2 * ============LICENSE_START=======================================================
3 * Copyright (C) 2016-2018 Ericsson. All rights reserved.
4 * ================================================================================
5 * Licensed under the Apache License, Version 2.0 (the "License");
6 * you may not use this file except in compliance with the License.
7 * You may obtain a copy of the License at
9 * http://www.apache.org/licenses/LICENSE-2.0
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
17 * SPDX-License-Identifier: Apache-2.0
18 * ============LICENSE_END=========================================================
21 package org.onap.policy.apex.examples.adaptive;
23 import static org.junit.Assert.assertEquals;
24 import static org.junit.Assert.assertNotNull;
25 import static org.junit.Assert.assertTrue;
27 import java.io.IOException;
28 import java.util.Random;
30 import org.junit.After;
31 import org.junit.Before;
32 import org.junit.Test;
33 import org.onap.policy.apex.context.impl.schema.java.JavaSchemaHelperParameters;
34 import org.onap.policy.apex.context.parameters.ContextParameterConstants;
35 import org.onap.policy.apex.context.parameters.ContextParameters;
36 import org.onap.policy.apex.context.parameters.SchemaParameters;
37 import org.onap.policy.apex.core.engine.EngineParameters;
38 import org.onap.policy.apex.core.engine.engine.ApexEngine;
39 import org.onap.policy.apex.core.engine.engine.impl.ApexEngineFactory;
40 import org.onap.policy.apex.core.engine.event.EnEvent;
41 import org.onap.policy.apex.examples.adaptive.model.AdaptiveDomainModelFactory;
42 import org.onap.policy.apex.model.basicmodel.concepts.ApexException;
43 import org.onap.policy.apex.model.basicmodel.concepts.AxArtifactKey;
44 import org.onap.policy.apex.model.basicmodel.concepts.AxValidationResult;
45 import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
46 import org.onap.policy.apex.plugins.executor.java.JavaExecutorParameters;
47 import org.onap.policy.apex.plugins.executor.mvel.MVELExecutorParameters;
48 import org.onap.policy.common.parameters.ParameterService;
49 import org.slf4j.ext.XLogger;
50 import org.slf4j.ext.XLoggerFactory;
53 * Test Auto learning in TSL.
55 * @author John Keeney (John.Keeney@ericsson.com)
57 public class TestAutoLearnTSLUseCase {
58 private static final XLogger LOGGER = XLoggerFactory.getXLogger(TestAutoLearnTSLUseCase.class);
60 private static final int MAXITERATIONS = 1000;
61 private static final Random rand = new Random(System.currentTimeMillis());
63 private SchemaParameters schemaParameters;
64 private ContextParameters contextParameters;
65 private EngineParameters engineParameters;
68 public void beforeTest() {
69 schemaParameters = new SchemaParameters();
71 schemaParameters.setName(ContextParameterConstants.SCHEMA_GROUP_NAME);
72 schemaParameters.getSchemaHelperParameterMap().put("JAVA", new JavaSchemaHelperParameters());
74 ParameterService.register(schemaParameters);
76 contextParameters = new ContextParameters();
78 contextParameters.setName(ContextParameterConstants.MAIN_GROUP_NAME);
79 contextParameters.getDistributorParameters().setName(ContextParameterConstants.DISTRIBUTOR_GROUP_NAME);
80 contextParameters.getLockManagerParameters().setName(ContextParameterConstants.LOCKING_GROUP_NAME);
81 contextParameters.getPersistorParameters().setName(ContextParameterConstants.PERSISTENCE_GROUP_NAME);
83 ParameterService.register(contextParameters);
84 ParameterService.register(contextParameters.getDistributorParameters());
85 ParameterService.register(contextParameters.getLockManagerParameters());
86 ParameterService.register(contextParameters.getPersistorParameters());
88 engineParameters = new EngineParameters();
89 engineParameters.getExecutorParameterMap().put("MVEL", new MVELExecutorParameters());
90 engineParameters.getExecutorParameterMap().put("JAVA", new JavaExecutorParameters());
91 ParameterService.register(engineParameters);
95 public void afterTest() {
96 ParameterService.deregister(engineParameters);
98 ParameterService.deregister(contextParameters.getDistributorParameters());
99 ParameterService.deregister(contextParameters.getLockManagerParameters());
100 ParameterService.deregister(contextParameters.getPersistorParameters());
101 ParameterService.deregister(contextParameters);
103 ParameterService.deregister(schemaParameters);
107 // once through the long running test below
108 public void TestAutoLearnTSL() throws ApexException, InterruptedException, IOException {
109 final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
110 assertNotNull(apexPolicyModel);
112 final AxValidationResult validationResult = new AxValidationResult();
