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From myui <...@git.apache.org>
Subject [GitHub] incubator-hivemall pull request #87: [HIVEMALL-108] Support `-iter` option i...
Date Wed, 28 Jun 2017 11:51:29 GMT
Github user myui commented on a diff in the pull request:

    https://github.com/apache/incubator-hivemall/pull/87#discussion_r124519932
  
    --- Diff: core/src/main/java/hivemall/GeneralLearnerBaseUDTF.java ---
    @@ -318,66 +412,240 @@ protected void onlineUpdate(@Nonnull final FeatureValue[] features,
final float
         @Override
         public final void close() throws HiveException {
             super.close();
    -        if (model != null) {
    -            if (accumulated != null) { // Update model with accumulated delta
    -                batchUpdate();
    -                this.accumulated = null;
    -            }
    -            int numForwarded = 0;
    -            if (useCovariance()) {
    -                final WeightValueWithCovar probe = new WeightValueWithCovar();
    -                final Object[] forwardMapObj = new Object[3];
    -                final FloatWritable fv = new FloatWritable();
    -                final FloatWritable cov = new FloatWritable();
    -                final IMapIterator<Object, IWeightValue> itor = model.entries();
    -                while (itor.next() != -1) {
    -                    itor.getValue(probe);
    -                    if (!probe.isTouched()) {
    -                        continue; // skip outputting untouched weights
    +        finalizeTraining();
    +        forwardModel();
    +        this.accumulated = null;
    +        this.model = null;
    +    }
    +
    +    @VisibleForTesting
    +    public void finalizeTraining() throws HiveException {
    +        if (count == 0L) {
    +            this.model = null;
    +            return;
    +        }
    +        if (is_mini_batch) { // Update model with accumulated delta
    +            batchUpdate();
    +        }
    +        if (iterations > 1) {
    +            runIterativeTraining(iterations);
    +        }
    +    }
    +
    +    protected final void runIterativeTraining(@Nonnegative final int iterations)
    +            throws HiveException {
    +        final ByteBuffer buf = this.inputBuf;
    +        final NioStatefullSegment dst = this.fileIO;
    +        assert (buf != null);
    +        assert (dst != null);
    +        final long numTrainingExamples = count;
    +
    +        final Reporter reporter = getReporter();
    +        final Counters.Counter iterCounter = (reporter == null) ? null : reporter.getCounter(
    +            "hivemall.GeneralLearnerBase$Counter", "iteration");
    +
    +        try {
    +            if (dst.getPosition() == 0L) {// run iterations w/o temporary file
    +                if (buf.position() == 0) {
    +                    return; // no training example
    +                }
    +                buf.flip();
    +
    +                int iter = 2;
    +                double cumLossPrev;
    +                for (; iter <= iterations; iter++) {
    +                    cumLossPrev = cumLoss;
    +                    this.cumLoss = 0.d;
    +
    +                    reportProgress(reporter);
    +                    setCounterValue(iterCounter, iter);
    +
    +                    while (buf.remaining() > 0) {
    +                        int recordBytes = buf.getInt();
    +                        assert (recordBytes > 0) : recordBytes;
    +                        int featureVectorLength = buf.getInt();
    +                        final FeatureValue[] featureVector = new FeatureValue[featureVectorLength];
    +                        for (int j = 0; j < featureVectorLength; j++) {
    +                            featureVector[j] = new FeatureValue(buf);
    +                        }
    +                        float target = buf.getFloat();
    +                        train(featureVector, target);
    +                    }
    +                    buf.rewind();
    +
    +                    if (is_mini_batch) { // Update model with accumulated delta
    +                        batchUpdate();
    +                    }
    +
    +                    logger.info("[iter " + iter + "] cumulative loss: " + cumLoss);
    +
    +                    if (Math.abs(cumLossPrev - cumLoss) < tol) {
    --- End diff --
    
    `cumLoss` is always 0 due to ` this.cumLoss = 0.d;`. So, this conversion check is not
working properly.


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