/**
* Delete v1, v2 after potential modifying operations during processing: enums and/or train/test
* adaptation.
*/
private void simpleCMTest(
Frame v1,
Frame v2,
String[] actualDomain,
String[] predictedDomain,
String[] expectedDomain,
double[][] expectedCM,
boolean debug,
boolean toEnum) {
Scope.enter();
try {
ConfusionMatrix cm = buildCM(v1.vecs()[0].toEnum(), v2.vecs()[0].toEnum());
// -- DEBUG --
if (debug) {
System.err.println("actual : " + Arrays.toString(actualDomain));
System.err.println("predicted : " + Arrays.toString(predictedDomain));
System.err.println("CM domain : " + Arrays.toString(cm._domain));
System.err.println("expected CM domain: " + Arrays.toString(expectedDomain) + "\n");
for (int i = 0; i < cm._cm.length; i++) System.err.println(Arrays.toString(cm._cm[i]));
System.err.println("");
System.err.println(cm.toASCII());
}
// -- -- --
assertCMEqual(expectedDomain, expectedCM, cm);
} finally {
if (v1 != null) v1.delete();
if (v2 != null) v2.delete();
Scope.exit();
}
}
DataAdapter(
Frame fr,
SpeeDRFModel model,
int[] modelDataMap,
int rows,
long unique,
long seed,
int binLimit,
double[] classWt) {
// assert model._dataKey == fr._key;
_seed = seed + (unique << 16); // This is important to preserve sampling selection!!!
/* Maximum arity for a column (not a hard limit) */
_numRows = rows;
_numClasses = model.regression ? 1 : model.classes();
_regression = model.regression;
_c = new Col[model.fr.numCols()];
for (int i = 0; i < _c.length; i++) {
assert fr._names[modelDataMap[i]].equals(model.fr._names[i]);
Vec v = fr.vecs()[i];
if (isByteCol(v, rows, i == _c.length - 1, _regression)) // we do not bin for small values
_c[i] = new Col(fr._names[i], rows, i == _c.length - 1);
else
_c[i] =
new Col(fr._names[i], rows, i == _c.length - 1, binLimit, !(v.isEnum() || v.isInt()));
}
boolean trivial = true;
if (classWt != null) for (double f : classWt) if (f != 1.0) trivial = false;
_classWt = trivial ? null : classWt;
}
@Override
public void compute2() {
_in.read_lock(_jobKey);
// simply create a bogus new vector (don't even put it into KV) with appropriate number of lines
// per chunk and then use it as a source to do multiple makeZero calls
// to create empty vecs and than call RebalanceTask on each one of them.
// RebalanceTask will fetch the appropriate src chunks and fetch the data from them.
int rpc = (int) (_in.numRows() / _nchunks);
int rem = (int) (_in.numRows() % _nchunks);
long[] espc = new long[_nchunks + 1];
Arrays.fill(espc, rpc);
for (int i = 0; i < rem; ++i) ++espc[i];
long sum = 0;
for (int i = 0; i < espc.length; ++i) {
long s = espc[i];
espc[i] = sum;
sum += s;
}
assert espc[espc.length - 1] == _in.numRows()
: "unexpected number of rows, expected " + _in.numRows() + ", got " + espc[espc.length - 1];
final Vec[] srcVecs = _in.vecs();
_out =
new Frame(
_okey,
_in.names(),
new Vec(Vec.newKey(), espc).makeZeros(srcVecs.length, _in.domains()));
_out.delete_and_lock(_jobKey);
new RebalanceTask(this, srcVecs).asyncExec(_out);
}
@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));
}
}
}
static Frame exec_str(String str, String id) {
Val val = Exec.exec(str);
switch (val.type()) {
case Val.FRM:
Frame fr = val.getFrame();
Key k = Key.make(id);
// Smart delete any prior top-level result
Iced i = DKV.getGet(k);
if (i instanceof Lockable) ((Lockable) i).delete();
else if (i instanceof Keyed) ((Keyed) i).remove();
else if (i != null)
throw new IllegalArgumentException("Attempting to overright an unexpected key");
DKV.put(fr = new Frame(k, fr._names, fr.vecs()));
System.out.println(fr);
checkSaneFrame();
return fr;
case Val.NUM:
System.out.println("num= " + val.getNum());
assert id == null;
checkSaneFrame();
return null;
case Val.STR:
System.out.println("str= " + val.getStr());
assert id == null;
checkSaneFrame();
return null;
default:
throw water.H2O.fail();
}
}
DRFTree(Frame fr, int ncols, char nbins, char nclass, int min_rows, int mtrys, long seed) {
super(fr._names, ncols, nbins, nclass, min_rows, seed);
_mtrys = mtrys;
_rand = createRNG(seed);
_seeds = new long[fr.vecs()[0].nChunks()];
for (int i = 0; i < _seeds.length; i++) _seeds[i] = _rand.nextLong();
}
@Override
Val apply(Env env, Env.StackHelp stk, AST asts[]) {
Val v = stk.track(asts[1].exec(env));
if (v instanceof ValRow) {
ValRow vv = (ValRow) v;
return vv.slice(asts[2].columns(vv._names));
}
Frame fr = v.getFrame();
int[] cols = asts[2].columns(fr.names());
Frame fr2 = new Frame();
if (cols.length == 0) { // Empty inclusion list?
