Examples with Chunk water.fvec.Chunk used on opensource projects

Example 31 with Chunk

use of water.fvec.Chunk in project h2o-2 by h2oai.

the class FrameTask method map.

/**
   * Extracts the values, applies standardization/normalization to numerics, adds appropriate offsets to categoricals,
   * and adapts response according to the CaseMode/CaseValue if set.
   */
@Override
public final void map(Chunk[] chunks, NewChunk[] outputs) {
    if (_jobKey != null && !Job.isRunning(_jobKey))
        throw new JobCancelledException();
    final int nrows = chunks[0]._len;
    final long offset = chunks[0]._start;
    chunkInit();
    double[] nums = MemoryManager.malloc8d(_dinfo._nums);
    int[] cats = MemoryManager.malloc4(_dinfo._cats);
    double[] response = _dinfo._responses == 0 ? null : MemoryManager.malloc8d(_dinfo._responses);
    int start = 0;
    int end = nrows;
    //random generator for skipping rows
    Random skip_rng = null;
    //Example:
    // _useFraction = 0.8 -> 1 repeat with fraction = 0.8
    // _useFraction = 1.0 -> 1 repeat with fraction = 1.0
    // _useFraction = 1.1 -> 2 repeats with fraction = 0.55
    // _useFraction = 2.1 -> 3 repeats with fraction = 0.7
    // _useFraction = 3.0 -> 3 repeats with fraction = 1.0
    final int repeats = (int) Math.ceil(_useFraction);
    final float fraction = _useFraction / repeats;
    if (fraction < 1.0)
        skip_rng = water.util.Utils.getDeterRNG(new Random().nextLong());
    long[] shuf_map = null;
    if (_shuffle) {
        shuf_map = new long[end - start];
        for (int i = 0; i < shuf_map.length; ++i) shuf_map[i] = start + i;
        Utils.shuffleArray(shuf_map, new Random().nextLong());
    }
    long num_processed_rows = 0;
    for (int rrr = 0; rrr < repeats; ++rrr) {
        OUTER: for (int rr = start; rr < end; ++rr) {
            final int r = shuf_map != null ? (int) shuf_map[rr - start] : rr;
            final long lr = r + chunks[0]._start;
            if ((_dinfo._nfolds > 0 && (lr % _dinfo._nfolds) == _dinfo._foldId) || (skip_rng != null && skip_rng.nextFloat() > fraction))
                continue;
            //count rows with missing values even if they are skipped
            ++num_processed_rows;
            // skip rows with NAs!
            for (Chunk c : chunks) if (skipMissing() && c.isNA0(r))
                continue OUTER;
            int i = 0, ncats = 0;
            for (; i < _dinfo._cats; ++i) {
                int c;
                if (chunks[i].isNA0(r)) {
                    //missing value turns into extra (last) factor
                    cats[ncats++] = (_dinfo._catOffsets[i + 1] - 1);
                } else {
                    c = (int) chunks[i].at80(r);
                    if (_dinfo._catLvls != null) {
                        // some levels are ignored?
                        c = Arrays.binarySearch(_dinfo._catLvls[i], c);
                        if (c >= 0)
                            cats[ncats++] = c + _dinfo._catOffsets[i];
                    } else if (_dinfo._useAllFactorLevels)
                        cats[ncats++] = c + _dinfo._catOffsets[i];
                    else if (c != 0)
                        cats[ncats++] = c + _dinfo._catOffsets[i] - 1;
                }
            }
            final int n = chunks.length - _dinfo._responses;
            for (; i < n; ++i) {
                //can be NA if skipMissing() == false
                double d = chunks[i].at0(r);
                if (_dinfo._normSub != null)
                    d -= _dinfo._normSub[i - _dinfo._cats];
                if (_dinfo._normMul != null)
                    d *= _dinfo._normMul[i - _dinfo._cats];
                nums[i - _dinfo._cats] = d;
            }
            for (i = 0; i < _dinfo._responses; ++i) {
                response[i] = chunks[chunks.length - _dinfo._responses + i].at0(r);
                if (_dinfo._normRespSub != null)
                    response[i] -= _dinfo._normRespSub[i];
                if (_dinfo._normRespMul != null)
                    response[i] *= _dinfo._normRespMul[i];
                // skip rows without a valid response (no supervised training possible)
                if (Double.isNaN(response[i]))
                    continue OUTER;
            }
            long seed = offset + rrr * (end - start) + r;
            if (outputs != null && outputs.length > 0)
                processRow(seed, nums, ncats, cats, response, outputs);
            else
                processRow(seed, nums, ncats, cats, response);
        }
    }
    chunkDone(num_processed_rows);
}

