import { DataFrameInterface, Scikit1D, Transformer, Tensor2D } from '../types';
import { Serialize } from '../simpleSerializer';
declare type Selection = string | string[] | number[] | number;
declare type SingleTransformation = [string, Transformer, Selection];
declare type TransformerTriple = Array<SingleTransformation>;
/**
 * The parameters for the Column Transormer
 */
export interface ColumnTransformerParams {
    /**
     * A list of transformations. Every element is itself a list [name, Transformer, Selection]. **default = []**
     */
    transformers?: TransformerTriple;
    /**
     * What should we do with the remainder columns? Possible values for remainder are a Transformer that
     * will be applied to all remaining columns. It can also be 'passthrough' which simply passes the columns
     * untouched through this, or 'drop', which drops all untransformed columns. **default = "drop"**
     */
    remainder?: Transformer | 'drop' | 'passthrough';
}
/**
 * The ColumnTransformer transformers a 2D matrix of mixed types, with possibly missing values
 * into a 2DMatrix that is ready to be put into a machine learning model. Usually this class does
 * the heavy lifting associated with imputing missing data, one hot encoding categorical variables,
 * and any other preprocessing steps that are deemed necessary (standard scaling, etc).
 *
 * @example
 * ```typescript
    const X = [
      [2, 2],
      [2, 3],
      [0, NaN],
      [2, 0]
    ]

    const transformer = new ColumnTransformer({
      transformers: [
        ['minmax', new MinMaxScaler(), [0]],
        ['simpleImpute', new SimpleImputer({ strategy: 'median' }), [1]]
      ]
    })

    let result = transformer.fitTransform(X)
    const expected = [
      [1, 2],
      [1, 3],
      [0, 2],
      [1, 0]
    ]
 * ```
 */
export declare class ColumnTransformer extends Serialize {
    transformers: TransformerTriple;
    remainder: Transformer | 'drop' | 'passthrough';
    /** Useful for pipelines and column transformers to have a default name for transforms */
    name: string;
    tf: any;
    constructor({ transformers, remainder }?: ColumnTransformerParams);
    fit(X: Tensor2D | DataFrameInterface, y?: Scikit1D): this;
    transform(X: Tensor2D | DataFrameInterface, y?: Scikit1D): any;
    fitTransform(X: Tensor2D | DataFrameInterface, y?: Scikit1D): any;
    getColumns(X: DataFrameInterface | Tensor2D, selectedColumns: Selection): Tensor2D;
}
export {};