@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);
}
@Test
public void testImpute() {
Frame fr = null;
try {
// Impute fuel economy via the "mean" method, no.
String tree = "(h2o.impute hex 1 \"mean\" \"low\" [])";
fr = chkTree(tree, "smalldata/junit/cars.csv");
chkDim(fr, 8, 406);
Assert.assertEquals(0, fr.vec(1).naCnt()); // No NAs anymore
Assert.assertEquals(23.51, fr.vec(1).at(26), 1e-1); // Row 26 was an NA, now as mean economy
fr.delete();
// Impute fuel economy via the "mean" method, after grouping by year. Update in place.
tree = "(h2o.impute hex 1 \"mean\" \"low\" [7])";
fr = chkTree(tree, "smalldata/junit/cars.csv");
chkDim(fr, 8, 406);
Assert.assertEquals(0, fr.vec(1).naCnt()); // No NAs anymore
Assert.assertEquals(
17.69, fr.vec(1).at(26), 1e-1); // Row 26 was an NA, now as 1970 mean economy
} finally {
if (fr != null) fr.delete();
Keyed.remove(Key.make("hex"));
}
}
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);
}
}
private void setTransform(
TransformType t, double[] normMul, double[] normSub, int vecStart, int n) {
int idx = 0; // idx!=i when interactions are in play, otherwise, it's just 'i'
for (int i = 0; i < n; ++i) {
Vec v = _adaptedFrame.vec(vecStart + i);
boolean isIWV = isInteractionVec(vecStart + i);
switch (t) {
case STANDARDIZE:
normMul[idx] = (v.sigma() != 0) ? 1.0 / v.sigma() : 1.0;
if (isIWV) for (int j = idx + 1; j < nextNumericIdx(i) + idx; j++) normMul[j] = 1;
normSub[idx] = v.mean();
break;
case NORMALIZE:
normMul[idx] = (v.max() - v.min() > 0) ? 1.0 / (v.max() - v.min()) : 1.0;
if (isIWV) for (int j = idx + 1; j < nextNumericIdx(i) + idx; j++) normMul[j] = 1;
normSub[idx] = v.mean();
break;
case DEMEAN:
normMul[idx] = 1;
if (isIWV) for (int j = idx + 1; j < nextNumericIdx(i) + idx; j++) normMul[j] = 1;
normSub[idx] = v.mean();
break;
case DESCALE:
normMul[idx] = (v.sigma() != 0) ? 1.0 / v.sigma() : 1.0;
if (isIWV) for (int j = idx + 1; j < nextNumericIdx(i) + idx; j++) normMul[j] = 1;
normSub[idx] = 0;
break;
default:
throw H2O.unimpl();
}
assert !Double.isNaN(normMul[idx]);
assert !Double.isNaN(normSub[idx]);
idx = isIWV ? (idx + nextNumericIdx(i)) : (idx + 1);
}
}
private void setTransform(
TransformType t, double[] normMul, double[] normSub, int vecStart, int n) {
for (int i = 0; i < n; ++i) {
Vec v = _adaptedFrame.vec(vecStart + i);
switch (t) {
case STANDARDIZE:
normMul[i] = (v.sigma() != 0) ? 1.0 / v.sigma() : 1.0;
normSub[i] = v.mean();
break;
case NORMALIZE:
normMul[i] = (v.max() - v.min() > 0) ? 1.0 / (v.max() - v.min()) : 1.0;
normSub[i] = v.mean();
break;
case DEMEAN:
normMul[i] = 1;
normSub[i] = v.mean();
break;
case DESCALE:
normMul[i] = (v.sigma() != 0) ? 1.0 / v.sigma() : 1.0;
normSub[i] = 0;
break;
default:
throw H2O.unimpl();
}
assert !Double.isNaN(normMul[i]);
assert !Double.isNaN(normSub[i]);
}
}
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 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();
}
@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 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;
}
@Test
public void testCategoricalProstate() throws InterruptedException, ExecutionException {
GLRM job = null;
GLRMModel model = null;
Frame train = null;
final int[] cats = new int[] {1, 3, 4, 5}; // Categoricals: CAPSULE, RACE, DPROS, DCAPS
try {
Scope.enter();
train = parse_test_file(Key.make("prostate.hex"), "smalldata/logreg/prostate.csv");
for (int i = 0; i < cats.length; i++)
Scope.track(train.replace(cats[i], train.vec(cats[i]).toCategoricalVec())._key);
train.remove("ID").remove();
DKV.put(train._key, train);
GLRMParameters parms = new GLRMParameters();
parms._train = train._key;
parms._k = 8;
parms._gamma_x = parms._gamma_y = 0.1;
parms._regularization_x = GLRMModel.GLRMParameters.Regularizer.Quadratic;
