org.deeplearning4j.eval.ConfusionMatrix Maven / Gradle / Ivy
package org.deeplearning4j.eval;
import java.util.HashMap;
import java.util.Map;
import java.util.SortedSet;
import java.util.TreeSet;
import com.google.common.collect.HashMultiset;
import com.google.common.collect.Multiset;
import com.google.common.collect.Ordering;
/**
* This data structure provides an easy way to build and output a confusion matrix. A confusion
* matrix is a two dimensional table with a row and table for each class. Each element in the matrix
* shows the number of test examples for which the actual class is the row and the predicted class
* is the column. Display of this matrix is useful for identifying when a system is confusing two
* classes
*
*
* For more info @see The wikipedia page on
* Confusion Matrices
*
*
* Copyright (c) 2011, Regents of the University of Colorado
* All rights reserved.
*
* @author Lee Becker
*
* @param
* The data type used to represent the class labels
*/
public class ConfusionMatrix> {
private Map> matrix;
private SortedSet classes;
/**
* Creates an empty confusion Matrix
*/
public ConfusionMatrix() {
this.matrix = new HashMap>();
this.classes = new TreeSet(Ordering.natural().nullsFirst());
}
/**
* Creates a new ConfusionMatrix initialized with the contents of another ConfusionMatrix.
*/
public ConfusionMatrix(ConfusionMatrix other) {
this();
this.add(other);
}
/**
* Increments the entry specified by actual and predicted by one.
*/
public void add(T actual, T predicted) {
add(actual, predicted, 1);
}
/**
* Increments the entry specified by actual and predicted by count.
*/
public void add(T actual, T predicted, int count) {
if (matrix.containsKey(actual)) {
matrix.get(actual).add(predicted, count);
} else {
Multiset counts = HashMultiset.create();
counts.add(predicted, count);
matrix.put(actual, counts);
}
classes.add(actual);
classes.add(predicted);
}
/**
* Adds the entries from another confusion matrix to this one.
*/
public void add(ConfusionMatrix other) {
for (T actual : other.matrix.keySet()) {
Multiset counts = other.matrix.get(actual);
for (T predicted : counts.elementSet()) {
int count = counts.count(predicted);
this.add(actual, predicted, count);
}
}
}
/**
* Gives the applyTransformToDestination of all classes in the confusion matrix.
*/
public SortedSet getClasses() {
return classes;
}
/**
* Gives the count of the number of times the "predicted" class was predicted for the "actual"
* class.
*/
public int getCount(T actual, T predicted) {
if (!matrix.containsKey(actual)) {
return 0;
} else {
return matrix.get(actual).count(predicted);
}
}
/**
* Computes the total number of times the class was predicted by the classifier.
*/
public int getPredictedTotal(T predicted) {
int total = 0;
for (T actual : classes) {
total += getCount(actual, predicted);
}
return total;
}
/**
* Computes the total number of times the class actually appeared in the data.
*/
public int getActualTotal(T actual) {
if (!matrix.containsKey(actual)) {
return 0;
} else {
int total = 0;
for (T elem : matrix.get(actual).elementSet()) {
total += matrix.get(actual).count(elem);
}
return total;
}
}
@Override
public String toString() {
return matrix.toString();
}
/**
* Outputs the ConfusionMatrix as comma-separated values for easy import into spreadsheets
*/
public String toCSV() {
StringBuilder builder = new StringBuilder();
// Header Row
builder.append(",,Predicted Class,\n");
// Predicted Classes Header Row
builder.append(",,");
for (T predicted : classes) {
builder.append(String.format("%s,", predicted));
}
builder.append("Total\n");
// Data Rows
String firstColumnLabel = "Actual Class,";
for (T actual : classes) {
builder.append(firstColumnLabel);
firstColumnLabel = ",";
builder.append(String.format("%s,", actual));
for (T predicted : classes) {
builder.append(getCount(actual, predicted));
builder.append(",");
}
// Actual Class Totals Column
builder.append(getActualTotal(actual));
builder.append("\n");
}
// Predicted Class Totals Row
builder.append(",Total,");
for (T predicted : classes) {
builder.append(getPredictedTotal(predicted));
builder.append(",");
}
builder.append("\n");
return builder.toString();
}
/**
* Outputs Confusion Matrix in an HTML table. Cascading Style Sheets (CSS) can control the table's
* appearance by defining the empty-space, actual-count-header, predicted-class-header, and
* count-element classes. For example
*
* @return html string
*/
public String toHTML() {
StringBuilder builder = new StringBuilder();
int numClasses = classes.size();
// Header Row
builder.append("\n");
builder.append("");
builder.append(String.format(
" Predicted Class ");
// builder.append("");
builder.append(predicted);
builder.append(" ");
}
builder.append("Total ");
builder.append(" \n");
// Data Rows
String firstColumnLabel = String.format(
"Actual Class ",
numClasses + 1);
for (T actual : classes) {
builder.append(firstColumnLabel);
firstColumnLabel = "";
builder.append(String.format("%s ", actual));
for (T predicted : classes) {
builder.append("");
builder.append(getCount(actual, predicted));
builder.append(" ");
}
// Actual Class Totals Column
builder.append("");
builder.append(getActualTotal(actual));
builder.append(" ");
builder.append(" \n");
}
// Predicted Class Totals Row
builder.append("Total ");
for (T predicted : classes) {
builder.append("");
builder.append(getPredictedTotal(predicted));
builder.append(" ");
}
builder.append("
\n");
return builder.toString();
}
public static void main(String[] args) {
ConfusionMatrix confusionMatrix = new ConfusionMatrix();
confusionMatrix.add("a", "a", 88);
confusionMatrix.add("a", "b", 10);
// confusionMatrix.add("a", "c", 2);
confusionMatrix.add("b", "a", 14);
confusionMatrix.add("b", "b", 40);
confusionMatrix.add("b", "c", 6);
confusionMatrix.add("c", "a", 18);
confusionMatrix.add("c", "b", 10);
confusionMatrix.add("c", "c", 12);
ConfusionMatrix confusionMatrix2 = new ConfusionMatrix(confusionMatrix);
confusionMatrix2.add(confusionMatrix);
System.out.println(confusionMatrix2.toHTML());
System.out.println(confusionMatrix2.toCSV());
}
} © 2015 - 2025 Weber Informatics LLC | Privacy Policy