@Override
protected Response serve() {
try {
// pull everything local
Log.info("ExportFiles processing (" + path + ")");
if (DKV.get(src_key) == null)
throw new IllegalArgumentException(src_key.toString() + " not found.");
Object value = DKV.get(src_key).get();
// create a stream to read the entire VA or Frame
if (!(value instanceof ValueArray) && !(value instanceof Frame))
throw new UnsupportedOperationException("Can only export Frames or ValueArrays.");
InputStream csv =
value instanceof ValueArray
? new ValueArray.CsvVAStream((ValueArray) value, null)
: ((Frame) value).toCSV(true);
String p2 = path.toLowerCase();
if (p2.startsWith("hdfs://")) serveHdfs(csv);
else if (p2.startsWith("s3n://")) serveHdfs(csv);
else serveLocalDisk(csv);
return RequestBuilders.Response.done(this);
} catch (Throwable t) {
return RequestBuilders.Response.error(t);
}
}
static Frame exec_str(String str, String id) {
Val val = Exec.exec(str);
switch (val.type()) {
case Val.FRM:
Frame fr = val.getFrame();
Key k = Key.make(id);
// Smart delete any prior top-level result
Iced i = DKV.getGet(k);
if (i instanceof Lockable) ((Lockable) i).delete();
else if (i instanceof Keyed) ((Keyed) i).remove();
else if (i != null)
throw new IllegalArgumentException("Attempting to overright an unexpected key");
DKV.put(fr = new Frame(k, fr._names, fr.vecs()));
System.out.println(fr);
checkSaneFrame();
return fr;
case Val.NUM:
System.out.println("num= " + val.getNum());
assert id == null;
checkSaneFrame();
return null;
case Val.STR:
System.out.println("str= " + val.getStr());
assert id == null;
checkSaneFrame();
return null;
default:
throw water.H2O.fail();
}
}
/**
* Score a frame with the given model and return just the metrics.
*
* <p>NOTE: ModelMetrics are now always being created by model.score. . .
*/
@SuppressWarnings("unused") // called through reflection by RequestServer
public ModelMetricsListSchemaV3 score(int version, ModelMetricsListSchemaV3 s) {
// parameters checking:
if (null == s.model) throw new H2OIllegalArgumentException("model", "predict", s.model);
if (null == DKV.get(s.model.name))
throw new H2OKeyNotFoundArgumentException("model", "predict", s.model.name);
if (null == s.frame) throw new H2OIllegalArgumentException("frame", "predict", s.frame);
if (null == DKV.get(s.frame.name))
throw new H2OKeyNotFoundArgumentException("frame", "predict", s.frame.name);
ModelMetricsList parms = s.createAndFillImpl();
parms
._model
.score(parms._frame, parms._predictions_name)
.remove(); // throw away predictions, keep metrics as a side-effect
ModelMetricsListSchemaV3 mm = this.fetch(version, s);
// TODO: for now only binary predictors write an MM object.
// For the others cons one up here to return the predictions frame.