parms._regularization_y = GLRMModel.GLRMParameters.Regularizer.Quadratic;
parms._init = GLRM.Initialization.PlusPlus;
parms._transform = DataInfo.TransformType.STANDARDIZE;
parms._recover_svd = false;
parms._max_iterations = 200;
try {
job = new GLRM(parms);
model = job.trainModel().get();
Log.info(
"Iteration "
+ model._output._iterations
+ ": Objective value = "
+ model._output._objective);
model.score(train).delete();
ModelMetricsGLRM mm = (ModelMetricsGLRM) ModelMetrics.getFromDKV(model, train);
Log.info(
"Numeric Sum of Squared Error = "
+ mm._numerr
+ "\tCategorical Misclassification Error = "
+ mm._caterr);
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
job.remove();
}
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
if (train != null) train.delete();
if (model != null) model.delete();
Scope.exit();
}
}
// 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 final int getCategoricalIdFromInteraction(int cid, int val) {
InteractionWrappedVec v;
if ((v = (InteractionWrappedVec) _adaptedFrame.vec(cid)).isCategorical())
return getCategoricalId(cid, val);
assert v.domains() != null
: "No domain levels found for interactions! cid: " + cid + " val: " + val;
if (val >= _numOffsets[cid + 1]) { // previously unseen interaction (aka new domain level)
assert _valid
: "interaction value out of bounds, got "
+ val
+ ", next cat starts at "
+ _numOffsets[cid + 1];
val = v.mode();
}
return val < 0 ? -1 : val + _numOffsets[cid];
}
/**
* 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);
}
public int getInteractionOffset(Chunk[] chunks, int cid, int rid) {
int v1 = -1, v2 = -1;
if (_adaptedFrame == null) {
Vec vec1 = ((InteractionWrappedVec) chunks[cid].vec()).v1();
Vec vec2 = ((InteractionWrappedVec) chunks[cid].vec()).v2();
for (int i = 0; i < chunks.length; ++i) {
if (v1 >= 0 && v2 >= 0) break; // found both vecs already
if (v1 == -1 && chunks[i].vec() == vec1) v1 = i;
if (v2 == -1 && chunks[i].vec() == vec2) v2 = i;
}
} else {
InteractionWrappedVec iwv = ((InteractionWrappedVec) _adaptedFrame.vec(cid));
v1 = _adaptedFrame.find(iwv.v1());
v2 = _adaptedFrame.find(iwv.v2());
}
if (v1 < _cats) return (int) chunks[v1].at8(rid); // v1 is some categorical column
else if (v2 < _cats) return (int) chunks[v2].at8(rid); // or v2 is some categorical column
return 0; // or neither is categorical
}
private void checkTree(String tree, boolean expectThrow) {
// Frame r = frame(new double[][]{{-1},{1},{2},{3},{4},{5},{6},{254}});
// Key ahex = Key.make("a.hex");
// Frame fr = new Frame(ahex, null, new Vec[]{r.remove(0)});
// r.delete();
// DKV.put(ahex, fr);
Frame fr = parse_test_file(Key.make("a.hex"), "smalldata/iris/iris_wheader.csv");
fr.remove(4).remove();
try {
Val val = Exec.exec(tree);
Assert.assertFalse(expectThrow);
System.out.println(val.toString());
if (val instanceof ValFrame) {
Frame fr2 = ((ValFrame) val)._fr;
System.out.println(fr2.vec(0));
fr2.remove();
}
} catch (IllegalArgumentException iae) {
if (!expectThrow) throw iae;
} finally {
fr.delete();
}
}
public Vec getOutputVec(int i) {
return _adaptedFrame.vec(outputChunkId(i));
}
/**
* The train/valid Frame instances are sorted by categorical (themselves sorted by cardinality
* greatest to least) with all numerical columns following. The response column(s) are placed at
* the end.
*
* <p>Interactions: 1. Num-Num (Note: N(0,1) * N(0,1) ~ N(0,1) ) 2. Num-Enum 3. Enum-Enum
*
* <p>Interactions are produced on the fly and are dense (in all 3 cases). Consumers of DataInfo
* should not have to care how these interactions are generated. Any heuristic using the fullN
* value should continue functioning the same.
*
* <p>Interactions are specified in two ways: A. As a list of pairs of column indices. B. As a
* list of pairs of column indices with limited enums.
