HOL-master/examples/lassie/sempre/src/edu/stanford/nlp/sempre/Learner.java
package edu.stanford.nlp.sempre;

import com.google.common.base.Joiner;
import com.google.common.collect.Maps;
import fig.basic.*;
import fig.exec.Execution;

import java.io.PrintWriter;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

/**
 * The main learning loop.  Goes over a dataset multiple times, calling the
 * parser and updating parameters.
 *
 * @author Percy Liang
 */
public class Learner {
  public static class Options {
    @Option(gloss = "Number of iterations to train")
    public int maxTrainIters = 0;

    @Option(gloss = "When using mini-batch updates for SGD, this is the batch size")
    public int batchSize = 1;  // Default is SGD

    @Option(gloss = "Write predDerivations to examples file (huge)")
    public boolean outputPredDerivations = false;
    @Option(gloss = "Write predicted values to a TSV file")
    public boolean outputPredValues = false;

    @Option(gloss = "Dump all features and compatibility scores")
    public boolean dumpFeaturesAndCompatibility = false;

    @Option(gloss = "Whether to add feedback")
    public boolean addFeedback = false;
    @Option(gloss = "Whether to sort on feedback")
    public boolean sortOnFeedback = true;

    @Option(gloss = "Verbosity") public int verbose = 0;

    @Option(gloss = "Initialize with these parameters")
    public List<Pair<String, Double>> initialization;

    @Option(gloss = "Whether to update weights")
    public boolean updateWeights = true;
    @Option(gloss = "Whether to check gradient")
    public boolean checkGradient = false;

    @Option(gloss = "Whether to skip the 'train' group in the last iteration and non-'train' groups in other iterations")
    public boolean skipUnnecessaryGroups = false;

    @Option(gloss = "Number of threads to parallelize")
    public int numParallelThreads = 1;
  }
  public static Options opts = new Options();

  private Parser parser;
  private final Params params;
  private final Dataset dataset;
  private final PrintWriter eventsOut;  // For printing a machine-readable log
  private final List<SemanticFn> semFuncsToUpdate;

  public Learner(Parser parser, Params params, Dataset dataset) {
    this.parser = parser;
    this.params = params;
    this.dataset = dataset;
    this.eventsOut = IOUtils.openOutAppendEasy(Execution.getFile("learner.events"));
    if (opts.initialization != null && this.params.isEmpty())
      this.params.init(opts.initialization);

    // Collect all semantic functions to update.
    semFuncsToUpdate = new ArrayList<>();
    for (Rule rule : parser.grammar.getRules()) {
      SemanticFn currSemFn = rule.getSem();
      boolean toAdd = true;
      for (SemanticFn semFuncToUpdate : semFuncsToUpdate) {
        if (semFuncToUpdate.getClass().equals(currSemFn.getClass())) {
          toAdd = false;
          break;
        }
      }
      if (toAdd)
        semFuncsToUpdate.add(currSemFn);
    }
  }

  public void learn() {
    learn(opts.maxTrainIters, Maps.newHashMap());
  }

  /**
   * @param evaluations Evaluations per iteration per group.
   */
  public void learn(int numIters, Map<String, List<Evaluation>> evaluations) {
    LogInfo.begin_track("Learner.learn()");
    // if when we start we have parameters already - need to sort the semantic functions.
    if (!params.isEmpty())
      sortOnFeedback();
    // For each iteration, go through the groups and parse (updating if train).
    for (int iter = 0; iter <= numIters; iter++) {

      LogInfo.begin_track("Iteration %s/%s", iter, numIters);
      Execution.putOutput("iter", iter);

      // Averaged over all iterations
      // Group -> evaluation for that group.
      Map<String, Evaluation> meanEvaluations = Maps.newHashMap();

      // Clear
      for (String group : dataset.groups())
        meanEvaluations.put(group, new Evaluation());

