QSVC#
- class QSVC(*, quantum_kernel=None, **kwargs)[source]#
Bases:
SVC
,SerializableModelMixin
Quantum Support Vector Classifier that extends the scikit-learn sklearn.svm.SVC classifier and introduces an additional quantum_kernel parameter.
This class shows how to use a quantum kernel for classification. The class inherits its methods like
fit
andpredict
from scikit-learn, see the example below. Read more in the scikit-learn user guide.Example
qsvc = QSVC(quantum_kernel=qkernel) qsvc.fit(sample_train,label_train) qsvc.predict(sample_test)
- Parameters:
quantum_kernel (BaseKernel | None) – A quantum kernel to be used for classification. Has to be
None
when a precomputed kernel is used. If None, default toFidelityQuantumKernel
.*args – Variable length argument list to pass to SVC constructor.
**kwargs – Arbitrary keyword arguments to pass to SVC constructor.
Attributes
- coef_#
Weights assigned to the features when kernel=”linear”.
- Return type:
ndarray of shape (n_features, n_classes)
- n_support_#
Number of support vectors for each class.
- probA_#
Parameter learned in Platt scaling when probability=True.
- Return type:
ndarray of shape (n_classes * (n_classes - 1) / 2)
- probB_#
Parameter learned in Platt scaling when probability=True.
- Return type:
ndarray of shape (n_classes * (n_classes - 1) / 2)
- quantum_kernel#
Returns quantum kernel
- unused_param = 'nu'#
Methods
- decision_function(X)#
Evaluate the decision function for the samples in X.
- Parameters:
X (array-like of shape (n_samples, n_features)) – The input samples.
- Returns:
X – Returns the decision function of the sample for each class in the model. If decision_function_shape=’ovr’, the shape is (n_samples, n_classes).
- Return type:
ndarray of shape (n_samples, n_classes * (n_classes-1) / 2)
Notes
If decision_function_shape=’ovo’, the function values are proportional to the distance of the samples X to the separating hyperplane. If the exact distances are required, divide the function values by the norm of the weight vector (
coef_
). See also this question for further details. If decision_function_shape=’ovr’, the decision function is a monotonic transformation of ovo decision function.
- fit(X, y, sample_weight=None)#
Fit the SVM model according to the given training data.
- Parameters:
X ({array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples)) – Training vectors, where n_samples is the number of samples and n_features is the number of features. For kernel=”precomputed”, the expected shape of X is (n_samples, n_samples).
y (array-like of shape (n_samples,)) – Target values (class labels in classification, real numbers in regression).
sample_weight (array-like of shape (n_samples,), default=None) – Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.
- Returns:
self – Fitted estimator.
- Return type:
Notes
If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied.
If X is a dense array, then the other methods will not support sparse matrices as input.
- get_metadata_routing()#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
routing – A
MetadataRequest
encapsulating routing information.- Return type:
MetadataRequest
- get_params(deep=True)#
Get parameters for this estimator.
- classmethod load(file_name)#
Loads a model from the file. If the loaded model is not an instance of the class whose method was called, then a warning is raised. Nevertheless, the loaded model may be a valid model.
- predict(X)#
Perform classification on samples in X.
For an one-class model, +1 or -1 is returned.
- Parameters:
X ({array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples_test, n_samples_train)) – For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).
- Returns:
y_pred – Class labels for samples in X.
- Return type:
ndarray of shape (n_samples,)
- predict_log_proba(X)#
Compute log probabilities of possible outcomes for samples in X.
The model need to have probability information computed at training time: fit with attribute probability set to True.
- Parameters:
X (array-like of shape (n_samples, n_features) or (n_samples_test, n_samples_train)) – For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).
- Returns:
T – Returns the log-probabilities of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.
- Return type:
ndarray of shape (n_samples, n_classes)
Notes
The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets.
- predict_proba(X)#
Compute probabilities of possible outcomes for samples in X.
The model needs to have probability information computed at training time: fit with attribute probability set to True.
- Parameters:
X (array-like of shape (n_samples, n_features)) – For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).
- Returns:
T – Returns the probability of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.
- Return type:
ndarray of shape (n_samples, n_classes)
Notes
The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets.
- save(file_name)#
Saves this model to the specified file. Internally, the model is serialized via
dill
. All parameters are saved, including a primitive instance that is referenced by internal objects. That means if a model is loaded from a file and is used, for instance, for inference, the same primitive will be used even if a cloud primitive was used.- Parameters:
file_name (str) – a file name or path where to save the model.
- score(X, y, sample_weight=None)#
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
- Parameters:
X (array-like of shape (n_samples, n_features)) – Test samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.
- Returns:
score – Mean accuracy of
self.predict(X)
w.r.t. y.- Return type:
- set_fit_request(*, sample_weight='$UNCHANGED$')#
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.
- set_params(**params)#
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
**params (dict) – Estimator parameters.
- Returns:
self – Estimator instance.
- Return type:
estimator instance
- set_score_request(*, sample_weight='$UNCHANGED$')#
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.