From 1aceb2f3371c41ff381af54f9aadb1505e6d4774 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Sultan?= Date: Tue, 5 Oct 2021 23:44:34 +0200 Subject: [PATCH 1/2] feat: isolation forest (#71) * feat: isolation forests * test: isolation forest test * feat: dataframe csv writer * refactor: example refactor * test: datafame writer * feat: TPR threshold evaluator --- .../example/IsolationForestExample.java | 30 + .../src/main/resources/http/http.csv | 567499 +++++++++++++++ .../src/main/resources/http/http_reduced.csv | 12212 + .../main/java/org/rsultan/core/Evaluator.java | 10 + .../java/org/rsultan/core/RawTrainable.java | 13 + .../ensemble/domain/IsolationNode.java | 30 + .../evaluation/TPRThresholdEvaluator.java | 74 + .../isolationforest/IsolationForest.java | 75 + .../isolationforest/IsolationTree.java | 88 + .../isolationforest/utils/ScoreUtils.java | 20 + .../java/org/rsultan/dataframe/Dataframe.java | 5 + .../dataframe/printer/DataframeWriter.java | 43 + .../main/java/org/rsultan/utils/CSVUtils.java | 2 + .../main/java/org/rsultan/utils/Matrices.java | 2 +- .../core/clustering/IsolationForestTest.java | 59 + .../dataframe/DataframeWriterTest.java | 34 + 16 files changed, 580195 insertions(+), 1 deletion(-) create mode 100644 java-ml-example/src/main/java/org/rsultan/example/IsolationForestExample.java create mode 100644 java-ml-example/src/main/resources/http/http.csv create mode 100644 java-ml-example/src/main/resources/http/http_reduced.csv create mode 100644 java-ml/src/main/java/org/rsultan/core/Evaluator.java create mode 100644 java-ml/src/main/java/org/rsultan/core/RawTrainable.java create mode 100644 java-ml/src/main/java/org/rsultan/core/clustering/ensemble/domain/IsolationNode.java create mode 100644 java-ml/src/main/java/org/rsultan/core/clustering/ensemble/evaluation/TPRThresholdEvaluator.java create mode 100644 java-ml/src/main/java/org/rsultan/core/clustering/ensemble/isolationforest/IsolationForest.java create mode 100644 java-ml/src/main/java/org/rsultan/core/clustering/ensemble/isolationforest/IsolationTree.java create mode 100644 java-ml/src/main/java/org/rsultan/core/clustering/ensemble/isolationforest/utils/ScoreUtils.java create mode 100644 java-ml/src/main/java/org/rsultan/dataframe/printer/DataframeWriter.java create mode 100644 java-ml/src/test/java/org/rsultan/core/clustering/IsolationForestTest.java create mode 100644 java-ml/src/test/java/org/rsultan/dataframe/DataframeWriterTest.java diff --git a/java-ml-example/src/main/java/org/rsultan/example/IsolationForestExample.java b/java-ml-example/src/main/java/org/rsultan/example/IsolationForestExample.java new file mode 100644 index 0000000..a7fb3a3 --- /dev/null +++ b/java-ml-example/src/main/java/org/rsultan/example/IsolationForestExample.java @@ -0,0 +1,30 @@ +package org.rsultan.example; + +import java.io.IOException; +import org.nd4j.linalg.api.buffer.DataType; +import org.nd4j.linalg.factory.Nd4j; +import org.rsultan.core.clustering.ensemble.evaluation.TPRThresholdEvaluator; +import org.rsultan.core.clustering.ensemble.isolationforest.IsolationForest; +import org.rsultan.dataframe.Dataframes; +import org.rsultan.dataframe.TrainTestDataframe; + +public class IsolationForestExample { + + /* + You can use the http dataset --> args[0] + */ + static { + Nd4j.setDefaultDataTypes(DataType.DOUBLE, DataType.DOUBLE); + } + + public static void main(String[] args) throws IOException { + var df = Dataframes.csv(args[0], ",", "\"", true); + var trainTestDataframe = Dataframes.trainTest(df.getColumns()).setSplitValue(0.5); + + IsolationForest model = new IsolationForest(200).train(df.mapWithout("attack")); + var evaluator = new TPRThresholdEvaluator("attack", "anomalies").setDesiredTPR(0.9).setLearningRate(0.02); + Double threshold = evaluator.evaluate(model, trainTestDataframe); + System.out.println("threshold = " + threshold); + evaluator.showMetrics(); + } +} diff --git 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+-2.3025850929940455,5.670225691127358,7.92374653896191,0 +-2.3025850929940455,5.6736668507515775,7.818470496523107,0 +-2.3025850929940455,5.6598305871270185,8.907084318886218,0 diff --git a/java-ml/src/main/java/org/rsultan/core/Evaluator.java b/java-ml/src/main/java/org/rsultan/core/Evaluator.java new file mode 100644 index 0000000..2fd3754 --- /dev/null +++ b/java-ml/src/main/java/org/rsultan/core/Evaluator.java @@ -0,0 +1,10 @@ +package org.rsultan.core; + +import org.rsultan.core.Trainable; +import org.rsultan.dataframe.TrainTestDataframe; + +public interface Evaluator> { + + V evaluate(T trainable, TrainTestDataframe dataframe); + +} diff --git a/java-ml/src/main/java/org/rsultan/core/RawTrainable.java b/java-ml/src/main/java/org/rsultan/core/RawTrainable.java new file mode 100644 index 0000000..d7617ff --- /dev/null +++ b/java-ml/src/main/java/org/rsultan/core/RawTrainable.java @@ -0,0 +1,13 @@ +package org.rsultan.core; + +import java.io.Serializable; +import org.nd4j.linalg.api.ndarray.INDArray; +import org.rsultan.dataframe.Dataframe; + +public interface RawTrainable extends Serializable { + + T train(INDArray matrix); + + INDArray predict(INDArray matrix); + +} diff --git a/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/domain/IsolationNode.java b/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/domain/IsolationNode.java new file mode 100644 index 0000000..0cbdd9c --- /dev/null +++ b/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/domain/IsolationNode.java @@ -0,0 +1,30 @@ +package org.rsultan.core.clustering.ensemble.domain; + +import static java.util.Objects.nonNull; + +import java.io.Serializable; +import org.nd4j.linalg.api.ndarray.INDArray; + +public record IsolationNode( + int feature, + double featureThreshold, + INDArray data, + IsolationNode left, + IsolationNode right +) implements Serializable { + + public IsolationNode(INDArray data) { + this(-1, -1, data, null, null); + } + public IsolationNode( + int feature, + double featureThreshold, + IsolationNode left, + IsolationNode right) { + this(feature, featureThreshold, null, left, right); + } + + public boolean isLeaf() { + return nonNull(data); + } +} \ No newline at end of file diff --git a/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/evaluation/TPRThresholdEvaluator.java b/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/evaluation/TPRThresholdEvaluator.java new file mode 100644 index 0000000..9024390 --- /dev/null +++ b/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/evaluation/TPRThresholdEvaluator.java @@ -0,0 +1,74 @@ +package org.rsultan.core.clustering.ensemble.evaluation; + +import org.rsultan.core.Evaluator; +import org.rsultan.core.clustering.ensemble.isolationforest.IsolationForest; +import org.rsultan.dataframe.Dataframes; +import org.rsultan.dataframe.Row; +import org.rsultan.dataframe.TrainTestDataframe; + +public class TPRThresholdEvaluator implements Evaluator { + + private final String responseVariable; + private final String predictionColumn; + private double desiredTPR = 0.9; + private double learningRate = 0.01; + private double TPR = 0; + private double FPR = 0; + + public TPRThresholdEvaluator(String responseVariable, String predictionColumn) { + this.responseVariable = responseVariable; + this.predictionColumn = predictionColumn; + } + + @Override + public Double evaluate(IsolationForest trainable, TrainTestDataframe dataframe) { + var dfSplit = dataframe.shuffle().split(); + double threshold = 1; + while (threshold > 0 && TPR <= desiredTPR) { + threshold -= learningRate; + var trained = trainable.setAnomalyThreshold(threshold) + .train(dfSplit.train().mapWithout(responseVariable)); + var responses = dfSplit.test().get(responseVariable); + var predictions = trained.predict(dfSplit.test().mapWithout(responseVariable)) + .get(predictionColumn); + + double truePositives = 0; + double trueNegatives = 0; + double falsePositives = 0; + double falseNegative = 0; + + for (int i = 0; i < responses.size(); i++) { + var response = responses.get(i); + var prediction = predictions.get(i); + truePositives += response == 1L && prediction == 1L ? 