Example 32 with Chunk

use of water.fvec.Chunk in project h2o-2 by h2oai.

the class SpeeDRFModel method scoreOnTest.

private void scoreOnTest(Frame fr, Vec modelResp) {
    Frame scored = score(fr);
    water.api.ConfusionMatrix cm = new water.api.ConfusionMatrix();
    cm.vactual = fr.lastVec();
    cm.vpredict = scored.anyVec();
    cm.invoke();
    // Regression scoring
    if (regression) {
        float mse = (float) cm.mse;
        errs[errs.length - 1] = mse;
        cms[cms.length - 1] = null;
    // Classification scoring
    } else {
        Vec lv = scored.lastVec();
        double mse = CMTask.MSETask.doTask(scored.add("actual", fr.lastVec()));
        this.cm = cm.cm;
        errs[errs.length - 1] = (float) mse;
        ConfusionMatrix new_cm = new ConfusionMatrix(this.cm);
        cms[cms.length - 1] = new_cm;
        // Create the ROC Plot
        if (classes() == 2) {
            Vec v = null;
            Frame fa = null;
            if (lv.isInt()) {
                fa = new MRTask2() {

                    @Override
                    public void map(Chunk[] cs, NewChunk nchk) {
                        int rows = cs[0]._len;
                        int cols = cs.length - 1;
                        for (int r = 0; r < rows; ++r) {
                            nchk.addNum(cs[cols].at0(r) == 0 ? 1e-10 : 1.0 - 1e-10);
                        }
                    }
                }.doAll(1, scored).outputFrame(null, null);
                v = fa.anyVec();
            }
            AUC auc_calc = new AUC();
            auc_calc.vactual = cm.vactual;
            // lastVec is class1
            auc_calc.vpredict = v == null ? lv : v;
            auc_calc.invoke();
            validAUC = auc_calc.data();
            if (v != null)
                UKV.remove(v._key);
            if (fa != null)
                fa.delete();
            UKV.remove(lv._key);
        }
    }
    scored.remove("actual");
    scored.delete();
}

Example 33 with Chunk

use of water.fvec.Chunk in project h2o-3 by h2oai.

the class DataInfoTestAdapt method checkFrame.

private void checkFrame(final Frame checkMe, final Frame gold) {
    Vec[] vecs = new Vec[checkMe.numCols() + gold.numCols()];
    new MRTask() {

        @Override
        public void map(Chunk[] cs) {
            int off = checkMe.numCols();
            for (int i = 0; i < off; ++i) {
                for (int r = 0; r < cs[0]._len; ++r) {
                    double check = cs[i].atd(r);
                    double gold = cs[i + off].atd(r);
                    if (Math.abs(check - gold) > 1e-12)
                        throw new RuntimeException("bonk");
                }
            }
        }
    }.doAll(vecs);
}

Example 34 with Chunk

use of water.fvec.Chunk in project h2o-3 by h2oai.

the class XValPredictionsCheck method checkModel.

void checkModel(Model m, Vec foldId, int nclass) {
    if (// DRF does out of back instead of true training, nobs might be different
    !(m instanceof DRFModel))
        assertEquals(m._output._training_metrics._nobs, m._output._cross_validation_metrics._nobs);
    m.delete();
    m.deleteCrossValidationModels();
    Key[] xvalKeys = m._output._cross_validation_predictions;
    Key xvalKey = m._output._cross_validation_holdout_predictions_frame_id;
    final int[] id = new int[1];
    for (Key k : xvalKeys) {
        Frame preds = DKV.getGet(k);
        assert preds.numRows() == foldId.length();
        Vec[] vecs = new Vec[nclass + 1];
        vecs[0] = foldId;
        if (nclass == 1)
            vecs[1] = preds.anyVec();
        else
            System.arraycopy(preds.vecs(ArrayUtils.range(1, nclass)), 0, vecs, 1, nclass);
        new MRTask() {

            @Override
            public void map(Chunk[] cs) {
                Chunk foldId = cs[0];
                for (int r = 0; r < cs[0]._len; ++r) if (foldId.at8(r) != id[0])
                    for (int i = 1; i < cs.length; ++i) // no prediction for this row!
                    assert cs[i].atd(r) == 0;
            }
        }.doAll(vecs);
        id[0]++;
        preds.delete();
    }
    xvalKey.remove();
}

Example 35 with Chunk

use of water.fvec.Chunk in project h2o-3 by h2oai.

the class DeepLearningGradientCheck method gradientCheck.

