Java tutorial
/* * * * 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. * * */ package eu.amidst.core.inference; import eu.amidst.core.Main; import eu.amidst.core.models.BayesianNetwork; import eu.amidst.core.utils.BayesianNetworkGenerator; import eu.amidst.core.utils.Utils; import eu.amidst.core.variables.Assignment; import eu.amidst.core.variables.HashMapAssignment; import eu.amidst.core.variables.Variable; import org.apache.commons.lang3.ArrayUtils; import java.util.Arrays; import java.util.List; import java.util.Random; /** * Created by dario on 01/06/15. */ public class MPEInferenceExperiments_Deliv1 { private static Assignment randomEvidence(long seed, double evidenceRatio, BayesianNetwork bn) throws UnsupportedOperationException { if (evidenceRatio <= 0 || evidenceRatio >= 1) { throw new UnsupportedOperationException("Error: invalid ratio"); } int numVariables = bn.getVariables().getNumberOfVars(); Random random = new Random(seed); //1823716125 int numVarEvidence = (int) Math.ceil(numVariables * evidenceRatio); // Evidence on 20% of variables //numVarEvidence = 0; //List<Variable> varEvidence = new ArrayList<>(numVarEvidence); double[] evidence = new double[numVarEvidence]; Variable aux; HashMapAssignment assignment = new HashMapAssignment(numVarEvidence); int[] indexesEvidence = new int[numVarEvidence]; //indexesEvidence[0]=varInterest.getVarID(); //if (Main.VERBOSE) System.out.println(variable.getVarID()); if (Main.VERBOSE) System.out.println("Evidence:"); for (int k = 0; k < numVarEvidence; k++) { int varIndex = -1; do { varIndex = random.nextInt(bn.getNumberOfVars()); //if (Main.VERBOSE) System.out.println(varIndex); aux = bn.getVariables().getVariableById(varIndex); double thisEvidence; if (aux.isMultinomial()) { thisEvidence = random.nextInt(aux.getNumberOfStates()); } else { thisEvidence = random.nextGaussian(); } evidence[k] = thisEvidence; } while (ArrayUtils.contains(indexesEvidence, varIndex)); indexesEvidence[k] = varIndex; //if (Main.VERBOSE) System.out.println(Arrays.toString(indexesEvidence)); if (Main.VERBOSE) System.out.println("Variable " + aux.getName() + " = " + evidence[k]); assignment.setValue(aux, evidence[k]); } if (Main.VERBOSE) System.out.println(); return assignment; } /** * The class constructor. * @param args Array of options: "filename variable a b N useVMP" if variable is continuous or "filename variable w N useVMP" for discrete */ public static void main(String[] args) throws Exception { // args: seedNetwork numberGaussians numberDiscrete seedAlgorithms int seedNetwork = 23423523; int numberOfGaussians = 20; int numberOfMultinomials = 20; int seed = 634634534; int parallelSamples = 100; int samplingMethodSize = 10000; int repetitions = 10; int numberOfIterations = 200; if (args.length != 8) { if (Main.VERBOSE) System.out.println("Invalid number of parameters. Using default values"); } else { try { seedNetwork = Integer.parseInt(args[0]); numberOfGaussians = Integer.parseInt(args[1]); numberOfMultinomials = Integer.parseInt(args[2]); seed = Integer.parseInt(args[3]); parallelSamples = Integer.parseInt(args[4]); samplingMethodSize = Integer.parseInt(args[5]); repetitions = Integer.parseInt(args[6]); numberOfIterations = Integer.parseInt(args[7]); } catch (NumberFormatException ex) { if (Main.VERBOSE) System.out.println( "Invalid parameters. Provide integers: seedNetwork numberGaussians numberDiscrete seedAlgorithms parallelSamples sampleSize