// Stopping criteria
boolean isDone(KMeansModel model, double[][] newCenters, double[][] oldCenters) {
if (!isRunning()) return true; // Stopped/cancelled
// Stopped for running out iterations
if (model._output._iterations >= _parms._max_iterations) return true;
// Compute average change in standardized cluster centers
if (oldCenters == null) return false; // No prior iteration, not stopping
double average_change = 0;
for (int clu = 0; clu < _parms._k; clu++)
average_change +=
hex.genmodel.GenModel.KMeans_distance(
oldCenters[clu], newCenters[clu], _isCats, null, null);
average_change /= _parms._k; // Average change per cluster
model._output._avg_centroids_chg =
ArrayUtils.copyAndFillOf(
model._output._avg_centroids_chg,
model._output._avg_centroids_chg.length + 1,
average_change);
model._output._training_time_ms =
ArrayUtils.copyAndFillOf(
model._output._training_time_ms,
model._output._training_time_ms.length + 1,
System.currentTimeMillis());
return average_change < TOLERANCE;
}
protected void calcCounts(CoxPHModel model, final CoxPHTask coxMR) {
CoxPHModel.CoxPHParameters p = model._parms;
CoxPHModel.CoxPHOutput o = model._output;
o.n_missing = o.n - coxMR.n;
o.n = coxMR.n;
for (int j = 0; j < o.x_mean_cat.length; j++)
o.x_mean_cat[j] = coxMR.sumWeightedCatX[j] / coxMR.sumWeights;
for (int j = 0; j < o.x_mean_num.length; j++)
o.x_mean_num[j] = coxMR.dinfo()._normSub[j] + coxMR.sumWeightedNumX[j] / coxMR.sumWeights;
System.arraycopy(
coxMR.dinfo()._normSub, o.x_mean_num.length, o.mean_offset, 0, o.mean_offset.length);
int nz = 0;
for (int t = 0; t < coxMR.countEvents.length; ++t) {
o.total_event += coxMR.countEvents[t];
if (coxMR.sizeEvents[t] > 0 || coxMR.sizeCensored[t] > 0) {
o.time[nz] = o.min_time + t;
o.n_risk[nz] = coxMR.sizeRiskSet[t];
o.n_event[nz] = coxMR.sizeEvents[t];
o.n_censor[nz] = coxMR.sizeCensored[t];
nz++;
}
}
if (p.start_column == null)
for (int t = o.n_risk.length - 2; t >= 0; --t) o.n_risk[t] += o.n_risk[t + 1];
}
protected void initStats(final CoxPHModel model, final DataInfo dinfo) {
CoxPHModel.CoxPHParameters p = model._parms;
CoxPHModel.CoxPHOutput o = model._output;
o.n = p.stop_column.length();
o.data_info = dinfo;
final int n_offsets = (p.offset_columns == null) ? 0 : p.offset_columns.length;
final int n_coef = o.data_info.fullN() - n_offsets;
final String[] coefNames = o.data_info.coefNames();
o.coef_names = new String[n_coef];
System.arraycopy(coefNames, 0, o.coef_names, 0, n_coef);
o.coef = MemoryManager.malloc8d(n_coef);
o.exp_coef = MemoryManager.malloc8d(n_coef);
o.exp_neg_coef = MemoryManager.malloc8d(n_coef);
o.se_coef = MemoryManager.malloc8d(n_coef);
o.z_coef = MemoryManager.malloc8d(n_coef);
o.gradient = MemoryManager.malloc8d(n_coef);
o.hessian = malloc2DArray(n_coef, n_coef);
o.var_coef = malloc2DArray(n_coef, n_coef);
o.x_mean_cat = MemoryManager.malloc8d(n_coef - (o.data_info._nums - n_offsets));
o.x_mean_num = MemoryManager.malloc8d(o.data_info._nums - n_offsets);
o.mean_offset = MemoryManager.malloc8d(n_offsets);
o.offset_names = new String[n_offsets];
System.arraycopy(coefNames, n_coef, o.offset_names, 0, n_offsets);
final Vec start_column = p.start_column;
final Vec stop_column = p.stop_column;
o.min_time =
p.start_column == null ? (long) stop_column.min() : (long) start_column.min() + 1;
o.max_time = (long) stop_column.max();
final int n_time = new Vec.CollectDomain().doAll(stop_column).domain().length;
o.time = MemoryManager.malloc8(n_time);
o.n_risk = MemoryManager.malloc8d(n_time);
o.n_event = MemoryManager.malloc8d(n_time);