1L : 0L; + trueNegatives += response == 0L && prediction == 0L ? 1L : 0L; + falsePositives += response == 0L && prediction == 1L ? 1L : 0L; + falseNegative += response == 1L && prediction == 0L ? 1L : 0L; + } + TPR = truePositives / (truePositives + falseNegative); + TPR = Double.isNaN(TPR) ? 0 : TPR; + + FPR = falsePositives / (falsePositives + trueNegatives); + FPR = Double.isNaN(FPR) ? 0L : FPR; + } + if (threshold < 0) { + throw new IllegalArgumentException("Cannot have desired TPR"); + } + + return threshold; + } + + public TPRThresholdEvaluator setDesiredTPR(double desiredTPR) { + this.desiredTPR = desiredTPR; + return this; + } + + public TPRThresholdEvaluator setLearningRate(double learningRate) { + this.learningRate = learningRate; + return this; + } + + public void showMetrics() { + Dataframes.create(new String[]{"TPR", "FPR"}, new Row(TPR, FPR)).tail(); + } +} diff --git a/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/isolationforest/IsolationForest.java b/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/isolationforest/IsolationForest.java new file mode 100644 index 0000000..6be020a --- /dev/null +++ b/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/isolationforest/IsolationForest.java @@ -0,0 +1,75 @@ +package org.rsultan.core.clustering.ensemble.isolationforest; + +import static java.util.stream.IntStream.range; +import static org.rsultan.core.clustering.ensemble.isolationforest.utils.ScoreUtils.averagePathLength; + +import java.util.List; +import java.util.stream.DoubleStream; +import org.apache.commons.lang3.RandomUtils; +import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.factory.Nd4j; +import org.nd4j.linalg.ops.transforms.Transforms; +import org.rsultan.core.Trainable; +import org.rsultan.dataframe.Column; +import org.rsultan.dataframe.Dataframe; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +public class IsolationForest implements Trainable { + + private static final Logger LOG = LoggerFactory.getLogger(IsolationTree.class); + private final int nbTrees; + private double anomalyThreshold = 0.5; + private List isolationTrees; + private int sampleSize = 256; + + public IsolationForest(int nbTrees) { + this.nbTrees = nbTrees; + } + + public IsolationForest setSampleSize(int sampleSize) { + this.sampleSize = sampleSize; + return this; + } + + public IsolationForest setAnomalyThreshold(double anomalyThreshold) { + this.anomalyThreshold = anomalyThreshold; + return this; + } + + @Override + public IsolationForest train(Dataframe dataframe) { + var matrix = dataframe.toMatrix(); + int realSample = sampleSize >= matrix.rows() ? sampleSize / 10 : sampleSize; + int treeDepth = (int) Math.ceil(Math.log(realSample) / Math.log(2)); + isolationTrees = range(0, nbTrees).parallel() + .peek(i -> LOG.info("Tree number: {}", i)) + .mapToObj(i -> range(0, realSample) + .map(idx -> RandomUtils.nextInt(0, matrix.rows())) + .toArray()).map(matrix::getRows) + .map(m -> new IsolationTree(treeDepth).train(m)) + .toList(); + return this; + } + + @Override + public Dataframe predict(Dataframe dataframe) { + var matrix = dataframe.toMatrix(); + var anomalyScores = computeAnomalyScore(matrix); + var isAnomaly = new Column<>("anomalies", DoubleStream.of( + anomalyScores.toDoubleVector() + ).mapToObj(score -> score >= anomalyThreshold ? 1L : 0L).toArray()); + return dataframe.addColumn(isAnomaly); + } + + private INDArray computeAnomalyScore(INDArray matrix) { + var pathLengths = isolationTrees.stream().parallel().map(tree -> { + LOG.info("Compute paths for tree {}", isolationTrees.indexOf(tree) + 1); + return tree.predict(matrix); + }).toList(); + int[] shape = {pathLengths.size(), pathLengths.get(0).columns()}; + var avgLength = Nd4j.create(pathLengths, shape).mean(true, 0); + var twos = Nd4j.ones(avgLength.shape()).mul(2D); + return Transforms.pow(twos, avgLength.neg().div(averagePathLength(sampleSize))); + } +} diff --git a/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/isolationforest/IsolationTree.java b/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/isolationforest/IsolationTree.java