@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();
}
}