o.n_censor = MemoryManager.malloc8d(n_time);
o.cumhaz_0 = MemoryManager.malloc8d(n_time);
o.var_cumhaz_1 = MemoryManager.malloc8d(n_time);
o.var_cumhaz_2 = malloc2DArray(n_time, n_coef);
}
private static double[][] destandardize(
double[][] centers, String[][] isCats, double[] means, double[] mults) {
int K = centers.length;
int N = centers[0].length;
double[][] value = new double[K][N];
for (int clu = 0; clu < K; clu++) {
System.arraycopy(centers[clu], 0, value[clu], 0, N);
if (mults != null) { // Reverse standardization
for (int col = 0; col < N; col++)
if (isCats[col] == null) value[clu][col] = value[clu][col] / mults[col] + means[col];
}
}
return value;
}
public static class PCAParameters extends Model.Parameters {
public DataInfo.TransformType _transform = DataInfo.TransformType.NONE; // Data transformation
public Method _pca_method = Method.GramSVD; // Method for computing PCA
public int _k = 1; // Number of principal components
public int _max_iterations = 1000; // Max iterations
public long _seed = System.nanoTime(); // RNG seed
// public Key<Frame> _loading_key;
public String _loading_name; // Loading only generated if pca_method = Power
public boolean _keep_loading = true;
public boolean _use_all_factor_levels =
false; // When expanding categoricals, should first level be kept or dropped?
public boolean _compute_metrics =
true; // Should a second pass be made through data to compute metrics?
public enum Method {
GramSVD,
Power,
GLRM
}
}
/**
* 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;
}
protected double doScoringAndSaveModel(
boolean finalScoring, boolean oob, boolean build_tree_one_node) {
double training_r2 = Double.NaN; // Training R^2 value, if computed
long now = System.currentTimeMillis();
if (_firstScore == 0) _firstScore = now;
long sinceLastScore = now - _timeLastScoreStart;
boolean updated = false;
new ProgressUpdate(
"Built " + _model._output._ntrees + " trees so far (out of " + _parms._ntrees + ").")
.fork(_progressKey);
// Now model already contains tid-trees in serialized form
if (_parms._score_each_iteration
|| finalScoring
|| (now - _firstScore < 4000)
|| // Score every time for 4 secs
// Throttle scoring to keep the cost sane; limit to a 10% duty cycle & every 4 secs
(sinceLastScore > 4000
&& // Limit scoring updates to every 4sec
(double) (_timeLastScoreEnd - _timeLastScoreStart) / sinceLastScore
< 0.1)) { // 10% duty cycle
checkMemoryFootPrint();
// If validation is specified we use a model for scoring, so we need to
// update it! First we save model with trees (i.e., make them available
// for scoring) and then update it with resulting error
_model.update(_key);
updated = true;
Log.info("============================================================== ");
SharedTreeModel.SharedTreeOutput out = _model._output;
_timeLastScoreStart = now;
// Score on training data
new ProgressUpdate("Scoring the model.").fork(_progressKey);
Score sc =
new Score(this, true, oob, _model._output.getModelCategory())
.doAll(train(), build_tree_one_node);
ModelMetrics mm = sc.makeModelMetrics(_model, _parms.train());
out._training_metrics = mm;
if (oob)
out._training_metrics._description = "Metrics reported on Out-Of-Bag training samples";
out._scored_train[out._ntrees].fillFrom(mm);
if (out._ntrees > 0) Log.info("Training " + out._scored_train[out._ntrees].toString());
// Score again on validation data
if (_parms._valid != null) {
Score scv =
new Score(this, false, false, _model._output.getModelCategory())
.doAll(valid(), build_tree_one_node);