      // Test and train
      for (String group : dataset.groups()) {
        boolean lastIter = (iter == numIters);
        boolean updateWeights = opts.updateWeights && group.equals("train") && !lastIter;  // Don't train on last iteration
        if (opts.skipUnnecessaryGroups) {
          if ((group.equals("train") && lastIter) || (!group.equals("train") && !lastIter))
            continue;
        }
        // Allow the parser to change behavior based on current group and iteration
        parser.onBeginDataGroup(iter, numIters, group);
        Evaluation eval = processExamples(iter, group, dataset.examples(group), updateWeights);
        MapUtils.addToList(evaluations, group, eval);
        meanEvaluations.get(group).add(eval);
        StopWatchSet.logStats();
        writeParams(iter);
      }
      LogInfo.end_track();
    }
    LogInfo.end_track();
  }

  private void writeParams(int iter) {
    String path = Execution.getFile("params." + iter);
    if (path != null) {
      params.write(path);
      Utils.systemHard("ln -sf params." + iter + " " + Execution.getFile("params"));
    }
  }

  public void onlineLearnExample(Example ex) {
    LogInfo.begin_track("onlineLearnExample: %s derivations", ex.predDerivations.size());
    HashMap<String, Double> counts = new HashMap<>();
    for (Derivation deriv : ex.predDerivations)
      deriv.compatibility = parser.valueEvaluator.getCompatibility(ex.targetValue, deriv.value);
    ParserState.computeExpectedCounts(ex.predDerivations, counts);
    params.update(counts);
    LogInfo.end_track();
  }

  public void onlineLearnExampleByFormula(Example ex, List<Formula> formulas) {
    HashMap<String, Double> counts = new HashMap<>();
    for (Derivation deriv : ex.predDerivations)
      deriv.compatibility = formulas.contains(deriv.formula)? 1 : 0;
    ParserState.computeExpectedCounts(ex.predDerivations, counts);
    params.update(counts);
  }

  private Evaluation processExamples(int iter, String group,
      List<Example> examples, boolean computeExpectedCounts) {
    Evaluation evaluation = new Evaluation();

    if (examples.size() == 0)
      return evaluation;

    final String prefix = "iter=" + iter + "." + group;

    Execution.putOutput("group", group);
    LogInfo.begin_track_printAll(
        "Processing %s: %s examples", prefix, examples.size());
    LogInfo.begin_track("Examples");

    if (opts.numParallelThreads > 1) {
      // Parallelize!
      Parallelizer<Example> paral = new Parallelizer<>(opts.numParallelThreads);
      LearnerParallelProcessor processor = new LearnerParallelProcessor(
          parser, params, prefix, computeExpectedCounts, evaluation);
      LogInfo.begin_threads();
      paral.process(examples, processor);
      LogInfo.end_threads();

    } else {
      // Original code (single-threaded)

      Map<String, Double> counts = new HashMap<>();
      int batchSize = 0;
      for (int e = 0; e < examples.size(); e++) {

        Example ex = examples.get(e);

        LogInfo.begin_track_printAll(
            "%s: example %s/%s: %s", prefix, e, examples.size(), ex.id);
        ex.log();
        Execution.putOutput("example", e);

        ParserState state = parseExample(params, ex, computeExpectedCounts);
        if (computeExpectedCounts) {
          if (opts.checkGradient) {
            LogInfo.begin_track("Checking gradient");
            checkGradient(ex, state);
            LogInfo.end_track();
          }

          SempreUtils.addToDoubleMap(counts, state.expectedCounts);

          batchSize++;
          if (batchSize >= opts.batchSize) {
            // Gathered enough examples, update parameters
            updateWeights(counts);
            batchSize = 0;
          }
        }

        LogInfo.logs("Current: %s", ex.evaluation.summary());
        evaluation.add(ex.evaluation);
        LogInfo.logs("Cumulative(%s): %s", prefix, evaluation.summary());

        printLearnerEventsIter(ex, iter, group);
        LogInfo.end_track();
        if (opts.addFeedback && computeExpectedCounts)
          addFeedback(ex);

        // Write out examples and predictions
        if (opts.outputPredDerivations) {
          ExampleUtils.writeParaphraseSDF(iter, group, ex, true);
        }
        if (opts.outputPredValues) {
          ExampleUtils.writePredictionTSV(iter, group, ex);
        }

        // To save memory
        ex.predDerivations.clear();
      }

      if (computeExpectedCounts && batchSize > 0)
        updateWeights(counts);

    }

    params.finalizeWeights();
    if (opts.sortOnFeedback && computeExpectedCounts)
      sortOnFeedback();