} else if (cols[0] >= 0) { // Positive (inclusion) list
if (cols[cols.length - 1] > fr.numCols())
throw new IllegalArgumentException(
"Column must be an integer from 0 to " + (fr.numCols() - 1));
for (int col : cols) fr2.add(fr.names()[col], fr.vecs()[col]);
} else { // Negative (exclusion) list
fr2 = new Frame(fr); // All of them at first
Arrays.sort(cols); // This loop depends on the values in sorted order
for (int col : cols)
if (0 <= -col - 1 && -col - 1 < fr.numCols()) fr2.remove(-col - 1); // Remove named column
}
return new ValFrame(fr2);
}
@Override
protected void init() {
if (validation != null && n_folds != 0)
throw new UnsupportedOperationException(
"Cannot specify a validation dataset and non-zero number of cross-validation folds.");
if (n_folds < 0)
throw new UnsupportedOperationException(
"The number of cross-validation folds must be >= 0.");
super.init();
xval_models = new Key[n_folds];
for (int i = 0; i < xval_models.length; ++i)
xval_models[i] = Key.make(dest().toString() + "_xval" + i);
int rIndex = 0;
for (int i = 0; i < source.vecs().length; i++)
if (source.vecs()[i] == response) {
rIndex = i;
break;
}
_responseName = source._names != null && rIndex >= 0 ? source._names[rIndex] : "response";
_train = selectVecs(source);
_names = new String[cols.length];
for (int i = 0; i < cols.length; i++) _names[i] = source._names[cols[i]];
// Compute source response domain
if (classification) _sourceResponseDomain = getVectorDomain(response);
// Is validation specified?
if (validation != null) {
// Extract a validation response
int idx = validation.find(source.names()[rIndex]);
if (idx == -1)
throw new IllegalArgumentException(
"Validation set does not have a response column called " + _responseName);
_validResponse = validation.vecs()[idx];
// Compute output confusion matrix domain for classification:
// - if validation dataset is specified then CM domain is union of train and validation
// response domains
// else it is only domain of response column.
if (classification) {
_validResponseDomain = getVectorDomain(_validResponse);
if (_validResponseDomain != null) {
_cmDomain = Utils.domainUnion(_sourceResponseDomain, _validResponseDomain);
if (!Arrays.deepEquals(_sourceResponseDomain, _validResponseDomain)) {
_fromModel2CM =
Model.getDomainMapping(
_cmDomain,
_sourceResponseDomain,
false); // transformation from model produced response ~> cmDomain
_fromValid2CM =
Model.getDomainMapping(
_cmDomain,
_validResponseDomain,
false); // transformation from validation response domain ~> cmDomain
}
} else _cmDomain = _sourceResponseDomain;
} /* end of if classification */
} else if (classification) _cmDomain = _sourceResponseDomain;
}
@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));
}
}
}
protected final Frame selectFrame(Frame frame) {
Vec[] vecs = new Vec[cols.length];
String[] names = new String[cols.length];
for( int i = 0; i < cols.length; i++ ) {
vecs[i] = frame.vecs()[cols[i]];
names[i] = frame.names()[cols[i]];
}
return new Frame(names, vecs);
}
/**
* Global redistribution of a Frame (balancing of chunks), done by calling process (all-to-one +
* one-to-all)
*
* @param fr Input frame
* @param seed RNG seed
* @param shuffle whether to shuffle the data globally
* @return Shuffled frame
*/
public static Frame shuffleAndBalance(
final Frame fr, int splits, long seed, final boolean local, final boolean shuffle) {
if ((fr.vecs()[0].nChunks() < splits || shuffle) && fr.numRows() > splits) {
Vec[] vecs = fr.vecs().clone();
