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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 java.util.Arrays;
import smile.classification.Classifier;
import smile.classification.DataFrameClassifier;
import smile.data.DataFrame;
import smile.data.formula.Formula;
import smile.math.MathEx;
import smile.validation.metric.*;
import smile.validation.metric.Error;
/** The classification validation metrics. */
public class ClassificationMetrics 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 number of errors. */
public final int error;
/** The accuracy on validation data. */
public final double accuracy;
/** The sensitivity on validation data. */
public final double sensitivity;
/** The specificity on validation data. */
public final double specificity;
/** The precision on validation data. */
public final double precision;
/** The F-1 score on validation data. */
public final double f1;
/** The Matthews correlation coefficient on validation data. */
public final double mcc;
/** The AUC on validation data. */
public final double auc;
/** The log loss on validation data. */
public final double logloss;
/** The cross entropy on validation data. */
public final double crossentropy;
/**
* 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 error the number of errors.
* @param accuracy the accuracy on validation data.
*/
public ClassificationMetrics(double fitTime, double scoreTime, int size, int error, double accuracy) {
this(fitTime, scoreTime, size, error, accuracy, Double.NaN);
}
/**
* Constructor of multiclass soft classifier validation.
* @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 error the number of errors.
* @param accuracy the accuracy on validation data.
* @param crossentropy the cross entropy on validation data.
*/
public ClassificationMetrics(double fitTime, double scoreTime, int size, int error, double accuracy, double crossentropy) {
this.fitTime = fitTime;
this.scoreTime = scoreTime;
this.size = size;
this.error = error;
this.accuracy = accuracy;
this.crossentropy = crossentropy;
this.sensitivity = Double.NaN;
this.specificity = Double.NaN;
this.precision = Double.NaN;
this.f1 = Double.NaN;
this.mcc = Double.NaN;
this.auc = Double.NaN;
this.logloss = Double.NaN;
}
/**
* Constructor of binary classifier validation.
* @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 error the number of errors.
* @param accuracy the accuracy on validation data.
* @param sensitivity the sensitivity on validation data.
* @param specificity the specificity on validation data.
* @param precision the precision on validation data.
* @param f1 the F-1 score on validation data.
* @param mcc the Matthews correlation coefficient on validation data.
*/
public ClassificationMetrics(double fitTime, double scoreTime, int size, int error,
double accuracy, double sensitivity, double specificity,
double precision, double f1, double mcc) {
this(fitTime, scoreTime, size, error, accuracy, sensitivity, specificity, precision, f1, mcc, Double.NaN, Double.NaN);
}
/**
* Constructor of binary soft classifier validation.
* @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 error the number of errors.
* @param accuracy the accuracy on validation data.
* @param sensitivity the sensitivity on validation data.
* @param specificity the specificity on validation data.
* @param precision the precision on validation data.
* @param f1 the F-1 score on validation data.
* @param mcc the Matthews correlation coefficient on validation data.
* @param auc the AUC on validation data.
* @param logloss the log loss on validation data.
*/
public ClassificationMetrics(double fitTime, double scoreTime, int size, int error,
double accuracy, double sensitivity, double specificity,
double precision, double f1, double mcc, double auc,
double logloss) {
this.fitTime = fitTime;
this.scoreTime = scoreTime;
this.size = size;
this.error = error;
this.accuracy = accuracy;
this.sensitivity = sensitivity;
this.specificity = specificity;
this.precision = precision;
this.f1 = f1;
this.mcc = mcc;
this.auc = auc;
this.logloss = logloss;
this.crossentropy = logloss;
}
/**
* 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 error the number of errors.
* @param accuracy the accuracy on validation data.
* @param sensitivity the sensitivity on validation data.
* @param specificity the specificity on validation data.
* @param precision the precision on validation data.
* @param f1 the F-1 score on validation data.
* @param mcc the Matthews correlation coefficient on validation data.
* @param auc the AUC on validation data.
* @param logloss the log loss on validation data.
* @param crossentropy the cross entropy on validation data.
