labels)
Train the SVM
Parameters
- 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