ModelMetrics mmv = scv.makeModelMetrics(_model, _parms.valid());
out._validation_metrics = mmv;
out._scored_valid[out._ntrees].fillFrom(mmv);
if (out._ntrees > 0) Log.info("Validation " + out._scored_valid[out._ntrees].toString());
}
if (out._ntrees > 0) { // Compute variable importances
out._model_summary = createModelSummaryTable(out);
out._scoring_history = createScoringHistoryTable(out);
out._varimp = new hex.VarImp(_improvPerVar, out._names);
out._variable_importances = hex.ModelMetrics.calcVarImp(out._varimp);
Log.info(out._model_summary.toString());
// For Debugging:
// Log.info(out._scoring_history.toString());
// Log.info(out._variable_importances.toString());
}
ConfusionMatrix cm = mm.cm();
if (cm != null) {
if (cm._cm.length <= _parms._max_confusion_matrix_size) {
Log.info(cm.toASCII());
} else {
Log.info(
"Confusion Matrix is too large (max_confusion_matrix_size="
+ _parms._max_confusion_matrix_size
+ "): "
+ _nclass
+ " classes.");
}
}
_timeLastScoreEnd = System.currentTimeMillis();
}
// Double update - after either scoring or variable importance
if (updated) _model.update(_key);
return training_r2;
}
public static void userMain(String[] args) {
H2O.main(args);
TestUtil.stall_till_cloudsize(NODES);
List<Class> tests = new ArrayList<Class>();
// Classes to test:
// tests = JUnitRunner.all();
// Neural Net - deprecated
// tests.add(NeuralNetSpiralsTest.class); //compare NeuralNet vs reference
// tests.add(NeuralNetIrisTest.class); //compare NeuralNet vs reference
// Chunk tests
// tests.add(C0LChunkTest.class);
// tests.add(C0DChunkTest.class);
// tests.add(C1ChunkTest.class);
// tests.add(C1NChunkTest.class);
// tests.add(C1SChunkTest.class);
// tests.add(C2ChunkTest.class);
// tests.add(C2SChunkTest.class);
// tests.add(C4ChunkTest.class);
// tests.add(C4FChunkTest.class);
// tests.add(C4SChunkTest.class);
// tests.add(C8ChunkTest.class);
// tests.add(C8DChunkTest.class);
// tests.add(C16ChunkTest.class);
// tests.add(CBSChunkTest.class);
// tests.add(CX0ChunkTest.class);
// tests.add(CXIChunkTest.class);
// tests.add(CXDChunkTest.class);
// tests.add(VecTest.class);
// Deep Learning tests
// tests.add(DeepLearningVsNeuralNet.class); //only passes for NODES=1, not clear why
// tests.add(DeepLearningAutoEncoderTest.class); //test Deep Learning convergence
// tests.add(DeepLearningAutoEncoderCategoricalTest.class); //test Deep Learning
// convergence
// tests.add(DeepLearningSpiralsTest.class); //test Deep Learning convergence
// tests.add(DeepLearningIrisTest.Short.class); //compare Deep Learning vs reference
// tests.add(DeepLearningIrisTest.Long.class); //compare Deep Learning vs reference
tests.add(DeepLearningProstateTest.Short.class); // test Deep Learning
// tests.add(DeepLearningMissingTest.class); //test Deep Learning
// tests.add(DeepLearningProstateTest.Long.class); //test Deep Learning
// tests.add(NeuronsTest.class); //test Deep Learning
// tests.add(MRUtilsTest.class); //test MR sampling/rebalancing
// tests.add(DropoutTest.class); //test NN Dropput
// tests.add(ParserTest2.class);
// tests.add(ParserTest2.ParseAllSmalldata.class);
// tests.add(KMeans2Test.class);
// tests.add(KMeans2RandomTest.class);
// tests.add(GLMRandomTest.Short.class);
// tests.add(SpeeDRFTest.class);
// tests.add(SpeeDRFTest2.class);
//// tests.add(GLMTest2.class);
// tests.add(DRFTest.class);
// tests.add(DRFTest2.class);
// tests.add(GBMTest.class);
// tests.add(KMeans2Test.class);
// tests.add(PCATest.class);
// tests.add(NetworkTestTest.class);
// Uncomment this to sleep here and use the browser.