*/
public DataInfo(
Frame train,
Frame valid,
int nResponses,
boolean useAllFactorLevels,
TransformType predictor_transform,
TransformType response_transform,
boolean skipMissing,
boolean imputeMissing,
boolean missingBucket,
boolean weight,
boolean offset,
boolean fold,
Model.InteractionPair[] interactions) {
super(Key.<DataInfo>make());
_valid = valid != null;
assert predictor_transform != null;
assert response_transform != null;
_offset = offset;
_weights = weight;
_fold = fold;
assert !(skipMissing && imputeMissing) : "skipMissing and imputeMissing cannot both be true";
_skipMissing = skipMissing;
_imputeMissing = imputeMissing;
_predictor_transform = predictor_transform;
_response_transform = response_transform;
_responses = nResponses;
_useAllFactorLevels = useAllFactorLevels;
_interactions = interactions;
// create dummy InteractionWrappedVecs and shove them onto the front
if (_interactions != null) {
_interactionVecs = new int[_interactions.length];
train =
Model.makeInteractions(
train,
false,
_interactions,
_useAllFactorLevels,
_skipMissing,
predictor_transform == TransformType.STANDARDIZE)
.add(train);
if (valid != null)
valid =
Model.makeInteractions(
valid,
true,
_interactions,
_useAllFactorLevels,
_skipMissing,
predictor_transform == TransformType.STANDARDIZE)
.add(valid); // FIXME: should be using the training subs/muls!
}
_permutation = new int[train.numCols()];
final Vec[] tvecs = train.vecs();
// Count categorical-vs-numerical
final int n = tvecs.length - _responses - (offset ? 1 : 0) - (weight ? 1 : 0) - (fold ? 1 : 0);
int[] nums = MemoryManager.malloc4(n);
int[] cats = MemoryManager.malloc4(n);
int nnums = 0, ncats = 0;
for (int i = 0; i < n; ++i)
if (tvecs[i].isCategorical()) cats[ncats++] = i;
else nums[nnums++] = i;
_nums = nnums;
_cats = ncats;
_catLvls = new int[ncats][];
// sort the cats in the decreasing order according to their size
for (int i = 0; i < ncats; ++i)
for (int j = i + 1; j < ncats; ++j)
if (tvecs[cats[i]].domain().length < tvecs[cats[j]].domain().length) {
int x = cats[i];
cats[i] = cats[j];
cats[j] = x;
}
String[] names = new String[train.numCols()];
Vec[] tvecs2 = new Vec[train.numCols()];
// Compute the cardinality of each cat
_catModes = new int[ncats];
_catOffsets = MemoryManager.malloc4(ncats + 1);
_catMissing = new boolean[ncats];
int len = _catOffsets[0] = 0;
int interactionIdx = 0; // simple index into the _interactionVecs array
ArrayList<Integer> interactionIds;
if (_interactions == null) {
interactionIds = new ArrayList<>();
for (int i = 0; i < tvecs.length; ++i)
if (tvecs[i] instanceof InteractionWrappedVec) {
interactionIds.add(i);
}
_interactionVecs = new int[interactionIds.size()];
for (int i = 0; i < _interactionVecs.length; ++i) _interactionVecs[i] = interactionIds.get(i);
}
for (int i = 0; i < ncats; ++i) {
names[i] = train._names[cats[i]];
Vec v = (tvecs2[i] = tvecs[cats[i]]);
_catMissing[i] = missingBucket; // needed for test time
if (v instanceof InteractionWrappedVec) {
if (_interactions != null) _interactions[interactionIdx].vecIdx = i;
_interactionVecs[interactionIdx++] =
i; // i (and not cats[i]) because this is the index in _adaptedFrame
_catOffsets[i + 1] = (len += v.domain().length + (missingBucket ? 1 : 0));
} else
_catOffsets[i + 1] =
(len +=
v.domain().length
- (useAllFactorLevels ? 0 : 1)
+ (missingBucket ? 1 : 0)); // missing values turn into a new factor level
_catModes[i] =
imputeMissing ? imputeCat(train.vec(cats[i])) : _catMissing[i] ? v.domain().length : -100;
_permutation[i] = cats[i];
}
_numMeans = new double[nnums];
_numOffsets = MemoryManager.malloc4(nnums + 1);
_numOffsets[0] = len;
boolean isIWV; // is InteractionWrappedVec?
