Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

chore():Reformat Codebase with Google Formatter #78 #79

Merged
merged 1 commit into from
Nov 1, 2023
Merged

chore():Reformat Codebase with Google Formatter #78 #79

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
245 changes: 131 additions & 114 deletions example/src/main/java/de/example/benchmark/Benchmark.java
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -13,127 +13,144 @@
import de.edux.ml.randomforest.RandomForest;
import de.edux.ml.svm.SVMKernel;
import de.edux.ml.svm.SupportVectorMachine;

import java.io.File;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
import java.util.stream.IntStream;

/**
* Compare the performance of different classifiers
*/
/** Compare the performance of different classifiers */
public class Benchmark {
private static final double TRAIN_TEST_SPLIT_RATIO = 0.70;
private static final File CSV_FILE = new File("example" + File.separator + "datasets" + File.separator + "iris" + File.separator + "iris.csv");
private static final boolean SKIP_HEAD = true;

private double[][] trainFeatures;
private double[][] trainLabels;
private double[][] testFeatures;
private double[][] testLabels;
private MultilayerPerceptron multilayerPerceptron;
private NetworkConfiguration networkConfiguration;

public static void main(String[] args) {
new Benchmark().run();
}

private void run() {
initFeaturesAndLabels();

Classifier knn = new KnnClassifier(2);
Classifier decisionTree = new DecisionTree(2, 2, 3, 12);
Classifier randomForest = new RandomForest(500, 10, 2, 3, 3, 60);
Classifier svm = new SupportVectorMachine(SVMKernel.LINEAR, 1);

networkConfiguration = new NetworkConfiguration(trainFeatures[0].length, List.of(128, 256, 512), 3, 0.01, 300, ActivationFunction.LEAKY_RELU, ActivationFunction.SOFTMAX, LossFunction.CATEGORICAL_CROSS_ENTROPY, Initialization.XAVIER, Initialization.XAVIER);
multilayerPerceptron = new MultilayerPerceptron(networkConfiguration, testFeatures, testLabels);
Map<String, Classifier> classifiers = Map.of(
"KNN", knn,
"DecisionTree", decisionTree,
"RandomForest", randomForest,
"SVM", svm,
"MLP", multilayerPerceptron
);

Map<String, List<Double>> results = new ConcurrentHashMap<>();
results.put("KNN", new ArrayList<>());
results.put("DecisionTree", new ArrayList<>());
results.put("RandomForest", new ArrayList<>());
results.put("SVM", new ArrayList<>());
results.put("MLP", new ArrayList<>());


IntStream.range(0, 5).forEach(i -> {
knn.train(trainFeatures, trainLabels);
decisionTree.train(trainFeatures, trainLabels);
randomForest.train(trainFeatures, trainLabels);
svm.train(trainFeatures, trainLabels);
multilayerPerceptron.train(trainFeatures, trainLabels);

double knnAccuracy = knn.evaluate(testFeatures, testLabels);
double decisionTreeAccuracy = decisionTree.evaluate(testFeatures, testLabels);
double randomForestAccuracy = randomForest.evaluate(testFeatures, testLabels);
double svmAccuracy = svm.evaluate(testFeatures, testLabels);
double multilayerPerceptronAccuracy = multilayerPerceptron.evaluate(testFeatures, testLabels);

results.get("KNN").add(knnAccuracy);
results.get("DecisionTree").add(decisionTreeAccuracy);
results.get("RandomForest").add(randomForestAccuracy);
results.get("SVM").add(svmAccuracy);
results.get("MLP").add(multilayerPerceptronAccuracy);
initFeaturesAndLabels();
updateMLP(testFeatures, testLabels);
});

System.out.println("Classifier performances (sorted by average accuracy):");
results.entrySet().stream()
.map(entry -> {
double avgAccuracy = entry.getValue().stream()
.mapToDouble(Double::doubleValue)
.average()
.orElse(0.0);
return Map.entry(entry.getKey(), avgAccuracy);
})
.sorted(Map.Entry.<String, Double>comparingByValue().reversed())
.forEachOrdered(entry -> {
System.out.printf("%s: %.2f%%\n", entry.getKey(), entry.getValue() * 100);
});

