From f763cf0edacc2156ee63e7be3de63689fb8add22 Mon Sep 17 00:00:00 2001
From: rschoene <rene.schoene@tu-dresden.de>
Date: Fri, 28 Jun 2019 12:26:21 +0200
Subject: [PATCH] Fix Learner bugs.
- Let ExternalMachineLearningModel.check() return true instead of throwing an exception
- In Learner, use temporary files to store encog model
- In MachineLearningImpl, do not use result for now (as newData won't work at the moment)
- Minor fixes for base (add logger, add correct input to jastadd gradle task)
---
eraser-base/build.gradle | 1 +
eraser-base/src/main/jastadd/Logging.jadd | 17 +++++++---
.../src/main/jastadd/MachineLearning.jrag | 3 +-
.../inf/st/eraser/starter/EraserStarter.java | 8 ++---
.../inf/st/eraser/starter/Setting.java | 2 +-
eraser.starter/starter-setting.yaml | 4 ++-
.../feedbackloop.learner_backup/Learner.java | 34 ++++++++++++++-----
.../MachineLearningImpl.java | 6 ++--
8 files changed, 51 insertions(+), 24 deletions(-)
diff --git a/eraser-base/build.gradle b/eraser-base/build.gradle
index 2d0b1f8e..fbe5f76e 100644
--- a/eraser-base/build.gradle
+++ b/eraser-base/build.gradle
@@ -118,6 +118,7 @@ task copyRagdoc(type: Copy, dependsOn: cleanRagdoc) {
}
generateAst.dependsOn preprocess
+generateAst.inputs.files file("./src/main/jastadd/mainGen.ast"), file("./src/main/jastadd/mainGen.jadd")
//compileJava.dependsOn jastadd
//
//// always run jastadd
diff --git a/eraser-base/src/main/jastadd/Logging.jadd b/eraser-base/src/main/jastadd/Logging.jadd
index dd41bc70..710bd79a 100644
--- a/eraser-base/src/main/jastadd/Logging.jadd
+++ b/eraser-base/src/main/jastadd/Logging.jadd
@@ -1,12 +1,21 @@
aspect Logging {
+ // Base
protected org.apache.logging.log4j.Logger Item.logger = org.apache.logging.log4j.LogManager.getLogger(Item.class);
protected org.apache.logging.log4j.Logger ItemPreference.logger = org.apache.logging.log4j.LogManager.getLogger(ItemPreference.class);
- protected org.apache.logging.log4j.Logger Neuron.logger = org.apache.logging.log4j.LogManager.getLogger(Neuron.class);
+ // MachineLearning
private org.apache.logging.log4j.Logger DummyMachineLearningModel.logger = org.apache.logging.log4j.LogManager.getLogger(DummyMachineLearningModel.class);
- private org.apache.logging.log4j.Logger Rule.logger = org.apache.logging.log4j.LogManager.getLogger(Rule.class);
- private org.apache.logging.log4j.Logger MqttRoot.logger = org.apache.logging.log4j.LogManager.getLogger(MqttRoot.class);
- private org.apache.logging.log4j.Logger InternalMachineLearningModel.logger = org.apache.logging.log4j.LogManager.getLogger(MachineLearningModel.class);
+ private org.apache.logging.log4j.Logger InternalMachineLearningModel.logger = org.apache.logging.log4j.LogManager.getLogger(InternalMachineLearningModel.class);
+ private org.apache.logging.log4j.Logger ExternalMachineLearningModel.logger = org.apache.logging.log4j.LogManager.getLogger(ExternalMachineLearningModel.class);
+
+ // NeuralNetwork
private org.apache.logging.log4j.Logger NeuralNetworkRoot.logger = org.apache.logging.log4j.LogManager.getLogger(NeuralNetworkRoot.class);
private org.apache.logging.log4j.Logger OutputLayer.logger = org.apache.logging.log4j.LogManager.getLogger(OutputLayer.class);
+ protected org.apache.logging.log4j.Logger Neuron.logger = org.apache.logging.log4j.LogManager.getLogger(Neuron.class);
+
+ // Rules
+ private org.apache.logging.log4j.Logger Rule.logger = org.apache.logging.log4j.LogManager.getLogger(Rule.class);
+
+ // MQTT
+ private org.apache.logging.log4j.Logger MqttRoot.logger = org.apache.logging.log4j.LogManager.getLogger(MqttRoot.class);
}
diff --git a/eraser-base/src/main/jastadd/MachineLearning.jrag b/eraser-base/src/main/jastadd/MachineLearning.jrag
index e5fc2f6f..98f5ba3b 100644
--- a/eraser-base/src/main/jastadd/MachineLearning.jrag
+++ b/eraser-base/src/main/jastadd/MachineLearning.jrag
@@ -112,7 +112,8 @@ aspect MachineLearning {
@Override
