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.
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
__init__
__init__(training_dataset, test_dataset=None, datapoints=None, gamma=None, multiclass_extension=None)
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_datasetDict[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 |
class_to_label
returns class to label
label_to_class
returns label to class
load_model
load_model(file_path)
Load a model from a file path.
Parameters
file_path (str) – the path of the saved model.
predict
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
random
Return a numpy random.
ret
returns result
run
run()
Execute the classical algorithm.
Returns
results of an algorithm.
Return type
dict
save_model
save_model(file_path)
Save the model to a file path.
Parameters
file_path (str) – a path to save the model.
test
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
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