if (null == mm) mm = new ModelMetricsListSchemaV3();
if (null == mm.model_metrics || 0 == mm.model_metrics.length) {
Log.warn(
"Score() did not return a ModelMetrics for model: " + s.model + " on frame: " + s.frame);
}
return mm;
}
public GLMModelV3 make_model(int version, MakeGLMModelV3 args) {
GLMModel model = DKV.getGet(args.model.key());
if (model == null) throw new IllegalArgumentException("missing source model " + args.model);
String[] names = model._output.coefficientNames();
Map<String, Double> coefs = model.coefficients();
for (int i = 0; i < args.names.length; ++i) coefs.put(args.names[i], args.beta[i]);
double[] beta = model.beta().clone();
for (int i = 0; i < beta.length; ++i) beta[i] = coefs.get(names[i]);
GLMModel m =
new GLMModel(
args.dest != null ? args.dest.key() : Key.make(),
model._parms,
null,
new double[] {.5},
Double.NaN,
Double.NaN,
-1);
DataInfo dinfo = model.dinfo();
dinfo.setPredictorTransform(TransformType.NONE);
// GLMOutput(DataInfo dinfo, String[] column_names, String[][] domains, String[]
// coefficient_names, boolean binomial) {
m._output =
new GLMOutput(
model.dinfo(),
model._output._names,
model._output._domains,
model._output.coefficientNames(),
model._output._binomial,
beta);
DKV.put(m._key, m);
GLMModelV3 res = new GLMModelV3();
res.fillFromImpl(m);
return res;
}
@Override
public float progress() {
if (DKV.get(dest()) == null) return 0;
GLMModel m = DKV.get(dest()).get();
float progress = (float) m.iteration() / (float) max_iter; // TODO, do something smarter here
return progress;
}
/** Score a frame with the given model and return the metrics AND the prediction frame. */
@SuppressWarnings("unused") // called through reflection by RequestServer
public JobV3 predict2(int version, final ModelMetricsListSchemaV3 s) {
// parameters checking:
if (null == s.model) throw new H2OIllegalArgumentException("model", "predict", s.model);
if (null == DKV.get(s.model.name))
throw new H2OKeyNotFoundArgumentException("model", "predict", s.model.name);
if (null == s.frame) throw new H2OIllegalArgumentException("frame", "predict", s.frame);
if (null == DKV.get(s.frame.name))
throw new H2OKeyNotFoundArgumentException("frame", "predict", s.frame.name);
final ModelMetricsList parms = s.createAndFillImpl();
// predict2 does not return modelmetrics, so cannot handle deeplearning: reconstruction_error
// (anomaly) or GLRM: reconstruct and archetypes
// predict2 can handle deeplearning: deepfeatures and predict
if (s.deep_features_hidden_layer > 0) {
if (null == parms._predictions_name)
parms._predictions_name =
"deep_features"
+ Key.make().toString().substring(0, 5)
+ "_"
+ parms._model._key.toString()
+ "_on_"
+ parms._frame._key.toString();
} else if (null == parms._predictions_name)
parms._predictions_name =
"predictions"
+ Key.make().toString().substring(0, 5)
+ "_"
+ parms._model._key.toString()
+ "_on_"
+ parms._frame._key.toString();
final Job<Frame> j =
new Job(Key.make(parms._predictions_name), Frame.class.getName(), "prediction");
H2O.H2OCountedCompleter work =
new H2O.H2OCountedCompleter() {
@Override
public void compute2() {
if (s.deep_features_hidden_layer < 0) {
parms._model.score(parms._frame, parms._predictions_name, j);
} else {
Frame predictions =
((Model.DeepFeatures) parms._model)
.scoreDeepFeatures(parms._frame, s.deep_features_hidden_layer, j);
predictions =
new Frame(
Key.make(parms._predictions_name), predictions.names(), predictions.vecs());
DKV.put(predictions._key, predictions);
}
tryComplete();
}
};
j.start(work, parms._frame.anyVec().nChunks());
return new JobV3().fillFromImpl(j);
}
public Vec replace(int col, Vec nv) {
assert col < _names.length;
Vec rv = vecs()[col];
assert rv.group().equals(nv.group());
_vecs[col] = nv;
_keys[col] = nv._key;
if (DKV.get(nv._key) == null) // If not already in KV, put it there
DKV.put(nv._key, nv);
return rv;
}
public static Response redirect(JsonObject fromPageResponse, Key rfModelKey) {
RFModel rfModel = DKV.get(rfModelKey).get();
ValueArray data = DKV.get(rfModel._dataKey).get();
return redirect(
fromPageResponse,
null,
rfModelKey,
rfModel._dataKey,
rfModel._totalTrees,
data.numCols() - 1,
null,
true,
false);
}
/**
* Creates the value header based on the calculated columns.
*
* <p>Also stores the header to its appropriate key. This will be the VA header of the parsed
* dataset.