@Test
public void gradientCheck() {
    Frame tfr = null;
    DeepLearningModel dl = null;
    try {
        tfr = parse_test_file("smalldata/glm_test/cancar_logIn.csv");
        for (String s : new String[] { "Merit", "Class" }) {
            Vec f = tfr.vec(s).toCategoricalVec();
            tfr.remove(s).remove();
            tfr.add(s, f);
        }
        DKV.put(tfr);
        tfr.add("Binary", tfr.anyVec().makeZero());
        new MRTask() {

            public void map(Chunk[] c) {
                for (int i = 0; i < c[0]._len; ++i) if (c[0].at8(i) == 1)
                    c[1].set(i, 1);
            }
        }.doAll(tfr.vecs(new String[] { "Class", "Binary" }));
        Vec cv = tfr.vec("Binary").toCategoricalVec();
        tfr.remove("Binary").remove();
        tfr.add("Binary", cv);
        DKV.put(tfr);
        Random rng = new Random(0xDECAF);
        int count = 0;
        int failedcount = 0;
        double maxRelErr = 0;
        double meanRelErr = 0;
        for (DistributionFamily dist : new DistributionFamily[] { DistributionFamily.gaussian, DistributionFamily.laplace, DistributionFamily.quantile, DistributionFamily.huber, // DistributionFamily.modified_huber,
        DistributionFamily.gamma, DistributionFamily.poisson, DistributionFamily.AUTO, DistributionFamily.tweedie, DistributionFamily.multinomial, DistributionFamily.bernoulli }) {
            for (DeepLearningParameters.Activation act : new DeepLearningParameters.Activation[] { //            DeepLearningParameters.Activation.ExpRectifier,
            DeepLearningParameters.Activation.Tanh, DeepLearningParameters.Activation.Rectifier }) {
                for (String response : new String[] { //binary classification
                "Binary", //multi-class
                "Class", //regression
                "Cost" }) {
                    for (boolean adaptive : new boolean[] { true, false }) {
                        for (int miniBatchSize : new int[] { 1 }) {
                            if (response.equals("Class")) {
                                if (dist != DistributionFamily.multinomial && dist != DistributionFamily.AUTO)
                                    continue;
                            } else if (response.equals("Binary")) {
                                if (dist != DistributionFamily.modified_huber && dist != DistributionFamily.bernoulli && dist != DistributionFamily.AUTO)
                                    continue;
                            } else {
                                if (dist == DistributionFamily.multinomial || dist == DistributionFamily.modified_huber || dist == DistributionFamily.bernoulli)
                                    continue;
                            }
                            DeepLearningParameters parms = new DeepLearningParameters();
                            parms._huber_alpha = rng.nextDouble() + 0.1;
                            parms._tweedie_power = 1.01 + rng.nextDouble() * 0.9;
                            parms._quantile_alpha = 0.05 + rng.nextDouble() * 0.9;
                            parms._train = tfr._key;
                            //converge to a reasonable model to avoid too large gradients
                            parms._epochs = 100;
                            parms._l1 = 1e-3;
                            parms._l2 = 1e-3;
                            parms._force_load_balance = false;
                            parms._hidden = new int[] { 10, 10, 10 };
                            //otherwise we introduce small bprop errors
                            parms._fast_mode = false;
                            parms._response_column = response;
                            parms._distribution = dist;
                            parms._max_w2 = 10;
                            parms._seed = 0xaaabbb;
                            parms._activation = act;
                            parms._adaptive_rate = adaptive;
                            parms._rate = 1e-4;
                            parms._momentum_start = 0.9;
                            parms._momentum_stable = 0.99;
                            parms._mini_batch_size = miniBatchSize;
                            //                DeepLearningModelInfo.gradientCheck = null;
                            //tell it what gradient to collect
                            DeepLearningModelInfo.gradientCheck = new DeepLearningModelInfo.GradientCheck(0, 0, 0);
                            // Build a first model; all remaining models should be equal
                            DeepLearning job = new DeepLearning(parms);
                            try {
                                dl = job.trainModel().get();
                                boolean classification = response.equals("Class") || response.equals("Binary");
                                if (!classification) {
                                    Frame p = dl.score(tfr);
                                    hex.ModelMetrics mm = hex.ModelMetrics.getFromDKV(dl, tfr);
                                    double resdev = ((ModelMetricsRegression) mm)._mean_residual_deviance;
                                    Log.info("Mean residual deviance: " + resdev);
                                    p.delete();
                                }
                                //golden version
                                DeepLearningModelInfo modelInfo = IcedUtils.deepCopy(dl.model_info());
                                //                Log.info(modelInfo.toStringAll());
                                long before = dl.model_info().checksum_impl();
                                float meanLoss = 0;
                                // loop over every row in the dataset and check that the predictions
                                for (int rId = 0; rId < tfr.numRows(); rId += 1) /*miniBatchSize*/
                                {
                                    // start from scratch - with a clean model
                                    dl.set_model_info(IcedUtils.deepCopy(modelInfo));
                                    final DataInfo di = dl.model_info().data_info();
                                    // populate miniBatch (consecutive rows)
