@Override
public void callback(final GLMIterationTask glmt) {
if (!isRunning(self())) throw new JobCancelledException();
boolean converged = false;
if (glmt._beta != null && glmt._val != null && _glm.family != Family.tweedie) {
glmt._val.finalize_AIC_AUC();
_model.setAndTestValidation(_lambdaIdx, glmt._val); // .store();
_model.clone().update(self());
converged = true;
double l1pen = alpha[0] * lambda[_lambdaIdx] * glmt._n;
double l2pen = (1 - alpha[0]) * lambda[_lambdaIdx] * glmt._n;
final double eps = 1e-2;
for (int i = 0; i < glmt._grad.length - 1; ++i) { // add l2 reg. term to the gradient
glmt._grad[i] += l2pen * glmt._beta[i];
if (glmt._beta[i] < 0) converged &= Math.abs(glmt._grad[i] - l1pen) < eps;
else if (glmt._beta[i] > 0) converged &= Math.abs(glmt._grad[i] + l1pen) < eps;
else converged &= LSMSolver.shrinkage(glmt._grad[i], l1pen + eps) == 0;
}
if (converged) Log.info("GLM converged by reaching 0 gradient/subgradient.");
double objval = glmt._val.residual_deviance + 0.5 * l2pen * l2norm(glmt._beta);
if (!converged && _lastResult != null && needLineSearch(glmt._beta, objval, 1)) {
new GLMTask.GLMLineSearchTask(
GLM2.this,
_dinfo,
_glm,
_lastResult._glmt._beta,
glmt._beta,
1e-8,
new LineSearchIteration())
.asyncExec(_dinfo._adaptedFrame);
return;
}
_lastResult = new IterationInfo(GLM2.this._iter - 1, objval, glmt);
}
double[] newBeta =
glmt._beta != null ? glmt._beta.clone() : MemoryManager.malloc8d(glmt._xy.length);
double[] newBetaDeNorm = null;
ADMMSolver slvr = new ADMMSolver(lambda[_lambdaIdx], alpha[0], _addedL2);
slvr.solve(glmt._gram, glmt._xy, glmt._yy, newBeta);
_addedL2 = slvr._addedL2;
if (Utils.hasNaNsOrInfs(newBeta)) {
Log.info("GLM forcibly converged by getting NaNs and/or Infs in beta");
} else {
if (_dinfo._standardize) {
newBetaDeNorm = newBeta.clone();
double norm = 0.0; // Reverse any normalization on the intercept
// denormalize only the numeric coefs (categoricals are not normalized)
final int numoff = newBeta.length - _dinfo._nums - 1;
for (int i = numoff; i < newBeta.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;
}
_model.setLambdaSubmodel(
_lambdaIdx,
newBetaDeNorm == null ? newBeta : newBetaDeNorm,
newBetaDeNorm == null ? null : newBeta,
_iter);
if (beta_diff(glmt._beta, newBeta) < beta_epsilon) {
Log.info("GLM converged by reaching fixed-point.");
converged = true;
}
if (!converged && _glm.family != Family.gaussian && _iter < max_iter) {
++_iter;
new GLMIterationTask(GLM2.this, _dinfo, glmt._glm, newBeta, _ymu, _reg, new Iteration())
.asyncExec(_dinfo._adaptedFrame);
return;
}
}
// done with this lambda
nextLambda(glmt);
}
@Override
public void callback(final GLMIterationTask glmt) {
_model.stop_training();
Log.info(
"GLM2 iteration("
+ _iter
+ ") done in "
+ (System.currentTimeMillis() - _iterationStartTime)
+ "ms");
if (!isRunning(self())) throw new JobCancelledException();
currentLambdaIter++;
if (glmt._val != null) {
if (!(glmt._val.residual_deviance
< glmt._val
.null_deviance)) { // complete fail, look if we can restart with higher_accuracy on
if (!highAccuracy()) {
Log.info(
"GLM2 reached negative explained deviance without line-search, rerunning with high accuracy settings.");
setHighAccuracy();
if (_lastResult != null)
new GLMIterationTask(
GLM2.this,
_activeData,
glmt._glm,
true,
true,
true,
_lastResult._glmt._beta,
_ymu,
_reg,
new Iteration())
.asyncExec(_activeData._adaptedFrame);
else if (_lambdaIdx > 2) // > 2 because 0 is null model, we don't wan to run with that
new GLMIterationTask(
GLM2.this,
_activeData,
glmt._glm,
true,
true,
true,
_model.submodels[_lambdaIdx - 1].norm_beta,
_ymu,
_reg,
new Iteration())
.asyncExec(_activeData._adaptedFrame);
else // no sane solution to go back to, start from scratch!