Log.info("Load balancing dataset, splitting it into up to " + splits + " chunks.");
long[] idx = null;
if (shuffle) {
idx = new long[splits];
for (int r = 0; r < idx.length; ++r) idx[r] = r;
Utils.shuffleArray(idx, seed);
}
Key keys[] = new Vec.VectorGroup().addVecs(vecs.length);
final long rows_per_new_chunk = (long) (Math.ceil((double) fr.numRows() / splits));
// loop over cols (same indexing for each column)
Futures fs = new Futures();
for (int col = 0; col < vecs.length; col++) {
AppendableVec vec = new AppendableVec(keys[col]);
// create outgoing chunks for this col
NewChunk[] outCkg = new NewChunk[splits];
for (int i = 0; i < splits; ++i) outCkg[i] = new NewChunk(vec, i);
// loop over all incoming chunks
for (int ckg = 0; ckg < vecs[col].nChunks(); ckg++) {
final Chunk inCkg = vecs[col].chunkForChunkIdx(ckg);
// loop over local rows of incoming chunks (fast path)
for (int row = 0; row < inCkg._len; ++row) {
int outCkgIdx =
(int) ((inCkg._start + row) / rows_per_new_chunk); // destination chunk idx
if (shuffle)
outCkgIdx = (int) (idx[outCkgIdx]); // shuffle: choose a different output chunk
assert (outCkgIdx >= 0 && outCkgIdx < splits);
outCkg[outCkgIdx].addNum(inCkg.at0(row));
}
}
for (int i = 0; i < outCkg.length; ++i) outCkg[i].close(i, fs);
Vec t = vec.close(fs);
t._domain = vecs[col]._domain;
vecs[col] = t;
}
fs.blockForPending();
Log.info("Load balancing done.");
return new Frame(fr.names(), vecs);
}
return fr;
}
@Override
public void compute2() {
// Lock all possible data
dataset.read_lock(jobKey);
// Create a template vector for each segment
final Vec[][] templates = makeTemplates(dataset, ratios);
final int nsplits = templates.length;
assert nsplits == ratios.length + 1 : "Unexpected number of split templates!";
// Launch number of distributed FJ for each split part
final Vec[] datasetVecs = dataset.vecs();
splits = new Frame[nsplits];
for (int s = 0; s < nsplits; s++) {
Frame split = new Frame(destKeys[s], dataset.names(), templates[s]);
split.delete_and_lock(jobKey);
splits[s] = split;
}
setPendingCount(1);
H2O.submitTask(
new H2OCountedCompleter(FrameSplitter.this) {
@Override
public void compute2() {
setPendingCount(nsplits);
for (int s = 0; s < nsplits; s++) {
new FrameSplitTask(
new H2OCountedCompleter(this) { // Completer for this task
@Override
public void compute2() {}
@Override
public boolean onExceptionalCompletion(
Throwable ex, CountedCompleter caller) {
synchronized (
FrameSplitter
.this) { // synchronized on this since can be accessed from
// different workers
workersExceptions =
workersExceptions != null
? Arrays.copyOf(workersExceptions, workersExceptions.length + 1)
: new Throwable[1];
workersExceptions[workersExceptions.length - 1] = ex;
}
tryComplete(); // we handle the exception so wait perform normal
// completion
return false;
}
},
datasetVecs,
ratios,
s)
.asyncExec(splits[s]);
}
tryComplete(); // complete the computation of nsplits-tasks
}
});
tryComplete(); // complete the computation of thrown tasks
}
// --------------------------------------------------------------------------
// Build an entire layer of all K trees
protected DHistogram[][][] buildLayer(
final Frame fr,
final int nbins,
int nbins_cats,
final DTree ktrees[],
final int leafs[],
final DHistogram hcs[][][],
boolean subset,
boolean build_tree_one_node) {
// Build K trees, one per class.
// Build up the next-generation tree splits from the current histograms.
// Nearly all leaves will split one more level. This loop nest is
// O( #active_splits * #bins * #ncols )
// but is NOT over all the data.
