@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
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));
}
}
}
@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);
}
private static void assertRowFrameEquals(double[] expected, Frame actual) {
assertEquals(1, actual.numRows());
assertEquals(expected.length, actual.numCols());
for (int i = 0; i < expected.length; i++) {
assertEquals("Wrong sum in column " + actual.name(i), expected[i], actual.vec(i).at(0), 1e-8);
}
}
/**
* 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;
}
private static void assertColFrameEquals(double[] expected, Frame actual) {
assertEquals(1, actual.numCols());
assertEquals(expected.length, actual.numRows());
for (int i = 0; i < expected.length; i++) {
assertEquals("Wrong sum in row " + i, expected[i], actual.vec(0).at(i), 1e-8);
}
}
@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;
}
/**
* 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;
}
/**
* Annotate the number of columns and rows of the training data set in the job parameter JSON
* @return JsonObject annotated with num_cols and num_rows of the training data set
*/
@Override protected JsonObject toJSON() {
JsonObject jo = super.toJSON();
if (source != null) {
jo.getAsJsonObject("source").addProperty("num_cols", source.numCols());
jo.getAsJsonObject("source").addProperty("num_rows", source.numRows());
}
return jo;
}
/**
* Annotate the number of columns and rows of the validation data set in the job parameter JSON
* @return JsonObject annotated with num_cols and num_rows of the validation data set
*/
@Override protected JsonObject toJSON() {
JsonObject jo = super.toJSON();
if (validation != null) {
jo.getAsJsonObject("validation").addProperty("num_cols", validation.numCols());
jo.getAsJsonObject("validation").addProperty("num_rows", validation.numRows());
}
return jo;
}
@Test
public void testColumnwisesumOnEmptyFrame() {
Frame fr = register(new Frame(Key.<Frame>make()));
Val val = Rapids.exec("(sumaxis " + fr._key + " 0 0)");
assertTrue(val instanceof ValFrame);
Frame res = register(val.getFrame());
assertEquals(res.numCols(), 0);
assertEquals(res.numRows(), 0);
}
/**
* This method applies a stacked autoencoders model to a given dataset and make predictions
*
* @param ctxt JavaSparkContext
* @param deeplearningModel Stacked Autoencoders model
* @param test Testing dataset as a JavaRDD of labeled points
* @return
*/
public JavaPairRDD<Double, Double> test(
JavaSparkContext ctxt,
final DeepLearningModel deeplearningModel,
JavaRDD<LabeledPoint> test,
MLModel mlModel)
throws MLModelBuilderException {
Scope.enter();
if (deeplearningModel == null) {
throw new MLModelBuilderException("DeeplearningModel is Null");
}
int numberOfFeatures = mlModel.getFeatures().size();
List<Feature> features = mlModel.getFeatures();
String[] names = new String[numberOfFeatures + 1];
for (int i = 0; i < numberOfFeatures; i++) {
names[i] = features.get(i).getName();
}
names[numberOfFeatures] = mlModel.getResponseVariable();
Frame testData = DeeplearningModelUtils.javaRDDToFrame(names, test);
Frame testDataWithoutLabels = testData.subframe(0, testData.numCols() - 1);
int numRows = (int) testDataWithoutLabels.numRows();
Vec predictionsVector = deeplearningModel.score(testDataWithoutLabels).vec(0);
double[] predictionValues = new double[numRows];
for (int i = 0; i < numRows; i++) {
predictionValues[i] = predictionsVector.at(i);
}
Vec labelsVector = testData.vec(testData.numCols() - 1);
double[] labels = new double[numRows];
for (int i = 0; i < numRows; i++) {
labels[i] = labelsVector.at(i);
}
Scope.exit();
ArrayList<Tuple2<Double, Double>> tupleList = new ArrayList<Tuple2<Double, Double>>();
for (int i = 0; i < labels.length; i++) {
tupleList.add(new Tuple2<Double, Double>(predictionValues[i], labels[i]));
}
return ctxt.parallelizePairs(tupleList);
}
// private constructor called by filterExpandedColumns
private DataInfo(
DataInfo dinfo,
Frame fr,
double[] normMul,
double[] normSub,
int[][] catLevels,
int[] catModes) {
_fullCatOffsets = dinfo._catOffsets;
if (!dinfo._useAllFactorLevels) {
_fullCatOffsets = dinfo._catOffsets.clone();
for (int i = 0; i < _fullCatOffsets.length; ++i)
_fullCatOffsets[i] += i; // add for the skipped zeros.
