public Vec replace(int col, Vec nv) {
assert col < _names.length;
Vec rv = vecs()[col];
assert rv.group().equals(nv.group());
_vecs[col] = nv;
_keys[col] = nv._key;
if (DKV.get(nv._key) == null) // If not already in KV, put it there
DKV.put(nv._key, nv);
return rv;
}
@Override
public void modifyParmsForCrossValidationMainModel(ModelBuilder[] cvModelBuilders) {
_parms._overwrite_with_best_model = false;
if (_parms._stopping_rounds == 0 && _parms._max_runtime_secs == 0)
return; // No exciting changes to stopping conditions
// Extract stopping conditions from each CV model, and compute the best stopping answer
_parms._stopping_rounds = 0;
_parms._max_runtime_secs = 0;
double sum = 0;
for (ModelBuilder cvmb : cvModelBuilders)
sum += ((DeepLearningModel) DKV.getGet(cvmb.dest())).last_scored().epoch_counter;
_parms._epochs = sum / cvModelBuilders.length;
if (!_parms._quiet_mode) {
warn(
"_epochs",
"Setting optimal _epochs to "
+ _parms._epochs
+ " for cross-validation main model based on early stopping of cross-validation models.");
warn(
"_stopping_rounds",
"Disabling convergence-based early stopping for cross-validation main model.");
warn(
"_max_runtime_secs",
"Disabling maximum allowed runtime for cross-validation main model.");
}
}
public Frame(String[] names, Vec[] vecs) {
// assert names==null || names.length == vecs.length : "Number of columns does not match to
// number of cols' names.";
_names = names;
_vecs = vecs;
_keys = new Key[vecs.length];
for (int i = 0; i < vecs.length; i++) {
Key k = _keys[i] = vecs[i]._key;
if (DKV.get(k) == null) // If not already in KV, put it there
DKV.put(k, vecs[i]);
}
Vec v0 = anyVec();
if (v0 == null) return;
VectorGroup grp = v0.group();
for (int i = 0; i < vecs.length; i++) assert grp.equals(vecs[i].group());
}
// ------------------------------------------------------------------------
// Zipped file; no parallel decompression; decompress into local chunks,
// parse local chunks; distribute chunks later.
ParseWriter streamParseZip(final InputStream is, final StreamParseWriter dout, InputStream bvs)
throws IOException {
// All output into a fresh pile of NewChunks, one per column
if (!_setup._parse_type._parallelParseSupported) throw H2O.unimpl();
StreamData din = new StreamData(is);
int cidx = 0;
StreamParseWriter nextChunk = dout;
int zidx = bvs.read(null, 0, 0); // Back-channel read of chunk index
assert zidx == 1;
while (is.available() > 0) {
int xidx = bvs.read(null, 0, 0); // Back-channel read of chunk index
if (xidx > zidx) { // Advanced chunk index of underlying ByteVec stream?
zidx = xidx; // Record advancing of chunk
nextChunk.close(); // Match output chunks to input zipfile chunks
if (dout != nextChunk) {
dout.reduce(nextChunk);
if (_jobKey != null && ((Job) DKV.getGet(_jobKey)).isCancelledOrCrashed()) break;
}
nextChunk = nextChunk.nextChunk();
}
parseChunk(cidx++, din, nextChunk);
}
parseChunk(cidx, din, nextChunk); // Parse the remaining partial 32K buffer
nextChunk.close();
if (dout != nextChunk) dout.reduce(nextChunk);
return dout;
}
public static ValueArray loadAndParseKey(Key okey, String path) {
FileIntegrityChecker c = FileIntegrityChecker.check(new File(path),false);
Key k = c.syncDirectory(null,null,null,null);
ParseDataset.forkParseDataset(okey, new Key[] { k }, null).get();
UKV.remove(k);
ValueArray res = DKV.get(okey).get();
return res;
}
// Close all AppendableVec
public Futures closeAppendables(Futures fs) {
_col0 = null; // Reset cache
int len = vecs().length;
for (int i = 0; i < len; i++) {
Vec v = _vecs[i];
if (v instanceof AppendableVec)
DKV.put(_keys[i], _vecs[i] = ((AppendableVec) v).close(fs), fs);
}
return fs;
}
ParseWriter streamParse(final InputStream is, final ParseWriter dout) throws IOException {