for (int i = 0; i < nnums; ++i) {
names[i + ncats] = train._names[nums[i]];
Vec v = train.vec(nums[i]);
tvecs2[i + ncats] = v;
isIWV = v instanceof InteractionWrappedVec;
if (isIWV) {
if (null != _interactions) _interactions[interactionIdx].vecIdx = i + ncats;
_interactionVecs[interactionIdx++] = i + ncats;
}
_numOffsets[i + 1] = (len += (isIWV ? ((InteractionWrappedVec) v).expandedLength() : 1));
_numMeans[i] = train.vec(nums[i]).mean();
_permutation[i + ncats] = nums[i];
}
for (int i = names.length - nResponses - (weight ? 1 : 0) - (offset ? 1 : 0) - (fold ? 1 : 0);
i < names.length;
++i) {
names[i] = train._names[i];
tvecs2[i] = train.vec(i);
}
_adaptedFrame = new Frame(names, tvecs2);
train.restructure(names, tvecs2);
if (valid != null) valid.restructure(names, valid.vecs(names));
// _adaptedFrame = train;
setPredictorTransform(predictor_transform);
if (_responses > 0) setResponseTransform(response_transform);
}
public boolean isInteractionVec(int colid) {
if (null == _interactions || null == _interactionVecs) return false;
if (_adaptedFrame != null) return _adaptedFrame.vec(colid) instanceof InteractionWrappedVec;
else return Arrays.binarySearch(_interactionVecs, colid) >= 0;
}
@Test
public void testLosses() throws InterruptedException, ExecutionException {
long seed = 0xDECAF;
Random rng = new Random(seed);
Frame train = null;
final int[] cats = new int[] {1, 3, 4, 5}; // Categoricals: CAPSULE, RACE, DPROS, DCAPS
final GLRMParameters.Regularizer[] regs =
new GLRMParameters.Regularizer[] {
GLRMParameters.Regularizer.Quadratic,
GLRMParameters.Regularizer.L1,
GLRMParameters.Regularizer.NonNegative,
GLRMParameters.Regularizer.OneSparse,
GLRMParameters.Regularizer.UnitOneSparse,
GLRMParameters.Regularizer.Simplex
};
Scope.enter();
try {
train = parse_test_file(Key.make("prostate.hex"), "smalldata/logreg/prostate.csv");
for (int i = 0; i < cats.length; i++)
Scope.track(train.replace(cats[i], train.vec(cats[i]).toCategoricalVec())._key);
train.remove("ID").remove();
DKV.put(train._key, train);
for (GLRMParameters.Loss loss :
new GLRMParameters.Loss[] {
GLRMParameters.Loss.Quadratic,
GLRMParameters.Loss.Absolute,
GLRMParameters.Loss.Huber,
GLRMParameters.Loss.Poisson,
GLRMParameters.Loss.Hinge,
GLRMParameters.Loss.Logistic
}) {
for (GLRMParameters.Loss multiloss :
new GLRMParameters.Loss[] {
GLRMParameters.Loss.Categorical, GLRMParameters.Loss.Ordinal
}) {
GLRMModel model = null;
try {
Scope.enter();
long myseed = rng.nextLong();
Log.info("GLRM using seed = " + myseed);
GLRMParameters parms = new GLRMParameters();
parms._train = train._key;
parms._transform = DataInfo.TransformType.NONE;
parms._k = 5;
parms._loss = loss;
parms._multi_loss = multiloss;
parms._init = GLRM.Initialization.SVD;
parms._regularization_x = regs[rng.nextInt(regs.length)];
parms._regularization_y = regs[rng.nextInt(regs.length)];
parms._gamma_x = Math.abs(rng.nextDouble());
parms._gamma_y = Math.abs(rng.nextDouble());
parms._recover_svd = false;
parms._seed = myseed;
parms._verbose = false;
parms._max_iterations = 500;
GLRM job = new GLRM(parms);
try {
model = job.trainModel().get();
Log.info(
"Iteration "
+ model._output._iterations
+ ": Objective value = "
+ model._output._objective);
model.score(train).delete();
ModelMetricsGLRM mm = (ModelMetricsGLRM) ModelMetrics.getFromDKV(model, train);
Log.info(
"Numeric Sum of Squared Error = "
+ mm._numerr
+ "\tCategorical Misclassification Error = "
+ mm._caterr);
} catch (Throwable t) {
throw t;
} finally {
job.remove();
}
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
if (model != null) model.delete();
Scope.exit();
}
}
}
} finally {
if (train != null) train.delete();
Scope.exit();
}
}
@Override
protected void compute2() {
_model = null; // Resulting model!