System.out.println("\nClassifier best and worst performances:");
results.forEach((classifierName, accuracies) -> {
double maxAccuracy = accuracies.stream()
.mapToDouble(Double::doubleValue)
.max()
.orElse(0.0);
double minAccuracy = accuracies.stream()
.mapToDouble(Double::doubleValue)
.min()
.orElse(0.0);
System.out.printf("%s: Best: %.2f%%, Worst: %.2f%%\n", classifierName, maxAccuracy * 100, minAccuracy * 100);
private static final double TRAIN_TEST_SPLIT_RATIO = 0.70;
private static final File CSV_FILE =
new File(
"example"
+ File.separator
+ "datasets"
+ File.separator
+ "iris"
+ File.separator
+ "iris.csv");
private static final boolean SKIP_HEAD = true;

private double[][] trainFeatures;
private double[][] trainLabels;
private double[][] testFeatures;
private double[][] testLabels;
private MultilayerPerceptron multilayerPerceptron;
private NetworkConfiguration networkConfiguration;

public static void main(String[] args) {
new Benchmark().run();
}

private void run() {
initFeaturesAndLabels();

Classifier knn = new KnnClassifier(2);
Classifier decisionTree = new DecisionTree(2, 2, 3, 12);
Classifier randomForest = new RandomForest(500, 10, 2, 3, 3, 60);
Classifier svm = new SupportVectorMachine(SVMKernel.LINEAR, 1);

networkConfiguration =
new NetworkConfiguration(
trainFeatures[0].length,
List.of(128, 256, 512),
3,
0.01,
300,
ActivationFunction.LEAKY_RELU,
ActivationFunction.SOFTMAX,
LossFunction.CATEGORICAL_CROSS_ENTROPY,
Initialization.XAVIER,
Initialization.XAVIER);
multilayerPerceptron = new MultilayerPerceptron(networkConfiguration, testFeatures, testLabels);
Map<String, Classifier> classifiers =
Map.of(
"KNN", knn,
"DecisionTree", decisionTree,
"RandomForest", randomForest,
"SVM", svm,
"MLP", multilayerPerceptron);

Map<String, List<Double>> results = new ConcurrentHashMap<>();
results.put("KNN", new ArrayList<>());
results.put("DecisionTree", new ArrayList<>());
results.put("RandomForest", new ArrayList<>());
results.put("SVM", new ArrayList<>());
results.put("MLP", new ArrayList<>());

IntStream.range(0, 5)
.forEach(
i -> {
knn.train(trainFeatures, trainLabels);
decisionTree.train(trainFeatures, trainLabels);
randomForest.train(trainFeatures, trainLabels);
svm.train(trainFeatures, trainLabels);
multilayerPerceptron.train(trainFeatures, trainLabels);

double knnAccuracy = knn.evaluate(testFeatures, testLabels);
double decisionTreeAccuracy = decisionTree.evaluate(testFeatures, testLabels);
double randomForestAccuracy = randomForest.evaluate(testFeatures, testLabels);
double svmAccuracy = svm.evaluate(testFeatures, testLabels);
double multilayerPerceptronAccuracy =
multilayerPerceptron.evaluate(testFeatures, testLabels);

results.get("KNN").add(knnAccuracy);
results.get("DecisionTree").add(decisionTreeAccuracy);
results.get("RandomForest").add(randomForestAccuracy);
results.get("SVM").add(svmAccuracy);
results.get("MLP").add(multilayerPerceptronAccuracy);
initFeaturesAndLabels();
updateMLP(testFeatures, testLabels);
});

System.out.println("Classifier performances (sorted by average accuracy):");
results.entrySet().stream()
.map(
entry -> {
double avgAccuracy =
entry.getValue().stream().mapToDouble(Double::doubleValue).average().orElse(0.0);
return Map.entry(entry.getKey(), avgAccuracy);
})
.sorted(Map.Entry.<String, Double>comparingByValue().reversed())
.forEachOrdered(
entry -> {
System.out.printf("%s: %.2f%%\n", entry.getKey(), entry.getValue() * 100);
});

