@Override
public ValFrame apply(Env env, Env.StackHelp stk, AstRoot asts[]) {
Frame f = stk.track(asts[1].exec(env)).getFrame();
AstRoot axisAR = asts[2];
for (Vec v : f.vecs()) {
if (v.isCategorical() || v.isString() || v.isUUID())
throw new IllegalArgumentException(
"Cumulative functions not applicable to enum, string, or UUID values");
}
double axis = axisAR.exec(env).getNum();
if (axis != 1.0 && axis != 0.0) throw new IllegalArgumentException("Axis must be 0 or 1");
if (f.numCols() == 1) {
if (axis == 0.0) {
AstCumu.CumuTask t = new AstCumu.CumuTask(f.anyVec().nChunks(), init());
t.doAll(new byte[] {Vec.T_NUM}, f.anyVec());
final double[] chkCumu = t._chkCumu;
Vec cumuVec = t.outputFrame().anyVec();
new MRTask() {
@Override
public void map(Chunk c) {
if (c.cidx() != 0) {
double d = chkCumu[c.cidx() - 1];
for (int i = 0; i < c._len; ++i) c.set(i, op(c.atd(i), d));
}
}
}.doAll(cumuVec);
return new ValFrame(new Frame(cumuVec));
} else {
return new ValFrame(new Frame(f));
}
} else {
if (axis == 0.0) { // down the column implementation
AstCumu.CumuTaskWholeFrame t =
new AstCumu.CumuTaskWholeFrame(f.anyVec().nChunks(), init(), f.numCols());
Frame fr2 = t.doAll(f.numCols(), Vec.T_NUM, f).outputFrame(null, f.names(), null);
final double[][] chkCumu = t._chkCumu;
new MRTask() {
@Override
public void map(Chunk cs[]) {
if (cs[0].cidx() != 0) {
for (int i = 0; i < cs.length; i++) {
double d = chkCumu[i][cs[i].cidx() - 1];
for (int j = 0; j < cs[i]._len; ++j) cs[i].set(j, op(cs[i].atd(j), d));
}
}
}
}.doAll(fr2);
return new ValFrame(new Frame(fr2));
} else {
AstCumu.CumuTaskAxis1 t = new AstCumu.CumuTaskAxis1(init());
Frame fr2 = t.doAll(f.numCols(), Vec.T_NUM, f).outputFrame(null, f.names(), null);
return new ValFrame(new Frame(fr2));
}
}
}
@Override
protected Frame predictScoreImpl(Frame orig, Frame adaptedFr, String destination_key) {
Frame adaptFrm = new Frame(adaptedFr);
for (int i = 0; i < _parms._k; i++)
adaptFrm.add("PC" + String.valueOf(i + 1), adaptFrm.anyVec().makeZero());
new MRTask() {
@Override
public void map(Chunk chks[]) {
double tmp[] = new double[_output._names.length];
double preds[] = new double[_parms._k];
for (int row = 0; row < chks[0]._len; row++) {
double p[] = score0(chks, row, tmp, preds);
for (int c = 0; c < preds.length; c++) chks[_output._names.length + c].set(row, p[c]);
}
}
}.doAll(adaptFrm);
// Return the projection into principal component space
int x = _output._names.length, y = adaptFrm.numCols();
Frame f =
adaptFrm.extractFrame(
x, y); // this will call vec_impl() and we cannot call the delete() below just yet
f =
new Frame(
(null == destination_key ? Key.make() : Key.make(destination_key)),
f.names(),
f.vecs());
DKV.put(f);
makeMetricBuilder(null).makeModelMetrics(this, orig);
return f;
}
@Override
public void map(Chunk[] ix, NewChunk[] ncs) {
final Vec[] vecs = new Vec[_cols.length];
final Vec anyv = _base.anyVec();
final long nrow = anyv.length();
long r = ix[0].at80(0);
int last_ci = anyv.elem2ChunkIdx(r < nrow ? r : 0); // memoize the last chunk index
long last_c0 = anyv._espc[last_ci]; // ... last chunk start
long last_c1 = anyv._espc[last_ci + 1]; // ... last chunk end
Chunk[] last_cs = new Chunk[vecs.length]; // ... last chunks
for (int c = 0; c < _cols.length; c++) {
vecs[c] = _base.vecs()[_cols[c]];
last_cs[c] = vecs[c].elem2BV(last_ci);
}
for (int i = 0; i < ix[0]._len; i++) {
// select one row
r = ix[0].at80(i) - 1; // next row to select
if (r < 0) continue;
if (r >= nrow) {
for (int c = 0; c < vecs.length; c++) ncs[c].addNum(Double.NaN);
} else {
if (r < last_c0 || r >= last_c1) {
last_ci = anyv.elem2ChunkIdx(r);
last_c0 = anyv._espc[last_ci];
last_c1 = anyv._espc[last_ci + 1];
for (int c = 0; c < vecs.length; c++) last_cs[c] = vecs[c].elem2BV(last_ci);
}
for (int c = 0; c < vecs.length; c++) ncs[c].addNum(last_cs[c].at(r));