// try { Thread.sleep(10000000); } catch (Exception _) {}
JUnitCore junit = new JUnitCore();
junit.addListener(new LogListener());
Result result = junit.run(tests.toArray(new Class[0]));
if (result.getFailures().size() == 0) {
Log.info("SUCCESS!");
System.exit(0);
} else {
Log.info("FAIL!");
System.exit(1);
}
}
public static class GLRMParameters extends Model.Parameters {
public String algoName() {
return "GLRM";
}
public String fullName() {
return "Generalized Low Rank Modeling";
}
public String javaName() {
return GLRMModel.class.getName();
}
public DataInfo.TransformType _transform =
DataInfo.TransformType.NONE; // Data transformation (demean to compare with PCA)
public int _k = 1; // Rank of resulting XY matrix
public GLRM.Initialization _init = GLRM.Initialization.PlusPlus; // Initialization of Y matrix
public SVDParameters.Method _svd_method =
SVDParameters.Method.Randomized; // SVD initialization method (for _init = SVD)
public Key<Frame> _user_y; // User-specified Y matrix (for _init = User)
public Key<Frame> _user_x; // User-specified X matrix (for _init = User)
public boolean _expand_user_y =
true; // Should categorical columns in _user_y be expanded via one-hot encoding? (for _init
// = User)
// Loss functions
public Loss _loss = Loss.Quadratic; // Default loss function for numeric cols
public Loss _multi_loss = Loss.Categorical; // Default loss function for categorical cols
public int _period = 1; // Length of the period when _loss = Periodic
public Loss[] _loss_by_col; // Override default loss function for specific columns
public int[] _loss_by_col_idx;
// Regularization functions
public Regularizer _regularization_x = Regularizer.None; // Regularization function for X matrix
public Regularizer _regularization_y = Regularizer.None; // Regularization function for Y matrix
public double _gamma_x = 0; // Regularization weight on X matrix
public double _gamma_y = 0; // Regularization weight on Y matrix
// Optional parameters
public int _max_iterations = 1000; // Max iterations
public int _max_updates = 2 * _max_iterations; // Max number of updates (X or Y)
public double _init_step_size = 1.0; // Initial step size (decrease until we hit min_step_size)
public double _min_step_size = 1e-4; // Min step size
public long _seed = System.nanoTime(); // RNG seed
@Override
protected long nFoldSeed() {
return _seed;
}
// public Key<Frame> _representation_key; // Key to save X matrix
public String _representation_name;
public boolean _recover_svd =
false; // Recover singular values and eigenvectors of XY at the end?
public boolean _impute_original =
false; // Reconstruct original training data by reversing _transform?
public boolean _verbose = true; // Log when objective increases each iteration?
// Quadratic -> Gaussian distribution ~ exp(-(a-u)^2)
// Absolute -> Laplace distribution ~ exp(-|a-u|)
public enum Loss {
Quadratic(true),
Absolute(true),
Huber(true),
Poisson(true),
Periodic(true), // One-dimensional loss (numeric)
Logistic(true, true),
Hinge(true, true), // Boolean loss (categorical)
Categorical(false),
Ordinal(false); // Multi-dimensional loss (categorical)
private boolean forNumeric;
private boolean forBinary;
Loss(boolean forNumeric) {
this(forNumeric, false);
}
Loss(boolean forNumeric, boolean forBinary) {
this.forNumeric = forNumeric;
this.forBinary = forBinary;
}
public boolean isForNumeric() {
return forNumeric;
}
public boolean isForCategorical() {
return !forNumeric;
}
public boolean isForBinary() {
return forBinary;
}
}
// Non-negative matrix factorization (NNMF): r_x = r_y = NonNegative
// Orthogonal NNMF: r_x = OneSparse, r_y = NonNegative
// K-means clustering: r_x = UnitOneSparse, r_y = 0 (\gamma_y = 0)
// Quadratic mixture: r_x = Simplex, r_y = 0 (\gamma_y = 0)
public enum Regularizer {
None,
Quadratic,
L2,
L1,
NonNegative,
OneSparse,
UnitOneSparse,
Simplex
}
