private DRFModel runDRF(Frame data, PrepData dprep) {
DRF drf = new DRF();
drf.source = data;
drf.response = dprep.prep(data);
drf.ntrees = 1;
drf.invoke();
return UKV.get(drf.dest());
}
// ==========================================================================
public void basicGBM(String fname, String hexname, PrepData prep) {
File file = TestUtil.find_test_file(fname);
if (file == null) return; // Silently abort test if the file is missing
Key fkey = NFSFileVec.make(file);
Key dest = Key.make(hexname);
GBM gbm = null;
Frame fr = null;
try {
gbm = new GBM();
gbm.source = fr = ParseDataset2.parse(dest, new Key[] {fkey});
UKV.remove(fkey);
int idx = prep.prep(fr);
if (idx < 0) {
gbm.classification = false;
idx = ~idx;
}
String rname = fr._names[idx];
gbm.response = fr.vecs()[idx];
fr.remove(idx); // Move response to the end
fr.add(rname, gbm.response);
gbm.ntrees = 4;
gbm.max_depth = 4;
gbm.min_rows = 1;
gbm.nbins = 50;
gbm.cols = new int[fr.numCols()];
for (int i = 0; i < gbm.cols.length; i++) gbm.cols[i] = i;
gbm.learn_rate = .2f;
gbm.invoke();
fr = gbm.score(gbm.source);
GBM.GBMModel gbmmodel = UKV.get(gbm.dest());
// System.out.println(gbmmodel.toJava());
} finally {
UKV.remove(dest); // Remove original hex frame key
if (gbm != null) {
UKV.remove(gbm.dest()); // Remove the model
UKV.remove(gbm.response._key);
gbm.remove(); // Remove GBM Job
if (fr != null) fr.remove();
}
}
}
void testModelAdaptation(String train, String test, PrepData dprep, boolean exactAdaptation) {
DRFModel model = null;
Frame frTest = null;
Frame frTrain = null;
Key trainKey = Key.make("train.hex");
Key testKey = Key.make("test.hex");
Frame[] frAdapted = null;
try {
// Prepare a simple model
frTrain = parseFrame(trainKey, train);
model = runDRF(frTrain, dprep);
// Load test dataset - test data contains input columns matching train data,
// BUT each input requires adaptation. Moreover, test data contains additional columns
// containing correct value mapping.
frTest = parseFrame(testKey, test);
Assert.assertEquals(
"TEST CONF ERROR: The test dataset should contain 2*<number of input columns>+1!",
2 * (frTrain.numCols() - 1) + 1,
frTest.numCols());
// Adapt test dataset
frAdapted = model.adapt(frTest, exactAdaptation); // do/do not perform translation to enums
Assert.assertEquals("Adapt method should return two frames", 2, frAdapted.length);
Assert.assertEquals(
"Test expects that all columns in test dataset has to be adapted",
dprep.needAdaptation(frTrain),
frAdapted[1].numCols());
// Compare vectors
Frame adaptedFrame = frAdapted[0];
// System.err.println(frTest.toStringAll());
// System.err.println(adaptedFrame.toStringAll());
for (int av = 0; av < frTrain.numCols() - 1; av++) {
int ev = av + frTrain.numCols();
Vec actV = adaptedFrame.vecs()[av];
Vec expV = frTest.vecs()[ev];
Assert.assertEquals(
"Different number of rows in test vectors", expV.length(), actV.length());
for (long r = 0; r < expV.length(); r++) {
if (expV.isNA(r))
Assert.assertTrue(
"Badly adapted vector - expected NA! Col: " + av + ", row: " + r, actV.isNA(r));
else {
Assert.assertTrue(
"Badly adapted vector - expected value but get NA! Col: " + av + ", row: " + r,
!actV.isNA(r));
Assert.assertEquals(
"Badly adapted vector - wrong values! Col: " + av + ", row: " + r,
expV.at8(r),
actV.at8(r));
}
}
}
} finally {
// Test cleanup
if (model != null) UKV.remove(model._selfKey);
if (frTrain != null) frTrain.remove();
UKV.remove(trainKey);
if (frTest != null) frTest.remove();
UKV.remove(testKey);
// Remove adapted vectors which were saved into KV-store, rest of vectors are remove by
// frTest.remove()
if (frAdapted != null) frAdapted[1].remove();
}
}