repetitions"); if (Main.VERBOSE) System.out.println("Using default parameters"); if (Main.VERBOSE) System.out.println(ex.toString()); System.exit(20); } } int numberOfLinks = (int) 1.3 * (numberOfGaussians + numberOfMultinomials); BayesianNetworkGenerator.setSeed(seedNetwork); BayesianNetworkGenerator.setNumberOfGaussianVars(numberOfGaussians); BayesianNetworkGenerator.setNumberOfMultinomialVars(numberOfMultinomials, 2); BayesianNetworkGenerator.setNumberOfLinks(numberOfLinks); String filename = "./networks/simulated/RandomBN_" + Integer.toString(numberOfMultinomials) + "D_" + Integer.toString(numberOfGaussians) + "C_" + Integer.toString(seedNetwork) + "_Seed.bn"; //BayesianNetworkGenerator.generateBNtoFile(numberOfMultinomials,2,numberOfGaussians,numberOfLinks,seedNetwork,filename); BayesianNetwork bn = BayesianNetworkGenerator.generateBayesianNetwork(); //if (Main.VERBOSE) System.out.println(bn.getDAG()); //if (Main.VERBOSE) System.out.println(bn.toString()); MPEInference mpeInference = new MPEInference(); mpeInference.setModel(bn); mpeInference.setParallelMode(true); //if (Main.VERBOSE) System.out.println("CausalOrder: " + Arrays.toString(Utils.getCausalOrder(mpeInference.getOriginalModel().getDAG()).stream().map(Variable::getName).toArray())); List<Variable> modelVariables = Utils.getTopologicalOrder(bn.getDAG()); if (Main.VERBOSE) System.out.println(); // Including evidence: //double observedVariablesRate = 0.00; //Assignment evidence = randomEvidence(seed, observedVariablesRate, bn); //mpeInference.setEvidence(evidence); mpeInference.setSampleSize(parallelSamples); mpeInference.setSeed(seed); mpeInference.setNumberOfIterations(numberOfIterations); double[] SA_All_prob = new double[repetitions]; double[] SA_Some_prob = new double[repetitions]; double[] HC_All_prob = new double[repetitions]; double[] HC_Some_prob = new double[repetitions]; double[] sampling_prob = new double[repetitions]; double[] SA_All_time = new double[repetitions]; double[] SA_Some_time = new double[repetitions]; double[] HC_All_time = new double[repetitions]; double[] HC_Some_time = new double[repetitions]; double[] sampling_time = new double[repetitions]; long timeStart; long timeStop; double execTime; Assignment mpeEstimate; mpeInference.setParallelMode(true); for (int k = 0; k < repetitions; k++) { mpeInference.setSampleSize(parallelSamples); /*********************************************** * SIMULATED ANNEALING ************************************************/ // MPE INFERENCE WITH SIMULATED ANNEALING, ALL VARIABLES //if (Main.VERBOSE) System.out.println(); timeStart = System.nanoTime(); mpeInference.runInference(MPEInference.SearchAlgorithm.SA_GLOBAL); //mpeEstimate = mpeInference.getEstimate(); //if (Main.VERBOSE) System.out.println("MPE estimate (SA.All): " + mpeEstimate.outputString(modelVariables)); //toString(modelVariables) //if (Main.VERBOSE) System.out.println("with probability: " + Math.exp(mpeInference.getLogProbabilityOfEstimate()) + ", logProb: " + mpeInference.getLogProbabilityOfEstimate()); timeStop = System.nanoTime(); execTime = (double) (timeStop - timeStart) / 1000000000.0; //if (Main.VERBOSE) System.out.println("computed in: " + Double.toString(execTime) + " seconds"); //if (Main.VERBOSE) System.out.println(.toString(mapInference.getOriginalModel().getStaticVariables().iterator().)); //if (Main.VERBOSE) System.out.println(); SA_All_prob[k] = mpeInference.getLogProbabilityOfEstimate(); SA_All_time[k] = execTime; // MPE