Exemple #1
0
    // Stopping criteria
    boolean isDone(KMeansModel model, double[][] newCenters, double[][] oldCenters) {
      if (!isRunning()) return true; // Stopped/cancelled
      // Stopped for running out iterations
      if (model._output._iterations >= _parms._max_iterations) return true;

      // Compute average change in standardized cluster centers
      if (oldCenters == null) return false; // No prior iteration, not stopping
      double average_change = 0;
      for (int clu = 0; clu < _parms._k; clu++)
        average_change +=
            hex.genmodel.GenModel.KMeans_distance(
                oldCenters[clu], newCenters[clu], _isCats, null, null);
      average_change /= _parms._k; // Average change per cluster
      model._output._avg_centroids_chg =
          ArrayUtils.copyAndFillOf(
              model._output._avg_centroids_chg,
              model._output._avg_centroids_chg.length + 1,
              average_change);
      model._output._training_time_ms =
          ArrayUtils.copyAndFillOf(
              model._output._training_time_ms,
              model._output._training_time_ms.length + 1,
              System.currentTimeMillis());
      return average_change < TOLERANCE;
    }
Exemple #2
0
    /**
     * Train a Deep Learning neural net model
     *
     * @param model Input model (e.g., from initModel(), or from a previous training run)
     * @return Trained model
     */
    public final DeepLearningModel trainModel(DeepLearningModel model) {
      Frame validScoreFrame = null;
      Frame train, trainScoreFrame;
      try {
        //      if (checkpoint == null && !quiet_mode) logStart(); //if checkpoint is given, some
        // Job's params might be uninitialized (but the restarted model's parameters are correct)
        if (model == null) {
          model = DKV.get(dest()).get();
        }
        Log.info(
            "Model category: "
                + (_parms._autoencoder
                    ? "Auto-Encoder"
                    : isClassifier() ? "Classification" : "Regression"));
        final long model_size = model.model_info().size();
        Log.info(
            "Number of model parameters (weights/biases): " + String.format("%,d", model_size));
        model.write_lock(_job);
        _job.update(0, "Setting up training data...");
        final DeepLearningParameters mp = model.model_info().get_params();

        // temporary frames of the same "name" as the orig _train/_valid (asking the parameter's
        // Key, not the actual frame)
        // Note: don't put into DKV or they would overwrite the _train/_valid frames!
        Frame tra_fr = new Frame(mp._train, _train.names(), _train.vecs());
        Frame val_fr = _valid != null ? new Frame(mp._valid, _valid.names(), _valid.vecs()) : null;

        train = tra_fr;
        if (model._output.isClassifier() && mp._balance_classes) {
          _job.update(0, "Balancing class distribution of training data...");
          float[] trainSamplingFactors =
              new float
                  [train
                      .lastVec()
                      .domain()
                      .length]; // leave initialized to 0 -> will be filled up below
          if (mp._class_sampling_factors != null) {
            if (mp._class_sampling_factors.length != train.lastVec().domain().length)
              throw new IllegalArgumentException(
                  "class_sampling_factors must have "
                      + train.lastVec().domain().length
                      + " elements");
            trainSamplingFactors =
                mp._class_sampling_factors.clone(); // clone: don't modify the original
          }
          train =
              sampleFrameStratified(
                  train,
                  train.lastVec(),
                  train.vec(model._output.weightsName()),
                  trainSamplingFactors,
                  (long) (mp._max_after_balance_size * train.numRows()),
                  mp._seed,
                  true,
                  false);
          Vec l = train.lastVec();
          Vec w = train.vec(model._output.weightsName());
          MRUtils.ClassDist cd = new MRUtils.ClassDist(l);
          model._output._modelClassDist =
              _weights != null ? cd.doAll(l, w).rel_dist() : cd.doAll(l).rel_dist();
        }
        model.training_rows = train.numRows();
        if (_weights != null && _weights.min() == 0 && _weights.max() == 1 && _weights.isInt()) {
          model.training_rows = Math.round(train.numRows() * _weights.mean());
          Log.warn(
              "Not counting "
                  + (train.numRows() - model.training_rows)
                  + " rows with weight=0 towards an epoch.");
        }
        Log.info("One epoch corresponds to " + model.training_rows + " training data rows.");
        trainScoreFrame =
            sampleFrame(
                train,
                mp._score_training_samples,
                mp._seed); // training scoring dataset is always sampled uniformly from the training
                           // dataset
        if (trainScoreFrame != train) Scope.track(trainScoreFrame);