public boolean ExternalMachineLearningModel.check() {
- throw new UnsupportedOperationException("check not available for external ML models (yet)!");
+ logger.warn("check not available for external ML models (yet)!");
+ return true;
}
//--- mlKind ---
diff --git a/eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/EraserStarter.java b/eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/EraserStarter.java
index 1715b337..2442c983 100644
--- a/eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/EraserStarter.java
+++ b/eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/EraserStarter.java
@@ -7,7 +7,6 @@ import de.tudresden.inf.st.eraser.feedbackloop.analyze.AnalyzeImpl;
import de.tudresden.inf.st.eraser.feedbackloop.api.Analyze;
import de.tudresden.inf.st.eraser.feedbackloop.api.Execute;
import de.tudresden.inf.st.eraser.feedbackloop.api.Plan;
-import de.tudresden.inf.st.eraser.feedbackloop.api.EncogModel;
import de.tudresden.inf.st.eraser.feedbackloop.execute.ExecuteImpl;
import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.Learner;
import de.tudresden.inf.st.eraser.feedbackloop.learner_backup.MachineLearningImpl;
@@ -16,7 +15,6 @@ import de.tudresden.inf.st.eraser.jastadd.model.*;
import de.tudresden.inf.st.eraser.openhab2.OpenHab2Importer;
import de.tudresden.inf.st.eraser.openhab2.mqtt.MQTTUpdater;
import de.tudresden.inf.st.eraser.spark.Application;
-import de.tudresden.inf.st.eraser.util.JavaUtils;
import de.tudresden.inf.st.eraser.util.ParserUtils;
import net.sourceforge.argparse4j.ArgumentParsers;
import net.sourceforge.argparse4j.annotation.Arg;
@@ -27,10 +25,8 @@ import org.apache.logging.log4j.Logger;
import java.io.File;
import java.io.IOException;
-import java.io.InputStream;
import java.net.MalformedURLException;
import java.net.URL;
-import java.util.Collections;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.locks.Condition;
import java.util.concurrent.locks.Lock;
@@ -124,7 +120,7 @@ public class EraserStarter {
MachineLearningImpl handler = new MachineLearningImpl(learner, MachineLearningImpl.GOAL_ACTIVITY_PHONE_AND_WATCH);
handler.setKnowledgeBaseRoot(root);
logger.info("Reading activity recognition from csv file {}", settings.activity.file);
- handler.initActivities(settings.activity.realURL().toString());
+ handler.initActivities(settings.activity.realURL().getFile());
ExternalMachineLearningModel machineLearningModel = new ExternalMachineLearningModel();
machineLearningModel.setEncoder(handler);
machineLearningModel.setDecoder(handler);
@@ -139,7 +135,7 @@ public class EraserStarter {
logger.info("Reading preference learning from csv file {}", settings.preference.file);
MachineLearningImpl handler = new MachineLearningImpl(learner, MachineLearningImpl.GOAL_PREFERENCE_BRIGHTNESS_IRIS);
handler.setKnowledgeBaseRoot(root);
- handler.initPreferences(settings.preference.realURL().toString());
+ handler.initPreferences(settings.preference.realURL().getFile());
ExternalMachineLearningModel machineLearningModel = new ExternalMachineLearningModel();
machineLearningModel.setEncoder(handler);
machineLearningModel.setDecoder(handler);
diff --git a/eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/Setting.java b/eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/Setting.java
index dacbb428..09af126b 100644
--- a/eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/Setting.java
+++ b/eraser.starter/src/main/java/de/tudresden/inf/st/eraser/starter/Setting.java
@@ -32,7 +32,7 @@ class Setting {
URL realURL() {
if (external) {
try {
- return Paths.get(file).toUri().toURL();
+ return Paths.get(file).toUri().normalize().toURL();
} catch (MalformedURLException e) {
logger.catching(e);
return null;
diff --git a/eraser.starter/starter-setting.yaml b/eraser.starter/starter-setting.yaml
index df759704..ca4c6fab 100644
--- a/eraser.starter/starter-setting.yaml
+++ b/eraser.starter/starter-setting.yaml
@@ -26,6 +26,7 @@ load:
activity:
