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/*
* 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.validation;
import java.io.Serializable;
import smile.data.DataFrame;
import smile.data.formula.Formula;
import smile.regression.DataFrameRegression;
import smile.regression.Regression;
import smile.validation.metric.*;
/** The regression validation metrics. */
public class RegressionMetrics implements Serializable {
private static final long serialVersionUID = 2L;
/** The time in milliseconds of fitting the model. */
public final double fitTime;
/** The time in milliseconds of scoring the validation data. */
public final double scoreTime;
/** The validation data size. */
public final int size;
/** The residual sum of squares on validation data. */
public final double rss;
/** The mean squared error on validation data. */
public final double mse;
/** The root mean squared error on validation data. */
public final double rmse;
/** The mean absolute deviation on validation data. */
public final double mad;
/** The R-squared score on validation data. */
public final double r2;
/**
* Constructor.
* @param fitTime the time in milliseconds of fitting the model.
* @param scoreTime the time in milliseconds of scoring the validation data.
* @param size the validation data size.
* @param rss the residual sum of squares on validation data.
* @param mse the mean squared error on validation data.
* @param rmse the root mean squared error on validation data.
* @param mad the mean absolute deviation on validation data.
* @param r2 the R-squared score on validation data.
*/
public RegressionMetrics(double fitTime, double scoreTime, int size, double rss, double mse, double rmse, double mad, double r2) {
this.fitTime = fitTime;
this.scoreTime = scoreTime;
this.size = size;
this.rss = rss;
this.mse = mse;
this.rmse = rmse;
this.mad = mad;
this.r2 = r2;
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder("{\n");
if (!Double.isNaN(fitTime)) sb.append(String.format(" fit time: %.3f ms,\n", fitTime));
sb.append(String.format(" score time: %.3f ms,\n", scoreTime));
sb.append(String.format(" validation data size:: %d,\n", size));
sb.append(String.format(" RSS: %.4f,\n", rss));
sb.append(String.format(" MSE: %.4f,\n", mse));
sb.append(String.format(" RMSE: %.4f,\n", rmse));
sb.append(String.format(" MAD: %.4f,\n", mad));
sb.append(String.format(" R2: %.2f%%\n}", 100 * r2));
return sb.toString();
}
/**
* Computes the regression metrics.
* @param fitTime the time in milliseconds of fitting the model.
* @param scoreTime the time in milliseconds of scoring the validation data.
* @param truth the ground truth.
* @param prediction the predictions.
* @return the validation metrics.
*/
public static RegressionMetrics of(double fitTime, double scoreTime, double[] truth, double[] prediction) {
return new RegressionMetrics(
fitTime, scoreTime, truth.length,
RSS.of(truth, prediction),
MSE.of(truth, prediction),
RMSE.of(truth, prediction),
MAD.of(truth, prediction),
R2.of(truth, prediction));
}
/**
* Validates a model on a test data.
* @param model the model.
* @param testx the validation data.
* @param testy the responsible variable of validation data.
* @param the data type of samples.
* @param the model type.
* @return the validation metrics.
*/
public static > RegressionMetrics of(M model, T[] testx, double[] testy) {
return of(Double.NaN, model, testx, testy);
}
/**
* Validates a model on a test data.
* @param fitTime the time in milliseconds of fitting the model.
* @param model the model.
* @param testx the validation data.
* @param testy the responsible variable of validation data.
* @param the data type of samples.
* @param the model type.
* @return the validation metrics.
*/
public static > RegressionMetrics of(double fitTime, M model, T[] testx, double[] testy) {
long start = System.nanoTime();
double[] prediction = model.predict(testx);
double scoreTime = (System.nanoTime() - start) / 1E6;
return new RegressionMetrics(
fitTime, scoreTime, testy.length,
RSS.of(testy, prediction),
MSE.of(testy, prediction),
RMSE.of(testy, prediction),
MAD.of(testy, prediction),
R2.of(testy, prediction));
}
/**
* Trains and validates a model on a train/validation split.
* @param model the model.
* @param formula the model formula.
* @param test the validation data.
* @param the model type.
* @return the validation metrics.
*/
public static RegressionMetrics of(M model, Formula formula, DataFrame test) {
return of(Double.NaN, model, formula, test);
}
/**
* Trains and validates a model on a train/validation split.
* @param fitTime the time in milliseconds of fitting the model.
* @param model the model.
* @param formula the model formula.
* @param test the validation data.
* @param the model type.
* @return the validation metrics.
*/
public static RegressionMetrics of(double fitTime, M model, Formula formula, DataFrame test) {
double[] testy = formula.y(test).toDoubleArray();
long start = System.nanoTime();
int n = test.nrow();
double[] prediction = new double[n];
for (int i = 0; i < n; i++) {
prediction[i] = model.predict(test.get(i));
}
double scoreTime = (System.nanoTime() - start) / 1E6;
return new RegressionMetrics(
fitTime, scoreTime, testy.length,
RSS.of(testy, prediction),
MSE.of(testy, prediction),
RMSE.of(testy, prediction),
MAD.of(testy, prediction),
R2.of(testy, prediction));
}
}