*/
private void createValueArrayHeader() {
assert (_phase == Pass.TWO);
Column[] cols = new Column[_ncolumns];
int off = 0;
for (int i = 0; i < cols.length; ++i) {
cols[i] = new Column();
cols[i]._n = _numRows - _invalidValues[i];
cols[i]._base = _bases[i];
assert (char) pow10i(-_scale[i]) == pow10i(-_scale[i])
: "scale out of bounds!, col = " + i + ", scale = " + _scale[i];
cols[i]._scale = (char) pow10i(-_scale[i]);
cols[i]._off = (char) off;
cols[i]._size = (byte) COL_SIZES[_colTypes[i]];
cols[i]._domain = _colDomains[i];
cols[i]._max = _max[i];
cols[i]._min = _min[i];
cols[i]._mean = _mean[i];
cols[i]._sigma = _sigma[i];
cols[i]._name = _colNames[i];
off += Math.abs(cols[i]._size);
}
// let any pending progress reports finish
DKV.write_barrier();
// finally make the value array header
ValueArray ary = new ValueArray(_resultKey, _numRows, off, cols);
UKV.put(_resultKey, ary.value());
}
// ------------------------------------------------------------------------
// Zipped file; no parallel decompression; decompress into local chunks,
// parse local chunks; distribute chunks later.
ParseWriter streamParseZip(final InputStream is, final StreamParseWriter dout, InputStream bvs)
throws IOException {
// All output into a fresh pile of NewChunks, one per column
if (!_setup._parse_type._parallelParseSupported) throw H2O.unimpl();
StreamData din = new StreamData(is);
int cidx = 0;
StreamParseWriter nextChunk = dout;
int zidx = bvs.read(null, 0, 0); // Back-channel read of chunk index
assert zidx == 1;
while (is.available() > 0) {
int xidx = bvs.read(null, 0, 0); // Back-channel read of chunk index
if (xidx > zidx) { // Advanced chunk index of underlying ByteVec stream?
zidx = xidx; // Record advancing of chunk
nextChunk.close(); // Match output chunks to input zipfile chunks
if (dout != nextChunk) {
dout.reduce(nextChunk);
if (_jobKey != null && ((Job) DKV.getGet(_jobKey)).isCancelledOrCrashed()) break;
}
nextChunk = nextChunk.nextChunk();
}
parseChunk(cidx++, din, nextChunk);
}
parseChunk(cidx, din, nextChunk); // Parse the remaining partial 32K buffer
nextChunk.close();
if (dout != nextChunk) dout.reduce(nextChunk);
return dout;
}
@Override
protected Frame predictScoreImpl(Frame orig, Frame adaptedFr, String destination_key) {
Frame adaptFrm = new Frame(adaptedFr);
for (int i = 0; i < _parms._k; i++)
adaptFrm.add("PC" + String.valueOf(i + 1), adaptFrm.anyVec().makeZero());
new MRTask() {
@Override
public void map(Chunk chks[]) {
double tmp[] = new double[_output._names.length];
double preds[] = new double[_parms._k];
for (int row = 0; row < chks[0]._len; row++) {
double p[] = score0(chks, row, tmp, preds);
for (int c = 0; c < preds.length; c++) chks[_output._names.length + c].set(row, p[c]);
}
}
}.doAll(adaptFrm);
// Return the projection into principal component space
int x = _output._names.length, y = adaptFrm.numCols();
Frame f =
adaptFrm.extractFrame(
x, y); // this will call vec_impl() and we cannot call the delete() below just yet
f =
new Frame(
(null == destination_key ? Key.make() : Key.make(destination_key)),
f.names(),
f.vecs());
DKV.put(f);
makeMetricBuilder(null).makeModelMetrics(this, orig);
return f;
}
// Delete the metrics that match model and/or frame
ModelMetricsList delete() {
ModelMetricsList matches = fetch();
for (ModelMetrics mm : matches._model_metrics) DKV.remove(mm._key);
return matches;
}
// Fetch all metrics that match model and/or frame
ModelMetricsList fetch() {
final Key[] modelMetricsKeys =
KeySnapshot.globalSnapshot()
.filter(
new KeySnapshot.KVFilter() {
@Override
public boolean filter(KeySnapshot.KeyInfo k) {
try {
if (!Value.isSubclassOf(k._type, ModelMetrics.class))
return false; // Fast-path cutout
ModelMetrics mm = DKV.getGet(k._key);
// If we're filtering by model filter by Model. :-)
if (_model != null && !mm.isForModel((Model) DKV.getGet(_model._key)))
return false;
// If we're filtering by frame filter by Frame. :-)
if (_frame != null && !mm.isForFrame((Frame) DKV.getGet(_frame._key)))
return false;
} catch (NullPointerException | ClassCastException ex) {
return false; // Handle all kinds of broken racey key updates
}
return true;
}
})
.keys();
_model_metrics = new ModelMetrics[modelMetricsKeys.length];
for (int i = 0; i < modelMetricsKeys.length; i++)
_model_metrics[i] = DKV.getGet(modelMetricsKeys[i]);
return this; // Flow coding
}
// Convert a chunk# into a chunk - does lazy-chunk creation. As chunks are
// asked-for the first time, we make the Key and an empty backing DVec.