if (!_setup._parse_type._parallelParseSupported) throw H2O.unimpl();
StreamData din = new StreamData(is);
int cidx = 0;
// FIXME leaving _jobKey == null until sampling is done, this mean entire zip files
// FIXME are parsed for parseSetup
while (is.available() > 0
&& (_jobKey == null || !((Job) DKV.getGet(_jobKey)).isCancelledOrCrashed()))
parseChunk(cidx++, din, dout);
parseChunk(cidx, din, dout); // Parse the remaining partial 32K buffer
return dout;
}
@Override
protected Key parse(String input) {
if (_validator != null) _validator.validateRaw(input);
Key k = Key.make(input);
Value v = DKV.get(k);
if (v == null && _mustExist)
throw new IllegalArgumentException("Key '" + input + "' does not exist!");
if (_type != null) {
if (v != null && !compatible(_type, v.get()))
throw new IllegalArgumentException(input + ":" + errors()[0]);
if (v == null && _required)
throw new IllegalArgumentException("Key '" + input + "' does not exist!");
}
return k;
}
@Override
public void map(Chunk cs) {
int idx = _chunkOffset + cs.cidx();
Key ckey = Vec.chunkKey(_v._key, idx);
if (_cmap != null) {
assert !cs.hasFloat()
: "Input chunk (" + cs.getClass() + ") has float, but is expected to be categorical";
NewChunk nc = new NewChunk(_v, idx);
// loop over rows and update ints for new domain mapping according to vecs[c].domain()
for (int r = 0; r < cs._len; ++r) {
if (cs.isNA(r)) nc.addNA();
else nc.addNum(_cmap[(int) cs.at8(r)], 0);
}
nc.close(_fs);
} else {
DKV.put(ckey, cs.deepCopy(), _fs, true);
}
}
@Override
protected void doGet(HttpServletRequest request, HttpServletResponse response)
throws IOException, ServletException {
String uri = getDecodedUri(request);
try {
boolean use_hex = false;
String f_name = request.getParameter("frame_id");
String hex_string = request.getParameter("hex_string");
if (f_name == null) {
throw new RuntimeException("Cannot find value for parameter \'frame_id\'");
}
if (hex_string != null && hex_string.toLowerCase().equals("true")) {
use_hex = true;
}
Frame dataset = DKV.getGet(f_name);
// TODO: Find a way to determing the hex_string parameter. It should not always be false
InputStream is = dataset.toCSV(true, use_hex);
response.setContentType("application/octet-stream");
// Clean up the file name
int x = f_name.length() - 1;
boolean dot = false;
for (; x >= 0; x--)
if (!Character.isLetterOrDigit(f_name.charAt(x)) && f_name.charAt(x) != '_')
if (f_name.charAt(x) == '.' && !dot) dot = true;
else break;
String suggested_fname = f_name.substring(x + 1).replace(".hex", ".csv");
if (!suggested_fname.endsWith(".csv")) suggested_fname = suggested_fname + ".csv";
f_name = suggested_fname;
response.addHeader("Content-Disposition", "attachment; filename=" + f_name);
setResponseStatus(response, HttpServletResponse.SC_OK);
OutputStream os = response.getOutputStream();
water.util.FileUtils.copyStream(is, os, 2048);
} catch (Exception e) {
sendErrorResponse(response, e, uri);
} finally {
logRequest("GET", request, response);
}
}
@Override
protected Frame rebalance(final Frame original_fr, boolean local, final String name) {
if (original_fr == null) return null;
if (_parms._force_load_balance) {
int original_chunks = original_fr.anyVec().nChunks();
_job.update(0, "Load balancing " + name.substring(name.length() - 5) + " data...");
int chunks = desiredChunks(original_fr, local);
if (!_parms._reproducible) {
if (original_chunks >= chunks) {
if (!_parms._quiet_mode)
Log.info(
"Dataset already contains " + original_chunks + " chunks. No need to rebalance.");
return original_fr;
}
} else { // reproducible, set chunks to 1
assert chunks == 1;
if (!_parms._quiet_mode)
Log.warn("Reproducibility enforced - using only 1 thread - can be slow.");
if (original_chunks == 1) return original_fr;
}
if (!_parms._quiet_mode)
Log.info(
