QSVR#
- class QSVR(*, quantum_kernel=None, **kwargs)[소스]#
기반 클래스:
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)
- 매개변수:
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”.
- 반환 형식:
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.
- 매개변수:
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.
- 반환:
self – Fitted estimator.
- 반환 형식:
참고
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.
- 반환:
routing – A
MetadataRequest
encapsulating routing information.- 반환 형식:
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.
- 매개변수:
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).
- 반환:
y_pred – The predicted values.
- 반환 형식:
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.- 매개변수:
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.- 매개변수:
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.
- 반환:
score – \(R^2\) of
self.predict(X)
w.r.t. y.- 반환 형식:
참고
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='$UNCHANGED$')#
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.버전 1.3에 추가.
참고
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.- 매개변수:
**params (dict) – Estimator parameters.
- 반환:
self – Estimator instance.
- 반환 형식:
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.버전 1.3에 추가.
참고
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.