public final Row extractDenseRow(double[] vals, Row row) {
row.bad = false;
row.rid = 0;
row.cid = 0;
if (row.weight == 0) return row;
if (_skipMissing)
for (double d : vals)
if (Double.isNaN(d)) {
row.bad = true;
return row;
}
int nbins = 0;
for (int i = 0; i < _cats; ++i) {
int c = getCategoricalId(i, Double.isNaN(vals[i]) ? _catModes[i] : (int) vals[i]);
if (c >= 0) row.binIds[nbins++] = c;
}
row.nBins = nbins;
final int n = _nums;
int numValsIdx = 0;
for (int i = 0; i < n; ++i) {
if (isInteractionVec(i)) {
int offset;
InteractionWrappedVec iwv = ((InteractionWrappedVec) _adaptedFrame.vec(_cats + i));
int v1 = _adaptedFrame.find(iwv.v1());
int v2 = _adaptedFrame.find(iwv.v2());
if (v1 < _cats)
offset = getCategoricalId(v1, Double.isNaN(vals[v1]) ? _catModes[v1] : (int) vals[v1]);
else if (v2 < _cats)
offset = getCategoricalId(v2, Double.isNaN(vals[v2]) ? _catModes[v1] : (int) vals[v2]);
else offset = 0;
row.numVals[numValsIdx + offset] = vals[_cats + i]; // essentially: vals[v1] * vals[v2])
numValsIdx += nextNumericIdx(i);
} else {
double d = vals[_cats + i]; // can be NA if skipMissing() == false
if (Double.isNaN(d)) d = _numMeans[numValsIdx];
if (_normMul != null && _normSub != null)
d = (d - _normSub[numValsIdx]) * _normMul[numValsIdx];
row.numVals[numValsIdx++] = d;
}
}
int off = responseChunkId(0);
for (int i = off; i < Math.min(vals.length, off + _responses); ++i) {
try {
row.response[i] = vals[responseChunkId(i)];
} catch (Throwable t) {
throw new RuntimeException(t);
}
if (_normRespMul != null)
row.response[i] = (row.response[i] - _normRespSub[i]) * _normRespMul[i];
if (Double.isNaN(row.response[i])) {
row.bad = true;
return row;
}
}
return row;
}
public void addNum(int id, double val) {
if (numIds.length == nNums) {
int newSz = Math.max(4, numIds.length + (numIds.length >> 1));
numIds = Arrays.copyOf(numIds, newSz);
numVals = Arrays.copyOf(numVals, newSz);
}
int i = nNums++;
numIds[i] = id;
numVals[i] = val;
}
public TestUtil(int minCloudSize) {
MINCLOUDSIZE = Math.max(MINCLOUDSIZE, minCloudSize);
String ignoreTests = System.getProperty("ignore.tests");
if (ignoreTests != null) {
ignoreTestsNames = ignoreTests.split(",");
if (ignoreTestsNames.length == 1 && ignoreTestsNames[0].equals("")) {
ignoreTestsNames = null;
}
}
String doonlyTests = System.getProperty("doonly.tests");
if (doonlyTests != null) {
doonlyTestsNames = doonlyTests.split(",");
if (doonlyTestsNames.length == 1 && doonlyTestsNames[0].equals("")) {
doonlyTestsNames = null;
}
}
}
/**
* 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
protected void compute2() {
final int ncols = _frs[1].numCols();
addToPendingCount(ncols - 1);
for (int i = 0; i < Math.min(MAXP, ncols); ++i) forkVecTask(i);
}
/**
* Stratified sampling for classifiers
*
* @param fr Input frame
* @param label Label vector (must be enum)
* @param sampling_ratios Optional: array containing the requested sampling ratios per class (in
* order of domains), will be overwritten if it contains all 0s
* @param maxrows Maximum number of rows in the returned frame
* @param seed RNG seed for sampling
* @param allowOversampling Allow oversampling of minority classes
* @param verbose Whether to print verbose info
* @return Sampled frame, with approximately the same number of samples from each class (or given
* by the requested sampling ratios)
*/
public static Frame sampleFrameStratified(
final Frame fr,
Vec label,
float[] sampling_ratios,
long maxrows,
final long seed,
final boolean allowOversampling,
final boolean verbose) {
if (fr == null) return null;
assert (label.isEnum());
assert (maxrows >= label.domain().length);
long[] dist = new ClassDist(label).doAll(label).dist();
assert (dist.length > 0);
Log.info(
"Doing stratified sampling for data set containing "
+ fr.numRows()
+ " rows from "
+ dist.length
+ " classes. Oversampling: "
+ (allowOversampling ? "on" : "off"));
if (verbose) {
for (int i = 0; i < dist.length; ++i) {
Log.info(
"Class "
+ label.domain(i)
+ ": count: "
+ dist[i]
+ " prior: "
+ (float) dist[i] / fr.numRows());
}
}
// create sampling_ratios for class balance with max. maxrows rows (fill existing array if not
// null)
if (sampling_ratios == null
|| (Utils.minValue(sampling_ratios) == 0 && Utils.maxValue(sampling_ratios) == 0)) {
// compute sampling ratios to achieve class balance
if (sampling_ratios == null) {
sampling_ratios = new float[dist.length];
}
assert (sampling_ratios.length == dist.length);
for (int i = 0; i < dist.length; ++i) {
sampling_ratios[i] =
((float) fr.numRows() / label.domain().length) / dist[i]; // prior^-1 / num_classes
}
final float inv_scale =
Utils.minValue(
sampling_ratios); // majority class has lowest required oversampling factor to achieve