// Check if all elements of _loss_by_col are equal to a specific loss function
private final boolean allLossEquals(Loss loss) {
if (null == _loss_by_col) return false;
boolean res = true;
for (int i = 0; i < _loss_by_col.length; i++) {
if (_loss_by_col[i] != loss) {
res = false;
break;
}
}
return res;
}
// Closed form solution only if quadratic loss, no regularization or quadratic regularization
// (same for X and Y), and no missing values
public final boolean hasClosedForm() {
long na_cnt = 0;
Frame train = _train.get();
for (int i = 0; i < train.numCols(); i++) na_cnt += train.vec(i).naCnt();
return hasClosedForm(na_cnt);
}
public final boolean hasClosedForm(long na_cnt) {
boolean loss_quad =
(null == _loss_by_col && _loss == Quadratic)
|| (null != _loss_by_col
&& allLossEquals(Quadratic)
&& (_loss_by_col.length == _train.get().numCols() || _loss == Quadratic));
return na_cnt == 0
&& ((loss_quad
&& (_gamma_x == 0
|| _regularization_x == Regularizer.None
|| _regularization_x == GLRMParameters.Regularizer.Quadratic)
&& (_gamma_y == 0
|| _regularization_y == Regularizer.None
|| _regularization_y == GLRMParameters.Regularizer.Quadratic)));
}
// L(u,a): Loss function
public final double loss(double u, double a) {
return loss(u, a, _loss);
}
public final double loss(double u, double a, Loss loss) {
assert loss.isForNumeric() : "Loss function " + loss + " not applicable to numerics";
switch (loss) {
case Quadratic:
return (u - a) * (u - a);
case Absolute:
return Math.abs(u - a);
case Huber:
return Math.abs(u - a) <= 1 ? 0.5 * (u - a) * (u - a) : Math.abs(u - a) - 0.5;
case Poisson:
assert a >= 0 : "Poisson loss L(u,a) requires variable a >= 0";
return Math.exp(u)
+ (a == 0 ? 0 : -a * u + a * Math.log(a) - a); // Since \lim_{a->0} a*log(a) = 0
case Hinge:
// return Math.max(1-a*u,0);
return Math.max(1 - (a == 0 ? -u : u), 0); // Booleans are coded {0,1} instead of {-1,1}
case Logistic:
// return Math.log(1 + Math.exp(-a * u));
return Math.log(
1 + Math.exp(a == 0 ? u : -u)); // Booleans are coded {0,1} instead of {-1,1}
case Periodic:
return 1 - Math.cos((a - u) * (2 * Math.PI) / _period);
default:
throw new RuntimeException("Unknown loss function " + loss);
}
}
// \grad_u L(u,a): Gradient of loss function with respect to u
public final double lgrad(double u, double a) {
return lgrad(u, a, _loss);
}
public final double lgrad(double u, double a, Loss loss) {
assert loss.isForNumeric() : "Loss function " + loss + " not applicable to numerics";
switch (loss) {
case Quadratic:
return 2 * (u - a);
case Absolute:
return Math.signum(u - a);
case Huber:
return Math.abs(u - a) <= 1 ? u - a : Math.signum(u - a);
case Poisson:
assert a >= 0 : "Poisson loss L(u,a) requires variable a >= 0";
return Math.exp(u) - a;
case Hinge:
// return a*u <= 1 ? -a : 0;
return a == 0
? (-u <= 1 ? 1 : 0)
: (u <= 1 ? -1 : 0); // Booleans are coded as {0,1} instead of {-1,1}
case Logistic:
// return -a/(1+Math.exp(a*u));
return a == 0
? 1 / (1 + Math.exp(-u))
: -1 / (1 + Math.exp(u)); // Booleans are coded as {0,1} instead of {-1,1}
case Periodic:
return ((2 * Math.PI) / _period) * Math.sin((a - u) * (2 * Math.PI) / _period);
default:
throw new RuntimeException("Unknown loss function " + loss);
}
}
// L(u,a): Multidimensional loss function
public final double mloss(double[] u, int a) {
return mloss(u, a, _multi_loss);
}
public static double mloss(double[] u, int a, Loss multi_loss) {
assert multi_loss.isForCategorical()
: "Loss function " + multi_loss + " not applicable to categoricals";
if (a < 0 || a > u.length - 1)
throw new IllegalArgumentException(
"Index must be between 0 and " + String.valueOf(u.length - 1));
double sum = 0;
switch (multi_loss) {
case Categorical:
for (int i = 0; i < u.length; i++) sum += Math.max(1 + u[i], 0);
sum += Math.max(1 - u[a], 0) - Math.max(1 + u[a], 0);
return sum;
case Ordinal:
for (int i = 0; i < u.length - 1; i++) sum += Math.max(a > i ? 1 - u[i] : 1, 0);