                                    final DataInfo.Row[] rowsMiniBatch = new DataInfo.Row[miniBatchSize];
                                    for (int i = 0; i < rowsMiniBatch.length; ++i) {
                                        if (0 <= rId + i && rId + i < tfr.numRows()) {
                                            rowsMiniBatch[i] = new FrameTask.ExtractDenseRow(di, rId + i).doAll(di._adaptedFrame)._row;
                                        }
                                    }
                                    // loss at weight
                                    long cs = dl.model_info().checksum_impl();
                                    double loss = dl.meanLoss(rowsMiniBatch);
                                    assert (cs == before);
                                    assert (before == dl.model_info().checksum_impl());
                                    meanLoss += loss;
                                    for (int layer = 0; layer <= parms._hidden.length; ++layer) {
                                        int rows = dl.model_info().get_weights(layer).rows();
                                        assert (dl.model_info().get_biases(layer).size() == rows);
                                        for (int row = 0; row < rows; ++row) {
                                            //check bias
                                            if (true) {
                                                // start from scratch - with a clean model
                                                dl.set_model_info(IcedUtils.deepCopy(modelInfo));
                                                // do one forward propagation pass (and fill the mini-batch gradients -> set training=true)
                                                Neurons[] neurons = DeepLearningTask.makeNeuronsForTraining(dl.model_info());
                                                double[] responses = new double[miniBatchSize];
                                                double[] offsets = new double[miniBatchSize];
                                                int n = 0;
                                                for (DataInfo.Row myRow : rowsMiniBatch) {
                                                    if (myRow == null)
                                                        continue;
                                                    ((Neurons.Input) neurons[0]).setInput(-1, myRow.numIds, myRow.numVals, myRow.nBins, myRow.binIds, n);
                                                    responses[n] = myRow.response(0);
                                                    offsets[n] = myRow.offset;
                                                    n++;
                                                }
                                                DeepLearningTask.fpropMiniBatch(-1, /*seed doesn't matter*/
                                                neurons, dl.model_info(), null, true, /*training*/
                                                responses, offsets, n);
                                                // check that we didn't change the model's weights/biases
                                                long after = dl.model_info().checksum_impl();
                                                assert (after == before);
                                                // record the gradient since gradientChecking is enabled
                                                //tell it what gradient to collect
                                                DeepLearningModelInfo.gradientCheck = new DeepLearningModelInfo.GradientCheck(layer, row, -1);
                                                //update the weights and biases
                                                DeepLearningTask.bpropMiniBatch(neurons, n);
                                                assert (before != dl.model_info().checksum_impl());
                                                // reset the model back to the trained model
                                                dl.set_model_info(IcedUtils.deepCopy(modelInfo));
                                                assert (before == dl.model_info().checksum_impl());
                                                double bpropGradient = DeepLearningModelInfo.gradientCheck.gradient;
                                                // FIXME: re-enable this once the loss is computed from the de-standardized prediction/response
                                                //                    double actualResponse=myRow.response[0];
                                                //                    double predResponseLinkSpace = neurons[neurons.length-1]._a.get(0);
                                                //                    if (di._normRespMul != null) {
                                                //                      bpropGradient /= di._normRespMul[0]; //no shift for gradient
                                                //                      actualResponse = (actualResponse / di._normRespMul[0] + di._normRespSub[0]);
                                                //                      predResponseLinkSpace = (predResponseLinkSpace / di._normRespMul[0] + di._normRespSub[0]);
                                                //                    }
                                                //                    bpropGradient *= new Distribution(parms._distribution).gradient(actualResponse, predResponseLinkSpace);
                                                final double bias = dl.model_info().get_biases(layer).get(row);
                                                //don't make the weight deltas too small, or the float weights "won't notice"
                                                double eps = 1e-4 * Math.abs(bias);
                                                if (eps == 0)
                                                    eps = 1e-6;
                                                // loss at bias + eps
                                                dl.model_info().get_biases(layer).set(row, bias + eps);
                                                double up = dl.meanLoss(rowsMiniBatch);