try {
Scope.enter(); // Cleanup temp keys
init(true); // Do any expensive tests & conversions now
// Do lock even before checking the errors, since this block is finalized by unlock
// (not the best solution, but the code is more readable)
_parms.read_lock_frames(SharedTree.this); // Fetch & read-lock input frames
if (error_count() > 0)
throw H2OModelBuilderIllegalArgumentException.makeFromBuilder(SharedTree.this);
// New Model? Or continuing from a checkpoint?
if (_parms._checkpoint && DKV.get(_parms._model_id) != null) {
_model = DKV.get(_dest).get();
_model.write_lock(_key); // do not delete previous model; we are extending it
} else { // New Model
// Compute the zero-tree error - guessing only the class distribution.
// MSE is stddev squared when guessing for regression.
// For classification, guess the largest class.
_model =
makeModel(
_dest,
_parms,
initial_MSE(_response, _response),
_valid == null
? Double.NaN
: initial_MSE(_response, _vresponse)); // Make a fresh model
_model.delete_and_lock(_key); // and clear & write-lock it (smashing any prior)
_model._output._init_f = _initialPrediction;
}
// Compute the response domain; makes for nicer printouts
String[] domain = _response.domain();
assert (_nclass > 1 && domain != null) || (_nclass == 1 && domain == null);
if (_nclass == 1) domain = new String[] {"r"}; // For regression, give a name to class 0
// Compute class distribution, used to for initial guesses and to
// upsample minority classes (if asked for).
if (_nclass > 1) { // Classification?
// Handle imbalanced classes by stratified over/under-sampling.
// initWorkFrame sets the modeled class distribution, and
// model.score() corrects the probabilities back using the
// distribution ratios
if (_model._output.isClassifier() && _parms._balance_classes) {
float[] trainSamplingFactors =
new float
[_train
.lastVec()
.domain()
.length]; // leave initialized to 0 -> will be filled up below
if (_parms._class_sampling_factors != null) {
if (_parms._class_sampling_factors.length != _train.lastVec().domain().length)
throw new IllegalArgumentException(
"class_sampling_factors must have "
+ _train.lastVec().domain().length
+ " elements");
trainSamplingFactors =
_parms._class_sampling_factors.clone(); // clone: don't modify the original
}
Frame stratified =
water.util.MRUtils.sampleFrameStratified(
_train,
_train.lastVec(),
_train.vec(_model._output.weightsName()),
trainSamplingFactors,
(long) (_parms._max_after_balance_size * _train.numRows()),
_parms._seed,
true,
false);
if (stratified != _train) {
_train = stratified;
_response = stratified.vec(_parms._response_column);
_weights = stratified.vec(_parms._weights_column);
// Recompute distribution since the input frame was modified
MRUtils.ClassDist cdmt2 =
_weights != null
? new MRUtils.ClassDist(_nclass).doAll(_response, _weights)
: new MRUtils.ClassDist(_nclass).doAll(_response);
_model._output._distribution = cdmt2.dist();
_model._output._modelClassDist = cdmt2.rel_dist();
}
}
Log.info("Prior class distribution: " + Arrays.toString(_model._output._priorClassDist));
Log.info("Model class distribution: " + Arrays.toString(_model._output._modelClassDist));
}
// Also add to the basic working Frame these sets:
// nclass Vecs of current forest results (sum across all trees)
// nclass Vecs of working/temp data
// nclass Vecs of NIDs, allowing 1 tree per class
// Current forest values: results of summing the prior M trees
for (int i = 0; i < _nclass; i++) _train.add("Tree_" + domain[i], _response.makeZero());
// Initial work columns. Set-before-use in the algos.
for (int i = 0; i < _nclass; i++) _train.add("Work_" + domain[i], _response.makeZero());
// One Tree per class, each tree needs a NIDs. For empty classes use a -1
// NID signifying an empty regression tree.
for (int i = 0; i < _nclass; i++)
_train.add(
"NIDs_" + domain[i],
_response.makeCon(
_model._output._distribution == null
? 0
: (_model._output._distribution[i] == 0 ? -1 : 0)));
// Tag out rows missing the response column
new ExcludeNAResponse().doAll(_train);
// Variable importance: squared-error-improvement-per-variable-per-split
_improvPerVar = new float[_ncols];
// Sub-class tree-model-builder specific build code
buildModel();
done(); // Job done!
