def __init__(
self,
feature_map: FeatureMap,
training_dataset: Optional[Dict[str, np.ndarray]] = None,
test_dataset: Optional[Dict[str, np.ndarray]] = None,
datapoints: Optional[np.ndarray] = None,
multiclass_extension: Optional[MulticlassExtension] = None,
quantum_instance: Optional[Union[QuantumInstance, BaseBackend]] = None
) -> None:
"""
Args:
feature_map: Feature map module, used to transform data
training_dataset: Training dataset.
test_dataset: Testing dataset.
datapoints: Prediction dataset.
multiclass_extension: If number of classes is greater than 2 then a multiclass scheme
must be supplied, in the form of a multiclass extension.
quantum_instance: Quantum Instance or Backend
Raises:
AquaError: Multiclass extension not supplied when number of classes > 2
"""
super().__init__(quantum_instance)
# check the validity of provided arguments if possible
if training_dataset is not None:
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")
self.training_dataset = None
self.test_dataset = None
self.datapoints = None
self.class_to_label = None
self.label_to_class = None
self.num_classes = None
self.setup_training_data(training_dataset)
self.setup_test_data(test_dataset)
self.setup_datapoint(datapoints)
self.feature_map = feature_map
self.num_qubits = self.feature_map.num_qubits
if multiclass_extension is None:
qsvm_instance = _QSVM_Binary(self)
else:
multiclass_extension.set_estimator(_QSVM_Estimator, [feature_map])
qsvm_instance = _QSVM_Multiclass(self, multiclass_extension)
self.instance = qsvm_instance
def __init__(self, feature_map, training_dataset, test_dataset=None, datapoints=None,
multiclass_extension=None):
"""Constructor.
Args:
feature_map (FeatureMap): feature map module, used to transform data
training_dataset (dict): training dataset.
test_dataset (Optional[dict]): testing dataset.
datapoints (Optional[numpy.ndarray]): prediction dataset.
multiclass_extension (Optional[MultiExtension]): if number of classes > 2 then
a multiclass scheme is needed.
Raises:
ValueError: if training_dataset is None
AquaError: use binary classifer for classes > 3
"""
super().__init__()
if training_dataset is None:
raise ValueError('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")
self.training_dataset, self.class_to_label = split_dataset_to_data_and_labels(
training_dataset)
if test_dataset is not None:
self.test_dataset = split_dataset_to_data_and_labels(test_dataset,
self.class_to_label)
else:
self.test_dataset = None
self.label_to_class = {label: class_name for class_name, label
in self.class_to_label.items()}
self.num_classes = len(list(self.class_to_label.keys()))
if datapoints is not None and not isinstance(datapoints, np.ndarray):
datapoints = np.asarray(datapoints)
self.datapoints = datapoints
self.feature_map = feature_map
self.num_qubits = self.feature_map.num_qubits
if multiclass_extension is None:
qsvm_instance = _QSVM_Binary(self)
else:
qsvm_instance = _QSVM_Multiclass(self, multiclass_extension)
self.instance = qsvm_instance
def __init__(
self,
feature_map: FeatureMap,
training_dataset: Optional[Dict[str, np.ndarray]] = None,
test_dataset: Optional[Dict[str, np.ndarray]] = None,
datapoints: Optional[np.ndarray] = None,
multiclass_extension: Optional[MulticlassExtension] = None
) -> None:
"""
Args:
feature_map: feature map module, used to transform data
training_dataset: training dataset.
test_dataset: testing dataset.
datapoints: prediction dataset.
multiclass_extension: if number of classes > 2 then
a multiclass scheme is needed.
Raises:
AquaError: Using binary classifier when number of classes > 2
"""
super().__init__()
# check the validity of provided arguments if possible
if training_dataset is not None:
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")
self.training_dataset = None
self.test_dataset = None
self.datapoints = None
self.class_to_label = None
self.label_to_class = None
self.num_classes = None
self.setup_training_data(training_dataset)
self.setup_test_data(test_dataset)
self.setup_datapoint(datapoints)
self.feature_map = feature_map
self.num_qubits = self.feature_map.num_qubits
if multiclass_extension is None:
qsvm_instance = _QSVM_Binary(self)
else:
qsvm_instance = _QSVM_Multiclass(self, multiclass_extension)
self.instance = qsvm_instance
def __init__(
self,
feature_map: Union[QuantumCircuit, FeatureMap],
training_dataset: Optional[Dict[str, np.ndarray]] = None,
test_dataset: Optional[Dict[str, np.ndarray]] = None,
datapoints: Optional[np.ndarray] = None,
multiclass_extension: Optional[MulticlassExtension] = None,
lambda2: float = 0.001,
quantum_instance: Optional[Union[QuantumInstance, BaseBackend,
Backend]] = None
) -> None:
"""
Args:
feature_map: Feature map module, used to transform data
training_dataset: Training dataset.
test_dataset: Testing dataset.
datapoints: Prediction dataset.
multiclass_extension: If number of classes is greater than 2 then a multiclass scheme
must be supplied, in the form of a multiclass extension.
lambda2: L2 norm regularization factor
quantum_instance: Quantum Instance or Backend
Raises:
AquaError: Multiclass extension not supplied when number of classes > 2
"""
super().__init__(quantum_instance)
warn_package('aqua.algorithms.classifiers',
'qiskit_machine_learning.algorithms.classifiers',
'qiskit-machine-learning')
# check the validity of provided arguments if possible
if training_dataset is not None:
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")
self.training_dataset = None
self.test_dataset = None
self.datapoints = None
self.class_to_label = None
self.label_to_class = None
self.num_classes = None
self.setup_training_data(training_dataset)
self.setup_test_data(test_dataset)
self.setup_datapoint(datapoints)
self.lambda2 = lambda2
self.feature_map = feature_map
self.num_qubits = self.feature_map.num_qubits
if isinstance(feature_map, QuantumCircuit):
# patch the feature dimension attribute to the circuit
self.feature_map.feature_dimension = len(feature_map.parameters)
if not hasattr(feature_map, 'ordered_parameters'):
self.feature_map.ordered_parameters = sorted(
feature_map.parameters, key=lambda p: p.name)
self.feature_map_params_x = ParameterVector(
'x', self.feature_map.feature_dimension)
self.feature_map_params_y = ParameterVector(
'y', self.feature_map.feature_dimension)
else:
warnings.warn("""
The {} object as input for the QSVM is deprecated as of 0.9.0 and will
be removed no earlier than 3 months after the release.
You should pass a QuantumCircuit object instead.
See also qiskit.circuit.library.data_preparation for a collection
of suitable circuits.""".format(type(feature_map)),
DeprecationWarning,
stacklevel=2)
self.feature_map_params_x = ParameterVector(
'x', feature_map.feature_dimension)
self.feature_map_params_y = ParameterVector(
'y', feature_map.feature_dimension)
qsvm_instance = None # type: Optional[Union[_QSVM_Binary, _QSVM_Multiclass]]
if multiclass_extension is None:
qsvm_instance = _QSVM_Binary(self)
else:
multiclass_extension.set_estimator(_QSVM_Estimator, [feature_map])
qsvm_instance = _QSVM_Multiclass(self, multiclass_extension)
self.instance = qsvm_instance