// Touching the DVec will force the file load.
@Override
public Value chunkIdx(int cidx) {
final long nchk = nChunks();
assert 0 <= cidx && cidx < nchk;
Key dkey = chunkKey(cidx);
Value val1 = DKV.get(dkey); // Check for an existing one... will fetch data as needed
if (val1 != null) return val1; // Found an existing one?
// Lazily create a DVec for this chunk
int len = (int) (cidx < nchk - 1 ? ValueArray.CHUNK_SZ : (_len - chunk2StartElem(cidx)));
// DVec is just the raw file data with a null-compression scheme
Value val2 = new Value(dkey, len, null, TypeMap.C1CHUNK, Value.NFS);
val2.setdsk(); // It is already on disk.
// Atomically insert: fails on a race, but then return the old version
Value val3 = DKV.DputIfMatch(dkey, val2, null, null);
return val3 == null ? val2 : val3;
}
@Override
public void modifyParmsForCrossValidationMainModel(ModelBuilder[] cvModelBuilders) {
_parms._overwrite_with_best_model = false;
if (_parms._stopping_rounds == 0 && _parms._max_runtime_secs == 0)
return; // No exciting changes to stopping conditions
// Extract stopping conditions from each CV model, and compute the best stopping answer
_parms._stopping_rounds = 0;
_parms._max_runtime_secs = 0;
double sum = 0;
for (ModelBuilder cvmb : cvModelBuilders)
sum += ((DeepLearningModel) DKV.getGet(cvmb.dest())).last_scored().epoch_counter;
_parms._epochs = sum / cvModelBuilders.length;
if (!_parms._quiet_mode) {
warn(
"_epochs",
"Setting optimal _epochs to "
+ _parms._epochs
+ " for cross-validation main model based on early stopping of cross-validation models.");
warn(
"_stopping_rounds",
"Disabling convergence-based early stopping for cross-validation main model.");
warn(
"_max_runtime_secs",
"Disabling maximum allowed runtime for cross-validation main model.");
}
}
@Override
public Response serve() {
Frame fr = DKV.get(data_key.value()).get();
if (fr == null) return RequestServer._http404.serve();
// Build a frame with the selected Vecs
Frame fr2 = new Frame(new String[0], new Vec[0]);
int[] idxs = vecs.value();
for (int idx : idxs) // The selected frame columns
fr2.add(fr._names[idx], fr._vecs[idx]);
// Add the class-vec last
Vec cvec = class_vec.value();
fr2.add(fr._names[class_vec._colIdx.get()], cvec);
domain = cvec.domain(); // Class/enum/factor names
mtrys = features.value() == null ? (int) (Math.sqrt(idxs.length) + 0.5) : features.value();
DRF drf =
DRF.start(
DRF.makeKey(),
fr2,
depth.value(),
ntrees.value(),
mtrys,
sample_rate.value(),
seed.value());
drf.get(); // Block for result
cm = drf.cm(); // Get CM result
return new Response(Response.Status.done, this, -1, -1, null);
}
@Test
public void testDomains() {
Frame frame = parse_test_file("smalldata/junit/weather.csv");
for (String s : new String[] {"MaxWindSpeed", "RelHumid9am", "Cloud9am"}) {
Vec v = frame.vec(s);
Vec newV = v.toCategoricalVec();
frame.remove(s);
frame.add(s, newV);
v.remove();
}
DKV.put(frame);
AggregatorModel.AggregatorParameters parms = new AggregatorModel.AggregatorParameters();
parms._train = frame._key;
parms._radius_scale = 10;
AggregatorModel agg = new Aggregator(parms).trainModel().get();
Frame output = agg._output._output_frame.get();
Assert.assertTrue(output.numRows() < 0.5 * frame.numRows());
boolean same = true;
for (int i = 0; i < frame.numCols(); ++i) {
if (frame.vec(i).isCategorical()) {
same = (frame.domains()[i].length == output.domains()[i].length);
if (!same) break;
}
}
frame.remove();
output.remove();
agg.remove();
Assert.assertFalse(same);
}
public Frame(String[] names, Vec[] vecs) {
// assert names==null || names.length == vecs.length : "Number of columns does not match to
// number of cols' names.";
_names = names;
_vecs = vecs;
_keys = new Key[vecs.length];
for (int i = 0; i < vecs.length; i++) {
Key k = _keys[i] = vecs[i]._key;
if (DKV.get(k) == null) // If not already in KV, put it there
DKV.put(k, vecs[i]);
}
Vec v0 = anyVec();
if (v0 == null) return;