new file mode 100644 index 0000000..4882ec6 --- /dev/null +++ b/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/isolationforest/IsolationTree.java @@ -0,0 +1,88 @@ +package org.rsultan.core.clustering.ensemble.isolationforest; + +import static java.util.stream.IntStream.range; +import static org.apache.commons.lang3.RandomUtils.nextDouble; +import static org.apache.commons.lang3.RandomUtils.nextInt; +import static org.rsultan.core.clustering.ensemble.isolationforest.utils.ScoreUtils.averagePathLength; + +import org.nd4j.linalg.api.ndarray.INDArray; +import org.nd4j.linalg.factory.Nd4j; +import org.rsultan.core.RawTrainable; +import org.rsultan.core.clustering.ensemble.domain.IsolationNode; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +public class IsolationTree implements RawTrainable { + + public static final Logger LOG = LoggerFactory.getLogger(IsolationTree.class); + private final int treeDepthLimit; + private IsolationNode tree; + + public IsolationTree(int treeDepthLimit) { + this.treeDepthLimit = treeDepthLimit; + } + + @Override + public IsolationTree train(INDArray matrix) { + this.tree = buildTree(matrix, treeDepthLimit); + return this; + } + + private IsolationNode buildTree(INDArray matrix, int currentDepth) { + LOG.info("Tree Depth {}", currentDepth); + if (currentDepth <= 0 || matrix.rows() <= 2) { + return new IsolationNode(matrix); + } + int numberOfFeatures = matrix.columns(); + int splitFeature = nextInt(0, numberOfFeatures); + var feature = matrix.getColumn(splitFeature); + double startInclusive = feature.minNumber().doubleValue(); + double endInclusive = feature.maxNumber().doubleValue(); + + double valueSplit = + getValueSplit(startInclusive, endInclusive); + + var leftIndices = range(0, feature.columns()).parallel() + .filter(idx -> feature.getDouble(idx) < valueSplit) + .toArray(); + var left = matrix.getRows(leftIndices); + + var rightIndices = range(0, feature.columns()).parallel() + .filter(idx -> feature.getDouble(idx) > valueSplit) + .toArray(); + var right = matrix.getRows(rightIndices); + + return new IsolationNode( + splitFeature, + valueSplit, + buildTree(left, currentDepth - 1), + buildTree(right, currentDepth - 1) + ); + } + + private double getValueSplit(double startInclusive, double endInclusive) { + if (startInclusive < 0 && endInclusive < 0) { + return -nextDouble(endInclusive * -1, startInclusive * -1); + } else if (startInclusive < 0 && endInclusive >= 0) { + return nextDouble(0, endInclusive + startInclusive * -1) + startInclusive; + } + return nextDouble(startInclusive, endInclusive); + } + + @Override + public INDArray predict(INDArray matrix) { + var pathLengths = Nd4j.zeros(1, matrix.rows()); + for (int i = 0; i < matrix.rows(); i++) { + var row = matrix.getRow(i); + var node = tree; + int length = 0; + while (!node.isLeaf()) { + node = row.getDouble(node.feature()) < node.featureThreshold() ? node.left() : node.right(); + length++; + } + int leafSize = node.data().rows(); + pathLengths.put(0, i, length + averagePathLength(leafSize)); + } + return pathLengths; + } +} diff --git a/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/isolationforest/utils/ScoreUtils.java b/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/isolationforest/utils/ScoreUtils.java new file mode 100644 index 0000000..fd4f95d --- /dev/null +++ b/java-ml/src/main/java/org/rsultan/core/clustering/ensemble/isolationforest/utils/ScoreUtils.java @@ -0,0 +1,20 @@ +package org.rsultan.core.clustering.ensemble.isolationforest.utils; + +public class ScoreUtils { + + private static final double EULER_CONSTANT = 0.5772156649; + + public static double averagePathLength(double leafSize) { + if (leafSize > 2) { + return 2 * harmonicNumber(leafSize) - (2 * (leafSize - 1) / leafSize); + } + if (leafSize == 2) { + return 1; + } + return 0; + } + + private static double harmonicNumber(double leafSize) { + return Math.log(leafSize - 1) + EULER_CONSTANT; + } +} diff --git a/java-ml/src/main/java/org/rsultan/dataframe/Dataframe.java