ScoreBuildOneTree sb1ts[] = new ScoreBuildOneTree[_nclass];
Vec vecs[] = fr.vecs();
for (int k = 0; k < _nclass; k++) {
final DTree tree = ktrees[k]; // Tree for class K
if (tree == null) continue;
// Build a frame with just a single tree (& work & nid) columns, so the
// nested MRTask ScoreBuildHistogram in ScoreBuildOneTree does not try
// to close other tree's Vecs when run in parallel.
Frame fr2 = new Frame(Arrays.copyOf(fr._names, _ncols + 1), Arrays.copyOf(vecs, _ncols + 1));
fr2.add(fr._names[idx_tree(k)], vecs[idx_tree(k)]);
fr2.add(fr._names[idx_work(k)], vecs[idx_work(k)]);
fr2.add(fr._names[idx_nids(k)], vecs[idx_nids(k)]);
if (idx_weight() >= 0) fr2.add(fr._names[idx_weight()], vecs[idx_weight()]);
// Start building one of the K trees in parallel
H2O.submitTask(
sb1ts[k] =
new ScoreBuildOneTree(
this,
k,
nbins,
nbins_cats,
tree,
leafs,
hcs,
fr2,
subset,
build_tree_one_node,
_improvPerVar,
_model._parms._distribution));
}
// Block for all K trees to complete.
boolean did_split = false;
for (int k = 0; k < _nclass; k++) {
final DTree tree = ktrees[k]; // Tree for class K
if (tree == null) continue;
sb1ts[k].join();
if (sb1ts[k]._did_split) did_split = true;
}
// The layer is done.
return did_split ? hcs : null;
}
@Override
ValFrame apply(Env env, Env.StackHelp stk, AST asts[]) {
Frame fr = stk.track(asts[1].exec(env)).getFrame();
double frac = asts[2].exec(env).getNum();
double nrow = fr.numRows() * frac;
Vec vecs[] = fr.vecs();
long[] idxs = new long[fr.numCols()];
int j = 0;
for (int i = 0; i < idxs.length; i++) if (vecs[i].naCnt() < nrow) idxs[j++] = i;
Vec vec = Vec.makeVec(Arrays.copyOf(idxs, j), null, Vec.VectorGroup.VG_LEN1.addVec());
return new ValFrame(new Frame(vec));
}
public final String[] coefNames() {
int k = 0;
final int n = fullN();
String[] res = new String[n];
final Vec[] vecs = _adaptedFrame.vecs();
for (int i = 0; i < _cats; ++i)
for (int j = 1; j < vecs[i]._domain.length; ++j)
res[k++] = _adaptedFrame._names[i] + "." + vecs[i]._domain[j];
final int nums = n - k;
for (int i = 0; i < nums; ++i) res[k + i] = _adaptedFrame._names[_cats + i];
return res;
}
// Scalar covariance for 1 row
private ValNum scalar(Frame frx, Frame fry, Mode mode) {
if (frx.numCols() != fry.numCols())
throw new IllegalArgumentException(
"Single rows must have the same number of columns, found "
+ frx.numCols()
+ " and "
+ fry.numCols());
Vec vecxs[] = frx.vecs();
Vec vecys[] = fry.vecs();
double xmean = 0, ymean = 0, ncols = frx.numCols(), NACount = 0, xval, yval, ss = 0;
for (int r = 0; r < ncols; r++) {
xval = vecxs[r].at(0);
yval = vecys[r].at(0);
if (Double.isNaN(xval) || Double.isNaN(yval)) NACount++;
else {
xmean += xval;
ymean += yval;
}
}
xmean /= (ncols - NACount);
ymean /= (ncols - NACount);
if (NACount != 0) {
if (mode.equals(Mode.AllObs))
throw new IllegalArgumentException("Mode is 'all.obs' but NAs are present");
if (mode.equals(Mode.Everything)) return new ValNum(Double.NaN);
}
for (int r = 0; r < ncols; r++) {
xval = vecxs[r].at(0);
yval = vecys[r].at(0);
if (!(Double.isNaN(xval) || Double.isNaN(yval)))
ss += (vecxs[r].at(0) - xmean) * (vecys[r].at(0) - ymean);
}
return new ValNum(ss / (ncols - NACount - 1));
}
@Test
public void test() {
Frame frame = null;
try {
Futures fs = new Futures();
Random random = new Random();
Vec[] vecs = new Vec[1];
AppendableVec vec = new AppendableVec(Vec.newKey(), Vec.T_NUM);