"Rebalancing "
+ name.substring(name.length() - 5)
+ " dataset into "
+ chunks
+ " chunks.");
Key newKey = Key.make(name + ".chks" + chunks);
RebalanceDataSet rb = new RebalanceDataSet(original_fr, newKey, chunks);
H2O.submitTask(rb).join();
Frame rebalanced_fr = DKV.get(newKey).get();
Scope.track(rebalanced_fr);
return rebalanced_fr;
}
return original_fr;
}
/**
* 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;
}
/**
* Train a Deep Learning model, assumes that all members are populated If checkpoint == null,
* then start training a new model, otherwise continue from a checkpoint
*/
public final void buildModel() {
DeepLearningModel cp = null;
if (_parms._checkpoint == null) {
cp =
new DeepLearningModel(
dest(),
_parms,
new DeepLearningModel.DeepLearningModelOutput(DeepLearning.this),
_train,
_valid,
nclasses());
cp.model_info().initializeMembers();
} else {
final DeepLearningModel previous = DKV.getGet(_parms._checkpoint);
if (previous == null) throw new IllegalArgumentException("Checkpoint not found.");
Log.info("Resuming from checkpoint.");
_job.update(0, "Resuming from checkpoint");
if (isClassifier() != previous._output.isClassifier())
throw new H2OIllegalArgumentException(
"Response type must be the same as for the checkpointed model.");
if (isSupervised() != previous._output.isSupervised())
throw new H2OIllegalArgumentException(
"Model type must be the same as for the checkpointed model.");
// check the user-given arguments for consistency
DeepLearningParameters oldP =
previous._parms; // sanitized parameters for checkpointed model
DeepLearningParameters newP = _parms; // user-given parameters for restart
DeepLearningParameters oldP2 = (DeepLearningParameters) oldP.clone();
DeepLearningParameters newP2 = (DeepLearningParameters) newP.clone();
DeepLearningParameters.Sanity.modifyParms(
oldP, oldP2, nclasses()); // sanitize the user-given parameters
DeepLearningParameters.Sanity.modifyParms(
newP, newP2, nclasses()); // sanitize the user-given parameters
DeepLearningParameters.Sanity.checkpoint(oldP2, newP2);
DataInfo dinfo;
try {
// PUBDEV-2513: Adapt _train and _valid (in-place) to match the frames that were used for
// the previous model
// This can add or remove dummy columns (can happen if the dataset is sparse and datasets
// have different non-const columns)
for (String st : previous.adaptTestForTrain(_train, true, false)) Log.warn(st);
for (String st : previous.adaptTestForTrain(_valid, true, false)) Log.warn(st);
dinfo = makeDataInfo(_train, _valid, _parms, nclasses());
DKV.put(dinfo);
cp = new DeepLearningModel(dest(), _parms, previous, false, dinfo);
cp.write_lock(_job);
if (!Arrays.equals(cp._output._names, previous._output._names)) {
throw new H2OIllegalArgumentException(
"The columns of the training data must be the same as for the checkpointed model. Check ignored columns (or disable ignore_const_cols).");
}
if (!Arrays.deepEquals(cp._output._domains, previous._output._domains)) {
throw new H2OIllegalArgumentException(
"Categorical factor levels of the training data must be the same as for the checkpointed model.");
}
if (dinfo.fullN() != previous.model_info().data_info().fullN()) {
throw new H2OIllegalArgumentException(
"Total number of predictors is different than for the checkpointed model.");
}
if (_parms._epochs <= previous.epoch_counter) {
throw new H2OIllegalArgumentException(
"Total number of epochs must be larger than the number of epochs already trained for the checkpointed model ("
+ previous.epoch_counter
+ ").");
}
// these are the mutable parameters that are to be used by the model (stored in
// model_info._parms)
final DeepLearningParameters actualNewP =
cp.model_info()
.get_params(); // actually used parameters for model building (defaults filled in,
// etc.)