VectorGroup grp = v0.group();
for (int i = 0; i < vecs.length; i++) assert grp.equals(vecs[i].group());
}
protected void testExecFail(String expr, int errorPos) {
DKV.write_barrier();
int keys = H2O.store_size();
try {
int i = UNIQUE.getAndIncrement();
System.err.println("result" + (new Integer(i).toString()) + ": " + expr);
Key key = Exec.exec(expr, "result" + (new Integer(i).toString()));
UKV.remove(key);
assertTrue("An exception should have been thrown.", false);
} catch (ParserException e) {
assertTrue(false);
} catch (EvaluationException e) {
if (errorPos != -1) assertEquals(errorPos, e._pos);
}
DKV.write_barrier();
assertEquals("Keys were not properly deleted for expression " + expr, keys, H2O.store_size());
}
public JobsV3 cancel(int version, JobsV3 c) {
Job j = DKV.getGet(c.job_id.key());
if (j == null) {
throw new IllegalArgumentException("No job with key " + c.job_id.key());
}
j.stop(); // Request Job stop
return c;
}
public static ValueArray loadAndParseKey(Key okey, String path) {
FileIntegrityChecker c = FileIntegrityChecker.check(new File(path),false);
Key k = c.syncDirectory(null,null,null,null);
ParseDataset.forkParseDataset(okey, new Key[] { k }, null).get();
UKV.remove(k);
ValueArray res = DKV.get(okey).get();
return res;
}
@Override
public byte[] atomic(byte[] bits1) {
byte[] mem = DKV.get(_key).get();
int len = Math.max(_dst_off + mem.length, bits1 == null ? 0 : bits1.length);
byte[] bits2 = MemoryManager.malloc1(len);
if (bits1 != null) System.arraycopy(bits1, 0, bits2, 0, bits1.length);
System.arraycopy(mem, 0, bits2, _dst_off, mem.length);
return bits2;
}
private FrameTask(Key jobKey, Key dinfoKey, int[] activeCols, long seed, int iteration) {
super(null);
assert dinfoKey == null || DKV.get(dinfoKey) != null;
_jobKey = jobKey;
_dinfoKey = dinfoKey;
_activeCols = activeCols;
_seed = seed;
_iteration = iteration;
}
/** Actually remove/delete all Vecs from memory, not just from the Frame. */
public void remove(Futures fs) {
if (_vecs.length > 0) {
VectorGroup vg = _vecs[0].group();
for (Vec v : _vecs) UKV.remove(v._key, fs);
DKV.remove(vg._key);
}
_names = new String[0];
_vecs = new Vec[0];
}
@Test
public void testCategoricalProstate() 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
try {
Scope.enter();
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 = 8;
parms._gamma_x = parms._gamma_y = 0.1;
parms._regularization_x = GLRMModel.GLRMParameters.Regularizer.Quadratic;
parms._regularization_y = GLRMModel.GLRMParameters.Regularizer.Quadratic;
parms._init = GLRM.Initialization.PlusPlus;
parms._transform = DataInfo.TransformType.STANDARDIZE;
parms._recover_svd = false;
parms._max_iterations = 200;
try {
job = new GLRM(parms);
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) {
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();
}
}
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 RuntimeException("Missing version");
JsonElement type = json.get(Constants.TYPE);
if (type == null) throw new RuntimeException("Missing type");
Class c = Class.forName(type.getAsString());
OldModel model = (OldModel) 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);
Log.info("PersistHdfs: DKV.put(" + k + ")");
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);
}
}
// Close all AppendableVec
public Futures closeAppendables(Futures fs) {
_col0 = null; // Reset cache
int len = vecs().length;
for (int i = 0; i < len; i++) {
Vec v = _vecs[i];
if (v instanceof AppendableVec)
DKV.put(_keys[i], _vecs[i] = ((AppendableVec) v).close(fs), fs);
}
return fs;
}
/**
* Initialize the ModelBuilder, validating all arguments and preparing the training frame. This
* call is expected to be overridden in the subclasses and each subclass will start with
* "super.init();". This call is made by the front-end whenever the GUI is clicked, and needs to
* be fast; heavy-weight prep needs to wait for the trainModel() call.
