smile.data.transform.Transform Maven / Gradle / Ivy
/*
* Copyright (c) 2010-2021 Haifeng Li. All rights reserved.
*
* Smile is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Smile is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with Smile. If not, see .
*/
package smile.data.transform;
import java.io.Serializable;
import java.util.function.Function;
import smile.data.DataFrame;
import smile.data.Tuple;
/**
* Data transformation interface. In general, learning algorithms benefit
* from standardization of the data set. If some outliers are present in
* the set, robust transformers are more appropriate.
*
* @author Haifeng Li
*/
public interface Transform extends Function, Serializable {
/**
* Fits a pipeline of data transforms.
*
* @param data the training data.
* @param trainers the training algorithm to fit the transforms to apply one after one.
* @return a composed transform.
*/
@SafeVarargs
static Transform fit(DataFrame data, Function... trainers) {
Transform pipeline = trainers[0].apply(data);
for (int i = 1; i < trainers.length; i++) {
data = pipeline.apply(data);
pipeline = pipeline.andThen(trainers[i].apply(data));
}
return pipeline;
}
/**
* Returns a pipeline of data transforms.
*
* @param transforms the transforms to apply one after one.
* @return a composed transform.
*/
static Transform pipeline(Transform... transforms) {
Transform pipeline = transforms[0];
for (int i = 1; i < transforms.length; i++) {
pipeline = pipeline.andThen(transforms[i]);
}
return pipeline;
}
/**
* Applies this transform to the given argument.
* @param data the input data frame.
* @return the transformed data frame.
*/
default DataFrame apply(DataFrame data) {
return data.stream().map(this).collect(DataFrame.Collectors.collect());
}
/**
* Returns a composed function that first applies this function
* to its input, and then applies the after function
* to the result.
*
* @param after the transform to apply after this transform is applied.
* @return a composed transform that first applies this transform and
* then applies the after transform.
*/
default Transform andThen(Transform after) {
return (Tuple t) -> after.apply(apply(t));
}
/**
* Returns a composed function that first applies the before
* function to its input, and then applies this function to the result.
*
* @param before the transform to apply before this transform is applied.
* @return a composed transform that first applies the before
* transform and then applies this transform.
*/
default Transform compose(Transform before) {
return (Tuple t) -> apply(before.apply(t));
}
}