From 8e142cf2b24b2fd012b637756748689ed201f2e1 Mon Sep 17 00:00:00 2001
From: Alex <croix.alexandre@gmail.com>
Date: Tue, 13 Aug 2019 18:16:41 +0200
Subject: [PATCH] Add new test for cross-validation
---
.../be/cylab/java/wowa/training/Trainer.java | 4 +-
.../cylab/java/wowa/training/TrainerTest.java | 98 ++++++++++++++++++-
2 files changed, 97 insertions(+), 5 deletions(-)
diff --git a/src/main/java/be/cylab/java/wowa/training/Trainer.java b/src/main/java/be/cylab/java/wowa/training/Trainer.java
index 91fc9d3..dd394b9 100644
--- a/src/main/java/be/cylab/java/wowa/training/Trainer.java
+++ b/src/main/java/be/cylab/java/wowa/training/Trainer.java
@@ -158,7 +158,7 @@ public class Trainer {
* @param expected
* @param increase_ratio
*/
- private TrainingDataset increaseTrueAlert(
+ TrainingDataset increaseTrueAlert(
final List<List<Double>> data,
final List<Double> expected,
final int increase_ratio) {
@@ -181,7 +181,7 @@ public class Trainer {
* @param fold_number
* @return
*/
- private List<TrainingDataset> prepareFolds(
+ List<TrainingDataset> prepareFolds(
final TrainingDataset dataset,
final int fold_number) {
//List<List<Double>> data = dataset.getData();
diff --git a/src/test/java/be/cylab/java/wowa/training/TrainerTest.java b/src/test/java/be/cylab/java/wowa/training/TrainerTest.java
index 03781fc..23ace5e 100644
--- a/src/test/java/be/cylab/java/wowa/training/TrainerTest.java
+++ b/src/test/java/be/cylab/java/wowa/training/TrainerTest.java
@@ -3,10 +3,12 @@ package be.cylab.java.wowa.training;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
-import java.util.*;
-import java.util.logging.Logger;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
-import static org.junit.jupiter.api.Assertions.*;
+import static org.junit.jupiter.api.Assertions.assertEquals;
class TrainerTest {
@@ -22,6 +24,50 @@ class TrainerTest {
void testRun() {
}
+ @Test
+ void testIncreaseTrueAlert() {
+ List<List<Double>> data = generateData(100, 5);
+ int original_data_set_size = data.size();
+ List<Double> expected = generateExpectedBinaryClassification(100);
+ int increase_ration = 10;
+ double number_of_true_alert = Utils.sumListElements(expected);
+ TrainingDataset ds = trainer.increaseTrueAlert(data, expected, increase_ration);
+
+ //Check if the length of the dataset us correct
+ //increase_ration * number_of_true_alert + expected.size()
+ assertEquals(630, ds.getLength());
+
+ //Check the number of true_alert in the dataset
+ //increase_ration * number_of_true_alert + number_of_true_alert
+ assertEquals(583, (double)Utils.sumListElements(ds.getExpected()));
+
+ //Check if each rue alert elements are present (increase_ratio time) in he dataset
+ //Here, we check if each true alert element is present 10 more times than the original one
+ for (int i = 0; i < expected.size(); i++) {
+ if (ds.getExpected().get(i) == 1.0) {
+ int cnt = 0;
+ for (int j = original_data_set_size; j < ds.getLength(); j++) {
+ if (ds.getExpected().get(i) == ds.getExpected().get(j)) {
+ cnt++;
+ }
+ }
+ assertEquals(increase_ration, cnt);
+ }
+ }
+
+ }
+
+ @Test
+ void testPrepareFolds() {
+ List<List<Double>> data = generateData(100,5);
+ List<Double> expected = generateExpectedBinaryClassification(100);
+ int increase_ratio = 5;
+ int fold_number = 5;
+ TrainingDataset ds = trainer.increaseTrueAlert(data, expected, increase_ratio);
+ List<TrainingDataset> folds = trainer.prepareFolds(ds, fold_number);
+ assertEquals(fold_number, folds.size());
+ }
+
@Test
void testFindBestSolution() {
List<AbstractSolution> population = new ArrayList<>();
@@ -49,6 +95,39 @@ class TrainerTest {
@Test
void testComputeDistances() {
+ List<AbstractSolution> population = new ArrayList<>();
+ Random rnd = new Random(5484);
+ double[] assertion = {1.5355936654577846,
+ 1.5671165947226067,
+ 1.5640401176636098,
+ 1.6622610655715315,
+ 1.5848944096279156,
+ 1.4772261163161986,
+ 1.5591155520984579,
+ 1.6280763047959521,
+ 1.640136934670875,
+ 1.5399006101316708,
+ 1.523255224403363,
+ 1.7676299421451587,
+ 1.5102792713787483,
+ 1.4568545985467831,
+ 1.6160572671527558,
+ 1.4822660627936635,
+ 1.7361461131034035,
+ 1.4686111015215462,
+ 1.551779317992214,
+ 1.4794365689675926};
+ for (int i = 0; i < 20; i++) {
+ AbstractSolution solution = new SolutionDistance(5, rnd.nextInt());
+ population.add(solution);
+ }
+ List<List<Double>> data = generateData(20, 5);
+ List<Double> expected = generateExpected(20);
+ List<AbstractSolution> computed_population = trainer.computeDistances(population, data, expected);
+ for (int i = 0; i < computed_population.size(); i++) {
+ assertEquals(assertion[i], computed_population.get(i).getFitnessScore());
+ }
+
}
@@ -137,4 +216,17 @@ class TrainerTest {
}
return expected;
}
+
+ static List<Double> generateExpectedBinaryClassification(final int size) {
+ Random rnd = new Random(5768);
+ List<Double> expected = new ArrayList<>();
+ for (int i = 0; i < size; i++) {
+ if (rnd.nextDouble() <= 0.5) {
+ expected.add(new Double(0.0));
+ } else {
+ expected.add(new Double(1.0));
+ }
+ }
+ return expected;
+ }
}
\ No newline at end of file
--
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