*
* <p>Validate the requested ntrees; precompute actual ntrees. Validate the number of classes to
* predict on; validate a checkpoint.
*/
@Override
public void init(boolean expensive) {
super.init(expensive);
if (H2O.ARGS.client && _parms._build_tree_one_node)
error("_build_tree_one_node", "Cannot run on a single node in client mode");
if (_vresponse != null) _vresponse_key = _vresponse._key;
if (_response != null) _response_key = _response._key;
if (_nclass > SharedTreeModel.SharedTreeParameters.MAX_SUPPORTED_LEVELS)
error("_nclass", "Too many levels in response column!");
if (_parms._min_rows < 0) error("_min_rows", "Requested min_rows must be greater than 0");
if (_parms._ntrees < 0 || _parms._ntrees > 100000)
error("_ntrees", "Requested ntrees must be between 1 and 100000");
_ntrees = _parms._ntrees; // Total trees in final model
if (_parms._checkpoint) { // Asking to continue from checkpoint?
Value cv = DKV.get(_parms._model_id);
if (cv != null) { // Look for prior model
M checkpointModel = cv.get();
if (_parms._ntrees < checkpointModel._output._ntrees + 1)
error(
"_ntrees",
"Requested ntrees must be between "
+ checkpointModel._output._ntrees
+ 1
+ " and 100000");
_ntrees = _parms._ntrees - checkpointModel._output._ntrees; // Needed trees
}
}
if (_parms._nbins <= 1) error("_nbins", "_nbins must be > 1.");
if (_parms._nbins >= 1 << 16) error("_nbins", "_nbins must be < " + (1 << 16));
if (_parms._nbins_cats <= 1) error("_nbins_cats", "_nbins_cats must be > 1.");
if (_parms._nbins_cats >= 1 << 16) error("_nbins_cats", "_nbins_cats must be < " + (1 << 16));
if (_parms._max_depth <= 0) error("_max_depth", "_max_depth must be > 0.");
if (_parms._min_rows <= 0) error("_min_rows", "_min_rows must be > 0.");
if (_parms._distribution == Distributions.Family.tweedie) {
_parms._distribution.tweedie.p = _parms._tweedie_power;
}
if (_train != null) {
double sumWeights =
_train.numRows() * (hasWeightCol() ? _train.vec(_parms._weights_column).mean() : 1);
if (sumWeights
< 2 * _parms._min_rows) // Need at least 2*min_rows weighted rows to split even once
error(
"_min_rows",
"The dataset size is too small to split for min_rows="
+ _parms._min_rows
+ ": must have at least "
+ 2 * _parms._min_rows
+ " (weighted) rows, but have only "
+ sumWeights
+ ".");
}
if (_train != null) _ncols = _train.numCols() - 1 - numSpecialCols();
}
/**
* Initialize the ModelBuilder, validating all arguments and preparing the training frame. This
* call is expected to be overridden in the subclasses and each subclass will start with
* "super.init();". This call is made by the front-end whenever the GUI is clicked, and needs to
* be fast; heavy-weight prep needs to wait for the trainModel() call.
*
* <p>Validate the requested ntrees; precompute actual ntrees. Validate the number of classes to
* predict on; validate a checkpoint.
