@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);
}
}
@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 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;
}
/** 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 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);
}
@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);
}
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;
}
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;
}
/**
* 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();
}
// Test kaggle/creditsample-test data
@org.junit.Test
public void kaggle_credit() {
Key okey = loadAndParseFile("credit.hex", "smalldata/kaggle/creditsample-training.csv.gz");
UKV.remove(Key.make("smalldata/kaggle/creditsample-training.csv.gz_UNZIPPED"));
UKV.remove(Key.make("smalldata\\kaggle\\creditsample-training.csv.gz_UNZIPPED"));
ValueArray val = DKV.get(okey).get();
// Check parsed dataset
final int n = new int[] {4, 2, 1}[ValueArray.LOG_CHK - 20];
assertEquals("Number of chunks", n, val.chunks());
assertEquals("Number of rows", 150000, val.numRows());
assertEquals("Number of cols", 12, val.numCols());
// setup default values for DRF
int ntrees = 3;
int depth = 30;
int gini = StatType.GINI.ordinal();
int seed = 42;
StatType statType = StatType.values()[gini];
final int cols[] =
new int[] {0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 1}; // ignore column 6, classify column 1
// Start the distributed Random Forest
final Key modelKey = Key.make("model");
DRFJob result =
hex.rf.DRF.execute(
modelKey,
cols,
val,
ntrees,
depth,
1024,
statType,
seed,
true,
null,
-1,
Sampling.Strategy.RANDOM,
1.0f,
null,
0,
0,
false);
// Wait for completion on all nodes
RFModel model = result.get();
assertEquals("Number of classes", 2, model.classes());
assertEquals("Number of trees", ntrees, model.size());
model.deleteKeys();
UKV.remove(modelKey);
UKV.remove(okey);
}
@Override
public void compute2() {
String path = null; // getPathFromValue(val);
ValueArray ary = DKV.get(_arykey).get();
Key self = selfKey();
while (_indexFrom < ary.chunks()) {
Key ckey = ary.getChunkKey(_indexFrom++);
if (!ckey.home()) { // Next chunk not At Home?
RPC.call(chunkHome(), this); // Hand the baton off to the next node/chunk
return;
}
Value val = DKV.get(ckey); // It IS home, so get the data
_err = PersistHdfs.appendChunk(_arykey, val);
if (_err != null) return;
UKV.put(self, this); // Update the progress/self key
}
// We did the last chunk. Removing the selfKey is the signal to the web
// thread that All Done.
UKV.remove(self);
}
// TODO: almost identical to ModelsHandler; refactor
public static ModelMetrics getFromDKV(Key key) {
if (null == key) throw new IllegalArgumentException("Got null key.");
Value v = DKV.get(key);
if (null == v) throw new IllegalArgumentException("Did not find key: " + key.toString());
Iced ice = v.get();
if (!(ice instanceof ModelMetrics))
throw new IllegalArgumentException(
"Expected a Model for key: " + key.toString() + "; got a: " + ice.getClass());
return (ModelMetrics) ice;
}
@Override
protected void setupLocal() {
_model_mem_size = 0;
for (int i = 0; i < trees_so_far; ++i) {
Key<CompressedTree>[] per_class = _treeKeys[i];
for (int j = 0; j < per_class.length; ++j) {
if (per_class[j] == null) continue;
if (!per_class[j].home()) continue;
// only look at homed tree keys
_model_mem_size += DKV.get(per_class[j])._max;
}
}
}
public static Frame createFrame(String fname, long[] chunkLayout, String[][] data) {
Frame f = new Frame(Key.make(fname));
f.preparePartialFrame(new String[] {"C0"});
f.update(null);
// Create chunks
for (int i = 0; i < chunkLayout.length; i++) {
createNC(fname, data[i], i, (int) chunkLayout[i]);
}
// Reload frame from DKV
f = DKV.get(fname).get();
// Finalize frame
f.finalizePartialFrame(chunkLayout, new String[][] {null}, new byte[] {Vec.T_STR});
return f;
}
@Test
public void testChunks() {
Frame frame = parse_test_file("smalldata/covtype/covtype.20k.data");
AggregatorModel.AggregatorParameters parms = new AggregatorModel.AggregatorParameters();
parms._train = frame._key;
parms._radius_scale = 3.0;
long start = System.currentTimeMillis();
AggregatorModel agg = new Aggregator(parms).trainModel().get(); // 0.418
System.out.println(
"AggregatorModel finished in: "
+ (System.currentTimeMillis() - start) / 1000.