        if (!_parms._quiet_mode)
          Log.info("Number of chunks of the training data: " + train.anyVec().nChunks());
        if (val_fr != null) {
          model.validation_rows = val_fr.numRows();
          // validation scoring dataset can be sampled in multiple ways from the given validation
          // dataset
          if (model._output.isClassifier()
              && mp._balance_classes
              && mp._score_validation_sampling
                  == DeepLearningParameters.ClassSamplingMethod.Stratified) {
            _job.update(0, "Sampling validation data (stratified)...");
            validScoreFrame =
                sampleFrameStratified(
                    val_fr,
                    val_fr.lastVec(),
                    val_fr.vec(model._output.weightsName()),
                    null,
                    mp._score_validation_samples > 0
                        ? mp._score_validation_samples
                        : val_fr.numRows(),
                    mp._seed + 1,
                    false /* no oversampling */,
                    false);
          } else {
            _job.update(0, "Sampling validation data...");
            validScoreFrame = sampleFrame(val_fr, mp._score_validation_samples, mp._seed + 1);
            if (validScoreFrame != val_fr) Scope.track(validScoreFrame);
          }
          if (!_parms._quiet_mode)
            Log.info(
                "Number of chunks of the validation data: " + validScoreFrame.anyVec().nChunks());
        }

        // Set train_samples_per_iteration size (cannot be done earlier since this depends on
        // whether stratified sampling is done)
        model.actual_train_samples_per_iteration =
            computeTrainSamplesPerIteration(mp, model.training_rows, model);
        // Determine whether shuffling is enforced
        if (mp._replicate_training_data
            && (model.actual_train_samples_per_iteration
                == model.training_rows * (mp._single_node_mode ? 1 : H2O.CLOUD.size()))
            && !mp._shuffle_training_data
            && H2O.CLOUD.size() > 1
            && !mp._reproducible) {
          if (!mp._quiet_mode)
            Log.info(
                "Enabling training data shuffling, because all nodes train on the full dataset (replicated training data).");
          mp._shuffle_training_data = true;
        }
        if (!mp._shuffle_training_data
            && model.actual_train_samples_per_iteration == model.training_rows
            && train.anyVec().nChunks() == 1) {
          if (!mp._quiet_mode)
            Log.info(
                "Enabling training data shuffling to avoid training rows in the same order over and over (no Hogwild since there's only 1 chunk).");
          mp._shuffle_training_data = true;
        }

        //        if (!mp._quiet_mode) Log.info("Initial model:\n" + model.model_info());
        long now = System.currentTimeMillis();
        model._timeLastIterationEnter = now;
        if (_parms._autoencoder) {
          _job.update(0, "Scoring null model of autoencoder...");
          if (!mp._quiet_mode) Log.info("Scoring the null model of the autoencoder.");
          model.doScoring(
              trainScoreFrame,
              validScoreFrame,
              _job._key,
              0,
              false); // get the null model reconstruction error
        }
        // put the initial version of the model into DKV
        model.update(_job);
        model.total_setup_time_ms += now - _job.start_time();
        Log.info("Total setup time: " + PrettyPrint.msecs(model.total_setup_time_ms, true));
        Log.info("Starting to train the Deep Learning model.");
        _job.update(0, "Training...");

        // main loop
        for (; ; ) {
          model.iterations++;
          model.set_model_info(
              mp._epochs == 0
                  ? model.model_info()
                  : H2O.CLOUD.size() > 1 && mp._replicate_training_data
                      ? (mp._single_node_mode
                          ? new DeepLearningTask2(
                                  _job._key,
                                  train,
                                  model.model_info(),
                                  rowFraction(train, mp, model),
                                  model.iterations)
                              .doAll(Key.make(H2O.SELF))
                              .model_info()
                          : // replicated data + single node mode
                          new DeepLearningTask2(
                                  _job._key,
                                  train,
                                  model.model_info(),
                                  rowFraction(train, mp, model),
                                  model.iterations)
                              .doAllNodes()
                              .model_info())
                      : // replicated data + multi-node mode
                      new DeepLearningTask(
                              _job._key,
                              model.model_info(),
                              rowFraction(train, mp, model),
                              model.iterations)
                          .doAll(train)
                          .model_info()); // distributed data (always in multi-node mode)
          if (stop_requested() && !timeout()) break; // cancellation
          if (!model.doScoring(
              trainScoreFrame, validScoreFrame, _job._key, model.iterations, false))
            break; // finished training (or early stopping or convergence)
          if (timeout()) break; // stop after scoring
        }

