@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();
}
}
@Test
public void testLosses() throws InterruptedException, ExecutionException {
long seed = 0xDECAF;
Random rng = new Random(seed);
Frame train = null;
final int[] cats = new int[] {1, 3, 4, 5}; // Categoricals: CAPSULE, RACE, DPROS, DCAPS
final GLRMParameters.Regularizer[] regs =
new GLRMParameters.Regularizer[] {
GLRMParameters.Regularizer.Quadratic,
GLRMParameters.Regularizer.L1,
GLRMParameters.Regularizer.NonNegative,
GLRMParameters.Regularizer.OneSparse,
GLRMParameters.Regularizer.UnitOneSparse,
GLRMParameters.Regularizer.Simplex
};
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);
for (GLRMParameters.Loss loss :
new GLRMParameters.Loss[] {
GLRMParameters.Loss.Quadratic,
GLRMParameters.Loss.Absolute,
GLRMParameters.Loss.Huber,
GLRMParameters.Loss.Poisson,
GLRMParameters.Loss.Hinge,
GLRMParameters.Loss.Logistic
}) {
for (GLRMParameters.Loss multiloss :
new GLRMParameters.Loss[] {
GLRMParameters.Loss.Categorical, GLRMParameters.Loss.Ordinal
}) {
GLRMModel model = null;
try {
Scope.enter();
long myseed = rng.nextLong();
Log.info("GLRM using seed = " + myseed);
GLRMParameters parms = new GLRMParameters();
parms._train = train._key;
parms._transform = DataInfo.TransformType.NONE;
parms._k = 5;
parms._loss = loss;
parms._multi_loss = multiloss;
parms._init = GLRM.Initialization.SVD;
parms._regularization_x = regs[rng.nextInt(regs.length)];
parms._regularization_y = regs[rng.nextInt(regs.length)];
parms._gamma_x = Math.abs(rng.nextDouble());
parms._gamma_y = Math.abs(rng.nextDouble());
parms._recover_svd = false;
parms._seed = myseed;
parms._verbose = false;
parms._max_iterations = 500;
GLRM job = new GLRM(parms);
try {
model = job.trainModel().get();
Log.info(
"Iteration "
+ model._output._iterations
+ ": Objective value = "
+ model._output._objective);
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) {
throw t;
} finally {
job.remove();
}
} catch (Throwable t) {
t.printStackTrace();
throw new RuntimeException(t);
} finally {
if (model != null) model.delete();
Scope.exit();
}
}
}
} finally {
if (train != null) train.delete();
Scope.exit();
}
}