QSVR#

class QSVR(*, quantum_kernel=None, **kwargs)[source]#

Bases: SVR, SerializableModelMixin

Quantum Support Vector Regressor that extends the scikit-learn sklearn.svm.SVR regressor and introduces an additional quantum_kernel parameter.

This class shows how to use a quantum kernel for regression. The class inherits its methods like fit and predict from scikit-learn, see the example below. Read more in the scikit-learn user guide.

Example

qsvr = QSVR(quantum_kernel=qkernel)
qsvr.fit(sample_train,label_train)
qsvr.predict(sample_test)

Parameters:

quantum_kernel (BaseKernel | None) -- A quantum kernel to be used for regression. If None, default to FidelityQuantumKernel.
*args -- Variable length argument list to pass to SVR constructor.
**kwargs -- Arbitrary keyword arguments to pass to SVR constructor.

Attributes

class_weight_#

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.

quantum_kernel#: Returns quantum kernel

unused_param = 'random_state'#

Methods

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:

object

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.

Parameters:: deep (bool, default=True) -- If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params -- Parameter names mapped to their values.
Return type:: dict

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.

Parameters:: file_name (str) -- a file name or path to load a model from.
Returns:: A loaded model.
Raises:: TypeError -- if a loaded model is not an instance of the expected class.
Return type:: Any

predict(X)#

Perform regression on samples in X.

For an one-class model, +1 (inlier) or -1 (outlier) is returned.

Parameters:: X ({array-like, sparse matrix} of shape (n_samples, n_features)) -- For kernel="precomputed", the expected shape of X is (n_samples_test, n_samples_train).
Returns:: y_pred -- The predicted values.
Return type:: ndarray of shape (n_samples,)

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 coefficient of determination of the prediction.

The coefficient of determination $R^2$ is defined as $(1 - \frac{u}{v})$, where $u$ is the residual sum of squares ((y_true - y_pred)** 2).sum() and $v$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $R^2$ score of 0.0.

Parameters:

X (array-like of shape (n_samples, n_features)) -- Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) -- True values for X.
sample_weight (array-like of shape (n_samples,), default=None) -- Sample weights.

Returns:

score -- $R^2$ of self.predict(X) w.r.t. y.

Return type:

float

Notes

The $R^2$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

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 (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
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.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) -- Metadata routing for sample_weight parameter in fit.
Returns:: self -- The updated object.
Return type:: object

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 (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
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.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) -- Metadata routing for sample_weight parameter in score.
Returns:: self -- The updated object.
Return type:: object