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/*-
* ============LICENSE_START=======================================================
* Copyright (C) 2016-2018 Ericsson. All rights reserved.
* ================================================================================
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* SPDX-License-Identifier: Apache-2.0
* ============LICENSE_END=========================================================
*/
package org.onap.policy.apex.examples.adaptive;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
import static org.junit.Assert.assertTrue;
import java.io.IOException;
import java.util.Random;
import org.junit.After;
import org.junit.Before;
import org.junit.Test;
import org.onap.policy.apex.context.impl.schema.java.JavaSchemaHelperParameters;
import org.onap.policy.apex.context.parameters.ContextParameterConstants;
import org.onap.policy.apex.context.parameters.ContextParameters;
import org.onap.policy.apex.context.parameters.SchemaParameters;
import org.onap.policy.apex.core.engine.EngineParameters;
import org.onap.policy.apex.core.engine.engine.ApexEngine;
import org.onap.policy.apex.core.engine.engine.impl.ApexEngineFactory;
import org.onap.policy.apex.core.engine.event.EnEvent;
import org.onap.policy.apex.examples.adaptive.model.AdaptiveDomainModelFactory;
import org.onap.policy.apex.model.basicmodel.concepts.ApexException;
import org.onap.policy.apex.model.basicmodel.concepts.AxArtifactKey;
import org.onap.policy.apex.model.basicmodel.concepts.AxValidationResult;
import org.onap.policy.apex.model.policymodel.concepts.AxPolicyModel;
import org.onap.policy.apex.plugins.executor.java.JavaExecutorParameters;
import org.onap.policy.apex.plugins.executor.mvel.MVELExecutorParameters;
import org.onap.policy.common.parameters.ParameterService;
import org.slf4j.ext.XLogger;
import org.slf4j.ext.XLoggerFactory;
/**
* Test Auto learning in TSL.
*
* @author John Keeney (John.Keeney@ericsson.com)
*/
public class TestAutoLearnTslUseCase {
private static final XLogger LOGGER = XLoggerFactory.getXLogger(TestAutoLearnTslUseCase.class);
private static final int MAXITERATIONS = 1000;
private static final Random rand = new Random(System.currentTimeMillis());
private SchemaParameters schemaParameters;
private ContextParameters contextParameters;
private EngineParameters engineParameters;
@Before
public void beforeTest() {
schemaParameters = new SchemaParameters();
schemaParameters.setName(ContextParameterConstants.SCHEMA_GROUP_NAME);
schemaParameters.getSchemaHelperParameterMap().put("JAVA", new JavaSchemaHelperParameters());
ParameterService.register(schemaParameters);
contextParameters = new ContextParameters();
contextParameters.setName(ContextParameterConstants.MAIN_GROUP_NAME);
contextParameters.getDistributorParameters().setName(ContextParameterConstants.DISTRIBUTOR_GROUP_NAME);
contextParameters.getLockManagerParameters().setName(ContextParameterConstants.LOCKING_GROUP_NAME);
contextParameters.getPersistorParameters().setName(ContextParameterConstants.PERSISTENCE_GROUP_NAME);
ParameterService.register(contextParameters);
ParameterService.register(contextParameters.getDistributorParameters());
ParameterService.register(contextParameters.getLockManagerParameters());
ParameterService.register(contextParameters.getPersistorParameters());
engineParameters = new EngineParameters();
engineParameters.getExecutorParameterMap().put("MVEL", new MVELExecutorParameters());
engineParameters.getExecutorParameterMap().put("JAVA", new JavaExecutorParameters());
ParameterService.register(engineParameters);
}
@After
public void afterTest() {
ParameterService.deregister(engineParameters);
ParameterService.deregister(contextParameters.getDistributorParameters());
ParameterService.deregister(contextParameters.getLockManagerParameters());
ParameterService.deregister(contextParameters.getPersistorParameters());
ParameterService.deregister(contextParameters);
ParameterService.deregister(schemaParameters);
}
@Test
// once through the long running test below
public void testAutoLearnTsl() throws ApexException, InterruptedException, IOException {
final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
assertNotNull(apexPolicyModel);
final AxValidationResult validationResult = new AxValidationResult();
apexPolicyModel.validate(validationResult);
assertTrue(validationResult.isValid());
final AxArtifactKey key = new AxArtifactKey("AADMApexEngine", "0.0.1");
final ApexEngine apexEngine1 = new ApexEngineFactory().createApexEngine(key);
