@Override
public float progress() {
if (DKV.get(dest()) == null) return 0;
GLMModel m = DKV.get(dest()).get();
float progress = (float) m.iteration() / (float) max_iter; // TODO, do something smarter here
return progress;
}
public void nextLambda(final GLMIterationTask glmt) {
// We're done with this lambda, launch validation
H2OCallback fin =
new H2OCallback<GLMValidationTask>() {
@Override
public void callback(GLMValidationTask tsk) {
boolean improved = _model.setAndTestValidation(_lambdaIdx, tsk._res);
_model.clone().update(self());
if ((improved || _runAllLambdas)
&& _lambdaIdx < (lambda.length - 1)) { // continue with next lambda value?
glmt._val = null;
++_lambdaIdx;
new Iteration().callback(glmt);
} else // nope, we're done
GLM2.this.complete(); // signal we're done to anyone waiting for the job
}
@Override
public boolean onExceptionalCompletion(Throwable ex, CountedCompleter cc) {
GLM2.this.cancel(ex);
return true;
}
};
if (GLM2.this.n_folds >= 2) xvalidate(_model.clone(), _lambdaIdx, fin);
else new GLMValidationTask(_model.clone(), _lambdaIdx, fin).asyncExec(_dinfo._adaptedFrame);
}
private double[] setNewBeta(final double[] newBeta) {
final double[] fullBeta;
if (_activeCols != null) {
fullBeta = MemoryManager.malloc8d(_dinfo.fullN() + 1);
int j = 0;
for (int i : _activeCols) fullBeta[i] = newBeta[j++];
assert j == newBeta.length - 1;
fullBeta[fullBeta.length - 1] = newBeta[j];
} else {
assert newBeta.length == _dinfo.fullN() + 1;
fullBeta = newBeta;
}
final double[] newBetaDeNorm;
if (_dinfo._standardize) {
newBetaDeNorm = fullBeta.clone();
double norm = 0.0; // Reverse any normalization on the intercept
// denormalize only the numeric coefs (categoricals are not normalized)
final int numoff = _dinfo.numStart();
for (int i = numoff; i < fullBeta.length - 1; i++) {
double b = newBetaDeNorm[i] * _dinfo._normMul[i - numoff];
norm += b * _dinfo._normSub[i - numoff]; // Also accumulate the intercept adjustment
newBetaDeNorm[i] = b;
}
newBetaDeNorm[newBetaDeNorm.length - 1] -= norm;
} else newBetaDeNorm = null;
_model.setLambdaSubmodel(
_lambdaIdx,
newBetaDeNorm == null ? fullBeta : newBetaDeNorm,
newBetaDeNorm == null ? null : fullBeta,
(_iter + 1));
_model.clone().update(self());
return fullBeta;
}
@Override
public void map(GLMModel m) {
_res = (GLMModel) m.clone();
_res._output = (GLMOutput) _res._output.clone();
Submodel sm =
Double.isNaN(_lambda)
? _res._output._submodels[_res._output._best_lambda_idx]
: _res._output.submodelForLambda(_lambda);
assert sm != null : "GLM[" + m._key + "]: missing submodel for lambda " + _lambda;
sm = (Submodel) sm.clone();
_res._output._submodels = new Submodel[] {sm};
_res._output.setSubmodelIdx(0);
}
protected void complete() {
if (_addedL2 > 0) {
String warn = "Added L2 penalty (rho = " + _addedL2 + ") due to non-spd matrix. ";
if (_model.warnings == null || _model.warnings.length == 0)
_model.warnings = new String[] {warn};
else {
_model.warnings = Arrays.copyOf(_model.warnings, _model.warnings.length + 1);
_model.warnings[_model.warnings.length - 1] = warn;
}
_model.update(self());
}
_model.unlock(self());
if (_dinfo._nfolds == 0) remove(); // Remove/complete job only for top-level, not xval GLM2s
if (_fjtask != null) _fjtask.tryComplete();
}
@Override
public void cancel(Throwable ex) {
if (isCancelledOrCrashed()) return;
if (_model != null) _model.unlock(self());
if (ex instanceof JobCancelledException) {
if (!isCancelledOrCrashed()) cancel();
} else super.cancel(ex);
}
public GLMModelV3 make_model(int version, MakeGLMModelV3 args) {
GLMModel model = DKV.getGet(args.model.key());
if (model == null) throw new IllegalArgumentException("missing source model " + args.model);
String[] names = model._output.coefficientNames();
Map<String, Double> coefs = model.coefficients();
for (int i = 0; i < args.names.length; ++i) coefs.put(args.names[i], args.beta[i]);
double[] beta = model.beta().clone();
for (int i = 0; i < beta.length; ++i) beta[i] = coefs.get(names[i]);
GLMModel m =
new GLMModel(
args.dest != null ? args.dest.key() : Key.make(),
model._parms,
null,
new double[] {.5},
Double.NaN,
Double.NaN,
-1);
DataInfo dinfo = model.dinfo();
dinfo.setPredictorTransform(TransformType.NONE);
