def init_args(self, training_dataset, test_dataset, datapoints,
feature_map, multiclass_extension=None):
if training_dataset is None:
raise AlgorithmError('Training dataset must be provided')
is_multiclass = get_num_classes(training_dataset) > 2
if is_multiclass:
if multiclass_extension is None:
raise AlgorithmError('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:
qsvm_instance = QSVM_Kernel_Binary()
else:
qsvm_instance = QSVM_Kernel_Multiclass(multiclass_extension)
if datapoints is not None:
if not isinstance(datapoints, np.ndarray):
datapoints = np.asarray(datapoints)
qsvm_instance.init_args(training_dataset, test_dataset, datapoints, feature_map, self)
self.instance = qsvm_instance
def init_params(self, params, algo_input):
if algo_input.training_dataset is None:
raise AlgorithmError("Training dataset is required.")
fea_map_params = params.get(QuantumAlgorithm.SECTION_KEY_FEATURE_MAP)
feature_map = get_feature_map_instance(fea_map_params['name'])
num_qubits = get_feature_dimension(algo_input.training_dataset)
fea_map_params['num_qubits'] = num_qubits
feature_map.init_params(fea_map_params)
is_multiclass = get_num_classes(algo_input.training_dataset) > 2
if is_multiclass:
multicls_ext_params = params.get(
QuantumAlgorithm.SECTION_KEY_MULTICLASS_EXTENSION)
multiclass_extension = get_multiclass_extension_instance(
multicls_ext_params['name'])
# we need to set this explicitly for quantum version
multicls_ext_params['params'] = [feature_map, self]
multicls_ext_params['estimator_cls'] = QKernalSVM_Estimator
multiclass_extension.init_params(multicls_ext_params)
logger.info("Multiclass classifcation algo:" +
multicls_ext_params['name'])
else:
logger.warning(
"Only two classes in the dataset, use binary classifer"
" and ignore all options of multiclass_extension")
multiclass_extension = None
self.init_args(algo_input.training_dataset, algo_input.test_dataset,
algo_input.datapoints, feature_map,
multiclass_extension)
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 (dict): testing dataset.
datapoints (numpy.ndarray): prediction dataset.
multiclass_extension (MultiExtension): if number of classes > 2, a multiclass scheme is
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()))
self.datapoints = datapoints
self.feature_map = feature_map
self.num_qubits = self.feature_map.num_qubits
if multiclass_extension is None:
qsvm_instance = _QSVM_Kernel_Binary(self)
else:
qsvm_instance = _QSVM_Kernel_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 (dict): testing dataset.
datapoints (numpy.ndarray): prediction dataset.
multiclass_extension (MultiExtension): if number of classes > 2, a multiclass scheme is
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"
)
if multiclass_extension is None:
qsvm_instance = _QSVM_Kernel_Binary(feature_map, self,
training_dataset, test_dataset,
datapoints)
else:
qsvm_instance = _QSVM_Kernel_Multiclass(feature_map, self,
training_dataset,
test_dataset, datapoints,
multiclass_extension)
self.instance = qsvm_instance