org.deeplearning4j.eval.ConfusionMatrix Maven / Gradle / Ivy
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*
* * Copyright 2015 Skymind,Inc.
* *
* * Licensed under the Apache License, Version 2.0 (the "License");
* * you may not use this file except in compliance with the License.
* * You may obtain a copy of the License at
* *
* * http://www.apache.org/licenses/LICENSE-2.0
* *
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS,
* * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* * See the License for the specific language governing permissions and
* * limitations under the License.
*
*/
package org.deeplearning4j.eval;
import com.google.common.collect.HashMultiset;
import com.google.common.collect.Multiset;
import lombok.Getter;
import java.io.Serializable;
import java.util.*;
import java.util.concurrent.ConcurrentHashMap;
public class ConfusionMatrix> implements Serializable {
@Getter
private volatile Map> matrix;
private List classes;
/**
* Creates an empty confusion Matrix
*/
public ConfusionMatrix(List classes) {
this.matrix = new ConcurrentHashMap<>();
this.classes = classes;
}
public ConfusionMatrix() {
this(new ArrayList());
}
/**
* Creates a new ConfusionMatrix initialized with the contents of another ConfusionMatrix.
*/
public ConfusionMatrix(ConfusionMatrix other) {
this(other.getClasses());
this.add(other);
}
/**
* Increments the entry specified by actual and predicted by one.
*/
public synchronized void add(T actual, T predicted) {
add(actual, predicted, 1);
}
/**
* Increments the entry specified by actual and predicted by count.
*/
public synchronized 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);
}
}
/**
* Adds the entries from another confusion matrix to this one.
*/
public synchronized 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 List getClasses() {
if (classes == null)
classes = new ArrayList<>();
return classes;
}
/**
* Gives the count of the number of times the "predicted" class was predicted for the "actual"
* class.
*/
public synchronized 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 synchronized 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 synchronized 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();
}
@Override
public boolean equals(Object o) {
if (!(o instanceof ConfusionMatrix))
return false;
ConfusionMatrix c = (ConfusionMatrix) o;
return matrix.equals(c.matrix) && classes.equals(c.classes);
}
@Override
public int hashCode() {
int result = 17;
result = 31 * result + (matrix == null ? 0 : matrix.hashCode());
result = 31 * result + (classes == null ? 0 : classes.hashCode());
return result;
}
public static void main(String[] args) {
ConfusionMatrix confusionMatrix = new ConfusionMatrix<>(Arrays.asList("a", "b", "c"));
confusionMatrix.add("a", "a", 88);
confusionMatrix.add("a", "b", 10);
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());
}
}
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