} catch (Throwable t) {
Job thisJob = DKV.getGet(_key);
if (thisJob._state == JobState.CANCELLED) {
Log.info("Job cancelled by user.");
} else {
t.printStackTrace();
failed(t);
throw t;
}
} finally {
if (_model != null) _model.unlock(_key);
_parms.read_unlock_frames(SharedTree.this);
if (_model == null) Scope.exit();
else {
Scope.exit(
_model._key,
ModelMetrics.buildKey(_model, _parms.train()),
ModelMetrics.buildKey(_model, _parms.valid()));
}
}
tryComplete();
}
public DataInfo(
Frame train,
Frame valid,
int nResponses,
boolean useAllFactorLevels,
TransformType predictor_transform,
TransformType response_transform,
boolean skipMissing,
boolean imputeMissing,
boolean missingBucket,
boolean weight,
boolean offset,
boolean fold) {
super(Key.<DataInfo>make());
_valid = false;
assert predictor_transform != null;
assert response_transform != null;
_offset = offset;
_weights = weight;
_fold = fold;
assert !(skipMissing && imputeMissing) : "skipMissing and imputeMissing cannot both be true";
_skipMissing = skipMissing;
_imputeMissing = imputeMissing;
_predictor_transform = predictor_transform;
_response_transform = response_transform;
_responses = nResponses;
_useAllFactorLevels = useAllFactorLevels;
_permutation = new int[train.numCols()];
final Vec[] tvecs = train.vecs();
// Count categorical-vs-numerical
final int n = tvecs.length - _responses - (offset ? 1 : 0) - (weight ? 1 : 0) - (fold ? 1 : 0);
int[] nums = MemoryManager.malloc4(n);
int[] cats = MemoryManager.malloc4(n);
int nnums = 0, ncats = 0;
for (int i = 0; i < n; ++i)
if (tvecs[i].isCategorical()) cats[ncats++] = i;
else nums[nnums++] = i;
_nums = nnums;
_cats = ncats;
_catLvls = new int[_cats][];
// sort the cats in the decreasing order according to their size
for (int i = 0; i < ncats; ++i)
for (int j = i + 1; j < ncats; ++j)
if (tvecs[cats[i]].domain().length < tvecs[cats[j]].domain().length) {
int x = cats[i];
cats[i] = cats[j];
cats[j] = x;
}
String[] names = new String[train.numCols()];
Vec[] tvecs2 = new Vec[train.numCols()];
// Compute the cardinality of each cat
_catModes = new int[_cats];
_catOffsets = MemoryManager.malloc4(ncats + 1);
_catMissing = new int[ncats];
int len = _catOffsets[0] = 0;
for (int i = 0; i < ncats; ++i) {
_catModes[i] = imputeCat(train.vec(cats[i]));
_permutation[i] = cats[i];
names[i] = train._names[cats[i]];
Vec v = (tvecs2[i] = tvecs[cats[i]]);
_catMissing[i] = missingBucket ? 1 : 0; // needed for test time
_catOffsets[i + 1] =
(len +=
v.domain().length
- (useAllFactorLevels ? 0 : 1)
+ (missingBucket ? 1 : 0)); // missing values turn into a new factor level
}
_numMeans = new double[_nums];
for (int i = 0; i < _nums; ++i) {
names[i + _cats] = train._names[nums[i]];
tvecs2[i + _cats] = train.vec(nums[i]);
_numMeans[i] = train.vec(nums[i]).mean();
_permutation[i + _cats] = nums[i];
}
for (int i = names.length - nResponses - (weight ? 1 : 0) - (offset ? 1 : 0) - (fold ? 1 : 0);
i < names.length;
++i) {
names[i] = train._names[i];
tvecs2[i] = train.vec(i);
}
_adaptedFrame = new Frame(names, tvecs2);
train.restructure(names, tvecs2);
if (valid != null) valid.restructure(names, valid.vecs(names));
// _adaptedFrame = train;
setPredictorTransform(predictor_transform);
if (_responses > 0) setResponseTransform(response_transform);
}
@Test
public void testSetColumnLossCats() throws InterruptedException, ExecutionException {
GLRM job = null;
GLRMModel model = null;
Frame train = null;
final int[] cats = new int[] {1, 3, 4, 5}; // Categoricals: CAPSULE, RACE, DPROS, DCAPS
Scope.enter();
try {
train = parse_test_file(Key.make("prostate.hex"), "smalldata/logreg/prostate.csv");
for (int i = 0; i < cats.length; i++)
Scope.track(train.replace(cats[i], train.vec(cats[i]).toCategoricalVec())._key);
train.remove("ID").remove();
DKV.put(train._key, train);
GLRMParameters parms = new GLRMParameters();
parms._train = train._key;
parms._k = 12;
parms._loss = GLRMParameters.Loss.Quadratic;
parms._multi_loss = GLRMParameters.Loss.Categorical;
