content: /*
   *
   *
   *    Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.
   *    See the NOTICE file distributed with this work for additional information regarding copyright ownership.
   *    The ASF licenses this file to You 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.
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  package eu.amidst.core.learning;
  
  import eu.amidst.core.Main;
  import eu.amidst.core.datastream.DataInstance;
  import eu.amidst.core.datastream.DataOnMemory;
  import eu.amidst.core.datastream.DataStream;
  import eu.amidst.core.io.BayesianNetworkLoader;
  import eu.amidst.core.learning.parametric.ParallelMaximumLikelihood;
  import eu.amidst.core.models.BayesianNetwork;
  import eu.amidst.core.utils.BayesianNetworkSampler;
  import eu.amidst.core.variables.Variable;
  import org.junit.Assert;
  import org.junit.Test;
  
  import java.io.IOException;
  
  /**
   * Created by Hanen on 08/01/15.
   */
  public class MLMultinomialsTest {
  
      @Test
      public void testingMLSequential() throws IOException, ClassNotFoundException {
  
          // load the true Asia Bayesian network
          BayesianNetwork asianet = BayesianNetworkLoader.loadFromFile("../networks/dataWeka/asia.bn");
  
          if (Main.VERBOSE) System.out.println("\nAsia network \n ");
          //if (Main.VERBOSE) System.out.println(asianet.getDAG().outputString());
          //if (Main.VERBOSE) System.out.println(asianet.outputString());
  
          //Sampling from Asia BN
          BayesianNetworkSampler sampler = new BayesianNetworkSampler(asianet);
          sampler.setSeed(0);
  
          //Load the sampled data
          DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
          //Structure learning is excluded from the test, i.e., we use directly the initial Asia network structure
          // and just learn then test the parameter learning
  
          //Parameter Learning
          ParallelMaximumLikelihood parallelMaximumLikelihood = new ParallelMaximumLikelihood();
          parallelMaximumLikelihood.setWindowsSize(1000);
          parallelMaximumLikelihood.setLaplace(false);
          parallelMaximumLikelihood.setDAG(asianet.getDAG());
          parallelMaximumLikelihood.initLearning();
          parallelMaximumLikelihood.updateModel(data);
          BayesianNetwork bnet = parallelMaximumLikelihood.getLearntBayesianNetwork();
  
  
          //Check if the probability distributions of each node
          for (Variable var : asianet.getVariables()) {
              if (Main.VERBOSE) System.out.println("\n------ Variable " + var.getName() + " ------");
              if (Main.VERBOSE) System.out.println("\nTrue distribution:\n"+ asianet.getConditionalDistribution(var));
              if (Main.VERBOSE) System.out.println("\nLearned distribution:\n"+ bnet.getConditionalDistribution(var));
              Assert.assertTrue(bnet.getConditionalDistribution(var).equalDist(asianet.getConditionalDistribution(var), 0.07));
          }
  
          //Or check directly if the true and learned networks are equals
          Assert.assertTrue(bnet.equalBNs(asianet, 0.07));
      }
  
  
      @Test
      public void testingMLParallel() throws IOException, ClassNotFoundException {
  
          // load the true Asia Bayesian network
          BayesianNetwork asianet = BayesianNetworkLoader.loadFromFile("../networks/dataWeka/asia.bn");
  
          if (Main.VERBOSE) System.out.println("\nAsia network \n ");
          //if (Main.VERBOSE) System.out.println(asianet.getDAG().outputString());
          //if (Main.VERBOSE) System.out.println(asianet.outputString());
  
          //Sampling from Asia BN
          BayesianNetworkSampler sampler = new BayesianNetworkSampler(asianet);
          sampler.setSeed(0);
  
          //Load the sampled data
          DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
          //Structure learning is excluded from the test, i.e., we use directly the initial Asia network structure
          // and just learn then test the parameter learning
  
          //Parameter Learning
          ParallelMaximumLikelihood parallelMaximumLikelihood = new ParallelMaximumLikelihood();
          parallelMaximumLikelihood.setWindowsSize(1000);
          parallelMaximumLikelihood.setParallelMode(true);
          parallelMaximumLikelihood.setLaplace(false);
          parallelMaximumLikelihood.setDAG(asianet.getDAG());
          parallelMaximumLikelihood.initLearning();
          parallelMaximumLikelihood.updateModel(data);
          BayesianNetwork bnet = parallelMaximumLikelihood.getLearntBayesianNetwork();
  
  
  
          //Check if the probability distributions of each node
          for (Variable var : asianet.getVariables()) {
              if (Main.VERBOSE) System.out.println("\n------ Variable " + var.getName() + " ------");
              if (Main.VERBOSE) System.out.println("\nTrue distribution:\n"+ asianet.getConditionalDistribution(var));
              if (Main.VERBOSE) System.out.println("\nLearned distribution:\n"+ bnet.getConditionalDistribution(var));
              Assert.assertTrue(bnet.getConditionalDistribution(var).equalDist(asianet.getConditionalDistribution(var), 0.07));
          }
  
          //Or check directly if the true and learned networks are equals
          Assert.assertTrue(bnet.equalBNs(asianet, 0.07));
      }
  
      @Test
      public void testingMLBatches() throws IOException, ClassNotFoundException {
  
          // load the true Asia Bayesian network
          BayesianNetwork asianet = BayesianNetworkLoader.loadFromFile("../networks/dataWeka/asia.bn");
  
          if (Main.VERBOSE) System.out.println("\nAsia network \n ");
          //if (Main.VERBOSE) System.out.println(asianet.getDAG().outputString());
          //if (Main.VERBOSE) System.out.println(asianet.outputString());
  
          //Sampling from Asia BN
          BayesianNetworkSampler sampler = new BayesianNetworkSampler(asianet);
          sampler.setSeed(0);
  
          //Load the sampled data
          DataStream<DataInstance> data = sampler.sampleToDataStream(10000);
          //Structure learning is excluded from the test, i.e., we use directly the initial Asia network structure
          // and just learn then test the parameter learning
  
          //Parameter Learning
          ParallelMaximumLikelihood parallelMaximumLikelihood = new ParallelMaximumLikelihood();
          parallelMaximumLikelihood.setDAG(asianet.getDAG());
          parallelMaximumLikelihood.initLearning();
  
          for (DataOnMemory<DataInstance> batch : data.iterableOverBatches(100)){
              parallelMaximumLikelihood.updateModel(batch);
          }
  
          BayesianNetwork bnet = parallelMaximumLikelihood.getLearntBayesianNetwork();
  
          //Check if the probability distributions of each node
          for (Variable var : asianet.getVariables()) {
              if (Main.VERBOSE) System.out.println("\n------ Variable " + var.getName() + " ------");
              if (Main.VERBOSE) System.out.println("\nTrue distribution:\n"+ asianet.getConditionalDistribution(var));
              if (Main.VERBOSE) System.out.println("\nLearned distribution:\n"+ bnet.getConditionalDistribution(var));
              Assert.assertTrue(bnet.getConditionalDistribution(var).equalDist(asianet.getConditionalDistribution(var), 0.07));
          }
  
          //Or check directly if the true and learned networks are equals
          Assert.assertTrue(bnet.equalBNs(asianet, 0.07));
      }
  }
  