                                                // loss at bias - eps
                                                dl.model_info().get_biases(layer).set(row, bias - eps);
                                                double down = dl.meanLoss(rowsMiniBatch);
                                                if (Math.abs(up - down) / Math.abs(up + down) < 1e-8) {
                                                    //relative change in loss function is too small -> skip
                                                    continue;
                                                }
                                                double gradient = ((up - down) / (2. * eps));
                                                double relError = 2 * Math.abs(bpropGradient - gradient) / (Math.abs(gradient) + Math.abs(bpropGradient));
                                                count++;
                                                // if either gradient is tiny, check if both are tiny
                                                if (Math.abs(gradient) < 1e-7 || Math.abs(bpropGradient) < 1e-7) {
                                                    //all good
                                                    if (Math.abs(bpropGradient - gradient) < 1e-7)
                                                        continue;
                                                }
                                                meanRelErr += relError;
                                                if (relError > MAX_TOLERANCE) {
                                                    Log.info("\nDistribution: " + dl._parms._distribution);
                                                    Log.info("\nRow: " + rId);
                                                    Log.info("bias (layer " + layer + ", row " + row + "): " + bias + " +/- " + eps);
                                                    Log.info("loss: " + loss);
                                                    Log.info("losses up/down: " + up + " / " + down);
                                                    Log.info("=> Finite differences gradient: " + gradient);
                                                    Log.info("=> Back-propagation gradient  : " + bpropGradient);
                                                    Log.info("=> Relative error             : " + PrettyPrint.formatPct(relError));
                                                    failedcount++;
                                                }
                                            }
                                            int cols = dl.model_info().get_weights(layer).cols();
                                            for (int col = 0; col < cols; ++col) {
                                                if (rng.nextFloat() >= SAMPLE_RATE)
                                                    continue;
                                                // start from scratch - with a clean model
                                                dl.set_model_info(IcedUtils.deepCopy(modelInfo));
                                                // do one forward propagation pass (and fill the mini-batch gradients -> set training=true)
                                                Neurons[] neurons = DeepLearningTask.makeNeuronsForTraining(dl.model_info());
                                                double[] responses = new double[miniBatchSize];
                                                double[] offsets = new double[miniBatchSize];
                                                int n = 0;
                                                for (DataInfo.Row myRow : rowsMiniBatch) {
                                                    if (myRow == null)
                                                        continue;
                                                    ((Neurons.Input) neurons[0]).setInput(-1, myRow.numIds, myRow.numVals, myRow.nBins, myRow.binIds, n);
                                                    responses[n] = myRow.response(0);
                                                    offsets[n] = myRow.offset;
                                                    n++;
                                                }
                                                DeepLearningTask.fpropMiniBatch(-1, /*seed doesn't matter*/
                                                neurons, dl.model_info(), null, true, /*training*/
                                                responses, offsets, n);
                                                // check that we didn't change the model's weights/biases
                                                long after = dl.model_info().checksum_impl();
                                                assert (after == before);
                                                // record the gradient since gradientChecking is enabled
                                                //tell it what gradient to collect
                                                DeepLearningModelInfo.gradientCheck = new DeepLearningModelInfo.GradientCheck(layer, row, col);
                                                //update the weights
                                                DeepLearningTask.bpropMiniBatch(neurons, n);
                                                assert (before != dl.model_info().checksum_impl());
                                                // reset the model back to the trained model
                                                dl.set_model_info(IcedUtils.deepCopy(modelInfo));
                                                assert (before == dl.model_info().checksum_impl());
                                                double bpropGradient = DeepLearningModelInfo.gradientCheck.gradient;
                                                // FIXME: re-enable this once the loss is computed from the de-standardized prediction/response
                                                //                    double actualResponse=myRow.response[0];
                                                //                    double predResponseLinkSpace = neurons[neurons.length-1]._a.get(0);