}
_offset = dinfo._offset;
_weights = dinfo._weights;
_fold = dinfo._fold;
_valid = false;
_interactions = dinfo._interactions;
_interactionVecs = dinfo._interactionVecs;
assert dinfo._predictor_transform != null;
assert dinfo._response_transform != null;
_predictor_transform = dinfo._predictor_transform;
_response_transform = dinfo._response_transform;
_skipMissing = dinfo._skipMissing;
_imputeMissing = dinfo._imputeMissing;
_adaptedFrame = fr;
_catOffsets = MemoryManager.malloc4(catLevels.length + 1);
_catMissing = new boolean[catLevels.length];
Arrays.fill(_catMissing, !(dinfo._imputeMissing || dinfo._skipMissing));
int s = 0;
for (int i = 0; i < catLevels.length; ++i) {
_catOffsets[i] = s;
s += catLevels[i].length;
}
_catLvls = catLevels;
_catOffsets[_catOffsets.length - 1] = s;
_responses = dinfo._responses;
_cats = catLevels.length;
_nums =
fr.numCols()
- _cats
- dinfo._responses
- (_offset ? 1 : 0)
- (_weights ? 1 : 0)
- (_fold ? 1 : 0);
_numOffsets = _nums == 0 ? new int[0] : dinfo._numOffsets.clone();
int diff = _numOffsets.length > 0 ? _numOffsets[0] - s : 0;
for (int i = 0; i < _numOffsets.length; i++) // need to shift everyone down by the offset!
_numOffsets[i] -= diff;
_useAllFactorLevels = true; // dinfo._useAllFactorLevels;
_numMeans = new double[_nums];
_normMul = normMul;
_normSub = normSub;
_catModes = catModes;
for (int i = 0; i < _nums; i++) _numMeans[i] = _adaptedFrame.vec(_cats + i).mean();
}
public void setResponseTransform(TransformType t) {
_response_transform = t;
if (t == TransformType.NONE) {
_normRespMul = null;
_normRespSub = null;
} else {
_normRespMul = MemoryManager.malloc8d(_responses);
_normRespSub = MemoryManager.malloc8d(_responses);
setTransform(t, _normRespMul, _normRespSub, _adaptedFrame.numCols() - _responses, _responses);
}
}
@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
}
@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));
}
// 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 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;
}
// 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;
}
// 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));
}
// private constructor called by filterExpandedColumns
private DataInfo(
Key<DataInfo> selfKey,
Frame fr,
double[] normMul,
double[] normSub,
int[][] catLevels,
int responses,
TransformType predictor_transform,
TransformType response_transform,
boolean skipMissing,
boolean imputeMissing,
boolean weight,
boolean offset,
boolean fold) {
super(selfKey);
_offset = offset;
_weights = weight;
_fold = fold;
_valid = false;
assert predictor_transform != null;
assert response_transform != null;
_predictor_transform = predictor_transform;
_response_transform = response_transform;
_skipMissing = skipMissing;
_imputeMissing = imputeMissing;
_adaptedFrame = fr;
_catOffsets = MemoryManager.malloc4(catLevels.length + 1);
_catMissing = new int[catLevels.length];
int s = 0;
for (int i = 0; i < catLevels.length; ++i) {
_catOffsets[i] = s;
s += catLevels[i].length;
}
_catLvls = catLevels;
_catOffsets[_catOffsets.length - 1] = s;
_responses = responses;
_cats = catLevels.length;
_nums =
fr.numCols() - _cats - responses - (_offset ? 1 : 0) - (_weights ? 1 : 0) - (_fold ? 1 : 0);
_useAllFactorLevels = true;
_catModes = new int[_cats];
_numMeans = new double[_nums];
_normMul = normMul;
_normSub = normSub;
for (int i = 0; i < _cats; i++) _catModes[i] = imputeCat(_adaptedFrame.vec(i));
for (int i = 0; i < _nums; i++) _numMeans[i] = _adaptedFrame.vec(_cats + i).mean();
}
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;
}
@Test
