qxmt.models.qkernels.qrigge module#

class qxmt.models.qkernels.qrigge.QRiggeRegressor(kernel, n_jobs=2, show_progress=True, alpha=1.0, **kwargs)

Bases: BaseKernelModel

Quantum Rigge Regressor model.

Detail of Kernel Rigge Regressor model is refered to the sklearn.kernel_ridge.KernelRidge class. Reference: https://scikit-learn.org/stable/modules/generated/sklearn.kernel_ridge.KernelRidge.html

Parameters:

BaseKernelModel (_type_) – kernel instance of BaseKernel class
kernel (BaseKernel)
n_jobs (int)
show_progress (bool)
alpha (float)
kwargs (Any)

__init__(kernel, n_jobs=2, show_progress=True, alpha=1.0, **kwargs)

Initialize the QRiggeRegressor model.

Parameters:

kernel (BaseKernel) – kernel instance of BaseKernel class
n_jobs (int) – number of jobs for parallel computation
show_progress (bool) – flag for showing progress bar
alpha (float) – Regularization strength; must be a positive float
kwargs (Any)

Return type:

None

cross_val_score(X, y, **kwargs)

Cross validation score of the Quantum Kernel Ridge Regressor model. Default to use the R^2 score.

Parameters:

X (np.ndarray) – numpy array of input data
y (np.ndarray) – numpy array of target data
**kwargs (dict) – additional arguments

Returns:

array of scores of the estimator for each run of the cross validation

Return type:

np.ndarray

fit(X, y, save_shots_path=None)

Fit the model with given input data and target data.

Parameters:

X (np.ndarray) – numpy array of input data
y (np.ndarray) – numpy array of target data
save_shots_path (Optional[Path | str], optional) – save path for the shot results. Defaults to None.

Return type:

None

get_params()

Get the parameters of the model.

Return type:: dict

hyperparameter_search(X, y, sampler_type, search_space, search_args, objective=None, refit=True)

Search the best hyperparameters for the Quantum Kernel Ridge Regressor model.

Parameters:

X (np.ndarray) – dataset for search
y (np.ndarray) – target values for search
sampler_type (str) – sampler type for hyperparameter search (grid, random, tpe)
search_space (dict[str, list[Any]]) – search space for hyperparameter search
search_args (dict[str, Any]) – search arguments for hyperparameter search
objective (Optional[Callable], optional) – objective function for search. Defaults to None.
refit (bool, optional) – refit the model with best hyperparameters. Defaults to True.

Raises:

ValueError – Not supported search type

Returns:

best hyperparameters

Return type:

dict[str, Any]

load(path)

Load the trained model from the given path.

Parameters:: path (str | Path) – path to load the model
Returns:: loaded QRiggeRegressor model
Return type:: QRiggeRegressor

predict(X, bar_label='')

Predict target values of input data.

Parameters:

X (np.ndarray) – numpy array of input data
bar_label (str) – label for progress bar. Defaults to “”.

Returns:

numpy array of predicted target values

Return type:

np.ndarray

save(path)

Save the model to the given path.

Parameters:: path (str | Path) – path to save the model
Return type:: None

score(X, y, sample_weight=None)

Return the coefficient of determination R^2 of the prediction.

Parameters:

X (np.ndarray) – numpy array of input data
y (np.ndarray) – numpy array of target data
sample_weight (ndarray | None)

Returns:

R^2 of the prediction

Return type:

float

set_params(params)

Set the parameters of the model.

Parameters:: params (dict)
Return type:: None