@Override
protected Response serve() {
init();
link = family.defaultLink; // TODO
tweedie_link_power = 1 - tweedie_variance_power; // TODO
Frame fr =
DataInfo.prepareFrame(source, response, ignored_cols, family == Family.binomial, true);
_dinfo = new DataInfo(fr, 1, standardize);
_glm = new GLMParams(family, tweedie_variance_power, link, tweedie_link_power);
if (alpha.length > 1) { // grid search
if (destination_key == null) destination_key = Key.make("GLMGridModel_" + Key.make());
if (job_key == null) job_key = Key.make("GLMGridJob_" + Key.make());
Job j = gridSearch(self(), destination_key, _dinfo, _glm, lambda, alpha, n_folds);
return GLMGridView.redirect(this, j.dest());
} else {
if (destination_key == null) destination_key = Key.make("GLMModel_" + Key.make());
if (job_key == null) job_key = Key.make("GLM2Job_" + Key.make());
fork();
return GLMProgress.redirect(this, job_key, dest());
}
}
@Override
public void init() {
super.init();
if (lambda_search && lambda.length > 1)
throw new IllegalArgumentException(
"Can not supply both lambda_search and multiple lambdas. If lambda_search is on, GLM expects only one value of lambda, representing the lambda min (smallest lambda in the lambda search).");
// check the response
if (response.isEnum() && family != Family.binomial)
throw new IllegalArgumentException(
"Invalid response variable, trying to run regression with categorical response!");
switch (family) {
case poisson:
case tweedie:
if (response.min() < 0)
throw new IllegalArgumentException(
"Illegal response column for family='" + family + "', response must be >= 0.");
break;
case gamma:
if (response.min() <= 0)
throw new IllegalArgumentException(
"Invalid response for family='Gamma', response must be > 0!");
break;
case binomial:
if (response.min() < 0 || response.max() > 1)
throw new IllegalArgumentException(
"Illegal response column for family='Binomial', response must in <0,1> range!");
break;
default:
// pass
}
Frame fr =
DataInfo.prepareFrame(
source, response, ignored_cols, family == Family.binomial, true, true);
_dinfo = new DataInfo(fr, 1, use_all_factor_levels || lambda_search, standardize, false);
if (higher_accuracy) setHighAccuracy();
}