*/
public ClassificationMetrics(double fitTime, double scoreTime, int size, int error,
double accuracy, double sensitivity, double specificity,
double precision, double f1, double mcc, double auc,
double logloss, double crossentropy) {
this.fitTime = fitTime;
this.scoreTime = scoreTime;
this.size = size;
this.error = error;
this.accuracy = accuracy;
this.sensitivity = sensitivity;
this.specificity = specificity;
this.precision = precision;
this.f1 = f1;
this.mcc = mcc;
this.auc = auc;
this.logloss = logloss;
this.crossentropy = crossentropy;
}
@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(" error: %d,\n", error));
sb.append(String.format(" accuracy: %.2f%%", 100 * accuracy));
if (!Double.isNaN(sensitivity)) sb.append(String.format(",\n sensitivity: %.2f%%", 100 * sensitivity));
if (!Double.isNaN(specificity)) sb.append(String.format(",\n specificity: %.2f%%", 100 * specificity));
if (!Double.isNaN(precision)) sb.append(String.format(",\n precision: %.2f%%", 100 * precision));
if (!Double.isNaN(f1)) sb.append(String.format(",\n F1 score: %.2f%%", 100 * f1));
if (!Double.isNaN(mcc)) sb.append(String.format(",\n MCC: %.2f%%", 100 * mcc));
if (!Double.isNaN(auc)) sb.append(String.format(",\n AUC: %.2f%%", 100 * auc));
if (!Double.isNaN(logloss)) sb.append(String.format(",\n log loss: %.4f", logloss));
else if (!Double.isNaN(crossentropy)) sb.append(String.format(",\n cross entropy: %.4f", crossentropy));
sb.append("\n}");
return sb.toString();
}
/**
* Computes the classification 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 classification metrics.
*/
public static ClassificationMetrics of(double fitTime, double scoreTime, int[] truth, int[] prediction) {
if (MathEx.unique(truth).length == 2) {
return new ClassificationMetrics(fitTime, scoreTime, truth.length,
Error.of(truth, prediction),
Accuracy.of(truth, prediction),
Sensitivity.of(truth, prediction),
Specificity.of(truth, prediction),
Precision.of(truth, prediction),
FScore.F1.score(truth, prediction),
MatthewsCorrelation.of(truth, prediction));
} else {
return new ClassificationMetrics(fitTime, scoreTime, truth.length,
Error.of(truth, prediction),
Accuracy.of(truth, prediction));
}
}
/**
* Computes the soft classification 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.
* @param posteriori the posteriori probabilities of predictions.
* @return the classification metrics.
*/
public static ClassificationMetrics of(double fitTime, double scoreTime, int[] truth, int[] prediction, double[][] posteriori) {
if (posteriori[0].length == 2) {
double[] probability = Arrays.stream(posteriori).mapToDouble(p -> p[1]).toArray();
return new ClassificationMetrics(fitTime, scoreTime, truth.length,
Error.of(truth, prediction),
Accuracy.of(truth, prediction),
Sensitivity.of(truth, prediction),
Specificity.of(truth, prediction),
Precision.of(truth, prediction),
FScore.F1.score(truth, prediction),
MatthewsCorrelation.of(truth, prediction),
AUC.of(truth, probability),
LogLoss.of(truth, probability));
} else {
return new ClassificationMetrics(fitTime, scoreTime, truth.length,
Error.of(truth, prediction),
Accuracy.of(truth, prediction),
CrossEntropy.of(truth, posteriori));
}
}
/**
* Validates a model on a test data.
* @param model the model.
* @param testx the validation data.
* @param testy the class labels of validation data.
* @param the data type of samples.
* @param the model type.
* @return the validation results.
*/
public static > ClassificationMetrics of(M model, T[] testx, int[] 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 class labels of validation data.
* @param the data type of samples.
* @param the model type.
* @return the validation results.
*/
public static > ClassificationMetrics of(double fitTime, M model, T[] testx, int[] testy) {
int k = MathEx.unique(testy).length;
long start = System.nanoTime();
if (model.soft()) {
double[][] posteriori = new double[testx.length][k];
int[] prediction = model.predict(testx, posteriori);
double scoreTime = (System.nanoTime() - start) / 1E6;
return ClassificationMetrics.of(fitTime, scoreTime, testy, prediction, posteriori);
} else {
int[] prediction = model.predict(testx);
double scoreTime = (System.nanoTime() - start) / 1E6;
return ClassificationMetrics.of(fitTime, scoreTime, testy, prediction);
}
}
/**
* Validates a model on a test data.
* @param model the model.
* @param formula the model formula.
* @param test the validation data.
* @param the model type.
* @return the validation results.
*/
public static ClassificationMetrics of(M model, Formula formula, DataFrame test) {
return of(Double.NaN, model, formula, test);
}
/**
* Validates a model on a test data.
* @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 results.
*/
public static ClassificationMetrics of(double fitTime, M model, Formula formula, DataFrame test) {
int[] testy = formula.y(test).toIntArray();
int k = MathEx.unique(testy).length;
long start = System.nanoTime();
int n = test.nrow();
int[] prediction = new int[n];
if (model.soft()) {
double[][] posteriori = new double[n][k];
for (int i = 0; i < n; i++) {
prediction[i] = model.predict(test.get(i), posteriori[i]);
}
double scoreTime = (System.nanoTime() - start) / 1E6;
return ClassificationMetrics.of(fitTime, scoreTime, testy, prediction, posteriori);
} else {
for (int i = 0; i < n; i++) {
prediction[i] = model.predict(test.get(i));
}
double scoreTime = (System.nanoTime() - start) / 1E6;
return ClassificationMetrics.of(fitTime, scoreTime, testy, prediction);
}
}
}
