def __init__(self,
training_dataset,
test_dataset=None,
datapoints=None,
gamma=None,
multiclass_extension=None):
self.validate(locals())
super().__init__()
if training_dataset is None:
raise AquaError('Training dataset must be provided.')
is_multiclass = get_num_classes(training_dataset) > 2
if is_multiclass:
if multiclass_extension is None:
raise AquaError(
'Dataset has more than two classes. A multiclass extension must be provided.'
)
else:
if multiclass_extension is not None:
logger.warning(
"Dataset has just two classes. Supplied multiclass extension will be ignored"
)
if multiclass_extension is None:
svm_instance = _SVM_Classical_Binary(training_dataset,
test_dataset, datapoints,
gamma)
else:
svm_instance = _SVM_Classical_Multiclass(training_dataset,
test_dataset, datapoints,
gamma,
multiclass_extension)
self.instance = svm_instance
def __init__(
self,
training_dataset: Dict[str, np.ndarray],
test_dataset: Optional[Dict[str, np.ndarray]] = None,
datapoints: Optional[np.ndarray] = None,
gamma: Optional[int] = None,
multiclass_extension: Optional[MulticlassExtension] = None
) -> None:
# pylint: disable=line-too-long
"""
Args:
training_dataset: Training dataset.
test_dataset: Testing dataset.
datapoints: Prediction dataset.
gamma: Used as input for sklearn rbf_kernel which is used internally. See
`sklearn.metrics.pairwise.rbf_kernel
<https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.rbf_kernel.html>`_
for more information about gamma.
multiclass_extension: 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
"""
super().__init__()
warn_package('aqua.algorithms.classifiers',
'qiskit_machine_learning.algorithms.classifiers',
'qiskit-machine-learning')
if training_dataset is None:
raise AquaError('Training dataset must be provided.')
is_multiclass = get_num_classes(training_dataset) > 2
if is_multiclass:
if multiclass_extension is None:
raise AquaError('Dataset has more than two classes. '
'A multiclass extension must be provided.')
else:
if multiclass_extension is not None:
logger.warning(
"Dataset has just two classes. Supplied multiclass "
"extension will be ignored")
svm_instance = None # type: Optional[Union[_SklearnSVMBinary, _SklearnSVMMulticlass]]
if multiclass_extension is None:
svm_instance = _SklearnSVMBinary(training_dataset, test_dataset,
datapoints, gamma)
else:
multiclass_extension.set_estimator(_RBF_SVC_Estimator, [])
svm_instance = _SklearnSVMMulticlass(training_dataset,
test_dataset, datapoints,
gamma, multiclass_extension)
self.instance = svm_instance
def __init__(
self,
training_dataset: Dict[str, np.ndarray],
test_dataset: Optional[Dict[str, np.ndarray]] = None,
datapoints: Optional[np.ndarray] = None,
gamma: Optional[int] = None,
multiclass_extension: Optional[MulticlassExtension] = None
) -> None:
# pylint: disable=line-too-long
"""
Args:
training_dataset: training dataset.
test_dataset: testing dataset.
datapoints: prediction dataset.
gamma: Used as input for sklearn rbf_kernel internally. See
`sklearn.metrics.pairwise.rbf_kernel
<https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.rbf_kernel.html>`_
for more information about gamma.
multiclass_extension: if number of classes > 2 then
a multiclass scheme is needed.
Raises:
AquaError: If using binary classifier where num classes >= 3
"""
super().__init__()
if training_dataset is None:
raise AquaError('Training dataset must be provided.')
is_multiclass = get_num_classes(training_dataset) > 2
if is_multiclass:
if multiclass_extension is None:
raise AquaError('Dataset has more than two classes. '
'A multiclass extension must be provided.')
else:
if multiclass_extension is not None:
logger.warning(
"Dataset has just two classes. Supplied multiclass "
"extension will be ignored")
if multiclass_extension is None:
svm_instance = _SVM_Classical_Binary(training_dataset,
test_dataset, datapoints,
gamma)
else:
svm_instance = _SVM_Classical_Multiclass(training_dataset,
test_dataset, datapoints,
gamma,
multiclass_extension)
self.instance = svm_instance