INFERENCE WITH SIMULATED ANNEALING, SOME VARIABLES AT EACH TIME timeStart = System.nanoTime(); mpeInference.runInference(MPEInference.SearchAlgorithm.SA_LOCAL); //mpeEstimate = mpeInference.getEstimate(); //if (Main.VERBOSE) System.out.println("MPE estimate (SA.Some): " + mpeEstimate.outputString(modelVariables)); //toString(modelVariables) //if (Main.VERBOSE) System.out.println("with probability: "+ Math.exp(mpeInference.getLogProbabilityOfEstimate()) + ", logProb: " + mpeInference.getLogProbabilityOfEstimate()); timeStop = System.nanoTime(); execTime = (double) (timeStop - timeStart) / 1000000000.0; //if (Main.VERBOSE) System.out.println("computed in: " + Double.toString(execTime) + " seconds"); //if (Main.VERBOSE) System.out.println(.toString(mapInference.getOriginalModel().getStaticVariables().iterator().)); //if (Main.VERBOSE) System.out.println(); SA_Some_prob[k] = mpeInference.getLogProbabilityOfEstimate(); SA_Some_time[k] = execTime; /*********************************************** * HILL CLIMBING ************************************************/ // MPE INFERENCE WITH HILL CLIMBING, ALL VARIABLES timeStart = System.nanoTime(); mpeInference.runInference(MPEInference.SearchAlgorithm.HC_GLOBAL); //mpeEstimate = mpeInference.getEstimate(); //modelVariables = mpeInference.getOriginalModel().getVariables().getListOfVariables(); //if (Main.VERBOSE) System.out.println("MPE estimate (HC.All): " + mpeEstimate.outputString(modelVariables)); //if (Main.VERBOSE) System.out.println("with probability: " + Math.exp(mpeInference.getLogProbabilityOfEstimate()) + ", logProb: " + mpeInference.getLogProbabilityOfEstimate()); timeStop = System.nanoTime(); execTime = (double) (timeStop - timeStart) / 1000000000.0; //if (Main.VERBOSE) System.out.println("computed in: " + Double.toString(execTime) + " seconds"); //if (Main.VERBOSE) System.out.println(); HC_All_prob[k] = mpeInference.getLogProbabilityOfEstimate(); HC_All_time[k] = execTime; // MPE INFERENCE WITH HILL CLIMBING, ONE VARIABLE AT EACH TIME timeStart = System.nanoTime(); mpeInference.runInference(MPEInference.SearchAlgorithm.HC_LOCAL); //mpeEstimate = mpeInference.getEstimate(); //if (Main.VERBOSE) System.out.println("MPE estimate (HC.Some): " + mpeEstimate.outputString(modelVariables)); //toString(modelVariables) //if (Main.VERBOSE) System.out.println("with probability: " + Math.exp(mpeInference.getLogProbabilityOfEstimate()) + ", logProb: " + mpeInference.getLogProbabilityOfEstimate()); timeStop = System.nanoTime(); execTime = (double) (timeStop - timeStart) / 1000000000.0; //if (Main.VERBOSE) System.out.println("computed in: " + Double.toString(execTime) + " seconds"); //if (Main.VERBOSE) System.out.println(); HC_Some_prob[k] = mpeInference.getLogProbabilityOfEstimate(); HC_Some_time[k] = execTime; /*********************************************** * SAMPLING AND DETERMINISTIC ************************************************/ // MPE INFERENCE WITH SIMULATION AND PICKING MAX mpeInference.setSampleSize(samplingMethodSize); timeStart = System.nanoTime(); mpeInference.runInference(MPEInference.SearchAlgorithm.SAMPLING); //mpeEstimate = mpeInference.getEstimate(); //modelVariables = mpeInference.getOriginalModel().getVariables().getListOfVariables(); //if (Main.VERBOSE) System.out.println("MPE estimate (SAMPLING): " + mpeEstimate.outputString(modelVariables)); //if (Main.VERBOSE) System.out.println("with probability: " + Math.exp(mpeInference.getLogProbabilityOfEstimate()) + ", logProb: " + mpeInference.getLogProbabilityOfEstimate()); timeStop = System.nanoTime(); execTime = (double) (timeStop - timeStart) / 1000000000.0; //if (Main.VERBOSE) System.out.println("computed in: " + Double.toString(execTime) + " seconds"); //if (Main.VERBOSE) System.out.println(); sampling_prob[k] = mpeInference.getLogProbabilityOfEstimate(); sampling_time[k] = execTime; } double determ_prob = 0; double determ_time = 0; if (bn.getNumberOfVars() <= 50) { // MPE INFERENCE, DETERMINISTIC timeStart = System.nanoTime(); //mpeInference.runInference(-2); //mpeEstimate = mpeInference.getEstimate(); //modelVariables = mpeInference.getOriginalModel().getVariables().getListOfVariables(); //if (Main.VERBOSE) System.out.println("MPE estimate (DETERM.): " + mpeEstimate.outputString(modelVariables)); //if (Main.VERBOSE) System.out.println("with probability: " + Math.exp(mpeInference.getLogProbabilityOfEstimate()) + ", logProb: " + mpeInference.getLogProbabilityOfEstimate()); timeStop = System.nanoTime(); execTime = (double) (timeStop - timeStart) / 1000000000.0; //if (Main.VERBOSE) System.out.println("computed in: " + Double.toString(execTime) + " seconds"); //if (Main.VERBOSE) System.out.println(); determ_prob = mpeInference.getLogProbabilityOfEstimate(); determ_time = execTime; determ_prob = -34.64743236365595; determ_time = 0; } else { if (Main.VERBOSE) System.out.println("Too many variables for deterministic method"); } /*********************************************** * DISPLAY OF RESULTS ************************************************/ if (Main.VERBOSE) System.out.println("*** RESULTS ***"); // if (Main.VERBOSE) System.out.println("SA_All log-probabilities"); // if (Main.VERBOSE) System.out.println(Arrays.toString(SA_All_prob)); // if (Main.VERBOSE) System.out.println("SA_Some log-probabilities"); // if (Main.VERBOSE) System.out.println(Arrays.toString(SA_Some_prob)); // if (Main.VERBOSE) System.out.println("HC_All log-probabilities"); // if (Main.VERBOSE) System.out.println(Arrays.toString(HC_All_prob)); // if (Main.VERBOSE) System.out.println("HC_Some log-probabilities"); // if (Main.VERBOSE) System.out.println(Arrays.toString(HC_Some_prob)); // if (Main.VERBOSE) System.out.println("Sampling log-probabilities"); // if (Main.VERBOSE) System.out.println(Arrays.toString(sampling_prob)); // // if(bn.getNumberOfVars()<=50) { // if (Main.VERBOSE) System.out.println("Deterministic log-probability"); // if (Main.VERBOSE) System.out.println(Double.toString(determ_prob)); // } // if (Main.VERBOSE) System.out.println(); final double determ_prob_FINAL = determ_prob; // int SA_All_success = (int) Arrays.stream(SA_All_prob).filter(db -> (db <= determ_prob_FINAL+0.001 && db >=determ_prob_FINAL-0.001)).count(); // int SA_Some_success = (int) Arrays.stream(SA_Some_prob).filter(db -> (db <= determ_prob_FINAL+0.001 && db >=determ_prob_FINAL-0.001)).count(); // int HC_All_success = (int) Arrays.stream(HC_All_prob).filter(db -> (db <= determ_prob_FINAL+0.001 && db >=determ_prob_FINAL-0.001)).count(); // int HC_Some_success = (int) Arrays.stream(HC_Some_prob).filter(db -> (db <= determ_prob_FINAL+0.001 && db >=determ_prob_FINAL-0.001)).count(); // int sampling_success = (int) Arrays.stream(sampling_prob).filter(db -> (db <= determ_prob_FINAL+0.001 && db >=determ_prob_FINAL-0.001)).count(); // // if (Main.VERBOSE) System.out.println("SA_All % success"); // if (Main.VERBOSE) System.out.println(Double.toString( 100 * SA_All_success/repetitions )); // if (Main.VERBOSE) System.out.println("SA_Some % success"); // if (Main.VERBOSE) System.out.println(Double.toString( 100 * SA_Some_success/repetitions )); // if (Main.VERBOSE) System.out.println("HC_All % success"); // if (Main.VERBOSE) System.out.println(Double.toString( 100 * HC_All_success/repetitions )); // if (Main.VERBOSE) System.out.println("HC_Some % success"); // if (Main.VERBOSE) System.out.println(Double.toString( 100 * HC_Some_success/repetitions )); // if (Main.VERBOSE) System.out.println("Sampling % success"); // if (Main.VERBOSE) System.out.println(Double.toString( 100 * sampling_success/repetitions )); // if (Main.VERBOSE) System.out.println(); if (Main.VERBOSE) System.out.println("SA_All RMS probabilities"); if (Main.VERBOSE) System.out.println(Double.toString(Arrays.stream(SA_All_prob) .map(value -> Math.pow(value - determ_prob_FINAL, 2)).average().getAsDouble())); if (Main.VERBOSE) System.out.println("SA_Some RMS probabilities"); if (Main.VERBOSE) System.out.println(Double.toString(Arrays.stream(SA_Some_prob) .map(value -> Math.pow(value - determ_prob_FINAL, 2)).average().getAsDouble())); if (Main.VERBOSE) System.out.println("HC_All RMS probabilities"); if (Main.VERBOSE) System.out.println(Double.toString(Arrays.stream(HC_All_prob) .map(value -> Math.pow(value - determ_prob_FINAL, 2)).average().getAsDouble())); if (Main.VERBOSE) System.out.println("HC_Some RMS probabilities"); if (Main.VERBOSE) System.out.println(Double.toString(Arrays.stream(HC_Some_prob) .map(value -> Math.pow(value - determ_prob_FINAL, 2)).average().getAsDouble())); if (Main.VERBOSE) System.out.println("Sampling RMS probabilities"); if (Main.VERBOSE) System.out.println(Double.toString(Arrays.stream(sampling_prob) .map(value -> Math.pow(value - determ_prob_FINAL, 2)).average().getAsDouble())); if (Main.VERBOSE) System.out.println(); if (Main.VERBOSE) System.out.println("SA_All times"); //if (Main.VERBOSE) System.out.println(Arrays.toString(SA_All_time)); if (Main.VERBOSE) System.out.println("Mean time: " + Double.toString(Arrays.stream(SA_All_time).average().getAsDouble())); if (Main.VERBOSE) System.out.println("SA_Some times"); //if (Main.VERBOSE) System.out.println(Arrays.toString(SA_Some_time)); if (Main.VERBOSE) System.out .println("Mean time: " + Double.toString(Arrays.stream(SA_Some_time).average().getAsDouble())); if (Main.VERBOSE) System.out.println("HC_All times"); //if (Main.VERBOSE) System.out.println(Arrays.toString(HC_All_time)); if (Main.VERBOSE) System.out.println("Mean time: " + Double.toString(Arrays.stream(HC_All_time).average().getAsDouble())); if (Main.VERBOSE) System.out.println("HC_Some times"); //if (Main.VERBOSE) System.out.println(Arrays.toString(HC_Some_time)); if (Main.VERBOSE) System.out .println("Mean time: " + Double.toString(Arrays.stream(HC_Some_time).average().getAsDouble())); if (Main.VERBOSE) System.out.println("Sampling times"); //if (Main.VERBOSE) System.out.println(Arrays.toString(sampling_time)); if (Main.VERBOSE) System.out .println("Mean time: " + Double.toString(Arrays.stream(sampling_time).average().getAsDouble())); if (bn.getNumberOfVars() <= 50) { if (Main.VERBOSE) System.out.println("Deterministic time"); if (Main.VERBOSE) System.out.println(Double.toString(determ_time)); if (Main.VERBOSE) System.out.println("and probability"); if (Main.VERBOSE) System.out.println(determ_prob); } } }