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Commit 3228f596 authored by a.croix's avatar a.croix
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Merge master in neural-network

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src/main/java/be/cylab/java/wowa/training/Trainer.java
+ 6
− 0
View file @ 3228f596
...@@ -125,6 +125,12 @@ public class Trainer { ...@@ -125,6 +125,12 @@ public class Trainer {
testing.getData(), testing.getData(),
testing.getExpected()); testing.getExpected());
Utils.computeStatisticalInformationAndStoreInTextFile(
testing.getData(),
testing.getExpected(),
100,
"Fold_" + (i + 1) + ".txt",
sol);
map.put(sol, score); map.put(sol, score);
parameters.getLogger().log(Level.WARNING, parameters.getLogger().log(Level.WARNING,
"Solution fold " + i + ":" + sol + " - AUC: " + score); "Solution fold " + i + ":" + sol + " - AUC: " + score);
......
src/main/java/be/cylab/java/wowa/training/Utils.java
+ 327
− 0
View file @ 3228f596
...@@ -2,9 +2,12 @@ package be.cylab.java.wowa.training; ...@@ -2,9 +2,12 @@ package be.cylab.java.wowa.training;
import com.owlike.genson.GenericType; import com.owlike.genson.GenericType;
import com.owlike.genson.Genson; import com.owlike.genson.Genson;
import info.debatty.java.aggregation.WOWA;
import org.apache.commons.lang3.ArrayUtils; import org.apache.commons.lang3.ArrayUtils;
import java.io.FileOutputStream;
import java.io.IOException; import java.io.IOException;
import java.io.OutputStreamWriter;
import java.nio.charset.StandardCharsets; import java.nio.charset.StandardCharsets;
import java.nio.file.Files; import java.nio.file.Files;
import java.nio.file.Paths; import java.nio.file.Paths;
...@@ -242,4 +245,328 @@ final class Utils { ...@@ -242,4 +245,328 @@ final class Utils {
} }
} }
static void computeStatisticalInformationAndStoreInTextFile(
final List<List<Double>> data,
final List<Double> expected,
final int nbr_values,
final String filename,
final AbstractSolution solution) {
int wowa_true_positive_counter = 0;
int wowa_true_negative_counter = 0;
int average_true_positive_counter = 0;
int average_true_negative_counter = 0;
int wowa_false_positive_counter = 0;
int wowa_false_negative_counter = 0;
int average_false_positive_counter = 0;
int average_false_negative_counter = 0;
double iteration = 1 / (double) nbr_values;
for (double trigger_value = 0.00;
trigger_value < 1; trigger_value += iteration) {
for (int j = 0; j < data.size(); j++) {
WOWA wowa = new WOWA(Utils.convertListDoubleToArrayDouble(
solution.weights_w),
Utils.convertListDoubleToArrayDouble(
solution.weights_p));
double wowa_result = wowa.aggregate(
Utils.convertListDoubleToArrayDouble(data.get(j)));
double average = Utils.sumListElements(data.get(j))
/ data.get(j).size();
if (expected.get(j) == 1 && average > trigger_value
&& wowa_result > trigger_value) {
average_true_positive_counter++;
wowa_true_positive_counter++;
} else if (expected.get(j) == 1 && average < trigger_value
&& wowa_result > trigger_value) {
wowa_true_positive_counter++;
average_false_negative_counter++;
} else if (expected.get(j) == 1 && average > trigger_value
&& wowa_result < trigger_value) {
average_true_positive_counter++;
wowa_false_negative_counter++;
} else if (expected.get(j) == 1 && average < trigger_value
&& wowa_result < trigger_value) {
average_false_negative_counter++;
wowa_false_negative_counter++;
} else if (expected.get(j) == 0 && average < trigger_value
&& wowa_result < trigger_value) {
average_true_negative_counter++;
wowa_true_negative_counter++;
} else if (expected.get(j) == 0 && average < trigger_value
&& wowa_result > trigger_value) {
average_true_negative_counter++;
wowa_false_positive_counter++;
} else if (expected.get(j) == 0 && average > trigger_value
&& wowa_result < trigger_value) {
wowa_true_negative_counter++;
average_false_positive_counter++;
} else if (expected.get(j) == 0 && average > trigger_value
&& wowa_result > trigger_value) {
average_false_positive_counter++;
wowa_false_positive_counter++;
}
}
computeStatisticalInformation(wowa_true_positive_counter,
wowa_true_negative_counter,
wowa_false_positive_counter,
wowa_false_negative_counter,
average_true_positive_counter,
average_true_negative_counter,
average_false_positive_counter,
average_false_negative_counter,
expected.size(),
filename,
trigger_value);
wowa_true_positive_counter = 0;
wowa_true_negative_counter = 0;
average_true_positive_counter = 0;
average_true_negative_counter = 0;
wowa_false_positive_counter = 0;
wowa_false_negative_counter = 0;
average_false_positive_counter = 0;
average_false_negative_counter = 0;
}
}
static void computeStatisticalInformation(
final int wowa_true_positive_counter,
final int wowa_true_negative_counter,
final int wowa_false_positive_counter,
final int wowa_false_negative_counter,
final int average_true_positive_counter,
final int average_true_negative_counter,
final int average_false_positive_counter,
final int average_false_negative_counter,
final int expected_size,
final String filename,
final double trigger_value) {
//Positive conditions.