# File to read in. Expected format = csv
file: datasets/backup/activity_data.csv
+ external: true
# Use dummy model in which the current activity is directly editable. Default: false.
dummy: true
# Model id. Default: 1.
@@ -34,7 +35,8 @@ activity:
# Model for preference learning. If dummy is true, then the file parameter is ignored.
preference:
# File to read in. Expected format = csv
- file: datasets/backup/preference_data.csv
+ file: ../datasets/backup/preference_data.csv
+ external: true
# Use dummy model in which the current activity is directly editable. Default: false.
dummy: false
# Model id. Default: 1.
diff --git a/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Learner.java b/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Learner.java
index 52f357b1..e13e0fbe 100644
--- a/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Learner.java
+++ b/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Learner.java
@@ -1,6 +1,7 @@
package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
import java.io.File;
+import java.io.IOException;
//import com.sun.javafx.tools.packager.Log;
import org.encog.ConsoleStatusReportable;
import org.encog.Encog;
@@ -26,8 +27,8 @@ public class Learner {
* */
private String csv_url_activity;
private String csv_url_preference;
- private String save_activity_model_file = "datasets/backup/activity_model.eg";
- private String save_preference_model_file = "datasets/backup/preference_model.eg";
+ private File save_activity_model_file;
+ private File save_preference_model_file;
private File csv_file;
private VersatileDataSource a_souce;
private VersatileMLDataSet a_data;
@@ -43,6 +44,22 @@ public class Learner {
private String[] preference_result;
private String activity_result;
+ public Learner() {
+ try {
+ save_activity_model_file = File.createTempFile("activity_model", "eg");
+ } catch (IOException e) {
+ // use local alternative
+ save_activity_model_file = new File("activity_model.eq");
+ }
+ try {
+ save_preference_model_file = File.createTempFile("preference_model", "eg");
+ } catch (IOException e) {
+ // use local alternative
+ save_preference_model_file = new File("preference_model.eg");
+ }
+ save_activity_model_file.deleteOnExit();
+ save_preference_model_file.deleteOnExit();
+ }
private void activityDataAnalyser(String activity_csv_url){
this.csv_url_activity = activity_csv_url;
@@ -149,7 +166,7 @@ public class Learner {
public String activity_predictor(String[] new_data){
activityDataAnalyser("datasets/backup/activity_data.csv");
- BasicNetwork activity_method = (BasicNetwork) loadObject(new File(save_activity_model_file));
+ BasicNetwork activity_method = (BasicNetwork) loadObject(save_activity_model_file);
MLData input = activity_helper.allocateInputVector();
System.out.println("input"+activity_helper);
String[] activity_new_data = new String[12];
@@ -191,7 +208,7 @@ public class Learner {
public String[] preference_predictor(String[] new_data){
preference_result = new String[2];
preferenceDataAnalyser("datasets/backup/preference_data.csv");
- BasicNetwork preference_method = (BasicNetwork)loadObject(new File(save_preference_model_file));
+ BasicNetwork preference_method = (BasicNetwork)loadObject(save_preference_model_file);
MLData input = preference_helper.allocateInputVector();
//
// System.out.print("input: "+input);
@@ -206,10 +223,11 @@ public class Learner {
preference_result[1] = preference_helper.denormalizeOutputVectorToString(output)[1];
return preference_result;
}
- private void saveEncogModel(String model_file_url){
- if (model_file_url.equals(save_activity_model_file)){saveObject(new File(model_file_url), this.a_best_method);}
- else {
- saveObject(new File(model_file_url), this.p_best_method);
+ private void saveEncogModel(File modelFile){
+ if (modelFile.equals(save_activity_model_file)) {
+ saveObject(modelFile, this.a_best_method);
+ } else {
+ saveObject(modelFile, this.p_best_method);
}
}
diff --git a/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/MachineLearningImpl.java b/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/MachineLearningImpl.java
index f2f36db3..bebc211f 100644
--- a/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/MachineLearningImpl.java
+++ b/feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/MachineLearningImpl.java
@@ -135,14 +135,14 @@ public class MachineLearningImpl implements MachineLearningDecoder, MachineLearn
public MachineLearningResult classify() {
switch (this.goal) {
case GOAL_ACTIVITY_PHONE_AND_WATCH:
- String activityStringValue = result[0];
+// String activityStringValue = result[0];
Item activityItem = resolve(this.root.getOpenHAB2Model(), "activity");
// FIXME how to translate activityStringValue to a number? or should activity item state better be a String?
- logger.debug("Classify would return activity: {}", activityStringValue);
+// logger.debug("Classify would return activity: {}", activityStringValue);
ItemPreference classifiedActivity = new ItemPreferenceDouble(activityItem, 0);
return new MachineLearningResultImpl(classifiedActivity);
case GOAL_PREFERENCE_BRIGHTNESS_IRIS:
- String[] preference = {result[1], result[2]};
+// String[] preference = {result[1], result[2]};
// FIXME what is the meaning of result[1] and result[2]
Item iris1 = resolve(this.root.getOpenHAB2Model(), "iris1_item");
ItemPreference classifiedPreference = new ItemPreferenceColor(iris1, TupleHSB.of(0, 0, 0));
--
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