        // replace the model with the best model so far (if it's better)
        if (!stop_requested()
            && _parms._overwrite_with_best_model
            && model.actual_best_model_key != null
            && _parms._nfolds == 0) {
          DeepLearningModel best_model = DKV.getGet(model.actual_best_model_key);
          if (best_model != null
              && best_model.loss() < model.loss()
              && Arrays.equals(best_model.model_info().units, model.model_info().units)) {
            if (!_parms._quiet_mode)
              Log.info("Setting the model to be the best model so far (based on scoring history).");
            DeepLearningModelInfo mi = best_model.model_info().deep_clone();
            // Don't cheat - count full amount of training samples, since that's the amount of
            // training it took to train (without finding anything better)
            mi.set_processed_global(model.model_info().get_processed_global());
            mi.set_processed_local(model.model_info().get_processed_local());
            model.set_model_info(mi);
            model.update(_job);
            model.doScoring(trainScoreFrame, validScoreFrame, _job._key, model.iterations, true);
            assert (best_model.loss() == model.loss());
          }
        }
        // store coefficient names for future use
        // possibly change
        model.model_info().data_info().coefNames();
        if (!_parms._quiet_mode) {
          Log.info(
              "==============================================================================================================================================================================");
          if (stop_requested()) {
            Log.info("Deep Learning model training was interrupted.");
          } else {
            Log.info("Finished training the Deep Learning model.");
            Log.info(model);
          }
          Log.info(
              "==============================================================================================================================================================================");
        }
      } finally {
        if (model != null) {
          model.deleteElasticAverageModels();
          model.unlock(_job);
          if (model.actual_best_model_key != null) {
            assert (model.actual_best_model_key != model._key);
            DKV.remove(model.actual_best_model_key);
          }
        }
      }
      return model;
    }
Exemple #3
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  protected double doScoringAndSaveModel(
      boolean finalScoring, boolean oob, boolean build_tree_one_node) {
    double training_r2 = Double.NaN; // Training R^2 value, if computed
    long now = System.currentTimeMillis();
    if (_firstScore == 0) _firstScore = now;
    long sinceLastScore = now - _timeLastScoreStart;
    boolean updated = false;
    new ProgressUpdate(
            "Built " + _model._output._ntrees + " trees so far (out of " + _parms._ntrees + ").")
        .fork(_progressKey);
    // Now model already contains tid-trees in serialized form
    if (_parms._score_each_iteration
        || finalScoring
        || (now - _firstScore < 4000)
        || // Score every time for 4 secs
        // Throttle scoring to keep the cost sane; limit to a 10% duty cycle & every 4 secs
        (sinceLastScore > 4000
            && // Limit scoring updates to every 4sec
            (double) (_timeLastScoreEnd - _timeLastScoreStart) / sinceLastScore
                < 0.1)) { // 10% duty cycle

      checkMemoryFootPrint();

      // If validation is specified we use a model for scoring, so we need to
      // update it!  First we save model with trees (i.e., make them available
      // for scoring) and then update it with resulting error
      _model.update(_key);
      updated = true;

      Log.info("============================================================== ");
      SharedTreeModel.SharedTreeOutput out = _model._output;
      _timeLastScoreStart = now;
      // Score on training data
      new ProgressUpdate("Scoring the model.").fork(_progressKey);
      Score sc =
          new Score(this, true, oob, _model._output.getModelCategory())
              .doAll(train(), build_tree_one_node);
      ModelMetrics mm = sc.makeModelMetrics(_model, _parms.train());
      out._training_metrics = mm;
      if (oob)
        out._training_metrics._description = "Metrics reported on Out-Of-Bag training samples";
      out._scored_train[out._ntrees].fillFrom(mm);
      if (out._ntrees > 0) Log.info("Training " + out._scored_train[out._ntrees].toString());

      // Score again on validation data
      if (_parms._valid != null) {
        Score scv =
            new Score(this, false, false, _model._output.getModelCategory())
                .doAll(valid(), build_tree_one_node);
        ModelMetrics mmv = scv.makeModelMetrics(_model, _parms.valid());
        out._validation_metrics = mmv;
        out._scored_valid[out._ntrees].fillFrom(mmv);
        if (out._ntrees > 0) Log.info("Validation " + out._scored_valid[out._ntrees].toString());
      }

      if (out._ntrees > 0) { // Compute variable importances
        out._model_summary = createModelSummaryTable(out);
        out._scoring_history = createScoringHistoryTable(out);
        out._varimp = new hex.VarImp(_improvPerVar, out._names);
        out._variable_importances = hex.ModelMetrics.calcVarImp(out._varimp);
        Log.info(out._model_summary.toString());
        // For Debugging:
        //        Log.info(out._scoring_history.toString());
        //        Log.info(out._variable_importances.toString());
      }

      ConfusionMatrix cm = mm.cm();
      if (cm != null) {
        if (cm._cm.length <= _parms._max_confusion_matrix_size) {
          Log.info(cm.toASCII());
        } else {
          Log.info(
              "Confusion Matrix is too large (max_confusion_matrix_size="
                  + _parms._max_confusion_matrix_size
                  + "): "
                  + _nclass
                  + " classes.");
        }
      }
      _timeLastScoreEnd = System.currentTimeMillis();
    }

    // Double update - after either scoring or variable importance
    if (updated) _model.update(_key);
    return training_r2;
  }