public void testQuantile() {
Frame f = null;
try {
Frame fr =
frame(
ard(
ard(1.223292e-02),
ard(1.635312e-25),
ard(1.601522e-11),
ard(8.452298e-10),
ard(2.643733e-10),
ard(2.671520e-06),
ard(1.165381e-06),
ard(7.193265e-10),
ard(3.383532e-04),
ard(2.561221e-05)));
double[] probs = new double[] {0.001, 0.005, .01, .02, .05, .10, .50, .8883, .90, .99};
String x =
String.format("(quantile %%%s %s \"interpolate\")", fr._key, Arrays.toString(probs));
Val val = Exec.exec(x);
fr.delete();
f = val.getFrame();
Assert.assertEquals(2, f.numCols());
// Expected values computed as golden values from R's quantile call
double[] exp =
ard(
1.4413698000016206E-13,
7.206849000001562E-13,
1.4413698000001489E-12,
2.882739600000134E-12,
7.20684900000009E-12,
1.4413698000000017E-11,
5.831131148999999E-07,
3.3669567275300000E-04,
0.00152780988,
0.011162408988);
for (int i = 0; i < exp.length; i++)
Assert.assertTrue(
"expected " + exp[i] + " got " + f.vec(1).at(i),
water.util.MathUtils.compare(exp[i], f.vec(1).at(i), 1e-6, 1e-6));
} finally {
if (f != null) f.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();
}
}
}
private void applyTrainingFrameSideEffects() {
int numCols = _modelBuilderTrain.numCols();
String responseVecName = _modelBuilderTrain.names()[numCols - 1];
Vec responseVec = _modelBuilderTrain.remove(numCols - 1);
final boolean use_weights_column = (_parms.weights_column != null);
final boolean use_start_column = (_parms.start_column != null);
if (use_weights_column) {
Vec weightsVec = _parms.weights_column;
int idxInRawFrame = _train.find(weightsVec);
if (idxInRawFrame < 0) {
throw new RuntimeException("CoxPHDriver failed to find weightVec");
}
String weightsVecName = _parms.train().names()[idxInRawFrame];
_modelBuilderTrain.add(weightsVecName, weightsVec);
}
if (use_start_column) {
Vec startVec = _parms.start_column;
int idxInRawFrame = _train.find(startVec);
if (idxInRawFrame < 0) {
throw new RuntimeException("CoxPHDriver failed to find startVec");
}
String startVecName = _parms.train().names()[idxInRawFrame];
_modelBuilderTrain.add(startVecName, startVec);
}
{
Vec stopVec = _parms.stop_column;
int idxInRawFrame = _train.find(stopVec);
if (idxInRawFrame < 0) {
throw new RuntimeException("CoxPHDriver failed to find stopVec");
}
String stopVecName = _parms.train().names()[idxInRawFrame];
_modelBuilderTrain.add(stopVecName, stopVec);
}
_modelBuilderTrain.add(responseVecName, responseVec);
}
@Override
Val apply(Env env, Env.StackHelp stk, AST asts[]) {
QuantileModel.QuantileParameters parms = new QuantileModel.QuantileParameters();
Frame fr = stk.track(asts[1].exec(env)).getFrame();
Frame fr_wkey = new Frame(fr); // Force a bogus Key for Quantiles ModelBuilder
DKV.put(fr_wkey);
parms._train = fr_wkey._key;
parms._probs = ((ASTNumList) asts[2]).expand();
for (double d : parms._probs)
if (d < 0 || d > 1)
throw new IllegalArgumentException("Probability must be between 0 and 1: " + d);
String inter = asts[3].exec(env).getStr();
parms._combine_method = QuantileModel.CombineMethod.valueOf(inter.toUpperCase());
parms._weights_column = asts[4].str().equals("_") ? null : asts[4].str();
// Compute Quantiles
QuantileModel q = new Quantile(parms).trainModel().get();
// Remove bogus Key
DKV.remove(fr_wkey._key);
// Reshape all outputs as a Frame, with probs in col 0 and the
// quantiles in cols 1 thru fr.numCols() - except the optional weights vec
int ncols = fr.numCols();
if (parms._weights_column != null) ncols--;
Vec[] vecs = new Vec[1 /*1 more for the probs themselves*/ + ncols];
String[] names = new String[vecs.length];
