QSVR#

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

आधार: 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)

मापदण्ड:

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".

प्रदत्त प्रकार :: 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.

मापदण्ड:

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.

प्रदत्त :

self -- Fitted estimator.

प्रदत्त प्रकार :

object

टिप्पणियाँ

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.

प्रदत्त :: routing -- A MetadataRequest encapsulating routing information.
प्रदत्त प्रकार :: MetadataRequest

get_params(deep=True)#

Get parameters for this estimator.

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

मापदण्ड:: file_name (str) -- a file name or path to load a model from.
प्रदत्त :: A loaded model.
उभारता है :: TypeError -- if a loaded model is not an instance of the expected class.
प्रदत्त प्रकार :: Any

predict(X)#

Perform regression on samples in X.

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

मापदण्ड:: 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).
प्रदत्त :: y_pred -- The predicted values.
प्रदत्त प्रकार :: 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.

मापदण्ड:: 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.

मापदण्ड:

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.

प्रदत्त :

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

प्रदत्त प्रकार :

float

टिप्पणियाँ

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.

संस्करण 1.3 से नया .

टिप्पणी

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.

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

मापदण्ड:: **params (dict) -- Estimator parameters.
प्रदत्त :: self -- Estimator instance.
प्रदत्त प्रकार :: 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.

संस्करण 1.3 से नया .

टिप्पणी

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.

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