for (int i = 0; i < 2; i++) {
NewChunk chunk = new NewChunk(vec, i);
for (int r = 0; r < 1000; r++) chunk.addNum(random.nextInt(1000));
chunk.close(i, fs);
}
vecs[0] = vec.layout_and_close(fs);
fs.blockForPending();
frame = new Frame(Key.<Frame>make(), null, vecs);
// Make sure we test the multi-chunk case
vecs = frame.vecs();
assert vecs[0].nChunks() > 1;
long rows = frame.numRows();
Vec v = vecs[0];
double min = Double.POSITIVE_INFINITY, max = Double.NEGATIVE_INFINITY, mean = 0, sigma = 0;
for (int r = 0; r < rows; r++) {
double d = v.at(r);
if (d < min) min = d;
if (d > max) max = d;
mean += d;
}
mean /= rows;
for (int r = 0; r < rows; r++) {
double d = v.at(r);
sigma += (d - mean) * (d - mean);
}
sigma = Math.sqrt(sigma / (rows - 1));
double epsilon = 1e-9;
assertEquals(max, v.max(), epsilon);
assertEquals(min, v.min(), epsilon);
assertEquals(mean, v.mean(), epsilon);
assertEquals(sigma, v.sigma(), epsilon);
} finally {
if (frame != null) frame.delete();
}
}
// ==========================================================================
public void basicGBM(String fname, String hexname, PrepData prep) {
File file = TestUtil.find_test_file(fname);
if (file == null) return; // Silently abort test if the file is missing
Key fkey = NFSFileVec.make(file);
Key dest = Key.make(hexname);
GBM gbm = null;
Frame fr = null;
try {
gbm = new GBM();
gbm.source = fr = ParseDataset2.parse(dest, new Key[] {fkey});
UKV.remove(fkey);
int idx = prep.prep(fr);
if (idx < 0) {
gbm.classification = false;
idx = ~idx;
}
String rname = fr._names[idx];
gbm.response = fr.vecs()[idx];
fr.remove(idx); // Move response to the end
fr.add(rname, gbm.response);
gbm.ntrees = 4;
gbm.max_depth = 4;
gbm.min_rows = 1;
gbm.nbins = 50;
gbm.cols = new int[fr.numCols()];
for (int i = 0; i < gbm.cols.length; i++) gbm.cols[i] = i;
gbm.learn_rate = .2f;
gbm.invoke();
fr = gbm.score(gbm.source);
GBM.GBMModel gbmmodel = UKV.get(gbm.dest());
// System.out.println(gbmmodel.toJava());
} finally {
UKV.remove(dest); // Remove original hex frame key
if (gbm != null) {
UKV.remove(gbm.dest()); // Remove the model
UKV.remove(gbm.response._key);
gbm.remove(); // Remove GBM Job
if (fr != null) fr.remove();
}
}
}
public DataInfo validDinfo(Frame valid) {
DataInfo res =
new DataInfo(
_adaptedFrame,
null,
1,
_useAllFactorLevels,
TransformType.NONE,
TransformType.NONE,
_skipMissing,
_imputeMissing,
false,
_weights,
_offset,
_fold);
res._adaptedFrame = new Frame(_adaptedFrame.names(), valid.vecs(_adaptedFrame.names()));
res._valid = true;
return res;
}
public final String[] coefNames() {
if (_coefNames != null) return _coefNames;
int k = 0;
final int n = fullN();
String[] res = new String[n];
final Vec[] vecs = _adaptedFrame.vecs();
for (int i = 0; i < _cats; ++i) {
for (int j = _useAllFactorLevels ? 0 : 1; j < vecs[i].domain().length; ++j) {
int jj = getCategoricalId(i, j);
if (jj < 0) continue;
res[k++] = _adaptedFrame._names[i] + "." + vecs[i].domain()[j];
}
if (_catMissing[i] > 0) res[k++] = _adaptedFrame._names[i] + ".missing(NA)";
}
final int nums = n - k;
System.arraycopy(_adaptedFrame._names, _cats, res, k, nums);
_coefNames = res;
return res;
}
public final String[] coefNames() {
if (_coefNames != null) return _coefNames; // already computed
int k = 0;
final int n = fullN(); // total number of columns to compute
String[] res = new String[n];