}
}
}
@Override
public void onCompletion(CountedCompleter caller) {
assert _out.numRows() == _in.numRows();
assert _out.anyVec()._espc.length == (_nchunks + 1);
_in.unlock(_jobKey);
_out.update(_jobKey);
_out.unlock(_jobKey);
}
/** Appends an entire Frame */
public Frame add(Frame fr) {
assert anyVec().group().equals(fr.anyVec().group());
final int len0 = _names.length;
final int len1 = fr._names.length;
final int len = len0 + len1;
_names = Arrays.copyOf(_names, len);
_vecs = Arrays.copyOf(_vecs, len);
_keys = Arrays.copyOf(_keys, len);
System.arraycopy(fr._names, 0, _names, len0, len1);
System.arraycopy(fr._vecs, 0, _vecs, len0, len1);
System.arraycopy(fr._keys, 0, _keys, len0, len1);
return this;
}
// Make vector templates for all output frame vectors
private Vec[][] makeTemplates(Frame dataset, float[] ratios) {
Vec anyVec = dataset.anyVec();
final long[][] espcPerSplit = computeEspcPerSplit(anyVec._espc, anyVec.length(), ratios);
final int num = dataset.numCols(); // number of columns in input frame
final int nsplits = espcPerSplit.length; // number of splits
final String[][] domains = dataset.domains(); // domains
Vec[][] t = new Vec[nsplits][ /*num*/]; // resulting vectors for all
for (int i = 0; i < nsplits; i++) {
// vectors for j-th split
t[i] = new Vec(Vec.newKey(), espcPerSplit[i /*-th split*/]).makeZeros(num, domains);
}
return t;
}
@Override
Val apply(Env env, Env.StackHelp stk, AST asts[]) {
Frame fr = stk.track(asts[1].exec(env)).getFrame();
if (fr.numCols() == 1 && fr.numRows() == 1) {
if (fr.anyVec().isNumeric() || fr.anyVec().isBad()) return new ValNum(fr.anyVec().at(0));
else if (fr.anyVec().isString())
return new ValStr(fr.anyVec().atStr(new BufferedString(), 0).toString());
return new ValStr(fr.domains()[0][(int) fr.anyVec().at8(0)]);
}
return new ValFrame(fr); // did not flatten
}
@Test
public void testGroupbyTableSpeed() {
Frame ids = parse_test_file(Key.make("cov"), "smalldata/junit/id_cols.csv");
ids.replace(0, ids.anyVec().toCategoricalVec()).remove();
System.out.println(ids.toString(0, 10));
long start = System.currentTimeMillis();
Val v_gb = Exec.exec("(GB cov [0] nrow 0 \"all\")");
System.out.println("GB Time= " + (System.currentTimeMillis() - start) + "msec");
System.out.println(v_gb.toString());
((ValFrame) v_gb)._fr.delete();
long start2 = System.currentTimeMillis();
Val v_tb = Exec.exec("(table cov FALSE)");
System.out.println("Table Time= " + (System.currentTimeMillis() - start2) + "msec");
System.out.println(v_tb.toString());
((ValFrame) v_tb)._fr.delete();
ids.delete();
}
/**
* Compute the L2 norm for each row of the frame
*
* @param fr Input frame
* @return Vec containing L2 values for each row, is in K-V store
*/
public static Vec getL2(final Frame fr, final double[] scale) {
// add workspace vec at end
final int idx = fr.numCols();
assert (scale.length == idx) : "Mismatch for number of columns";
fr.add("L2", fr.anyVec().makeZero());
Vec res;
try {
new MRTask2() {
@Override
public void map(Chunk[] cs) {
for (int r = 0; r < cs[0]._len; r++) {
double norm2 = 0;
for (int i = 0; i < idx; i++) norm2 += Math.pow(cs[i].at0(r) * scale[i], 2);
cs[idx].set0(r, Math.sqrt(norm2));
}
}
}.doAll(fr);
} finally {
res = fr.remove(idx);
}
res.rollupStats();
return res;
}
@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;
}
// GLRM scoring is data imputation based on feature domains using reconstructed XY (see Udell
// (2015), Section 5.3)
private Frame reconstruct(
Frame orig,
Frame adaptedFr,
Key destination_key,
boolean save_imputed,
boolean reverse_transform) {
final int ncols = _output._names.length;
assert ncols == adaptedFr.numCols();
String prefix = "reconstr_";
// Need [A,X,P] where A = adaptedFr, X = loading frame, P = imputed frame
// Note: A is adapted to original training frame, P has columns shuffled so cats come before
// nums!