+ " seconds");
agg.checkConsistency();
Frame output = agg._output._output_frame.get();
Log.info("Number of exemplars: " + agg._exemplars.length);
// Assert.assertTrue(agg._exemplars.length==1993);
output.remove();
agg.remove();
for (int i : new int[] {1, 2, 5, 10, 50, 100}) {
Key key = Key.make();
RebalanceDataSet rb = new RebalanceDataSet(frame, key, i);
H2O.submitTask(rb);
rb.join();
Frame rebalanced = DKV.get(key).get();
parms = new AggregatorModel.AggregatorParameters();
parms._train = frame._key;
parms._radius_scale = 3.0;
start = System.currentTimeMillis();
AggregatorModel agg2 =
new Aggregator(parms).trainModel().get(); // 0.373 0.504 0.357 0.454 0.368 0.355
System.out.println(
"AggregatorModel finished in: "
+ (System.currentTimeMillis() - start) / 1000.
+ " seconds");
agg2.checkConsistency();
Log.info("Number of exemplars for " + i + " chunks: " + agg2._exemplars.length);
rebalanced.delete();
Assert.assertTrue(
Math.abs(agg._exemplars.length - agg2._exemplars.length)
== 0); // < agg._exemplars.length*0);
output = agg2._output._output_frame.get();
output.remove();
agg2.remove();
}
frame.delete();
}
@Override
public byte[] load(final Value v) {
final byte[] b = MemoryManager.malloc1(v._max);
long skip = 0;
Key k = v._key;
final Path p;
if (_iceRoot != null) {
p = new Path(_iceRoot, getIceName(v));
} else {
// Convert an arraylet chunk into a long-offset from the base file.
if (k._kb[0] == Key.ARRAYLET_CHUNK) {
skip = ValueArray.getChunkOffset(k); // The offset
k = ValueArray.getArrayKey(k); // From the base file key
if (k.toString().endsWith(Extensions.HEX)) { // Hex file?
int value_len = DKV.get(k).memOrLoad().length; // How long is the ValueArray header?
skip += value_len;
}
}
p = new Path(k.toString());
}
final long skip_ = skip;
run(
new Callable() {
@Override
public Object call() throws Exception {
FileSystem fs = FileSystem.get(p.toUri(), CONF);
FSDataInputStream s = null;
try {
s = fs.open(p);
// NOTE:
// The following line degrades performance of HDFS load from S3 API:
// s.readFully(skip,b,0,b.length);
// Google API's simple seek has better performance
// Load of 300MB file via Google API ~ 14sec, via s.readFully ~ 5min (under the same
// condition)
ByteStreams.skipFully(s, skip_);
ByteStreams.readFully(s, b);
assert v.isPersisted();
} finally {
Utils.close(s);
}
return null;
}
},
true,
v._max);
return b;
}
// 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;
}
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());
}
public static String store2Hdfs(Key srcKey) {
assert srcKey._kb[0] != Key.ARRAYLET_CHUNK;
assert PersistHdfs.getPathForKey(srcKey) != null; // Validate key name
Value v = DKV.get(srcKey);
if (v == null) return "Key " + srcKey + " not found";
if (v._isArray == 0) { // Simple chunk?