assert (actualNewP != previous.model_info().get_params());
assert (actualNewP != newP);
assert (actualNewP != oldP);
DeepLearningParameters.Sanity.update(actualNewP, newP, nclasses());
Log.info(
"Continuing training after "
+ String.format("%.3f", previous.epoch_counter)
+ " epochs from the checkpointed model.");
cp.update(_job);
} catch (H2OIllegalArgumentException ex) {
if (cp != null) {
cp.unlock(_job);
cp.delete();
cp = null;
}
throw ex;
} finally {
if (cp != null) cp.unlock(_job);
}
}
trainModel(cp);
// clean up, but don't delete weights and biases if user asked for export
List<Key> keep = new ArrayList<>();
try {
if (_parms._export_weights_and_biases
&& cp._output.weights != null
&& cp._output.biases != null) {
for (Key k : Arrays.asList(cp._output.weights)) {
keep.add(k);
for (Vec vk : ((Frame) DKV.getGet(k)).vecs()) {
keep.add(vk._key);
}
}
for (Key k : Arrays.asList(cp._output.biases)) {
keep.add(k);
for (Vec vk : ((Frame) DKV.getGet(k)).vecs()) {
keep.add(vk._key);
}
}
}
} finally {
Scope.exit(keep.toArray(new Key[keep.size()]));
}
}
@Override
public void onCompletion(CountedCompleter cc) {
DKV.put(_v);
}
public static byte [] getFirstUnzipedBytes(Key k){
return getFirstUnzipedBytes(DKV.get(k));
}
@Override
public boolean toHTML(StringBuilder sb) {
if (jobs != null) {
DocGen.HTML.arrayHead(sb);
sb.append("<tr class='warning'>");
ArrayList<Argument> args = jobs[0].arguments();
// Filter some keys to simplify UI
args = (ArrayList<Argument>) args.clone();
filter(
args,
"destination_key",
"source",
"cols",
"ignored_cols_by_name",
"response",
"classification",
"validation");
for (int i = 0; i < args.size(); i++)
sb.append("<td><b>").append(args.get(i)._name).append("</b></td>");
sb.append("<td><b>").append("run time").append("</b></td>");
String perf = jobs[0].speedDescription();
if (perf != null) sb.append("<td><b>").append(perf).append("</b></td>");
sb.append("<td><b>").append("model key").append("</b></td>");
sb.append("<td><b>").append("prediction error").append("</b></td>");
sb.append("<td><b>").append("F1 score").append("</b></td>");
sb.append("</tr>");
ArrayList<JobInfo> infos = new ArrayList<JobInfo>();
for (Job job : jobs) {
JobInfo info = new JobInfo();
info._job = job;
Object value = UKV.get(job.destination_key);
info._model = value instanceof Model ? (Model) value : null;
if (info._model != null) info._cm = info._model.cm();
if (info._cm != null) info._error = info._cm.err();
infos.add(info);
}
Collections.sort(
infos,
new Comparator<JobInfo>() {
@Override
public int compare(JobInfo a, JobInfo b) {
return Double.compare(a._error, b._error);
}
});
for (JobInfo info : infos) {
sb.append("<tr>");
for (Argument a : args) {
try {
Object value = a._field.get(info._job);
String s;
if (value instanceof int[]) s = Utils.sampleToString((int[]) value, 20);
else s = "" + value;
sb.append("<td>").append(s).append("</td>");
} catch (Exception e) {
throw new RuntimeException(e);
}
}
String runTime = "Pending", speed = "";
if (info._job.start_time != 0) {
runTime = PrettyPrint.msecs(info._job.runTimeMs(), true);
speed = perf != null ? PrettyPrint.msecs(info._job.speedValue(), true) : "";
}
sb.append("<td>").append(runTime).append("</td>");
if (perf != null) sb.append("<td>").append(speed).append("</td>");
String link = info._job.destination_key.toString();
if (info._job.start_time != 0 && DKV.get(info._job.destination_key) != null) {
if (info._model instanceof GBMModel)
link = GBMModelView.link(link, info._job.destination_key);
else if (info._model instanceof NeuralNetModel)
link = NeuralNetProgress.link(info._job.self(), info._job.destination_key, link);
if (info._model instanceof KMeans2Model)
link = KMeans2ModelView.link(link, info._job.destination_key);
else link = Inspect.link(link, info._job.destination_key);
}
sb.append("<td>").append(link).append("</td>");
String pct = "", f1 = "";
if (info._cm != null) {
pct = String.format("%.2f", 100 * info._error) + "%";
if (info._cm._arr.length == 2)
f1 = String.format("%.2f", info._cm.precisionAndRecall());
}
sb.append("<td><b>").append(pct).append("</b></td>");
sb.append("<td><b>").append(f1).append("</b></td>");
sb.append("</tr>");
}
DocGen.HTML.arrayTail(sb);
}
return true;
}