// GLMOutput(DataInfo dinfo, String[] column_names, String[][] domains, String[]
// coefficient_names, boolean binomial) {
m._output =
new GLMOutput(
model.dinfo(),
model._output._names,
model._output._domains,
model._output.coefficientNames(),
model._output._binomial,
beta);
DKV.put(m._key, m);
GLMModelV3 res = new GLMModelV3();
res.fillFromImpl(m);
return res;
}
public GLMXValidation(GLMModel mainModel, GLMModel[] xvalModels, int lambdaIdx, long nobs) {
super(mainModel._dataKey, mainModel.ymu, mainModel.glm, mainModel.rank(lambdaIdx));
xval_models = new Key[xvalModels.length];
for (int i = 0; i < xvalModels.length; ++i) {
add(xvalModels[i].validation());
xval_models[i] = xvalModels[i]._key;
}
this.nobs = nobs;
finalize_AIC_AUC();
}
protected void nextLambda(final GLMIterationTask glmt, GLMValidation val) {
currentLambdaIter = 0;
boolean improved = _model.setAndTestValidation(_lambdaIdx, val);
_model.clone().update(self());
boolean done = false; // _iter < max_iter && (improved || _runAllLambdas) && _lambdaIdx <
// (lambda.length-1);
if (_iter == max_iter) {
Log.info("GLM2 reached max #iterations.");
done = true;
} else if (!improved && !_runAllLambdas) {
Log.info("GLM2 converged as solution stopped improving with decreasing lambda.");
done = true;
} else if (_lambdaIdx == lambda.length - 1) {
Log.info("GLM2 done with all given lambdas.");
done = true;
} else if (_activeCols != null && _activeCols.length + 1 >= MAX_PREDICTORS) {
Log.info(
"GLM2 reached maximum allowed number of predictors at lambda = " + lambda[_lambdaIdx]);
done = true;
}
if (!done) { // continue with next lambda value?
++_lambdaIdx;
glmt._val = null;
if (glmt._gram == null) { // assume we had lambda search with strong rules
// we use strong rules so we can't really used this gram for the next lambda computation
// (different sets of coefficients)
// I expect that:
// 1) beta has been expanded to match current set of active cols
// 2) it is new GLMIteration ready to be launched
// caller (nextLambda(glmt,beta)) is expected to ensure this...
assert _activeCols == null || (glmt._beta.length == _activeCols.length + 1);
assert !glmt.isDone();
glmt.asyncExec(_activeData._adaptedFrame);
} else // we have the right gram, just solve with with next lambda
new Iteration().callback(glmt);
} else // nope, we're done
GLM2.this.complete(); // signal we're done to anyone waiting for the job
}
private void xvalidate(final GLMModel model, int lambdaIxd, final H2OCountedCompleter cmp) {
final Key[] keys = new Key[n_folds];
H2OCallback callback =
new H2OCallback() {
@Override
public void callback(H2OCountedCompleter t) {
try {
GLMModel[] models = new GLMModel[keys.length];
// we got the xval models, now compute their validations...
for (int i = 0; i < models.length; ++i) models[i] = DKV.get(keys[i]).get();
new GLMXValidationTask(model, _lambdaIdx, models, cmp)
.asyncExec(_dinfo._adaptedFrame);
} catch (Throwable ex) {
cmp.completeExceptionally(ex);
}
}
@Override
public boolean onExceptionalCompletion(Throwable ex, CountedCompleter caller) {
cmp.completeExceptionally(ex);
return true;
}
};
callback.addToPendingCount(n_folds - 1);
double proximal_penalty = 0;
for (int i = 0; i < n_folds; ++i)
new GLM2(
this.description + "xval " + i,
self(),
keys[i] = Key.make(destination_key + "_" + _lambdaIdx + "_xval" + i),
_dinfo.getFold(i, n_folds),
_glm,
new double[] {lambda[_lambdaIdx]},
model.alpha,
0,
model.beta_eps,
self(),
model.norm_beta(lambdaIxd),
proximal_penalty)
.run(callback);
}
private void xvalidate(final GLMModel model, int lambdaIxd, final H2OCountedCompleter cmp) {
final Key[] keys = new Key[n_folds];
GLM2[] glms = new GLM2[n_folds];
for (int i = 0; i < n_folds; ++i)
glms[i] =
new GLM2(
this.description + "xval " + i,
self(),
keys[i] = Key.make(destination_key + "_" + _lambdaIdx + "_xval" + i),
_dinfo.getFold(i, n_folds),
_glm,
new double[] {lambda[_lambdaIdx]},
model.alpha,
0,
model.beta_eps,
self(),
model.norm_beta(lambdaIxd),
higher_accuracy,
prior,
0);
H2O.submitTask(
new ParallelGLMs(
GLM2.this,
glms,
H2O.CLOUD.size(),
new H2OCallback(GLM2.this) {
@Override
public void callback(H2OCountedCompleter t) {
GLMModel[] models = new GLMModel[keys.length];
// we got the xval models, now compute their validations...
