@Override
protected void init() {
if (validation != null && n_folds != 0)
throw new UnsupportedOperationException(
"Cannot specify a validation dataset and non-zero number of cross-validation folds.");
if (n_folds < 0)
throw new UnsupportedOperationException(
"The number of cross-validation folds must be >= 0.");
super.init();
xval_models = new Key[n_folds];
for (int i = 0; i < xval_models.length; ++i)
xval_models[i] = Key.make(dest().toString() + "_xval" + i);
int rIndex = 0;
for (int i = 0; i < source.vecs().length; i++)
if (source.vecs()[i] == response) {
rIndex = i;
break;
}
_responseName = source._names != null && rIndex >= 0 ? source._names[rIndex] : "response";
_train = selectVecs(source);
_names = new String[cols.length];
for (int i = 0; i < cols.length; i++) _names[i] = source._names[cols[i]];
// Compute source response domain
if (classification) _sourceResponseDomain = getVectorDomain(response);
// Is validation specified?
if (validation != null) {
// Extract a validation response
int idx = validation.find(source.names()[rIndex]);
if (idx == -1)
throw new IllegalArgumentException(
"Validation set does not have a response column called " + _responseName);
_validResponse = validation.vecs()[idx];
// Compute output confusion matrix domain for classification:
// - if validation dataset is specified then CM domain is union of train and validation
// response domains
// else it is only domain of response column.
if (classification) {
_validResponseDomain = getVectorDomain(_validResponse);
if (_validResponseDomain != null) {
_cmDomain = Utils.domainUnion(_sourceResponseDomain, _validResponseDomain);
if (!Arrays.deepEquals(_sourceResponseDomain, _validResponseDomain)) {
_fromModel2CM =
Model.getDomainMapping(
_cmDomain,
_sourceResponseDomain,
false); // transformation from model produced response ~> cmDomain
_fromValid2CM =
Model.getDomainMapping(
_cmDomain,
_validResponseDomain,
false); // transformation from validation response domain ~> cmDomain
}
} else _cmDomain = _sourceResponseDomain;
} /* end of if classification */
} else if (classification) _cmDomain = _sourceResponseDomain;
}
private static void addFolder(FileSystem fs, Path p, JsonArray succeeded, JsonArray failed) {
try {
if (fs == null) return;
for (FileStatus file : fs.listStatus(p)) {
Path pfs = file.getPath();
if (file.isDir()) {
addFolder(fs, pfs, succeeded, failed);
} else {
Key k = Key.make(pfs.toString());
long size = file.getLen();
Value val = null;
if (pfs.getName().endsWith(Extensions.JSON)) {
JsonParser parser = new JsonParser();
JsonObject json = parser.parse(new InputStreamReader(fs.open(pfs))).getAsJsonObject();
JsonElement v = json.get(Constants.VERSION);
if (v == null) throw new InvalidDataException("Missing version");
JsonElement type = json.get(Constants.TYPE);
if (type == null) throw new InvalidDataException("Missing type");
Class c = Class.forName(type.getAsString());
Model model = (Model) c.newInstance();
model.fromJson(json);
} else if (pfs.getName().endsWith(Extensions.HEX)) { // Hex file?
FSDataInputStream s = fs.open(pfs);
int sz = (int) Math.min(1L << 20, size); // Read up to the 1st meg
byte[] mem = MemoryManager.malloc1(sz);
s.readFully(mem);
// Convert to a ValueArray (hope it fits in 1Meg!)
ValueArray ary = new ValueArray(k, 0).read(new AutoBuffer(mem));
val = new Value(k, ary, Value.HDFS);
} else if (size >= 2 * ValueArray.CHUNK_SZ) {
val =
new Value(
k,
new ValueArray(k, size),
Value.HDFS); // ValueArray byte wrapper over a large file
} else {
val = new Value(k, (int) size, Value.HDFS); // Plain Value
}
val.setdsk();
DKV.put(k, val);
JsonObject o = new JsonObject();
o.addProperty(Constants.KEY, k.toString());
o.addProperty(Constants.FILE, pfs.toString());
o.addProperty(Constants.VALUE_SIZE, file.getLen());
succeeded.add(o);
}
}
} catch (Exception e) {
Log.err(e);
JsonObject o = new JsonObject();
o.addProperty(Constants.FILE, p.toString());
o.addProperty(Constants.ERROR, e.getMessage());
failed.add(o);
}
}
protected final void prepareValidationWithModel(final Model model) {
if (validation == null) return;
Frame[] av = model.adapt(validation, false);
_adaptedValidation = av[0];
gtrash(av[1]); // delete this after computation
if (_fromValid2CM != null) {
assert classification
: "Validation response transformation should be declared only for classification!";
assert _fromModel2CM != null
: "Model response transformation should exist if validation response transformation exists!";
Vec tmp = _validResponse.toEnum();
_adaptedValidationResponse =
tmp.makeTransf(
_fromValid2CM, getCMDomain()); // Add an original response adapted to CM domain
gtrash(_adaptedValidationResponse); // Add the created vector to a clean-up list
gtrash(tmp);
}
}