Frame fullFrm = new Frame(adaptedFr);
Frame loadingFrm = DKV.get(_output._representation_key).get();
fullFrm.add(loadingFrm);
String[][] adaptedDomme = adaptedFr.domains();
for (int i = 0; i < ncols; i++) {
Vec v = fullFrm.anyVec().makeZero();
v.setDomain(adaptedDomme[i]);
fullFrm.add(prefix + _output._names[i], v);
}
GLRMScore gs = new GLRMScore(ncols, _parms._k, save_imputed, reverse_transform).doAll(fullFrm);
// Return the imputed training frame
int x = ncols + _parms._k, y = fullFrm.numCols();
Frame f =
fullFrm.extractFrame(
x, y); // this will call vec_impl() and we cannot call the delete() below just yet
f = new Frame((null == destination_key ? Key.make() : destination_key), f.names(), f.vecs());
DKV.put(f);
gs._mb.makeModelMetrics(
GLRMModel.this, orig, null, null); // save error metrics based on imputed data
return f;
}
/**
* 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;
}
@Override
Val apply(Env env, Env.StackHelp stk, AST asts[]) {
// Execute all args. Find a canonical frame; all Frames must look like this one.
// Each argument turns into either a Frame (whose rows are entirely
// inlined) or a scalar (which is replicated across as a single row).
Frame fr = null; // Canonical Frame; all frames have the same column count, types and names
int nchks = 0; // Total chunks
Val vals[] = new Val[asts.length]; // Computed AST results
for (int i = 1; i < asts.length; i++) {
vals[i] = stk.track(asts[i].exec(env));
if (vals[i].isFrame()) {
fr = vals[i].getFrame();
nchks += fr.anyVec().nChunks(); // Total chunks
} else nchks++; // One chunk per scalar
}
// No Frame, just a pile-o-scalars?
Vec zz = null; // The zero-length vec for the zero-frame frame
if (fr == null) { // Zero-length, 1-column, default name
fr = new Frame(new String[] {Frame.defaultColName(0)}, new Vec[] {zz = Vec.makeZero(0)});
if (asts.length == 1) return new ValFrame(fr);
}
// Verify all Frames are the same columns, names, and types. Domains can vary, and will be the
// union
final Frame frs[] = new Frame[asts.length]; // Input frame
final byte[] types = fr.types(); // Column types
final int ncols = fr.numCols();
final long[] espc = new long[nchks + 1]; // Compute a new layout!
int coffset = 0;
for (int i = 1; i < asts.length; i++) {
Val val = vals[i]; // Save values computed for pass 2
Frame fr0 =
val.isFrame()
? val.getFrame()
// Scalar: auto-expand into a 1-row frame
: stk.track(new Frame(fr._names, Vec.makeCons(val.getNum(), 1L, fr.numCols())));
// Check that all frames are compatible
if (fr.numCols() != fr0.numCols())
throw new IllegalArgumentException(
"rbind frames must have all the same columns, found "
+ fr.numCols()
+ " and "
+ fr0.numCols()
+ " columns.");
if (!Arrays.deepEquals(fr._names, fr0._names))
throw new IllegalArgumentException(
"rbind frames must have all the same column names, found "
+ Arrays.toString(fr._names)
+ " and "
+ Arrays.toString(fr0._names));
if (!Arrays.equals(types, fr0.types()))
throw new IllegalArgumentException(
"rbind frames must have all the same column types, found "
+ Arrays.toString(types)
+ " and "
+ Arrays.toString(fr0.types()));
frs[i] = fr0; // Save frame
// Roll up the ESPC row counts
long roffset = espc[coffset];
long[] espc2 = fr0.anyVec().espc();
for (int j = 1; j < espc2.length; j++) // Roll up the row counts
espc[coffset + j] = (roffset + espc2[j]);
coffset += espc2.length - 1; // Chunk offset
}
if (zz != null) zz.remove();
// build up the new domains for each vec
HashMap<String, Integer>[] dmap = new HashMap[types.length];
String[][] domains = new String[types.length][];
int[][][] cmaps = new int[types.length][][];
for (int k = 0; k < types.length; ++k) {
dmap[k] = new HashMap<>();
int c = 0;
byte t = types[k];
if (t == Vec.T_CAT) {
int[][] maps = new int[frs.length][];
for (int i = 1; i < frs.length; i++) {
maps[i] = new int[frs[i].vec(k).domain().length];
for (int j = 0; j < maps[i].length; j++) {
String s = frs[i].vec(k).domain()[j];
if (!dmap[k].containsKey(s)) dmap[k].put(s, maps[i][j] = c++);
else maps[i][j] = dmap[k].get(s);
}
}
cmaps[k] = maps;
} else {
cmaps[k] = new int[frs.length][];
}
domains[k] = c == 0 ? null : new String[c];
for (Map.Entry<String, Integer> e : dmap[k].entrySet()) domains[k][e.getValue()] = e.getKey();
}
// Now make Keys for the new Vecs
Key<Vec>[] keys = fr.anyVec().group().addVecs(fr.numCols());
Vec[] vecs = new Vec[fr.numCols()];
int rowLayout = Vec.ESPC.rowLayout(keys[0], espc);
for (int i = 0; i < vecs.length; i++)
vecs[i] = new Vec(keys[i], rowLayout, domains[i], types[i]);
// Do the row-binds column-by-column.