*/
@Override
public void init(boolean expensive) {
super.init(expensive);
if (H2O.ARGS.client && _parms._build_tree_one_node)
error("_build_tree_one_node", "Cannot run on a single node in client mode");
if (_vresponse != null) _vresponse_key = _vresponse._key;
if (_response != null) _response_key = _response._key;
if (_parms._min_rows < 0) error("_min_rows", "Requested min_rows must be greater than 0");
if (_parms._ntrees < 0 || _parms._ntrees > MAX_NTREES)
error("_ntrees", "Requested ntrees must be between 1 and " + MAX_NTREES);
_ntrees = _parms._ntrees; // Total trees in final model
if (_parms.hasCheckpoint()) { // Asking to continue from checkpoint?
Value cv = DKV.get(_parms._checkpoint);
if (cv != null) { // Look for prior model
M checkpointModel = cv.get();
try {
_parms.validateWithCheckpoint(checkpointModel._parms);
} catch (H2OIllegalArgumentException e) {
error(e.values.get("argument").toString(), e.values.get("value").toString());
}
if (_parms._ntrees < checkpointModel._output._ntrees + 1)
error(
"_ntrees",
"If checkpoint is specified then requested ntrees must be higher than "
+ (checkpointModel._output._ntrees + 1));
// Compute number of trees to build for this checkpoint
_ntrees = _parms._ntrees - checkpointModel._output._ntrees; // Needed trees
}
}
if (_parms._nbins <= 1) error("_nbins", "_nbins must be > 1.");
if (_parms._nbins >= 1 << 16) error("_nbins", "_nbins must be < " + (1 << 16));
if (_parms._nbins_cats <= 1) error("_nbins_cats", "_nbins_cats must be > 1.");
if (_parms._nbins_cats >= 1 << 16) error("_nbins_cats", "_nbins_cats must be < " + (1 << 16));
if (_parms._max_depth <= 0) error("_max_depth", "_max_depth must be > 0.");
if (_parms._min_rows <= 0) error("_min_rows", "_min_rows must be > 0.");
if (_train != null) {
double sumWeights =
_train.numRows() * (hasWeightCol() ? _train.vec(_parms._weights_column).mean() : 1);
if (sumWeights
< 2 * _parms._min_rows) // Need at least 2*min_rows weighted rows to split even once
error(
"_min_rows",
"The dataset size is too small to split for min_rows="
+ _parms._min_rows
+ ": must have at least "
+ 2 * _parms._min_rows
+ " (weighted) rows, but have only "
+ sumWeights
+ ".");
}
if (_train != null) _ncols = _train.numCols() - 1 - numSpecialCols();
}
@Override
Val apply(Env env, Env.StackHelp stk, AST asts[]) {
QuantileModel.QuantileParameters parms = new QuantileModel.QuantileParameters();
Frame fr = stk.track(asts[1].exec(env)).getFrame();
Frame fr_wkey = new Frame(fr); // Force a bogus Key for Quantiles ModelBuilder
DKV.put(fr_wkey);
parms._train = fr_wkey._key;
parms._probs = ((ASTNumList) asts[2]).expand();
for (double d : parms._probs)
if (d < 0 || d > 1)
throw new IllegalArgumentException("Probability must be between 0 and 1: " + d);
String inter = asts[3].exec(env).getStr();
parms._combine_method = QuantileModel.CombineMethod.valueOf(inter.toUpperCase());
parms._weights_column = asts[4].str().equals("_") ? null : asts[4].str();
// Compute Quantiles
QuantileModel q = new Quantile(parms).trainModel().get();
// Remove bogus Key
DKV.remove(fr_wkey._key);
// Reshape all outputs as a Frame, with probs in col 0 and the
// quantiles in cols 1 thru fr.numCols() - except the optional weights vec
int ncols = fr.numCols();
if (parms._weights_column != null) ncols--;
Vec[] vecs = new Vec[1 /*1 more for the probs themselves*/ + ncols];
String[] names = new String[vecs.length];
vecs[0] = Vec.makeCon(null, parms._probs);
names[0] = "Probs";
int w = 0;
for (int i = 0; i < vecs.length - 1; ++i) {
if (fr._names[i].equals(parms._weights_column)) w = 1;
assert (w == 0 || w == 1);
vecs[i + 1] = Vec.makeCon(null, q._output._quantiles[i]);
names[i + 1] = fr._names[w + i] + "Quantiles";
}
q.delete();
return new ValFrame(new Frame(names, vecs));
}