return sum;
default:
throw new RuntimeException("Unknown multidimensional loss function " + multi_loss);
}
}
// \grad_u L(u,a): Gradient of multidimensional loss function with respect to u
public final double[] mlgrad(double[] u, int a) {
return mlgrad(u, a, _multi_loss);
}
public static double[] mlgrad(double[] u, int a, Loss multi_loss) {
assert multi_loss.isForCategorical()
: "Loss function " + multi_loss + " not applicable to categoricals";
if (a < 0 || a > u.length - 1)
throw new IllegalArgumentException(
"Index must be between 0 and " + String.valueOf(u.length - 1));
double[] grad = new double[u.length];
switch (multi_loss) {
case Categorical:
for (int i = 0; i < u.length; i++) grad[i] = (1 + u[i] > 0) ? 1 : 0;
grad[a] = (1 - u[a] > 0) ? -1 : 0;
return grad;
case Ordinal:
for (int i = 0; i < u.length - 1; i++) grad[i] = (a > i && 1 - u[i] > 0) ? -1 : 0;
return grad;
default:
throw new RuntimeException("Unknown multidimensional loss function " + multi_loss);
}
}
// r_i(x_i), r_j(y_j): Regularization function for single row x_i or column y_j
public final double regularize_x(double[] u) {
return regularize(u, _regularization_x);
}
public final double regularize_y(double[] u) {
return regularize(u, _regularization_y);
}
public final double regularize(double[] u, Regularizer regularization) {
if (u == null) return 0;
double ureg = 0;
switch (regularization) {
case None:
return 0;
case Quadratic:
for (int i = 0; i < u.length; i++) ureg += u[i] * u[i];
return ureg;
case L2:
for (int i = 0; i < u.length; i++) ureg += u[i] * u[i];
return Math.sqrt(ureg);
case L1:
for (int i = 0; i < u.length; i++) ureg += Math.abs(u[i]);
return ureg;
case NonNegative:
for (int i = 0; i < u.length; i++) {
if (u[i] < 0) return Double.POSITIVE_INFINITY;
}
return 0;
case OneSparse:
int card = 0;
for (int i = 0; i < u.length; i++) {
if (u[i] < 0) return Double.POSITIVE_INFINITY;
else if (u[i] > 0) card++;
}
return card == 1 ? 0 : Double.POSITIVE_INFINITY;
case UnitOneSparse:
int ones = 0, zeros = 0;
for (int i = 0; i < u.length; i++) {
if (u[i] == 1) ones++;
else if (u[i] == 0) zeros++;
else return Double.POSITIVE_INFINITY;
}
return ones == 1 && zeros == u.length - 1 ? 0 : Double.POSITIVE_INFINITY;
case Simplex:
double sum = 0, absum = 0;
for (int i = 0; i < u.length; i++) {
if (u[i] < 0) return Double.POSITIVE_INFINITY;
else {
sum += u[i];
absum += Math.abs(u[i]);
}
}
return MathUtils.equalsWithinRecSumErr(sum, 1.0, u.length, absum)
? 0
: Double.POSITIVE_INFINITY;
default:
throw new RuntimeException("Unknown regularization function " + regularization);
}
}
// \sum_i r_i(x_i): Sum of regularization function for all entries of X
public final double regularize_x(double[][] u) {
return regularize(u, _regularization_x);
}
public final double regularize_y(double[][] u) {
return regularize(u, _regularization_y);
}
public final double regularize(double[][] u, Regularizer regularization) {
if (u == null || regularization == Regularizer.None) return 0;
double ureg = 0;
for (int i = 0; i < u.length; i++) {
ureg += regularize(u[i], regularization);
if (Double.isInfinite(ureg)) return ureg;
}
return ureg;
}
// \prox_{\alpha_k*r}(u): Proximal gradient of (step size) * (regularization function) evaluated
// at vector u
public final double[] rproxgrad_x(double[] u, double alpha, Random rand) {
return rproxgrad(u, alpha, _gamma_x, _regularization_x, rand);
}
public final double[] rproxgrad_y(double[] u, double alpha, Random rand) {
return rproxgrad(u, alpha, _gamma_y, _regularization_y, rand);
}
// public final double[] rproxgrad_x(double[] u, double alpha) { return rproxgrad(u, alpha,
// _gamma_x, _regularization_x, RandomUtils.getRNG(_seed)); }
// public final double[] rproxgrad_y(double[] u, double alpha) { return rproxgrad(u, alpha,
// _gamma_y, _regularization_y, RandomUtils.getRNG(_seed)); }
static double[] rproxgrad(