for (int i = 0; i < models.length; ++i) models[i] = DKV.get(keys[i]).get();
new GLMXValidationTask(model, _lambdaIdx, models, cmp)
.asyncExec(_dinfo._adaptedFrame);
}
}));
}
public GLMModel clone2() {
GLMModel res = clone();
res._output = (GLMOutput) res._output.clone();
return res;
}
@Override
protected void map(GLMModel glmModel) {
glmModel._output.pickBestModel(false);
glmModel.update(_jobKey);
glmModel.unlock(_jobKey);
}
@Override
public void cancel(Throwable ex) {
if (_model != null) _model.unlock(self());
if (ex instanceof JobCancelledException) cancel();
else super.cancel(ex);
}
private void run(final double ymu, final long nobs, LMAXTask lmaxt) {
String[] warns = null;
if ((!lambda_search || !strong_rules_enabled) && (_dinfo.fullN() > MAX_PREDICTORS))
throw new IllegalArgumentException(
"Too many predictors! GLM can only handle "
+ MAX_PREDICTORS
+ " predictors, got "
+ _dinfo.fullN()
+ ", try to run with strong_rules enabled.");
if (lambda_search) {
max_iter = Math.max(300, max_iter);
assert lmaxt != null : "running lambda search, but don't know what is the lambda max!";
final double lmax = lmaxt.lmax();
final double lambda_min_ratio =
_dinfo._adaptedFrame.numRows() > _dinfo.fullN() ? 0.0001 : 0.01;
final double d = Math.pow(lambda_min_ratio, 0.01);
lambda = new double[100];
lambda[0] = lmax;
for (int i = 1; i < lambda.length; ++i) lambda[i] = lambda[i - 1] * d;
_runAllLambdas = false;
} else if (alpha[0] > 0
&& lmaxt
!= null) { // make sure we start with lambda max (and discard all lambda > lambda max)
final double lmax = lmaxt.lmax();
int i = 0;
while (i < lambda.length && lambda[i] > lmax) ++i;
if (i != 0) {
Log.info(
"GLM: removing "
+ i
+ " lambdas > lambda_max: "
+ Arrays.toString(Arrays.copyOf(lambda, i)));
warns =
i == lambda.length
? new String[] {
"Removed " + i + " lambdas > lambda_max",
"No lambdas < lambda_max, returning null model."
}
: new String[] {"Removed " + i + " lambdas > lambda_max"};
}
lambda =
i == lambda.length
? new double[] {lambda_max}
: Arrays.copyOfRange(lambda, i, lambda.length);
}
_model =
new GLMModel(
GLM2.this,
dest(),
_dinfo,
_glm,
beta_epsilon,
alpha[0],
lambda_max,
lambda,
ymu,
prior);
_model.warnings = warns;
_model.clone().delete_and_lock(self());
if (lambda[0] == lambda_max && alpha[0] > 0) { // fill-in trivial solution for lambda max
_beta = MemoryManager.malloc8d(_dinfo.fullN() + 1);
_beta[_beta.length - 1] = _glm.link(ymu) + _iceptAdjust;
_model.setLambdaSubmodel(0, _beta, _beta, 0);
if (lmaxt != null) _model.setAndTestValidation(0, lmaxt._val);
_lambdaIdx = 1;
}
if (_lambdaIdx == lambda.length) // ran only with one lambda > lambda_max => return null model
GLM2.this.complete(); // signal we're done to anyone waiting for the job
else {
++_iter;
if (lmaxt != null && strong_rules_enabled)
activeCols(lambda[_lambdaIdx], lmaxt.lmax(), lmaxt.gradient(l2pen()));
Log.info(
"GLM2 staring GLM after "
+ (System.currentTimeMillis() - start)
+ "ms of preprocessing (mean/lmax/strong rules computation)");
new GLMIterationTask(
GLM2.this,
_activeData,
_glm,
true,
false,
false,
null,
_ymu = ymu,
_reg = 1.0 / nobs,
new Iteration())
.asyncExec(_activeData._adaptedFrame);
}
}