System.out.println("\nClassifier best and worst performances:");
results.forEach(
(classifierName, accuracies) -> {
double maxAccuracy =
accuracies.stream().mapToDouble(Double::doubleValue).max().orElse(0.0);
double minAccuracy =
accuracies.stream().mapToDouble(Double::doubleValue).min().orElse(0.0);
System.out.printf(
"%s: Best: %.2f%%, Worst: %.2f%%\n",
classifierName, maxAccuracy * 100, minAccuracy * 100);
});
}

private void updateMLP(double[][] testFeatures, double[][] testLabels) {
multilayerPerceptron = new MultilayerPerceptron(networkConfiguration, testFeatures, testLabels);
}

private void initFeaturesAndLabels() {
var featureColumnIndices = new int[]{0, 1, 2, 3};
var targetColumnIndex = 4;

var dataProcessor = new DataProcessor(new CSVIDataReader())
.loadDataSetFromCSV(CSV_FILE, ',', SKIP_HEAD, featureColumnIndices, targetColumnIndex)
.normalize()
.shuffle()
.split(TRAIN_TEST_SPLIT_RATIO);

trainFeatures = dataProcessor.getTrainFeatures(featureColumnIndices);
trainLabels = dataProcessor.getTrainLabels(targetColumnIndex);
testFeatures = dataProcessor.getTestFeatures(featureColumnIndices);
testLabels = dataProcessor.getTestLabels(targetColumnIndex);

}
}

private void updateMLP(double[][] testFeatures, double[][] testLabels) {
multilayerPerceptron = new MultilayerPerceptron(networkConfiguration, testFeatures, testLabels);
}

private void initFeaturesAndLabels() {
var featureColumnIndices = new int[] {0, 1, 2, 3};
var targetColumnIndex = 4;

var dataProcessor =
new DataProcessor(new CSVIDataReader())
.loadDataSetFromCSV(CSV_FILE, ',', SKIP_HEAD, featureColumnIndices, targetColumnIndex)
.normalize()
.shuffle()
.split(TRAIN_TEST_SPLIT_RATIO);

trainFeatures = dataProcessor.getTrainFeatures(featureColumnIndices);
trainLabels = dataProcessor.getTrainLabels(targetColumnIndex);
testFeatures = dataProcessor.getTestFeatures(featureColumnIndices);
testLabels = dataProcessor.getTestLabels(targetColumnIndex);
}
}
46 changes: 29 additions & 17 deletions example/src/main/java/de/example/decisiontree/DecisionTreeExampleOnIrisDataset.java
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,34 +4,46 @@
import de.edux.data.provider.DataProcessor;
import de.edux.data.reader.CSVIDataReader;
import de.edux.ml.decisiontree.DecisionTree;
import de.edux.ml.randomforest.RandomForest;

import java.io.File;

public class DecisionTreeExampleOnIrisDataset {
private static final double TRAIN_TEST_SPLIT_RATIO = 0.70;
private static final File CSV_FILE = new File("example" + File.separator + "datasets" + File.separator + "iris" + File.separator + "iris.csv");
private static final boolean SKIP_HEAD = true;
public static void main(String[] args) {
private static final double TRAIN_TEST_SPLIT_RATIO = 0.70;
private static final File CSV_FILE =
new File(
"example"
+ File.separator
+ "datasets"
+ File.separator
+ "iris"
+ File.separator
+ "iris.csv");
private static final boolean SKIP_HEAD = true;

public static void main(String[] args) {
/* IRIS Dataset...
+-------------+------------+-------------+------------+---------+
| sepal.length| sepal.width| petal.length| petal.width| variety |
+-------------+------------+-------------+------------+---------+
| 5.1 | 3.5 | 1.4 | .2 | Setosa |
+-------------+------------+-------------+------------+---------+
*/
var featureColumnIndices = new int[]{0, 1, 2, 3}; // First 4 columns are features
var targetColumnIndex = 4; // Last column is the target
var featureColumnIndices = new int[] {0, 1, 2, 3}; // First 4 columns are features
var targetColumnIndex = 4; // Last column is the target