int wowa_positive_condition = wowa_true_positive_counter
+ wowa_false_negative_counter;
int average_positive_condition = average_true_positive_counter
+ average_false_negative_counter;
//Negative conditions.
int wowa_negative_condition = wowa_true_negative_counter
+ wowa_false_positive_counter;
int average_negative_condition = average_true_negative_counter
+ average_false_positive_counter;
//True positive rate.
double wowa_true_positive_rate = wowa_true_positive_counter
/ (double) wowa_positive_condition;
double average_true_positive_rate = average_true_positive_counter
/ (double) average_positive_condition;
//True negative rate.
double wowa_true_negative_rate = wowa_true_negative_counter
/ (double) wowa_negative_condition;
double average_true_negative_rate = average_true_negative_counter
/ (double) average_negative_condition;
//False negative rate.
double wowa_false_negative_rate = 1 - wowa_true_positive_rate;
double average_false_negative_rate = 1 - average_true_positive_rate;
//Flase positive rate.
double wowa_false_positive_rate = 1 - wowa_true_negative_rate;
double average_false_positive_rate = 1 - average_true_negative_rate;
//Precision (or positive prediction value).
double wowa_precision = wowa_true_positive_counter / (double)
(wowa_true_positive_counter + wowa_false_positive_counter);
double average_precision = average_true_positive_counter / (double)
(average_true_positive_counter
+ average_false_positive_counter);
//Negative prediction value.
double wowa_negative_prediction_value = wowa_true_negative_counter
/ (double) (wowa_true_negative_counter
+ wowa_false_negative_counter);
double average_negative_prediction_value
= average_true_negative_counter / (double)
(average_true_negative_counter
+ average_false_negative_counter);
//False discovery rate.
double wowa_false_discovery_rate = 1 - wowa_precision;
double average_false_discovery_rate = 1 - average_precision;
//False omission rate.
double wowa_false_omission_rate
= 1 - wowa_negative_prediction_value;
double average_false_omission_rate
= 1 - average_negative_prediction_value;
//Accuracy.
double wowa_accuracy = (wowa_true_positive_counter
+ wowa_true_negative_counter) / (double) expected_size;
double average_accuracy = (average_true_positive_counter
+ average_true_negative_counter) / (double) expected_size;
//F1 score.