vecs[0] = Vec.makeCon(null, parms._probs);
names[0] = "Probs";
int w = 0;
for (int i = 0; i < vecs.length - 1; ++i) {
if (fr._names[i].equals(parms._weights_column)) w = 1;
assert (w == 0 || w == 1);
vecs[i + 1] = Vec.makeCon(null, q._output._quantiles[i]);
names[i + 1] = fr._names[w + i] + "Quantiles";
}
q.delete();
return new ValFrame(new Frame(names, vecs));
}
public ModelMetricsGLRM scoreMetricsOnly(Frame frame) {
final int ncols = _output._names.length;
// Need [A,X] where A = adapted test frame, X = loading frame
// Note: A is adapted to original training frame
Frame adaptedFr = new Frame(frame);
adaptTestForTrain(adaptedFr, true, false);
assert ncols == adaptedFr.numCols();
// Append loading frame X for calculating XY
Frame fullFrm = new Frame(adaptedFr);
Frame loadingFrm = DKV.get(_output._representation_key).get();
fullFrm.add(loadingFrm);
GLRMScore gs = new GLRMScore(ncols, _parms._k, false).doAll(fullFrm);
ModelMetrics mm =
gs._mb.makeModelMetrics(
GLRMModel.this, adaptedFr, null, null); // save error metrics based on imputed data
return (ModelMetricsGLRM) mm;
}
/**
* Perform A'x operation with a DRM and an in-core Vector to create a new DRM.
*
* @param drmA DRM representing matrix A.
* @param x in-core Mahout Vector.
* @return new DRM containing A'x.
*/
public static H2ODrm exec(H2ODrm drmA, Vector x) {
Frame A = drmA.frame;
final H2OBCast<Vector> bx = new H2OBCast<Vector>(x);
// A'x is computed into atx[] with an MRTask on A (with
// x available as a Broadcast
//
// x.size() == A.numRows()
// atx.length == chks.length == A.numCols()
class MRTaskAtx extends MRTask<MRTaskAtx> {
double atx[];
public void map(Chunk chks[]) {
int chunkSize = chks[0].len();
Vector x = bx.value();
long start = chks[0].start();
atx = new double[chks.length];
for (int r = 0; r < chunkSize; r++) {
double d = x.getQuick((int) start + r);
for (int c = 0; c < chks.length; c++) {
atx[c] += (chks[c].at0(r) * d);
}
}
}
public void reduce(MRTaskAtx other) {
ArrayUtils.add(atx, other.atx);
}
}
// Take the result in .atx[], and convert into a Frame
// using existing helper functions (creating a Matrix
// along the way for the Helper)
Vector v = new DenseVector(new MRTaskAtx().doAll(A).atx);
Matrix m = new DenseMatrix(A.numCols(), 1);
m.assignColumn(0, v);
return H2OHelper.drmFromMatrix(m, -1, -1);
}
/**
* Initialize the ModelBuilder, validating all arguments and preparing the training frame. This
* call is expected to be overridden in the subclasses and each subclass will start with
* "super.init();".
*
* <p>Validate K, max_iterations and the number of rows.
*/
@Override
public void init(boolean expensive) {
super.init(expensive);
if (_parms._max_iterations < 0 || _parms._max_iterations > 1e6)
error("_max_iterations", " max_iterations must be between 0 and 1e6");
if (_train == null) return;
if (_parms._init == Initialization.User && _parms._user_points == null)
error("_user_y", "Must specify initial cluster centers");
if (null != _parms._user_points) { // Check dimensions of user-specified centers
Frame user_points = _parms._user_points.get();
if (user_points.numCols() != _train.numCols() - numSpecialCols()) {
error(
"_user_y",
"The user-specified points must have the same number of columns ("
+ (_train.numCols() - numSpecialCols())
+ ") as the training observations");
} else if (user_points.numRows() != _parms._k)
error(
"_user_y",
"The number of rows in the user-specified points is not equal to k = " + _parms._k);
}
if (expensive && error_count() == 0) checkMemoryFootPrint();
}
/**
* 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;
}