final Vec[] vecs = _adaptedFrame.vecs();
// first do all of the expanded categorical names
for (int i = 0; i < _cats; ++i) {
for (int j = (_useAllFactorLevels || vecs[i] instanceof InteractionWrappedVec) ? 0 : 1;
j < vecs[i].domain().length;
++j) {
int jj = getCategoricalId(i, j);
if (jj < 0) continue;
res[k++] = _adaptedFrame._names[i] + "." + vecs[i].domain()[j];
}
if (_catMissing[i] && getCategoricalId(i, _catModes[i]) >= 0)
res[k++] = _adaptedFrame._names[i] + ".missing(NA)";
}
// now loop over the numerical columns, collecting up any expanded InteractionVec names
if (_interactions == null) {
final int nums = n - k;
System.arraycopy(_adaptedFrame._names, _cats, res, k, nums);
} else {
for (int i = _cats; i < _nums; ++i) {
InteractionWrappedVec v;
if (vecs[i] instanceof InteractionWrappedVec
&& ((v = (InteractionWrappedVec) vecs[i]).domains()
!= null)) { // in this case, get the categoricalOffset
for (int j = 0; k < v.domains().length; ++j) {
if (getCategoricalIdFromInteraction(i, j) < 0) continue;
res[k++] = _adaptedFrame._names[i] + "." + v.domains()[j];
}
} else res[k++] = _adaptedFrame._names[i];
}
}
_coefNames = res;
return res;
}
public static Frame expandDataset(Frame fr, Key destkey) { // , int[] ignored) {
ArrayList<Vec> nvecs = new ArrayList<Vec>();
ArrayList<Vec> evecs = new ArrayList<Vec>();
ArrayList<String> eNames = new ArrayList<String>();
ArrayList<String> nNames = new ArrayList<String>();
int[] offsets = new int[fr.numCols() + 1];
Vec[] vecs = fr.vecs();
int c = 0;
// int ip = 0; //ignored pointer
for (int i = 0; i < fr.numCols(); i++) {
if (vecs[i]
.isEnum()) { // && i != ignored[ip]) {//!fr._names {//_names[i]. { //equals(ignored)) {
offsets[evecs.size()] = c;
evecs.add(vecs[i]);
String name = fr._names[i];
c += vecs[i]._domain.length;
for (String s : vecs[i]._domain) eNames.add(name + "." + s);
} else {
// if(i == ignored[ip] && ip < ignored.length - 1) ip++;
nvecs.add(vecs[i]);
nNames.add(fr._names[i]);
}
}
offsets[evecs.size()] = c;
if (evecs.isEmpty()) return fr;
offsets = Arrays.copyOf(offsets, evecs.size() + 1);
OneHot ss = new OneHot();
ss._offsets = offsets;
int l = offsets[evecs.size()];
ss.doAll(l, evecs.toArray(new Vec[evecs.size()]));
Frame fr2 = ss.outputFrame(destkey, eNames.toArray(new String[eNames.size()]), new String[l][]);
fr2.add(
new Frame(nNames.toArray(new String[nNames.size()]), nvecs.toArray(new Vec[nvecs.size()])),
false);
return fr2;
}
static boolean checkSaneFrame_impl() {
for (Key k : H2O.localKeySet()) {
Value val = H2O.raw_get(k);
if (val.isFrame()) {
Frame fr = val.get();
Vec vecs[] = fr.vecs();
for (int i = 0; i < vecs.length; i++) {
Vec v = vecs[i];
if (DKV.get(v._key) == null) {
System.err.println(
"Frame "
+ fr._key
+ " in the DKV, is missing Vec "
+ v._key
+ ", name="
+ fr._names[i]);
return false;
}
}
}
}
return true;
}
@Override protected void init() {
super.init();
int rIndex = 0;
for( int i = 0; i < source.vecs().length; i++ )
if( source.vecs()[i] == response )
rIndex = i;
_responseName = source._names != null && rIndex >= 0 ? source._names[rIndex] : "response";
_train = selectVecs(source);
_names = new String[cols.length];
for( int i = 0; i < cols.length; i++ )
_names[i] = source._names[cols[i]];
// Compute source response domain
if (classification) _sourceResponseDomain = getVectorDomain(response);
// Is validation specified?