new GLMIterationTask(
GLM2.this,
_activeData,
glmt._glm,
true,
false,
false,
null,
_ymu,
_reg,
new Iteration())
.asyncExec(_activeData._adaptedFrame);
_lastResult = null;
return;
}
}
_model.setAndTestValidation(_lambdaIdx, glmt._val);
_model.clone().update(self());
}
if (glmt._val != null && glmt._computeGradient) { // check gradient
final double[] grad = glmt.gradient(l2pen());
ADMMSolver.subgrad(alpha[0], lambda[_lambdaIdx], glmt._beta, grad);
double err = 0;
for (double d : grad)
if (d > err) err = d;
else if (d < -err) err = -d;
Log.info("GLM2 gradient after " + _iter + " iterations = " + err);
if (err <= GLM_GRAD_EPS) {
Log.info(
"GLM2 converged by reaching small enough gradient, with max |subgradient| = " + err);
setNewBeta(glmt._beta);
nextLambda(glmt, glmt._beta);
return;
}
}
if (glmt._beta != null
&& glmt._val != null
&& glmt._computeGradient
&& _glm.family != Family.tweedie) {
if (_lastResult != null && needLineSearch(glmt._beta, objval(glmt), 1)) {
if (!highAccuracy()) {
setHighAccuracy();
if (_lastResult._iter
< (_iter - 2)) { // there is a gap form last result...return to it and start again
final double[] prevBeta =
_lastResult._activeCols != _activeCols
? resizeVec(_lastResult._glmt._beta, _activeCols, _lastResult._activeCols)
: _lastResult._glmt._beta;
new GLMIterationTask(
GLM2.this,
_activeData,
glmt._glm,
true,
true,
true,
prevBeta,
_ymu,
_reg,
new Iteration())
.asyncExec(_activeData._adaptedFrame);
return;
}
}
final double[] b =
resizeVec(_lastResult._glmt._beta, _activeCols, _lastResult._activeCols);
assert (b.length == glmt._beta.length)
: b.length + " != " + glmt._beta.length + ", activeCols = " + _activeCols.length;
new GLMTask.GLMLineSearchTask(
GLM2.this,
_activeData,
_glm,
resizeVec(_lastResult._glmt._beta, _activeCols, _lastResult._activeCols),
glmt._beta,
1e-4,
glmt._nobs,
alpha[0],
lambda[_lambdaIdx],
new LineSearchIteration())
.asyncExec(_activeData._adaptedFrame);
return;
}
_lastResult = new IterationInfo(GLM2.this._iter - 1, glmt, _activeCols);
}
final double[] newBeta = MemoryManager.malloc8d(glmt._xy.length);
ADMMSolver slvr = new ADMMSolver(lambda[_lambdaIdx], alpha[0], ADMM_GRAD_EPS, _addedL2);
slvr.solve(glmt._gram, glmt._xy, glmt._yy, newBeta);
_addedL2 = slvr._addedL2;
if (Utils.hasNaNsOrInfs(newBeta)) {
Log.info("GLM2 forcibly converged by getting NaNs and/or Infs in beta");
nextLambda(glmt, glmt._beta);
} else {
setNewBeta(newBeta);
final double bdiff = beta_diff(glmt._beta, newBeta);
if (_glm.family == Family.gaussian
|| bdiff < beta_epsilon
|| _iter
== max_iter) { // Gaussian is non-iterative and gradient is ADMMSolver's gradient =>
// just validate and move on to the next lambda
int diff = (int) Math.log10(bdiff);
int nzs = 0;
for (int i = 0; i < newBeta.length; ++i) if (newBeta[i] != 0) ++nzs;
if (newBeta.length < 20) System.out.println("beta = " + Arrays.toString(newBeta));
Log.info(
"GLM2 (lambda_"
+ _lambdaIdx
+ "="
+ lambda[_lambdaIdx]
+ ") converged (reached a fixed point with ~ 1e"
+ diff
+ " precision) after "
+ _iter
+ "iterations, got "
+ nzs
+ " nzs");
nextLambda(glmt, newBeta);
} else { // not done yet, launch next iteration
final boolean validate = higher_accuracy || (currentLambdaIter % 5) == 0;
++_iter;
System.out.println("Iter = " + _iter);
new GLMIterationTask(
GLM2.this,
_activeData,
glmt._glm,
true,
validate,
validate,
newBeta,
_ymu,
_reg,
new Iteration())
.asyncExec(_activeData._adaptedFrame);
}
}
}