// balance
if (!Float.isNaN(inv_scale) && !Float.isInfinite(inv_scale))
Utils.div(
sampling_ratios,
inv_scale); // want sampling_ratio 1.0 for majority class (no downsampling)
}
if (!allowOversampling) {
for (int i = 0; i < sampling_ratios.length; ++i) {
sampling_ratios[i] = Math.min(1.0f, sampling_ratios[i]);
}
}
// given these sampling ratios, and the original class distribution, this is the expected number
// of resulting rows
float numrows = 0;
for (int i = 0; i < sampling_ratios.length; ++i) {
numrows += sampling_ratios[i] * dist[i];
}
final long actualnumrows = Math.min(maxrows, Math.round(numrows)); // cap #rows at maxrows
assert (actualnumrows
>= 0); // can have no matching rows in case of sparse data where we had to fill in a
// makeZero() vector
Log.info("Stratified sampling to a total of " + String.format("%,d", actualnumrows) + " rows.");
if (actualnumrows != numrows) {
Utils.mult(
sampling_ratios,
(float) actualnumrows
/ numrows); // adjust the sampling_ratios by the global rescaling factor
if (verbose)
Log.info(
"Downsampling majority class by "
+ (float) actualnumrows / numrows
+ " to limit number of rows to "
+ String.format("%,d", maxrows));
}
Log.info(
"Majority class ("
+ label.domain()[Utils.minIndex(sampling_ratios)].toString()
+ ") sampling ratio: "
+ Utils.minValue(sampling_ratios));
Log.info(
"Minority class ("
+ label.domain()[Utils.maxIndex(sampling_ratios)].toString()
+ ") sampling ratio: "
+ Utils.maxValue(sampling_ratios));
return sampleFrameStratified(fr, label, sampling_ratios, seed, verbose);
}
public static int imputeCat(Vec v) {
if (v.isCategorical()) return v.mode();
return (int) Math.round(v.mean());
}
/**
* Extract (sparse) rows from given chunks. Note: 0 remains 0 - _normSub of DataInfo isn't used
* (mean shift during standarization is not reverted) - UNLESS offset is specified (for GLM only)
* Essentially turns the dataset 90 degrees.
*
* @param chunks - chunk of dataset
* @return array of sparse rows
*/
public final Row[] extractSparseRows(Chunk[] chunks) {
Row[] rows = new Row[chunks[0]._len];
long startOff = chunks[0].start();
for (int i = 0; i < rows.length; ++i) {
rows[i] =
new Row(
true,
Math.min(_nums, 16),
_cats,
_responses,
i,
startOff); // if sparse, _nums is the correct number of nonzero values! i.e., do not
// use numNums()
rows[i].rid = chunks[0].start() + i;
if (_offset) {
rows[i].offset = chunks[offsetChunkId()].atd(i);
if (Double.isNaN(rows[i].offset)) rows[i].bad = true;
}
if (_weights) {
rows[i].weight = chunks[weightChunkId()].atd(i);
if (Double.isNaN(rows[i].weight)) rows[i].bad = true;
}
if (_skipMissing) {
int N = _cats + _nums;
for (int c = 0; c < N; ++c) if (chunks[c].isNA(i)) rows[i].bad = true;
}
}
// categoricals
for (int i = 0; i < _cats; ++i) {
for (int r = 0; r < chunks[0]._len; ++r) {
Row row = rows[r];
if (row.bad) continue;
int cid = getCategoricalId(i, chunks[i].isNA(r) ? _catModes[i] : (int) chunks[i].at8(r));
if (cid >= 0) row.binIds[row.nBins++] = cid;
}
}
// generic numbers + interactions
int interactionOffset = 0;
for (int cid = 0; cid < _nums; ++cid) {
Chunk c = chunks[_cats + cid];
int oldRow = -1;
if (c
instanceof
InteractionWrappedVec
.InteractionWrappedChunk) { // for each row, only 1 value in an interaction is 'hot'
// all other values are off (i.e., are 0)
for (int r = 0;
r < c._len;
++r) { // the vec is "vertically" dense and "horizontally" sparse (i.e., every row has
// one, and only one, value)
Row row = rows[r];
if (row.bad) continue;
if (c.isNA(r)) row.bad = _skipMissing;
int cidVirtualOffset =
getInteractionOffset(
chunks, _cats + cid, r); // the "virtual" offset into the hot-expanded interaction
row.addNum(
_numOffsets[cid] + cidVirtualOffset,
c.atd(r)); // FIXME: if this produces a "true" NA then should sub with mean? with?
}
interactionOffset += nextNumericIdx(cid);
} else {
for (int r = c.nextNZ(-1); r < c._len; r = c.nextNZ(r)) {
if (c.atd(r) == 0) continue;
assert r > oldRow;
oldRow = r;
Row row = rows[r];
if (row.bad) continue;
if (c.isNA(r)) row.bad = _skipMissing;
double d = c.atd(r);
if (Double.isNaN(d)) d = _numMeans[cid];
if (_normMul != null) d *= _normMul[interactionOffset];
row.addNum(_numOffsets[cid], d);
}
interactionOffset++;
}
}
// response(s)
for (int i = 1; i <= _responses; ++i) {
int rid = responseChunkId(i - 1);
Chunk rChunk = chunks[rid];
for (int r = 0; r < chunks[0]._len; ++r) {
Row row = rows[r];
if (row.bad) continue;
row.response[i - 1] = rChunk.atd(r);
if (_normRespMul != null) {
row.response[i - 1] = (row.response[i - 1] - _normRespSub[i - 1]) * _normRespMul[i - 1];
}
if (Double.isNaN(row.response[row.response.length - i])) row.bad = true;
}
}
return rows;
}