if( validation != null ) {
// Extract a validation response
int idx = validation.find(source.names()[rIndex]);
if( idx == -1 ) throw new IllegalArgumentException("Validation set does not have a response column called "+_responseName);
_validResponse = validation.vecs()[idx];
// Compute output confusion matrix domain for classification:
// - if validation dataset is specified then CM domain is union of train and validation response domains
// else it is only domain of response column.
if (classification) {
_validResponseDomain = getVectorDomain(_validResponse);
if (_validResponseDomain!=null) {
_cmDomain = Utils.domainUnion(_sourceResponseDomain, _validResponseDomain);
if (!Arrays.deepEquals(_sourceResponseDomain, _validResponseDomain)) {
_fromModel2CM = Model.getDomainMapping(_cmDomain, _sourceResponseDomain, false); // transformation from model produced response ~> cmDomain
_fromValid2CM = Model.getDomainMapping(_cmDomain, _validResponseDomain , false); // transformation from validation response domain ~> cmDomain
}
} else _cmDomain = _sourceResponseDomain;
} /* end of if classification */
} else if (classification) _cmDomain = _sourceResponseDomain;
}
protected final Vec[] selectVecs(Frame frame) {
Vec[] vecs = new Vec[cols.length];
for( int i = 0; i < cols.length; i++ )
vecs[i] = frame.vecs()[cols[i]];
return vecs;
}
public static boolean checkIdx(Frame source, int[] idx) {
for (int i : idx) if (i<0 || i>source.vecs().length-1) return false;
return true;
}
/** Put given frame vectors into local trash which can be emptied by a user calling the {@link #emptyLTrash()} method.
* @see #emptyLTrash() */
protected final void ltrash(Frame fr) { ltrash(fr.vecs()); }
public static Frame[] shuffleSplitFrame(
Frame fr, Key[] keys, final double ratios[], final long seed) {
// Sanity check the ratios
assert keys.length == ratios.length;
double sum = ratios[0];
for (int i = 1; i < ratios.length; i++) {
sum += ratios[i];
ratios[i] = sum;
}
assert water.util.MathUtils.equalsWithinOneSmallUlp(sum, 1.0);
// Do the split, into ratios.length groupings of NewChunks
final int ncols = fr.numCols();
MRTask mr =
new MRTask() {
@Override
public void map(Chunk cs[], NewChunk ncs[]) {
Random rng = new Random(seed * cs[0].cidx());
int nrows = cs[0]._len;
for (int i = 0; i < nrows; i++) {
double r = rng.nextDouble();
int x = 0; // Pick the NewChunk split
for (; x < ratios.length - 1; x++) if (r < ratios[x]) break;
x *= ncols;
// Helper string holder
ValueString vstr = new ValueString();
// Copy row to correct set of NewChunks
for (int j = 0; j < ncols; j++) {
byte colType = cs[j].vec().get_type();
switch (colType) {
case Vec.T_BAD:
break; /* NOP */
case Vec.T_STR:
ncs[x + j].addStr(cs[j], i);
break;
case Vec.T_UUID:
ncs[x + j].addUUID(cs[j], i);
break;
case Vec.T_NUM: /* fallthrough */
case Vec.T_ENUM:
case Vec.T_TIME:
ncs[x + j].addNum(cs[j].atd(i));
break;
default:
if (colType > Vec.T_TIME && colType <= Vec.T_TIMELAST)
ncs[x + j].addNum(cs[j].atd(i));
else throw new IllegalArgumentException("Unsupported vector type: " + colType);
break;
}
}
}
}
}.doAll(ncols * ratios.length, fr);
// Build output frames
Frame frames[] = new Frame[ratios.length];
Vec[] vecs = fr.vecs();
String[] names = fr.names();
Futures fs = new Futures();
for (int i = 0; i < ratios.length; i++) {
Vec[] nvecs = new Vec[ncols];
for (int c = 0; c < ncols; c++) {
mr.appendables()[i * ncols + c].setDomain(vecs[c].domain());
nvecs[c] = mr.appendables()[i * ncols + c].close(fs);
}
frames[i] = new Frame(keys[i], fr.names(), nvecs);
DKV.put(frames[i], fs);
}
fs.blockForPending();
return frames;
}
public Frame(Frame fr) {
this(fr._names.clone(), fr.vecs().clone());
_col0 = null;
}