113 apexPolicyModel.validate(validationResult);
114 assertTrue(validationResult.isValid());
116 final AxArtifactKey key = new AxArtifactKey("AADMApexEngine", "0.0.1");
118 final ApexEngine apexEngine1 = new ApexEngineFactory().createApexEngine(key);
120 final TestApexActionListener listener1 = new TestApexActionListener("TestListener1");
121 apexEngine1.addEventListener("listener", listener1);
122 apexEngine1.updateModel(apexPolicyModel);
124 final EnEvent triggerEvent = apexEngine1.createEvent(new AxArtifactKey("AutoLearnTriggerEvent", "0.0.1"));
125 final double rval = rand.nextGaussian();
126 triggerEvent.put("MonitoredValue", rval);
127 triggerEvent.put("LastMonitoredValue", 0D);
128 LOGGER.info("Triggering policy in Engine 1 with " + triggerEvent);
129 apexEngine1.handleEvent(triggerEvent);
130 final EnEvent result = listener1.getResult();
131 LOGGER.info("Receiving action event {} ", result);
132 assertEquals("ExecutionIDs are different", triggerEvent.getExecutionID(), result.getExecutionID());
133 triggerEvent.clear();
140 * This policy passes, and receives a Double event context filed called "EVCDouble"<br>
141 * The policy tries to keep the value at 50, with a Min -100, Max 100 (These should probably be set using
142 * TaskParameters!)<br>
143 * The policy has 7 Decide Tasks that manipulate the value of this field in unknown ways.<br>
144 * The Decide TSL learns the effect of each task, and then selects the appropriate task to get the value back to
146 * After the value settles close to 50 for a while, the test Rests the value to to random number and then
148 * To plot the results grep stdout debug results for the string "*******", paste into excel and delete non-relevant
151 * @throws ApexException the apex exception
152 * @throws InterruptedException the interrupted exception
153 * @throws IOException Signals that an I/O exception has occurred.
156 public void TestAutoLearnTSL_main() throws ApexException, InterruptedException, IOException {
158 final double WANT = 50.0;
159 final double toleranceTileJump = 3.0;
161 final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
162 assertNotNull(apexPolicyModel);
164 final AxValidationResult validationResult = new AxValidationResult();
165 apexPolicyModel.validate(validationResult);
166 assertTrue(validationResult.isValid());
168 final AxArtifactKey key = new AxArtifactKey("AADMApexEngine", "0.0.1");
169 final EngineParameters parameters = new EngineParameters();
170 parameters.getExecutorParameterMap().put("MVEL", new MVELExecutorParameters());
171 parameters.getExecutorParameterMap().put("JAVA", new JavaExecutorParameters());
173 final ApexEngine apexEngine1 = new ApexEngineFactory().createApexEngine(key);
175 final TestApexActionListener listener1 = new TestApexActionListener("TestListener1");
176 apexEngine1.addEventListener("listener1", listener1);
177 apexEngine1.updateModel(apexPolicyModel);
180 final EnEvent triggerEvent = apexEngine1.createEvent(new AxArtifactKey("AutoLearnTriggerEvent", "0.0.1"));
181 assertNotNull(triggerEvent);
182 final double MIN = -100;
183 final double MAX = 100;
185 double rval = (((rand.nextGaussian() + 1) / 2) * (MAX - MIN)) + MIN;
186 triggerEvent.put("MonitoredValue", rval);
187 triggerEvent.put("LastMonitoredValue", 0);
193 for (int iteration = 0; iteration < MAXITERATIONS; iteration++) {
194 // Trigger the policy in engine 1
195 LOGGER.info("Triggering policy in Engine 1 with " + triggerEvent);
196 apexEngine1.handleEvent(triggerEvent);
197 final EnEvent result = listener1.getResult();
198 LOGGER.info("Receiving action event {} ", result);
199 triggerEvent.clear();
201 double val = (Double) result.get("MonitoredValue");
202 final double prevval = (Double) result.get("LastMonitoredValue");
204 triggerEvent.put("MonitoredValue", prevval);
205 triggerEvent.put("LastMonitoredValue", val);
207 avcount = Math.min((avcount + 1), 20); // maintain average of only the last 20 values
208 avval = ((avval * (avcount - 1)) + val) / (avcount);
210 distance = Math.abs(WANT - avval);
211 if (distance < toleranceTileJump) {
212 rval = (((rand.nextGaussian() + 1) / 2) * (MAX - MIN)) + MIN;
214 triggerEvent.put("MonitoredValue", val);
215 LOGGER.info("Iteration " + iteration + ": Average " + avval + " has become closer (" + distance
216 + ") than " + toleranceTileJump + " to " + WANT + " so reseting val:\t\t\t\t\t\t\t\t" + val);
220 LOGGER.info("Iteration " + iteration + ": \tpreval\t" + prevval + "\tval\t" + val + "\tavval\t" + avval);
231 public static void main(final String[] args) throws ApexException, InterruptedException, IOException {
232 new TestAutoLearnTSLUseCase().TestAutoLearnTSL_main();