@Test
public void testDomains() {
Frame frame = parse_test_file("smalldata/junit/weather.csv");
for (String s : new String[] {"MaxWindSpeed", "RelHumid9am", "Cloud9am"}) {
Vec v = frame.vec(s);
Vec newV = v.toCategoricalVec();
frame.remove(s);
frame.add(s, newV);
v.remove();
}
DKV.put(frame);
AggregatorModel.AggregatorParameters parms = new AggregatorModel.AggregatorParameters();
parms._train = frame._key;
parms._radius_scale = 10;
AggregatorModel agg = new Aggregator(parms).trainModel().get();
Frame output = agg._output._output_frame.get();
Assert.assertTrue(output.numRows() < 0.5 * frame.numRows());
boolean same = true;
for (int i = 0; i < frame.numCols(); ++i) {
if (frame.vec(i).isCategorical()) {
same = (frame.domains()[i].length == output.domains()[i].length);
if (!same) break;
}
}
frame.remove();
output.remove();
agg.remove();
Assert.assertFalse(same);
}
/**
* Sample rows from a frame. Can be unlucky for small sampling fractions - will continue calling
* itself until at least 1 row is returned.
*
* @param fr Input frame
* @param rows Approximate number of rows to sample (across all chunks)
* @param seed Seed for RNG
* @return Sampled frame
*/
public static Frame sampleFrame(Frame fr, final long rows, final long seed) {
if (fr == null) return null;
final float fraction = rows > 0 ? (float) rows / fr.numRows() : 1.f;
if (fraction >= 1.f) return fr;
Frame r =
new MRTask2() {
@Override
public void map(Chunk[] cs, NewChunk[] ncs) {
final Random rng = getDeterRNG(seed + cs[0].cidx());
int count = 0;
for (int r = 0; r < cs[0]._len; r++)
if (rng.nextFloat() < fraction || (count == 0 && r == cs[0]._len - 1)) {
count++;
for (int i = 0; i < ncs.length; i++) {
ncs[i].addNum(cs[i].at0(r));
}
}
}
}.doAll(fr.numCols(), fr).outputFrame(fr.names(), fr.domains());
if (r.numRows() == 0) {
Log.warn(
"You asked for "
+ rows
+ " rows (out of "
+ fr.numRows()
+ "), but you got none (seed="
+ seed
+ ").");
Log.warn("Let's try again. You've gotta ask yourself a question: \"Do I feel lucky?\"");
return sampleFrame(fr, rows, seed + 1);
}
return r;
}
/**
* Project each archetype into original feature space
*
* @param frame Original training data with m rows and n columns
* @param destination_key Frame Id for output
* @return Frame containing k rows and n columns, where each row corresponds to an archetype
*/
public Frame scoreArchetypes(Frame frame, Key destination_key, boolean reverse_transform) {
final int ncols = _output._names.length;
Frame adaptedFr = new Frame(frame);
adaptTestForTrain(adaptedFr, true, false);
assert ncols == adaptedFr.numCols();
String[][] adaptedDomme = adaptedFr.domains();
double[][] proj = new double[_parms._k][_output._nnums + _output._ncats];
// Categorical columns
for (int d = 0; d < _output._ncats; d++) {
double[][] block = _output._archetypes_raw.getCatBlock(d);
for (int k = 0; k < _parms._k; k++)
proj[k][_output._permutation[d]] = _parms.mimpute(block[k], _output._lossFunc[d]);
}
// Numeric columns
for (int d = _output._ncats; d < (_output._ncats + _output._nnums); d++) {
int ds = d - _output._ncats;
for (int k = 0; k < _parms._k; k++) {
double num = _output._archetypes_raw.getNum(ds, k);
proj[k][_output._permutation[d]] = _parms.impute(num, _output._lossFunc[d]);
if (reverse_transform)
proj[k][_output._permutation[d]] =
proj[k][_output._permutation[d]] / _output._normMul[ds] + _output._normSub[ds];
}
}
// Convert projection of archetypes into a frame with correct domains
Frame f =
ArrayUtils.frame(
(null == destination_key ? Key.make() : destination_key), adaptedFr.names(), proj);
for (int i = 0; i < ncols; i++) f.vec(i).setDomain(adaptedDomme[i]);
return f;
}
@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
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
}
// 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;
}
public static Frame shuffleFramePerChunk(Key outputFrameKey, Frame fr, final long seed) {
Frame r =
new MRTask2() {
@Override
public void map(Chunk[] cs, NewChunk[] ncs) {
long[] idx = new long[cs[0]._len];
for (int r = 0; r < idx.length; ++r) idx[r] = r;
Utils.shuffleArray(idx, seed);
for (int r = 0; r < idx.length; ++r) {
for (int i = 0; i < ncs.length; i++) {
ncs[i].addNum(cs[i].at0((int) idx[r]));
}
}
}
}.doAll(fr.numCols(), fr).outputFrame(outputFrameKey, fr.names(), fr.domains());
return r;
}
// 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;
}
@Override
Val apply(Env env, Env.StackHelp stk, AST asts[]) {
Frame fr = stk.track(asts[1].exec(env)).getFrame();
Frame returningFrame;
long nrows = fr.numRows();
if (asts[2] instanceof ASTNumList) {
final ASTNumList nums = (ASTNumList) asts[2];
long[] rows = nums._isList ? nums.expand8Sort() : null;
if (rows != null) {
if (rows.length == 0) { // Empty inclusion list?
