QSVR

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

Bases: SVR, SerializableModelMixin

Quantum Support Vector Regressor.

It extends scikit-learn’s sklearn.svm.SVR by introducing a quantum_kernel parameter for computing similarity between samples using a quantum kernel.

The class follows scikit-learn conventions and inherits methods such as fit() and predict(). For general SVR usage and parameters, refer to the scikit-learn user guide.

Notes

  • Passing kernel=... to the constructor is not supported; use quantum_kernel. If kernel is provided, it is discarded and a QiskitMachineLearningWarning is emitted.

  • If quantum_kernel is None, a FidelityQuantumKernel is created. A feature_map may be provided via kwargs and will be forwarded to the default fidelity kernel.

  • If quantum_kernel == "precomputed", the estimator is configured for scikit-learn’s precomputed-kernel mode and expects kernel matrices as input.

Example

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

Create a quantum-kernel SVR estimator.

Parameters:

  • quantum_kernel (BaseKernel | None) –

    Quantum kernel configuration.

    • If None, defaults to FidelityQuantumKernel. In this case, an optional feature_map may be provided via kwargs and is forwarded to the default fidelity kernel.

    • If equal to the string "precomputed", the estimator is configured for scikit-learn’s precomputed-kernel mode (i.e. it expects kernel matrices rather than raw samples).

    • Otherwise, it must be a BaseKernel instance and its evaluate() method will be used as the callable kernel by scikit-learn.

  • **kwargs –

    Keyword arguments forwarded to sklearn.svm.SVR.

    The kernel keyword is not supported and will be discarded (use quantum_kernel instead). If provided, a warning is emitted.

Warns:

QiskitMachineLearningWarning – If a kernel argument is provided in kwargs and is discarded.

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.

quantum_kernel

Quantum kernel configuration for this estimator.

Returns:

a BaseKernel instance.

Return type:

BaseKernel

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.

classmethod from_dill(file_name)

Loads a model from a 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.

Replaces the deprecated load() method.

Parameters:

file_name (str) – Path to the dill file containing the serialized model.

Returns:

An instance of the model loaded from disk.

Return type:

Any

Example

loaded = MyModel.from_dill('model_state.dill')
Raises:

TypeError – if a loaded model is not an instance of the expected class.

Return type:

Any

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(*args)

Backwards compatibility with from_dill(), deprecated in v0.9.0.

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(*args)

Backwards compatibility with to_dill(), deprecated in v0.9.0.

score(X, y, sample_weight=None)

Return coefficient of determination on test data.

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='$UNCHANGED$')

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Added in version 1.3.

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='$UNCHANGED$')

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

Added in version 1.3.

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

to_dill(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.

Warning

Replaces the deprecated save() method.

Parameters:

file_name (str) – Path where the serialized model will be written.

Example

model.to_dill('model_state.dill')