parms._loss_by_col =
new GLRMParameters.Loss[] {
GLRMParameters.Loss.Ordinal, GLRMParameters.Loss.Poisson, GLRMParameters.Loss.Absolute
};
parms._loss_by_col_idx = new int[] {3 /* DPROS */, 1 /* AGE */, 6 /* VOL */};
parms._init = GLRM.Initialization.PlusPlus;
parms._min_step_size = 1e-5;
parms._recover_svd = false;
parms._max_iterations = 2000;
try {
job = new GLRM(parms);
model = job.trainModel().get();
Log.info(
"Iteration "
+ model._output._iterations
+ ": Objective value = "
+ model._output._objective);
GLRMTest.checkLossbyCol(parms, model);
model.score(train).delete();
ModelMetricsGLRM mm = (ModelMetricsGLRM) ModelMetrics.getFromDKV(model, train);
Log.info(
"Numeric Sum of Squared Error = "
+ mm._numerr
+ "\tCategorical Misclassification Error = "
+ mm._caterr);
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
job.remove();
}
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
if (train != null) train.delete();
if (model != null) model.delete();
Scope.exit();
}
}
public static void assertValues(Frame f, String[] expValues) {
assertValues(f.vec(0), expValues);
}
private void chkFr(Frame fr, int col, int row, String exp) {
String[] dom = fr.vec(col).domain();
Assert.assertEquals(exp, dom[(int) fr.vec(col).at8(row)]);
}
public Vec getOffsetVec() {
return _adaptedFrame.vec(offsetChunkId());
}
/**
* 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;
}
@Test
public void testExpandCatsProstate() throws InterruptedException, ExecutionException {
double[][] prostate =
ard(
ard(0, 71, 1, 0, 0, 4.8, 14.0, 7),
ard(1, 70, 1, 1, 0, 8.4, 21.8, 5),
ard(0, 73, 1, 3, 0, 10.0, 27.4, 6),
ard(1, 68, 1, 0, 0, 6.7, 16.7, 6));
double[][] pros_expandR =
ard(
ard(1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 71, 4.8, 14.0, 7),
ard(0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 70, 8.4, 21.8, 5),
ard(0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 73, 10.0, 27.4, 6),
ard(1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 68, 6.7, 16.7, 6));
String[] pros_cols =
new String[] {"Capsule", "Age", "Race", "Dpros", "Dcaps", "PSA", "Vol", "Gleason"};
String[][] pros_domains =
new String[][] {
new String[] {"No", "Yes"},
null,
new String[] {"Other", "White", "Black"},
new String[] {"None", "UniLeft", "UniRight", "Bilobar"},
new String[] {"No", "Yes"},
null,
null,
null
};
final int[] cats = new int[] {1, 3, 4, 5}; // Categoricals: CAPSULE, RACE, DPROS, DCAPS
Frame fr = null;
try {
Scope.enter();
fr = parse_test_file(Key.make("prostate.hex"), "smalldata/logreg/prostate.csv");
for (int i = 0; i < cats.length; i++)
Scope.track(fr.replace(cats[i], fr.vec(cats[i]).toCategoricalVec())._key);
fr.remove("ID").remove();
DKV.put(fr._key, fr);
DataInfo dinfo =
new DataInfo(
Key.make(),
fr,
null,
0,
true,
DataInfo.TransformType.NONE,
DataInfo.TransformType.NONE,
false,
false,
false, /* weights */
false, /* offset */
false, /* fold */
false);
Log.info("Original matrix:\n" + colFormat(pros_cols, "%8.7s") + ArrayUtils.pprint(prostate));
double[][] pros_perm = ArrayUtils.permuteCols(prostate, dinfo._permutation);
Log.info(
"Permuted matrix:\n"
+ colFormat(pros_cols, "%8.7s", dinfo._permutation)
+ ArrayUtils.pprint(pros_perm));
double[][] pros_exp = GLRM.expandCats(pros_perm, dinfo);
Log.info(
"Expanded matrix:\n"
+ colExpFormat(pros_cols, pros_domains, "%8.7s", dinfo._permutation)
+ ArrayUtils.pprint(pros_exp));
Assert.assertArrayEquals(pros_expandR, pros_exp);
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
if (fr != null) fr.delete();
Scope.exit();
}
}
public void validate(GLM glm) {
if (_compute_p_values && _solver != Solver.AUTO && _solver != Solver.IRLSM)
glm.error(
"_compute_p_values",
"P values can only be computed with IRLSM solver, go solver = " + _solver);
if (_compute_p_values && (_lambda == null || _lambda[0] > 0))
glm.error(
"_compute_p_values",
"P values can only be computed with NO REGULARIZATION (lambda = 0)");
if (_compute_p_values && _family == Family.multinomial)