} else if (rows[0] >= 0) { // Positive (inclusion) list
if (rows[rows.length - 1] > nrows)
throw new IllegalArgumentException("Row must be an integer from 0 to " + (nrows - 1));
} else { // Negative (exclusion) list
// Invert the list to make a positive list, ignoring out-of-bounds values
BitSet bs = new BitSet((int) nrows);
for (int i = 0; i < rows.length; i++) {
int idx = (int) (-rows[i] - 1); // The positive index
if (idx >= 0 && idx < nrows) bs.set(idx); // Set column to EXCLUDE
}
rows = new long[(int) nrows - bs.cardinality()];
for (int i = bs.nextClearBit(0), j = 0; i < nrows; i = bs.nextClearBit(i + 1))
rows[j++] = i;
}
}
final long[] ls = rows;
returningFrame =
new MRTask() {
@Override
public void map(Chunk[] cs, NewChunk[] ncs) {
if (nums.cnt() == 0) return;
long start = cs[0].start();
long end = start + cs[0]._len;
long min = ls == null ? (long) nums.min() : ls[0],
max =
ls == null
? (long) nums.max() - 1
: ls[ls.length - 1]; // exclusive max to inclusive max when stride == 1
// [ start, ..., end ] the chunk
// 1 [] nums out left: nums.max() < start
// 2 [] nums out rite: nums.min() > end
// 3 [ nums ] nums run left: nums.min() < start && nums.max() <=
// end
// 4 [ nums ] nums run in : start <= nums.min() && nums.max() <=
// end
// 5 [ nums ] nums run rite: start <= nums.min() && end <
// nums.max()
if (!(max < start || min > end)) { // not situation 1 or 2 above
long startOffset = (min > start ? min : start); // situation 4 and 5 => min > start;
for (int i = (int) (startOffset - start); i < cs[0]._len; ++i) {
if ((ls == null && nums.has(start + i))
|| (ls != null && Arrays.binarySearch(ls, start + i) >= 0)) {
for (int c = 0; c < cs.length; ++c) {
if (cs[c] instanceof CStrChunk) ncs[c].addStr(cs[c], i);
else if (cs[c] instanceof C16Chunk) ncs[c].addUUID(cs[c], i);
else if (cs[c].isNA(i)) ncs[c].addNA();
else ncs[c].addNum(cs[c].atd(i));
}
}
}
}
}
}.doAll(fr.types(), fr).outputFrame(fr.names(), fr.domains());
} else if ((asts[2] instanceof ASTNum)) {
long[] rows = new long[] {(long) (((ASTNum) asts[2])._v.getNum())};
returningFrame = fr.deepSlice(rows, null);
} else if ((asts[2] instanceof ASTExec) || (asts[2] instanceof ASTId)) {
Frame predVec = stk.track(asts[2].exec(env)).getFrame();
if (predVec.numCols() != 1)
throw new IllegalArgumentException(
"Conditional Row Slicing Expression evaluated to "
+ predVec.numCols()
+ " columns. Must be a boolean Vec.");
returningFrame = fr.deepSlice(predVec, null);
} else
throw new IllegalArgumentException(
"Row slicing requires a number-list as the last argument, but found a "
+ asts[2].getClass());
return new ValFrame(returningFrame);
}
// internal version with repeat counter
// currently hardcoded to do up to 10 tries to get a row from each class, which can be impossible
// for certain wrong sampling ratios
private static Frame sampleFrameStratified(
final Frame fr,
Vec label,
final float[] sampling_ratios,
final long seed,
final boolean debug,
int count) {
if (fr == null) return null;
assert (label.isEnum());
assert (sampling_ratios != null && sampling_ratios.length == label.domain().length);
final int labelidx = fr.find(label); // which column is the label?
assert (labelidx >= 0);
final boolean poisson = false; // beta feature
Frame r =
new MRTask2() {
@Override
public void map(Chunk[] cs, NewChunk[] ncs) {
final Random rng = getDeterRNG(seed + cs[0].cidx());
for (int r = 0; r < cs[0]._len; r++) {
if (cs[labelidx].isNA0(r)) continue; // skip missing labels
final int label = (int) cs[labelidx].at80(r);
assert (sampling_ratios.length > label && label >= 0);
int sampling_reps;
if (poisson) {
sampling_reps = Utils.getPoisson(sampling_ratios[label], rng);
} else {
final float remainder = sampling_ratios[label] - (int) sampling_ratios[label];
sampling_reps =
(int) sampling_ratios[label] + (rng.nextFloat() < remainder ? 1 : 0);
}
for (int i = 0; i < ncs.length; i++) {
for (int j = 0; j < sampling_reps; ++j) {
ncs[i].addNum(cs[i].at0(r));
}
}
}
}
}.doAll(fr.numCols(), fr).outputFrame(fr.names(), fr.domains());
// Confirm the validity of the distribution
long[] dist = new ClassDist(r.vecs()[labelidx]).doAll(r.vecs()[labelidx]).dist();
// if there are no training labels in the test set, then there is no point in sampling the test
// set
if (dist == null) return fr;
if (debug) {
long sumdist = Utils.sum(dist);
Log.info("After stratified sampling: " + sumdist + " rows.");
for (int i = 0; i < dist.length; ++i) {
Log.info(
"Class "
+ r.vecs()[labelidx].domain(i)
+ ": count: "
+ dist[i]
+ " sampling ratio: "
+ sampling_ratios[i]
+ " actual relative frequency: "
+ (float) dist[i] / sumdist * dist.length);
}
}
// Re-try if we didn't get at least one example from each class
if (Utils.minValue(dist) == 0 && count < 10) {
Log.info(
"Re-doing stratified sampling because not all classes were represented (unlucky draw).");
r.delete();
return sampleFrameStratified(fr, label, sampling_ratios, seed + 1, debug, ++count);
}
// shuffle intra-chunk
Frame shuffled = shuffleFramePerChunk(r, seed + 0x580FF13);
r.delete();
return shuffled;
}
