/**
*  @license
* Copyright 2022, JsData. All rights reserved.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.

* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* ==========================================================================
*/
import { CrossValidator } from './CrossValidator';
import { Scikit1D, Scikit2D, Scalar, Tensor1D, Tensor2D } from '../types';
/**
 * Evaluates a score by cross-validation. This particular overload
 * of the function uses the given scorer function to cross validate
 * a supervised estimator.
 *
 * @param estimator A supervised estimator that has an async `fit(X,y)` function.
 *
 * @param Xy       A two element array containing the features and targets
 *                 of the cross validation dataset.
 *
 * @param params   Cross validation parameters. `cv` is the {@link CrossValidator}
 *                 responsible for splitting the dataset into training in test
 *                 data in one or more folds. `groups` is additional grouping
 *                 information which is used by some {@link CrossValidator}
 *                 instance to determine which groups of datapoints may not
 *                 appear both in test and training data at the same time.
 *                 `scoring` is the scorer function which is being called
 *                 with `estimator` as `this` and the test features `X_test`
 *                 and targets `y_test` as arguments.
 *
 * @returns        The test score for each split/fold that was generated by
 *                 the {@link CrossValidator} `cv`
 */
export declare function crossValScore<T extends {
    fit(X: Tensor2D, y: Tensor1D): Promise<unknown>;
}>(estimator: T, X: Scikit2D, y: Scikit1D, params: {
    cv?: CrossValidator;
    groups?: Scikit1D;
    scoring: (this: T, X: Tensor2D, y: Tensor1D) => Scalar;
}): Promise<Tensor1D>;
/**
 * Evaluates a score by cross-validation. This particular overload
 * of the function uses the given scorer to cross validate an
 * unsupervised estimator.
 *
 * @param estimator A supervised estimator that has an async `fit(X)` function.
 *
 * @param X        An single element array containing the features of the
 *                 cross validation dataset.
 *
 * @param params   Cross validation parameters. `cv` is the {@link CrossValidator}
 *                 responsible for splitting the dataset into training in test
 *                 data in one or more folds. `groups` is additional grouping
 *                 information which is used by some {@link CrossValidator}
 *                 instance to determine which groups of datapoints may not
 *                 appear both in test and training data at the same time.
 *                 `scoring` is the scorer function which is being called
 *                 with `estimator` as `this` and the test features `X_test`
 *                 as argument.
 *
 * @returns        The test score for each split/fold that was generated by
 *                 the {@link CrossValidator} `cv`
 */
export declare function crossValScore<T extends {
    fit(X: Tensor2D): Promise<unknown>;
}>(estimator: T, X: Scikit2D, params: {
    cv?: CrossValidator;
    groups?: Scikit1D;
    scoring: (this: T, X: Tensor2D) => Scalar;
}): Promise<Tensor1D>;
/**
 * Evaluates a score by cross-validation. This particular overload
 * of the function uses the default score of a supervised estimator
 * for scoring.
 *
 * @param estimator A supervised estimator that has an async `fit(X,y)`
 *                 function and a `score(X,y)` function.
 *
 * @param Xy       A two element array containing the features and targets
 *                 of the cross validation dataset.
 *
 * @param params   Cross validation parameters. `cv` is the {@link CrossValidator}
 *                 responsible for splitting the dataset into training in test
 *                 data in one or more folds. `groups` is additional grouping
 *                 information which is used by some {@link CrossValidator}
 *                 instance to determine which groups of datapoints may not
 *                 appear both in test and training data at the same time.
 *
 * @returns        The test score for each split/fold that was generated by
 *                 the {@link CrossValidator} `cv`
 */
export declare function crossValScore(estimator: {
    fit(X: Tensor2D, y: Tensor1D): Promise<unknown>;
    score(X: Tensor2D, y: Tensor1D): Scalar;
}, X: Scikit2D, y: Scikit1D, params: {
    cv?: CrossValidator;
    groups?: Scikit1D;
}): Promise<Tensor1D>;
/**
 * Evaluates a score by cross-validation. This particular overload
 * of the function uses the default score of an unsupervised estimator
 * for scoring.
 *
 * @param estimator A supervised estimator that has an async `fit(X)`
 *                 function and a `score(X)` function.
 *
 * @param X        An single element array containing the features of the
 *                 cross validation dataset.
 *
 * @param params   Cross validation parameters. `cv` is the {@link CrossValidator}
 *                 responsible for splitting the dataset into training in test
 *                 data in one or more folds. `groups` is additional grouping
 *                 information which is used by some {@link CrossValidator}
 *                 instance to determine which groups of datapoints may not
 *                 appear both in test and training data at the same time.
 *
 * @returns        The test score for each split/fold that was generated by
 *                 the {@link CrossValidator} `cv`
 */
export declare function crossValScore(estimator: {
    fit(X: Tensor2D): Promise<unknown>;
    score(X: Tensor2D): Scalar;
}, X: Scikit2D, params: {
    cv?: CrossValidator;
    groups?: Scikit1D;
}): Promise<Tensor1D>;