b/java-ml/src/main/java/org/rsultan/dataframe/Dataframe.java index 62e8df5..84c4e50 100644 --- a/java-ml/src/main/java/org/rsultan/dataframe/Dataframe.java +++ b/java-ml/src/main/java/org/rsultan/dataframe/Dataframe.java @@ -20,6 +20,7 @@ import java.util.function.Supplier; import org.nd4j.linalg.api.ndarray.INDArray; import org.rsultan.dataframe.printer.DataframePrinter; +import org.rsultan.dataframe.printer.DataframeWriter; import org.rsultan.dataframe.transform.filter.FilterDataframe; import org.rsultan.dataframe.transform.filter.FilterTransform; import org.rsultan.dataframe.transform.map.MapDataframe; @@ -137,6 +138,10 @@ public void show(int start, int end) { DataframePrinter.create(data).print(max(0, start), min(end, this.rowSize)); } + public void write(String filename, String separator, String enclosure) { + DataframeWriter.write(this, filename, separator, enclosure); + } + public void tail() { show(this.rowSize - 10, this.rowSize); } diff --git a/java-ml/src/main/java/org/rsultan/dataframe/printer/DataframeWriter.java b/java-ml/src/main/java/org/rsultan/dataframe/printer/DataframeWriter.java new file mode 100644 index 0000000..0d03172 --- /dev/null +++ b/java-ml/src/main/java/org/rsultan/dataframe/printer/DataframeWriter.java @@ -0,0 +1,43 @@ +package org.rsultan.dataframe.printer; + +import java.io.BufferedWriter; +import java.io.File; +import java.io.IOException; +import java.nio.file.Files; +import java.util.ArrayList; +import java.util.List; +import org.rsultan.dataframe.Dataframe; + +public class DataframeWriter { + + private final Dataframe dataframe; + + private DataframeWriter(Dataframe dataframe) { + this.dataframe = dataframe; + } + + public static void write(Dataframe dataframe, String fileName, String separator, + String enclosure) { + new DataframeWriter(dataframe).write(new File(fileName), separator, enclosure); + } + + + public void write(File file, String separator, String enclosure) { + try (BufferedWriter writer = Files.newBufferedWriter(file.toPath())) { + for (int i = 0; i < dataframe.getRowSize(); i++) { + List row = new ArrayList<>(); + for (int j = 0; j < dataframe.getColumnSize(); j++) { + Object o = dataframe.getColumns()[j].values().get(i); + if (o instanceof String s) { + row.add(enclosure + s + enclosure); + } else { + row.add(o.toString()); + } + } + writer.write(String.join(separator, row) + "\r\n"); + } + } catch (IOException e) { + e.printStackTrace(); + } + } +} diff --git a/java-ml/src/main/java/org/rsultan/utils/CSVUtils.java b/java-ml/src/main/java/org/rsultan/utils/CSVUtils.java index 7c06b55..af255fd 100644 --- a/java-ml/src/main/java/org/rsultan/utils/CSVUtils.java +++ b/java-ml/src/main/java/org/rsultan/utils/CSVUtils.java @@ -18,6 +18,8 @@ import org.junit.jupiter.params.shadow.com.univocity.parsers.csv.CsvFormat; import org.junit.jupiter.params.shadow.com.univocity.parsers.csv.CsvParser; import org.junit.jupiter.params.shadow.com.univocity.parsers.csv.CsvParserSettings; +import org.junit.jupiter.params.shadow.com.univocity.parsers.csv.CsvWriter; +import org.junit.jupiter.params.shadow.com.univocity.parsers.csv.CsvWriterSettings; import org.rsultan.dataframe.Column; public class CSVUtils { diff --git a/java-ml/src/main/java/org/rsultan/utils/Matrices.java b/java-ml/src/main/java/org/rsultan/utils/Matrices.java index b09217f..98778e5 100644 --- a/java-ml/src/main/java/org/rsultan/utils/Matrices.java +++ b/java-ml/src/main/java/org/rsultan/utils/Matrices.java @@ -23,7 +23,7 @@ public static INDArray diagonal(INDArray m) { .toArray(); return Nd4j.create(diagonal, m.rows(), 1); } - throw new IllegalArgumentException("Matrix is not a square matrix, shape: " + Arrays + throw new IllegalArgumentException("Matrix is not a square leafNode, shape: " + Arrays .toString(m.shape())); } diff --git a/java-ml/src/test/java/org/rsultan/core/clustering/IsolationForestTest.java b/java-ml/src/test/java/org/rsultan/core/clustering/IsolationForestTest.java new file mode 100644 index 0000000..c57f9f4 --- /dev/null +++ b/java-ml/src/test/java/org/rsultan/core/clustering/IsolationForestTest.java @@ -0,0 +1,59 @@ +package org.rsultan.core.clustering; + +import static java.lang.Boolean.FALSE; +import static java.lang.Boolean.TRUE; +import static java.util.stream.Collectors.toList; +import static java.util.stream.IntStream.rangeClosed; +import static org.apache.commons.lang3.RandomUtils.nextBoolean; +import static org.apache.commons.lang3.RandomUtils.nextDouble; +import static org.assertj.core.api.Assertions.assertThat; + +import java.util.List; +import org.junit.jupiter.api.Test; +import org.rsultan.core.clustering.ensemble.evaluation.TPRThresholdEvaluator; +import org.rsultan.core.clustering.ensemble.isolationforest.IsolationForest; +import org.rsultan.dataframe.Dataframes; +import org.rsultan.dataframe.Row; + +public class IsolationForestTest { + + private static Row[] rows; + + static { + rows = List.of(0, 3, 6, 7, 8).stream() + .map(IsolationForestTest::createCircleDataAroundCenter) + .flatMap(List::stream) + .toArray(Row[]::new); + } + + private static List createCircleDataAroundCenter(int radius) { + return rangeClosed(-radius, radius).boxed().flatMap(x -> { + double y = Math.sqrt(radius * radius - x * x); + long response = nextDouble() > 0.7 ? 1L : 0L; + return List.of(new Row(x, y, response), new Row(x, -y, response)).stream(); + }).collect(toList()); + } + + @Test + public void must_perform_isolation() { + var df = Dataframes.create(new String[]{"x", "y", "response"}, rows); + var predict = new IsolationForest(10).setAnomalyThreshold(0.56).setSampleSize(15).train(df).predict(df); + int anomalies = predict.filter("anomalies", obj -> obj.equals(1L)).getRowSize(); + int nonAnomalies = predict.filter("anomalies", obj -> obj.equals(0L)).getRowSize(); + + assertThat(anomalies).isBetween(1, predict.getRowSize()); + assertThat(nonAnomalies).isEqualTo(predict.getRowSize() - anomalies); + } + + @Test + public void should_evaluate_tpr() { + var df = Dataframes.trainTest(new String[]{"x", "y", "response"}, rows); + var model = new IsolationForest(10).setSampleSize(15); + var evaluator = new TPRThresholdEvaluator("response", "anomalies").setDesiredTPR(0.9).setLearningRate(0.01); + var threshold = evaluator.evaluate(model, df); + evaluator.showMetrics(); + assertThat(threshold).isGreaterThan(0.4); + } + + +} diff --git a/java-ml/src/test/java/org/rsultan/dataframe/DataframeWriterTest.java b/java-ml/src/test/java/org/rsultan/dataframe/DataframeWriterTest.java new file mode 100644 index 0000000..ec02f32 --- /dev/null +++ b/java-ml/src/test/java/org/rsultan/dataframe/DataframeWriterTest.java @@ -0,0 +1,34 @@ +package org.rsultan.dataframe; + +import static org.assertj.core.api.Assertions.assertThat; + +import java.io.File; +import java.io.IOException; +import java.nio.file.Files; +import java.util.UUID; +import org.junit.jupiter.api.Test; + +public class DataframeWriterTest { + + @Test + public void must_write_dataframe_to_file() throws IOException { + var headers = new String[]{"c1", "c2", "c3"}; + String filename = + System.getProperty("java.io.tmpdir") + File.separator + UUID.randomUUID() + ".csv"; + var inputDf = Dataframes.create(headers, + new Row("1", 2, 3.0D), + new Row("4", "5", 6), + new Row(7, 8, 9) + ); + inputDf.write(filename, ",", "\""); + + File file = new File(filename); + assertThat(Files.exists(file.toPath())).isTrue(); + + var dataframe = Dataframes.csv(file.getAbsolutePath(), ",", "\"", false); + assertThat(dataframe.getRowSize()).isEqualTo(dataframe.getRowSize()); + assertThat(dataframe.getColumnSize()).isEqualTo(dataframe.getColumnSize()); + + Files.delete(file.toPath()); + } +} From de6ebf3af1c76f3dfab249e9e0886bf2020b7180 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?R=C3=A9mi=20Sultan?= Date: Mon, 11 Oct 2021 20:53:56 +0200 Subject: [PATCH 2/2] chore: bump version for release --- version.properties | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/version.properties b/version.properties index 6657ba4..e1c4a24 100755 --- a/version.properties +++ b/version.properties @@ -1 +1 @@ -version.next=2.0.1 +version.next=2.1.0