var irisDataProcessor = new DataProcessor(new CSVIDataReader()).loadDataSetFromCSV(CSV_FILE, ',', SKIP_HEAD, featureColumnIndices, targetColumnIndex).normalize().shuffle().split(TRAIN_TEST_SPLIT_RATIO);
Classifier classifier = new DecisionTree(2, 2, 3, 12);
var irisDataProcessor =
new DataProcessor(new CSVIDataReader())
.loadDataSetFromCSV(CSV_FILE, ',', SKIP_HEAD, featureColumnIndices, targetColumnIndex)
.normalize()
.shuffle()
.split(TRAIN_TEST_SPLIT_RATIO);
Classifier classifier = new DecisionTree(2, 2, 3, 12);

var trainFeatures = irisDataProcessor.getTrainFeatures(featureColumnIndices);
var trainTestFeatures = irisDataProcessor.getTestFeatures(featureColumnIndices);
var trainLabels = irisDataProcessor.getTrainLabels(targetColumnIndex);
var trainTestLabels = irisDataProcessor.getTestLabels(targetColumnIndex);
var trainFeatures = irisDataProcessor.getTrainFeatures(featureColumnIndices);
var trainTestFeatures = irisDataProcessor.getTestFeatures(featureColumnIndices);
var trainLabels = irisDataProcessor.getTrainLabels(targetColumnIndex);
var trainTestLabels = irisDataProcessor.getTestLabels(targetColumnIndex);

classifier.train(trainFeatures, trainLabels);
classifier.evaluate(trainTestFeatures, trainTestLabels);
}
classifier.train(trainFeatures, trainLabels);
classifier.evaluate(trainTestFeatures, trainTestLabels);
}
}
59 changes: 33 additions & 26 deletions example/src/main/java/de/example/knn/KnnIrisExample.java
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,33 +4,40 @@
import de.edux.data.provider.DataProcessor;
import de.edux.data.reader.CSVIDataReader;
import de.edux.ml.knn.KnnClassifier;

import java.io.File;

public class KnnIrisExample {
private static final double TRAIN_TEST_SPLIT_RATIO = 0.70;
private static final File CSV_FILE = new File("example" + File.separator + "datasets" + File.separator + "iris" + File.separator + "iris.csv");
private static final boolean SKIP_HEAD = true;

public static void main(String[] args) {
var featureColumnIndices = new int[]{0, 1, 2, 3};
var targetColumnIndex = 4;

var irisDataProcessor = new DataProcessor(new CSVIDataReader())
.loadDataSetFromCSV(CSV_FILE, ',', SKIP_HEAD, featureColumnIndices, targetColumnIndex)
.normalize()
.shuffle()
.split(TRAIN_TEST_SPLIT_RATIO);


Classifier knn = new KnnClassifier(2);

var trainFeatures = irisDataProcessor.getTrainFeatures(featureColumnIndices);
var trainTestFeatures = irisDataProcessor.getTestFeatures(featureColumnIndices);
var trainLabels = irisDataProcessor.getTrainLabels(targetColumnIndex);
var trainTestLabels = irisDataProcessor.getTestLabels(targetColumnIndex);

knn.train(trainFeatures, trainLabels);
knn.evaluate(trainTestFeatures, trainTestLabels);
}
private static final double TRAIN_TEST_SPLIT_RATIO = 0.70;
private static final File CSV_FILE =
new File(
"example"
+ File.separator
+ "datasets"
+ File.separator
+ "iris"
+ File.separator
+ "iris.csv");
private static final boolean SKIP_HEAD = true;

public static void main(String[] args) {
var featureColumnIndices = new int[] {0, 1, 2, 3};
var targetColumnIndex = 4;

var irisDataProcessor =
new DataProcessor(new CSVIDataReader())
.loadDataSetFromCSV(CSV_FILE, ',', SKIP_HEAD, featureColumnIndices, targetColumnIndex)
.normalize()
.shuffle()
.split(TRAIN_TEST_SPLIT_RATIO);

Classifier knn = new KnnClassifier(2);

var trainFeatures = irisDataProcessor.getTrainFeatures(featureColumnIndices);
var trainTestFeatures = irisDataProcessor.getTestFeatures(featureColumnIndices);
var trainLabels = irisDataProcessor.getTrainLabels(targetColumnIndex);
var trainTestLabels = irisDataProcessor.getTestLabels(targetColumnIndex);

knn.train(trainFeatures, trainLabels);
knn.evaluate(trainTestFeatures, trainTestLabels);
}
}
Loading