From 79cfec617a12242b1bff771f156d715843415313 Mon Sep 17 00:00:00 2001
From: Alex <croix.alexandre@gmail.com>
Date: Mon, 9 Sep 2019 15:47:09 +0200
Subject: [PATCH] Add some logs
---
src/main/java/be/cylab/java/wowa/training/MainTest.java | 6 +++---
.../java/be/cylab/java/wowa/training/NeuralNetwork.java | 2 +-
src/main/java/be/cylab/java/wowa/training/Trainer.java | 7 ++++++-
3 files changed, 10 insertions(+), 5 deletions(-)
diff --git a/src/main/java/be/cylab/java/wowa/training/MainTest.java b/src/main/java/be/cylab/java/wowa/training/MainTest.java
index 16f3348..addb503 100644
--- a/src/main/java/be/cylab/java/wowa/training/MainTest.java
+++ b/src/main/java/be/cylab/java/wowa/training/MainTest.java
@@ -59,7 +59,7 @@ public final class MainTest {
NeuralNetwork nn = new NeuralNetwork(nn_parameters);
TrainingDataset dataset = new TrainingDataset(data, expected);
- List<TrainingDataset> folds = dataset.prepareFolds(10);
+ List<TrainingDataset> folds = dataset.prepareFolds(3);
HashMap<MultiLayerNetwork, Double> map_nn = nn.runKFold(folds, 10);
HashMap<AbstractSolution, Double> map_wt = trainer.runKFold(folds, 10);
@@ -72,12 +72,12 @@ public final class MainTest {
}
System.out.println("Average AUC for Neural Network learning : "
- + nn_score / 10);
+ + nn_score / 3);
for (Double d : map_wt.values()) {
wt_score = wt_score + d;
}
- System.out.println("Average AUC for WOWA learning : " + wt_score / 10);
+ System.out.println("Average AUC for WOWA learning : " + wt_score / 3);
}
}
diff --git a/src/main/java/be/cylab/java/wowa/training/NeuralNetwork.java b/src/main/java/be/cylab/java/wowa/training/NeuralNetwork.java
index 6c3eb66..fc3d513 100644
--- a/src/main/java/be/cylab/java/wowa/training/NeuralNetwork.java
+++ b/src/main/java/be/cylab/java/wowa/training/NeuralNetwork.java
@@ -265,11 +265,11 @@ public final class NeuralNetwork {
}
TrainingDataset dataset_increased = learning_fold.increaseTrueAlert(
increase_ratio);
+ System.out.println("Fold number : " + (i + 1));
MultiLayerNetwork nn = run(
dataset_increased.getData(),
dataset_increased.getExpected());
Double score = modelEvaluation(testingfold, nn);
-
map.put(nn, score);
}
return map;
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 0cece52..fda997a 100644
--- a/src/main/java/be/cylab/java/wowa/training/Trainer.java
+++ b/src/main/java/be/cylab/java/wowa/training/Trainer.java
@@ -1,7 +1,11 @@
package be.cylab.java.wowa.training;
-import java.util.*;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Random;
import java.util.logging.Level;
/**
@@ -178,6 +182,7 @@ public class Trainer {
Double score = sol.computeAUC(
testing.getData(),
testing.getExpected());
+ System.out.println("Fold number : " + (i + 1) + "AUC : " + score);
map.put(sol, score);
}
--
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