                                                //                    if (di._normRespMul != null) {
                                                //                      bpropGradient /= di._normRespMul[0]; //no shift for gradient
                                                //                      actualResponse = (actualResponse / di._normRespMul[0] + di._normRespSub[0]);
                                                //                      predResponseLinkSpace = (predResponseLinkSpace / di._normRespMul[0] + di._normRespSub[0]);
                                                //                    }
                                                //                    bpropGradient *= new Distribution(parms._distribution).gradient(actualResponse, predResponseLinkSpace);
                                                final float weight = dl.model_info().get_weights(layer).get(row, col);
                                                //don't make the weight deltas too small, or the float weights "won't notice"
                                                double eps = 1e-4 * Math.abs(weight);
                                                if (eps == 0)
                                                    eps = 1e-6;
                                                // loss at weight + eps
                                                dl.model_info().get_weights(layer).set(row, col, (float) (weight + eps));
                                                double up = dl.meanLoss(rowsMiniBatch);
                                                // loss at weight - eps
                                                dl.model_info().get_weights(layer).set(row, col, (float) (weight - eps));
                                                double down = dl.meanLoss(rowsMiniBatch);
                                                if (Math.abs(up - down) / Math.abs(up + down) < 1e-8) {
                                                    //relative change in loss function is too small -> skip
                                                    continue;
                                                }
                                                double gradient = ((up - down) / (2. * eps));
                                                double relError = 2 * Math.abs(bpropGradient - gradient) / (Math.abs(gradient) + Math.abs(bpropGradient));
                                                count++;
                                                // if either gradient is tiny, check if both are tiny
                                                if (Math.abs(gradient) < 1e-7 || Math.abs(bpropGradient) < 1e-7) {
                                                    //all good
                                                    if (Math.abs(bpropGradient - gradient) < 1e-7)
                                                        continue;
                                                }
                                                meanRelErr += relError;
                                                if (relError > MAX_TOLERANCE) {
                                                    Log.info("\nDistribution: " + dl._parms._distribution);
                                                    Log.info("\nRow: " + rId);
                                                    Log.info("weight (layer " + layer + ", row " + row + ", col " + col + "): " + weight + " +/- " + eps);
                                                    Log.info("loss: " + loss);
                                                    Log.info("losses up/down: " + up + " / " + down);
                                                    Log.info("=> Finite differences gradient: " + gradient);
                                                    Log.info("=> Back-propagation gradient  : " + bpropGradient);
                                                    Log.info("=> Relative error             : " + PrettyPrint.formatPct(relError));
                                                    failedcount++;
                                                }
                                                //                          Assert.assertTrue(failedcount==0);
                                                maxRelErr = Math.max(maxRelErr, relError);
                                                assert (!Double.isNaN(maxRelErr));
                                            }
                                        }
                                    }
                                }
                                meanLoss /= tfr.numRows();
                                Log.info("Mean loss: " + meanLoss);
                            //                  // FIXME: re-enable this
                            //                  if (parms._l1 == 0 && parms._l2 == 0) {
                            //                    assert(Math.abs(meanLoss-resdev)/Math.abs(resdev) < 1e-5);
                            //                  }
                            } catch (RuntimeException ex) {
                                dl = DKV.getGet(job.dest());
                                if (dl != null)
                                    Assert.assertTrue(dl.model_info().isUnstable());
                                else
                                    Assert.assertTrue(job.isStopped());
                            } finally {
                                if (dl != null)
                                    dl.delete();
                            }
                        }
                    }
                }
            }
        }
        Log.info("Number of tests: " + count);
        Log.info("Number of failed tests: " + failedcount);
        Log.info("Mean. relative error: " + meanRelErr / count);
        Log.info("Max. relative error: " + PrettyPrint.formatPct(maxRelErr));
        Assert.assertTrue("Error too large: " + maxRelErr + " >= " + MAX_TOLERANCE, maxRelErr < MAX_TOLERANCE);
        Assert.assertTrue("Failed count too large: " + failedcount + " > " + MAX_FAILED_COUNT, failedcount <= MAX_FAILED_COUNT);
    } finally {
        if (tfr != null)
            tfr.remove();
    }
}