// Switch to F/J thread for continuations
ParallelRbinds t;
H2O.submitTask(t = new ParallelRbinds(frs, espc, vecs, cmaps)).join();
return new ValFrame(new Frame(fr.names(), t._vecs));
}
public Frame deepSlice(Object orows, Object ocols) {
// ocols is either a long[] or a Frame-of-1-Vec
long[] cols;
if (ocols == null) {
cols = (long[]) ocols;
assert cols == null;
} else {
if (ocols instanceof long[]) {
cols = (long[]) ocols;
} else if (ocols instanceof Frame) {
Frame fr = (Frame) ocols;
if (fr.numCols() != 1) {
throw new IllegalArgumentException(
"Columns Frame must have only one column (actually has "
+ fr.numCols()
+ " columns)");
}
long n = fr.anyVec().length();
if (n > MAX_EQ2_COLS) {
throw new IllegalArgumentException(
"Too many requested columns (requested " + n + ", max " + MAX_EQ2_COLS + ")");
}
cols = new long[(int) n];
Vec v = fr._vecs[0];
for (long i = 0; i < v.length(); i++) {
cols[(int) i] = v.at8(i);
}
} else {
throw new IllegalArgumentException(
"Columns is specified by an unsupported data type ("
+ ocols.getClass().getName()
+ ")");
}
}
// Since cols is probably short convert to a positive list.
int c2[] = null;
if (cols == null) {
c2 = new int[numCols()];
for (int i = 0; i < c2.length; i++) c2[i] = i;
} else if (cols.length == 0) {
c2 = new int[0];
} else if (cols[0] > 0) {
c2 = new int[cols.length];
for (int i = 0; i < cols.length; i++)
c2[i] = (int) cols[i] - 1; // Convert 1-based cols to zero-based
} else {
c2 = new int[numCols() - cols.length];
int j = 0;
for (int i = 0; i < numCols(); i++) {
if (j >= cols.length || i < (-cols[j] - 1)) c2[i - j] = i;
else j++;
}
}
for (int i = 0; i < c2.length; i++)
if (c2[i] >= numCols())
throw new IllegalArgumentException(
"Trying to select column " + c2[i] + " but only " + numCols() + " present.");
if (c2.length == 0)
throw new IllegalArgumentException(
"No columns selected (did you try to select column 0 instead of column 1?)");
// Do Da Slice
// orows is either a long[] or a Vec
if (orows == null)
return new DeepSlice((long[]) orows, c2)
.doAll(c2.length, this)
.outputFrame(names(c2), domains(c2));
else if (orows instanceof long[]) {
final long CHK_ROWS = 1000000;
long[] rows = (long[]) orows;
if (rows.length == 0)
return new DeepSlice(rows, c2).doAll(c2.length, this).outputFrame(names(c2), domains(c2));
if (rows[0] < 0)
return new DeepSlice(rows, c2).doAll(c2.length, this).outputFrame(names(c2), domains(c2));
// Vec'ize the index array
AppendableVec av = new AppendableVec("rownames");
int r = 0;
int c = 0;
while (r < rows.length) {
NewChunk nc = new NewChunk(av, c);
long end = Math.min(r + CHK_ROWS, rows.length);
for (; r < end; r++) {
nc.addNum(rows[r]);
}
nc.close(c++, null);
}
Vec c0 = av.close(null); // c0 is the row index vec
Frame fr2 =
new Slice(c2, this)
.doAll(c2.length, new Frame(new String[] {"rownames"}, new Vec[] {c0}))
.outputFrame(names(c2), domains(c2));
UKV.remove(c0._key); // Remove hidden vector
return fr2;
}
Frame frows = (Frame) orows;
Vec vrows = frows.anyVec();
// It's a compatible Vec; use it as boolean selector.
// Build column names for the result.
Vec[] vecs = new Vec[c2.length + 1];
String[] names = new String[c2.length + 1];
for (int i = 0; i < c2.length; ++i) {
vecs[i] = _vecs[c2[i]];
names[i] = _names[c2[i]];
}
vecs[c2.length] = vrows;
names[c2.length] = "predicate";
return new DeepSelect()
.doAll(c2.length, new Frame(names, vecs))
.outputFrame(names(c2), domains(c2));
}