    LogInfo.end_track();
    logEvaluationStats(evaluation, prefix);
    evaluation.putOutput(prefix.replace('.', '-'));
    printLearnerEventsSummary(evaluation, iter, group);
    ExampleUtils.writeEvaluationSDF(iter, group, evaluation, examples.size());
    LogInfo.end_track();
    return evaluation;
  }

  private void checkGradient(Example ex, ParserState state) {
    double eps = 1e-2;
    for (String feature : state.expectedCounts.keySet()) {
      LogInfo.begin_track("feature=%s", feature);
      double computedGradient = state.expectedCounts.get(feature);
      Params perturbedParams = this.params.copyParams();
      perturbedParams.getWeights().put(feature, perturbedParams.getWeight(feature) + eps);
      ParserState perturbedState = parseExample(perturbedParams, ex, true);
      double checkedGradient = (perturbedState.objectiveValue - state.objectiveValue) / eps;
      LogInfo.logs("Learner.checkGradient(): weight=%s, pertWeight=%s, obj=%s, pertObj=%s, feature=%s, computed=%s, checked=%s, diff=%s",
              params.getWeight(feature), perturbedParams.getWeight(feature),
              state.objectiveValue, perturbedState.objectiveValue,
              feature,
              computedGradient, checkedGradient, Math.abs(checkedGradient - computedGradient));
      LogInfo.end_track();
    }
  }

  private void sortOnFeedback() {
    for (SemanticFn semFn : semFuncsToUpdate) {
      semFn.sortOnFeedback(parser.getSearchParams(params));
    }
  }

  private void addFeedback(Example ex) {
    for (SemanticFn semFn : semFuncsToUpdate) {
      semFn.addFeedback(ex);
    }
  }

  private ParserState parseExample(Params params, Example ex, boolean computeExpectedCounts) {
    StopWatchSet.begin("Parser.parse");
    ParserState res = this.parser.parse(params, ex, computeExpectedCounts);
    StopWatchSet.end();
    return res;
  }

  private void updateWeights(Map<String, Double> counts) {
    StopWatchSet.begin("Learner.updateWeights");
    LogInfo.begin_track("Updating learner weights");
    double sum = 0;
    for (double v : counts.values()) sum += v * v;
    if (opts.verbose >= 2)
      SempreUtils.logMap(counts, "gradient");
    LogInfo.logs("L2 norm: %s", Math.sqrt(sum));
    params.update(counts);
    if (opts.verbose >= 2)
      params.log();
    counts.clear();
    LogInfo.end_track();
    StopWatchSet.end();
  }

  // Print summary over all examples
  private void logEvaluationStats(Evaluation evaluation, String prefix) {
    LogInfo.logs("Stats for %s: %s", prefix, evaluation.summary());
    // evaluation.add(LexiconFn.lexEval);
    evaluation.logStats(prefix);
    evaluation.putOutput(prefix);
    evaluation.putOutput(prefix.replaceAll("iter=", "").replace('.', '_'));
  }

  private void printLearnerEventsIter(Example ex, int iter, String group) {
    if (eventsOut == null)
      return;
    List<String> fields = new ArrayList<>();
    fields.add("iter=" + iter);
    fields.add("group=" + group);
    fields.add("utterance=" + ex.utterance);
    fields.add("targetValue=" + ex.targetValue);
    if (ex.predDerivations.size() > 0) {
      Derivation deriv = ex.predDerivations.get(0);
      fields.add("predValue=" + deriv.value);
      fields.add("predFormula=" + deriv.formula);
    }
    fields.add(ex.evaluation.summary("\t"));
    eventsOut.println(Joiner.on('\t').join(fields));
    eventsOut.flush();

    // Print out features and the compatibility across all the derivations
    if (opts.dumpFeaturesAndCompatibility) {
      for (Derivation deriv : ex.predDerivations) {
        fields = new ArrayList<>();
        fields.add("iter=" + iter);
        fields.add("group=" + group);
        fields.add("utterance=" + ex.utterance);
        Map<String, Double> features = new HashMap<>();
        deriv.incrementAllFeatureVector(1, features);
        for (String f : features.keySet()) {
          double v = features.get(f);
          fields.add(f + "=" + v);
        }
        fields.add("comp=" + deriv.compatibility);
        eventsOut.println(Joiner.on('\t').join(fields));
      }
    }
  }

  private void printLearnerEventsSummary(Evaluation evaluation,
                                         int iter,
                                         String group) {
    if (eventsOut == null)
      return;
    List<String> fields = new ArrayList<>();
    fields.add("iter=" + iter);
    fields.add("group=" + group);
    fields.add(evaluation.summary("\t"));
    eventsOut.println(Joiner.on('\t').join(fields));
    eventsOut.flush();
  }
}