glm.error(
"_compute_p_values", "P values are currently not supported for family=multinomial");
if (_compute_p_values && _non_negative)
glm.error(
"_compute_p_values", "P values are currently not supported for family=multinomial");
if (_weights_column != null
&& _offset_column != null
&& _weights_column.equals(_offset_column))
glm.error("_offset_column", "Offset must be different from weights");
if (_lambda_search)
if (glm.nFoldCV())
glm.error(
"_lambda_search",
"Lambda search is not currently supported in conjunction with N-fold cross-validation");
if (_nlambdas == -1) _nlambdas = 100;
else _exactLambdas = false;
if (_obj_reg != -1 && _obj_reg <= 0)
glm.error("obj_reg", "Must be positive or -1 for default");
if (_prior != -1 && _prior <= 0 || _prior >= 1)
glm.error("_prior", "Prior must be in (exlusive) range (0,1)");
if (_family != Family.tweedie) {
glm.hide("_tweedie_variance_power", "Only applicable with Tweedie family");
glm.hide("_tweedie_link_power", "Only applicable with Tweedie family");
}
if (_beta_constraints != null) {
if (_family == Family.multinomial)
glm.error(
"beta_constraints", "beta constraints are not supported for family = multionomial");
Frame f = _beta_constraints.get();
if (f == null) glm.error("beta_constraints", "Missing frame for beta constraints");
Vec v = f.vec("names");
if (v == null)
glm.error(
"beta_constraints",
"Beta constraints parameter must have names column with valid coefficient names");
// todo: check the coefficient names
v = f.vec("upper_bounds");
if (v != null && !v.isNumeric())
glm.error("beta_constraints", "upper_bounds must be numeric if present");
v = f.vec("upper_bounds");
v = f.vec("lower_bounds");
if (v != null && !v.isNumeric())
glm.error("beta_constraints", "lower_bounds must be numeric if present");
v = f.vec("beta_given");
if (v != null && !v.isNumeric())
glm.error("beta_constraints", "beta_given must be numeric if present");
v = f.vec("upper_bounds");
v = f.vec("beta_start");
if (v != null && !v.isNumeric())
glm.error("beta_constraints", "beta_start must be numeric if present");
}
if (!_lambda_search) {
glm.hide("_lambda_min_ratio", "only applies if lambda search is on.");
glm.hide("_nlambdas", "only applies if lambda search is on.");
}
if (_link != Link.family_default) { // check we have compatible link
switch (_family) {
case gaussian:
if (_link != Link.identity && _link != Link.log && _link != Link.inverse)
throw new IllegalArgumentException(
"Incompatible link function for selected family. Only identity, log and inverse links are allowed for family=gaussian.");
break;
case binomial:
if (_link
!= Link
.logit) // fixme: R also allows log, but it's not clear when can be applied and
// what should we do in case the predictions are outside of 0/1.
throw new IllegalArgumentException(
"Incompatible link function for selected family. Only logit is allowed for family=binomial. Got "
+ _link);
break;
case poisson:
if (_link != Link.log && _link != Link.identity)
throw new IllegalArgumentException(
"Incompatible link function for selected family. Only log and identity links are allowed for family=poisson.");
break;
case gamma:
if (_link != Link.inverse && _link != Link.log && _link != Link.identity)
throw new IllegalArgumentException(
"Incompatible link function for selected family. Only inverse, log and identity links are allowed for family=gamma.");
break;
case tweedie:
if (_link != Link.tweedie)
throw new IllegalArgumentException(
"Incompatible link function for selected family. Only tweedie link allowed for family=tweedie.");
break;
case multinomial:
if (_link != Link.multinomial)
throw new IllegalArgumentException(
"Incompatible link function for selected family. Only multinomial link allowed for family=multinomial.");
break;
default:
H2O.fail();
}
}
}
private void chkFr(Frame fr, int col, int row, double exp, double tol) {
if (Double.isNaN(exp)) Assert.assertTrue(fr.vec(col).isNA(row));
else Assert.assertEquals(exp, fr.vec(col).at(row), tol);
}