double[] u, double alpha, double gamma, Regularizer regularization, Random rand) {
if (u == null || alpha == 0 || gamma == 0) return u;
double[] v = new double[u.length];
switch (regularization) {
case None:
return u;
case Quadratic:
for (int i = 0; i < u.length; i++) v[i] = u[i] / (1 + 2 * alpha * gamma);
return v;
case L2:
// Proof uses Moreau decomposition; see section 6.5.1 of Parikh and Boyd
// https://web.stanford.edu/~boyd/papers/pdf/prox_algs.pdf
double weight = 1 - alpha * gamma / ArrayUtils.l2norm(u);
if (weight < 0) return v; // Zero vector
for (int i = 0; i < u.length; i++) v[i] = weight * u[i];
return v;
case L1:
for (int i = 0; i < u.length; i++)
v[i] = Math.max(u[i] - alpha * gamma, 0) + Math.min(u[i] + alpha * gamma, 0);
return v;
case NonNegative:
for (int i = 0; i < u.length; i++) v[i] = Math.max(u[i], 0);
return v;
case OneSparse:
int idx = ArrayUtils.maxIndex(u, rand);
v[idx] = u[idx] > 0 ? u[idx] : 1e-6;
return v;
case UnitOneSparse:
idx = ArrayUtils.maxIndex(u, rand);
v[idx] = 1;
return v;
case Simplex:
// Proximal gradient algorithm by Chen and Ye in http://arxiv.org/pdf/1101.6081v2.pdf
// 1) Sort input vector u in ascending order: u[1] <= ... <= u[n]
int n = u.length;
int[] idxs = new int[n];
for (int i = 0; i < n; i++) idxs[i] = i;
ArrayUtils.sort(idxs, u);
// 2) Calculate cumulative sum of u in descending order
// cumsum(u) = (..., u[n-2]+u[n-1]+u[n], u[n-1]+u[n], u[n])
double[] ucsum = new double[n];
ucsum[n - 1] = u[idxs[n - 1]];
for (int i = n - 2; i >= 0; i--) ucsum[i] = ucsum[i + 1] + u[idxs[i]];
// 3) Let t_i = (\sum_{j=i+1}^n u[j] - 1)/(n - i)
// For i = n-1,...,1, set optimal t* to first t_i >= u[i]
double t = (ucsum[0] - 1) / n; // Default t* = (\sum_{j=1}^n u[j] - 1)/n
for (int i = n - 1; i >= 1; i--) {
double tmp = (ucsum[i] - 1) / (n - i);
if (tmp >= u[idxs[i - 1]]) {
t = tmp;
break;
}
}
// 4) Return max(u - t*, 0) as projection of u onto simplex
double[] x = new double[u.length];
for (int i = 0; i < u.length; i++) x[i] = Math.max(u[i] - t, 0);
return x;
default:
throw new RuntimeException("Unknown regularization function " + regularization);
}
}
// Project X,Y matrices into appropriate subspace so regularizer is finite. Used during
// initialization.
public final double[] project_x(double[] u, Random rand) {
return project(u, _regularization_x, rand);
}
public final double[] project_y(double[] u, Random rand) {
return project(u, _regularization_y, rand);
}
public final double[] project(double[] u, Regularizer regularization, Random rand) {
if (u == null) return u;
switch (regularization) {
// Domain is all real numbers
case None:
case Quadratic:
case L2:
case L1:
return u;
// Proximal operator of indicator function for a set C is (Euclidean) projection onto C
case NonNegative:
case OneSparse:
case UnitOneSparse:
return rproxgrad(u, 1, 1, regularization, rand);
case Simplex:
double reg =
regularize(
u,
regularization); // Check if inside simplex before projecting since algo is
// complicated
if (reg == 0) return u;
return rproxgrad(u, 1, 1, regularization, rand);
default:
throw new RuntimeException("Unknown regularization function " + regularization);
}
}
// \hat A_{i,j} = \argmin_a L_{i,j}(x_iy_j, a): Data imputation for real numeric values
public final double impute(double u) {
return impute(u, _loss);
}
public static double impute(double u, Loss loss) {
assert loss.isForNumeric() : "Loss function " + loss + " not applicable to numerics";
switch (loss) {
case Quadratic:
case Absolute:
case Huber:
case Periodic:
return u;
case Poisson:
return Math.exp(u) - 1;
case Hinge:
case Logistic:
return u > 0 ? 1 : 0; // Booleans are coded as {0,1} instead of {-1,1}
default:
throw new RuntimeException("Unknown loss function " + loss);
}
}
// \hat A_{i,j} = \argmin_a L_{i,j}(x_iy_j, a): Data imputation for categorical values
// {0,1,2,...}
// TODO: Is there a faster way to find the loss minimizer?