double wowa_f_1 = 2 * (wowa_precision * wowa_true_positive_rate)
/ (wowa_precision + wowa_true_positive_rate);
double average_f_1 = 2 * (average_precision
* average_true_positive_rate)
/ (average_precision + average_true_positive_rate);
writeWowaStatInFile(filename,
trigger_value,
wowa_true_positive_counter,
wowa_true_negative_counter,
wowa_false_positive_counter,
wowa_false_negative_counter,
wowa_true_positive_rate,
wowa_true_negative_rate,
wowa_false_positive_rate,
wowa_false_negative_rate,
wowa_precision,
wowa_negative_prediction_value,
wowa_false_discovery_rate,
wowa_false_omission_rate,
wowa_accuracy,
wowa_f_1);
writeAverageStatInFile(filename,
trigger_value,
average_true_positive_counter,
average_true_negative_counter,
average_false_positive_counter,
average_false_negative_counter,
average_true_positive_rate,
average_true_negative_rate,
average_false_positive_rate,
average_false_negative_rate,
average_precision,
average_negative_prediction_value,
average_false_discovery_rate,
average_false_omission_rate,
average_accuracy,
average_f_1);
}
static void writeWowaStatInFile(
final String filename,
final double trigger_value,
final int wowa_true_positive_counter,
final int wowa_true_negative_counter,
final int wowa_false_positive_counter,
final int wowa_false_negative_counter,
final double wowa_true_positive_rate,
final double wowa_true_negative_rate,
final double wowa_false_positive_rate,
final double wowa_false_negative_rate,
final double wowa_precision,
final double wowa_negative_prediction_value,
final double wowa_false_discovery_rate,
final double wowa_false_omission_rate,
final double wowa_accuracy,
final double wowa_f_1) {
try (OutputStreamWriter writer = new OutputStreamWriter(
new FileOutputStream(filename),
StandardCharsets.UTF_8)) {
writer.write("___________________________________________");
writer.write("|TRIGGER VALUE :" + trigger_value);
writer.write("|_________________________________________|");
writer.write("WOWA");
writer.write("Wowa true positive : "
+ wowa_true_positive_counter);
writer.write("Wowa true negative : "
+ wowa_true_negative_counter);
writer.write("Wowa false positive : "
+ wowa_false_positive_counter);
writer.write("Wowa false negative : "
+ wowa_false_negative_counter);
writer.write("Wowa true positive rate : "
+ wowa_true_positive_rate);
writer.write("Wowa true negative rate : "
+ wowa_true_negative_rate);
writer.write("Wowa false positive rate : "
+ wowa_false_positive_rate);
writer.write("Wowa false negative rate : "
+ wowa_false_negative_rate);
writer.write("Wowa precision : " + wowa_precision);
writer.write("Wowa negative predictive value : "
+ wowa_negative_prediction_value);
writer.write("Wowa false discovery rate : "
+ wowa_false_discovery_rate);
writer.write("Wowa false omission rate : "
+ wowa_false_omission_rate);
writer.write("Wowa accuracy : " + wowa_accuracy);
writer.write("Wowa F1 score : " + wowa_f_1 + "\n");
} catch (IOException e) {
e.printStackTrace();
}
}
static void writeAverageStatInFile(
final String filename,
final double trigger_value,
final int average_true_positive_counter,
final int average_true_negative_counter,
final int average_false_positive_counter,
final int average_false_negative_counter,
final double average_true_positive_rate,
final double average_true_negative_rate,
final double average_false_positive_rate,
final double average_false_negative_rate,
final double average_precision,
final double average_negative_prediction_value,
final double average_false_discovery_rate,
final double average_false_omission_rate,
final double average_accuracy,
final double average_f_1) {
try (OutputStreamWriter writer = new OutputStreamWriter(
new FileOutputStream(filename),
StandardCharsets.UTF_8)) {
writer.write("___________________________________________");
writer.write("|TRIGGER VALUE :" + trigger_value);
writer.write("|_________________________________________|");
writer.write("AVERAGE");
writer.write("Average true positive : "
+ average_true_positive_counter);
writer.write("Average true negative : "
+ average_true_negative_counter);
writer.write("Average false positive : "
+ average_false_positive_counter);
writer.write("Average false negative : "
+ average_false_negative_counter);
writer.write("Average true positive rate : "
+ average_true_positive_rate);
writer.write("Average true negative rate : "
+ average_true_negative_rate);
writer.write("Average false positive rate : "
+ average_false_positive_rate);
writer.write("Average false negative rate : "
+ average_false_negative_rate);
writer.write("Average precision : " + average_precision);
writer.write("Average negative predictive value : "
+ average_negative_prediction_value);
writer.write("Average false discovery rate : "
+ average_false_discovery_rate);
writer.write("Average false omission rate : "
+ average_false_omission_rate);
writer.write("Average accuracy : " + average_accuracy);
writer.write("Average F1 score : " + average_f_1);
} catch (IOException e) {
e.printStackTrace();
}
}
} }
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