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, 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, qalgo):
super().__init__()
self._qsvm_binary = _QSVM_Binary(qalgo)
self._ret = {}
def __init__(self, feature_map, qalgo): # pylint: disable=unused-argument
super().__init__()
self._qsvm_binary = _QSVM_Binary(qalgo)
self._ret = {}