public final int mimpute(double[] u) {
return mimpute(u, _multi_loss);
}
public static int mimpute(double[] u, Loss multi_loss) {
assert multi_loss.isForCategorical()
: "Loss function " + multi_loss + " not applicable to categoricals";
switch (multi_loss) {
case Categorical:
case Ordinal:
double[] cand = new double[u.length];
for (int a = 0; a < cand.length; a++) cand[a] = mloss(u, a, multi_loss);
return ArrayUtils.minIndex(cand);
default:
throw new RuntimeException("Unknown multidimensional loss function " + multi_loss);
}
}
}
@Override
protected void compute2() {
CoxPHModel model = null;
try {
Scope.enter();
_parms.read_lock_frames(CoxPH.this);
init(true);
applyScoringFrameSideEffects();
// The model to be built
model = new CoxPHModel(dest(), _parms, new CoxPHModel.CoxPHOutput(CoxPH.this));
model.delete_and_lock(_key);
applyTrainingFrameSideEffects();
int nResponses = 1;
boolean useAllFactorLevels = false;
final DataInfo dinfo =
new DataInfo(
Key.make(),
_modelBuilderTrain,
null,
nResponses,
useAllFactorLevels,
DataInfo.TransformType.DEMEAN,
TransformType.NONE,
true,
false,
false,
false,
false,
false);
initStats(model, dinfo);
final int n_offsets =
(model._parms.offset_columns == null) ? 0 : model._parms.offset_columns.length;
final int n_coef = dinfo.fullN() - n_offsets;
final double[] step = MemoryManager.malloc8d(n_coef);
final double[] oldCoef = MemoryManager.malloc8d(n_coef);
final double[] newCoef = MemoryManager.malloc8d(n_coef);
Arrays.fill(step, Double.NaN);
Arrays.fill(oldCoef, Double.NaN);
for (int j = 0; j < n_coef; ++j) newCoef[j] = model._parms.init;
double oldLoglik = -Double.MAX_VALUE;
final int n_time = (int) (model._output.max_time - model._output.min_time + 1);
final boolean has_start_column = (model._parms.start_column != null);
final boolean has_weights_column = (model._parms.weights_column != null);
for (int i = 0; i <= model._parms.iter_max; ++i) {
model._output.iter = i;
final CoxPHTask coxMR =
new CoxPHTask(
self(),
dinfo,
newCoef,
model._output.min_time,
n_time,
n_offsets,
has_start_column,
has_weights_column)
.doAll(dinfo._adaptedFrame);
final double newLoglik = calcLoglik(model, coxMR);
if (newLoglik > oldLoglik) {
if (i == 0) calcCounts(model, coxMR);
calcModelStats(model, newCoef, newLoglik);
calcCumhaz_0(model, coxMR);
if (newLoglik == 0) model._output.lre = -Math.log10(Math.abs(oldLoglik - newLoglik));
else model._output.lre = -Math.log10(Math.abs((oldLoglik - newLoglik) / newLoglik));
if (model._output.lre >= model._parms.lre_min) break;
Arrays.fill(step, 0);
for (int j = 0; j < n_coef; ++j)
for (int k = 0; k < n_coef; ++k)
step[j] -= model._output.var_coef[j][k] * model._output.gradient[k];
for (int j = 0; j < n_coef; ++j)
if (Double.isNaN(step[j]) || Double.isInfinite(step[j])) break;
oldLoglik = newLoglik;
System.arraycopy(newCoef, 0, oldCoef, 0, oldCoef.length);
} else {
for (int j = 0; j < n_coef; ++j) step[j] /= 2;
}
for (int j = 0; j < n_coef; ++j) newCoef[j] = oldCoef[j] - step[j];
}
model.update(_key);
} 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 {
updateModelOutput();
_parms.read_unlock_frames(CoxPH.this);
Scope.exit();
done(); // Job done!
}
tryComplete();
}