final TestApexActionListener listener1 = new TestApexActionListener("TestListener1");
apexEngine1.addEventListener("listener", listener1);
apexEngine1.updateModel(apexPolicyModel);
apexEngine1.start();
final EnEvent triggerEvent = apexEngine1.createEvent(new AxArtifactKey("AutoLearnTriggerEvent", "0.0.1"));
final double rval = rand.nextGaussian();
triggerEvent.put("MonitoredValue", rval);
triggerEvent.put("LastMonitoredValue", 0D);
LOGGER.info("Triggering policy in Engine 1 with " + triggerEvent);
apexEngine1.handleEvent(triggerEvent);
final EnEvent result = listener1.getResult();
LOGGER.info("Receiving action event {} ", result);
assertEquals("ExecutionIDs are different", triggerEvent.getExecutionID(), result.getExecutionID());
triggerEvent.clear();
result.clear();
Thread.sleep(1);
apexEngine1.stop();
}
/**
* This policy passes, and receives a Double event context filed called "EVCDouble"<br>
* The policy tries to keep the value at 50, with a Min -100, Max 100 (These should probably be set using
* TaskParameters!)<br>
* The policy has 7 Decide Tasks that manipulate the value of this field in unknown ways.<br>
* The Decide TSL learns the effect of each task, and then selects the appropriate task to get the value back to
* 50<br>
* After the value settles close to 50 for a while, the test Rests the value to to random number and then
* continues<br>
* To plot the results grep stdout debug results for the string "*******", paste into excel and delete non-relevant
* columns<br>
*
* @throws ApexException the apex exception
* @throws InterruptedException the interrupted exception
* @throws IOException Signals that an I/O exception has occurred.
*/
// @Test
public void testAutoLearnTslMain() throws ApexException, InterruptedException, IOException {
final double dwant = 50.0;
final double toleranceTileJump = 3.0;
final AxPolicyModel apexPolicyModel = new AdaptiveDomainModelFactory().getAutoLearnPolicyModel();
assertNotNull(apexPolicyModel);
final AxValidationResult validationResult = new AxValidationResult();
apexPolicyModel.validate(validationResult);
assertTrue(validationResult.isValid());
final AxArtifactKey key = new AxArtifactKey("AADMApexEngine", "0.0.1");
final EngineParameters parameters = new EngineParameters();
parameters.getExecutorParameterMap().put("MVEL", new MVELExecutorParameters());
parameters.getExecutorParameterMap().put("JAVA", new JavaExecutorParameters());
final ApexEngine apexEngine1 = new ApexEngineFactory().createApexEngine(key);
final TestApexActionListener listener1 = new TestApexActionListener("TestListener1");
apexEngine1.addEventListener("listener1", listener1);
apexEngine1.updateModel(apexPolicyModel);
apexEngine1.start();
final EnEvent triggerEvent = apexEngine1.createEvent(new AxArtifactKey("AutoLearnTriggerEvent", "0.0.1"));
assertNotNull(triggerEvent);
final double dmin = -100;
final double dmax = 100;
double rval = (((rand.nextGaussian() + 1) / 2) * (dmax - dmin)) + dmin;
triggerEvent.put("MonitoredValue", rval);
triggerEvent.put("LastMonitoredValue", 0);
double avval = 0;
double distance;
double avcount = 0;
for (int iteration = 0; iteration < MAXITERATIONS; iteration++) {
// Trigger the policy in engine 1
LOGGER.info("Triggering policy in Engine 1 with " + triggerEvent);
apexEngine1.handleEvent(triggerEvent);
final EnEvent result = listener1.getResult();
LOGGER.info("Receiving action event {} ", result);
triggerEvent.clear();
double val = (Double) result.get("MonitoredValue");
final double prevval = (Double) result.get("LastMonitoredValue");
triggerEvent.put("MonitoredValue", prevval);
triggerEvent.put("LastMonitoredValue", val);
avcount = Math.min((avcount + 1), 20); // maintain average of only the last 20 values
avval = ((avval * (avcount - 1)) + val) / (avcount);
distance = Math.abs(dwant - avval);
if (distance < toleranceTileJump) {
rval = (((rand.nextGaussian() + 1) / 2) * (dmax - dmin)) + dmin;
val = rval;
triggerEvent.put("MonitoredValue", val);
LOGGER.info("Iteration " + iteration + ": Average " + avval + " has become closer (" + distance
+ ") than " + toleranceTileJump + " to " + dwant + " so reseting val:\t\t\t\t\t\t\t\t" + val);
avval = 0;
avcount = 0;
}
LOGGER.info("Iteration " + iteration + ": \tpreval\t" + prevval + "\tval\t" + val + "\tavval\t" + avval);
result.clear();
Thread.sleep(1);
}
apexEngine1.stop();
Thread.sleep(1000);
}
public static void main(final String[] args) throws ApexException, InterruptedException, IOException {
new TestAutoLearnTslUseCase().testAutoLearnTslMain();
}
}
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