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
andpredict
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
- 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:
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 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 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)
, wheren_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:
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- 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
(seesklearn.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 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.Added in version 1.3.
- 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
(seesklearn.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 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.Added in version 1.3.