v.setHdfs(); // Set to HDFS and be done
return null; // Success
}
// For ValueArrays, make the .hex header
ValueArray ary = ValueArray.value(v);
String err = PersistHdfs.freeze(srcKey, ary);
if (err != null) return err;
// The task managing which chunks to write next,
// store in a known key
TaskStore2HDFS ts = new TaskStore2HDFS(srcKey);
Key selfKey = ts.selfKey();
UKV.put(selfKey, ts);
// Then start writing chunks in-order with the zero chunk
H2ONode chk0_home = ValueArray.getChunkKey(0, srcKey).home_node();
RPC.call(ts.chunkHome(), ts);
// Watch the progress key until it gets removed or an error appears
long idx = 0;
while (UKV.get(selfKey, ts) != null) {
if (ts._indexFrom != idx) {
System.out.print(" " + idx + "/" + ary.chunks());
idx = ts._indexFrom;
}
if (ts._err != null) { // Found an error?
UKV.remove(selfKey); // Cleanup & report
return ts._err;
}
try {
Thread.sleep(100);
} catch (InterruptedException e) {
}
}
System.out.println(" " + ary.chunks() + "/" + ary.chunks());
// PersistHdfs.refreshHDFSKeys();
return null;
}
/**
* Performs deep clone of given model.
*
* <p>FIXME: fetch all data to the caller node
*/
protected M getModelDeepClone(M model) {
M newModel = IcedUtils.clone(model, _dest);
// Do not clone model metrics
newModel._output._model_metrics = new Key[0];
newModel._output._training_metrics = null;
newModel._output._validation_metrics = null;
// Clone trees
Key[][] treeKeys = newModel._output._treeKeys;
for (int i = 0; i < treeKeys.length; i++) {
for (int j = 0; j < treeKeys[i].length; j++) {
if (treeKeys[i][j] == null) continue;
;
CompressedTree ct = DKV.get(treeKeys[i][j]).get();
CompressedTree newCt = IcedUtils.clone(ct, CompressedTree.makeTreeKey(i, j), true);
treeKeys[i][j] = newCt._key;
}
}
return newModel;
}
public Key importFile(int i, Futures fs) {
if (_ok[i] < H2O.CLOUD.size()) return null;
File f = new File(_files[i]);
Key k;
if (_newApi) {
k = PersistNFS.decodeFile(f);
NFSFileVec nfs = DKV.get(NFSFileVec.make(f, fs)).get();
UKV.put(k, new Frame(new String[] {"0"}, new Vec[] {nfs}), fs);
} else {
k = PersistNFS.decodeFile(f);
long size = f.length();
Value val =
(size < 2 * ValueArray.CHUNK_SZ)
? new Value(k, (int) size, Value.NFS)
: new Value(k, new ValueArray(k, size), Value.NFS);
val.setdsk();
UKV.put(k, val, fs);
}
return k;
}
public ModelMetricsGLRM scoreMetricsOnly(Frame frame) {
final int ncols = _output._names.length;
// Need [A,X] where A = adapted test frame, X = loading frame
// Note: A is adapted to original training frame
Frame adaptedFr = new Frame(frame);
adaptTestForTrain(adaptedFr, true, false);
assert ncols == adaptedFr.numCols();
// Append loading frame X for calculating XY
Frame fullFrm = new Frame(adaptedFr);
Frame loadingFrm = DKV.get(_output._representation_key).get();
fullFrm.add(loadingFrm);
GLRMScore gs = new GLRMScore(ncols, _parms._k, false).doAll(fullFrm);
ModelMetrics mm =
gs._mb.makeModelMetrics(
GLRMModel.this, adaptedFr, null, null); // save error metrics based on imputed data
return (ModelMetricsGLRM) mm;
}
@Override
public void map(Key key) {
_rows = new long[_clusters.length];
_dist = new double[_clusters.length];
assert key.home();
ValueArray va = DKV.get(_arykey).get();
AutoBuffer bits = va.getChunk(key);
int rows = va.rpc(ValueArray.getChunkIndex(key));
double[] values = new double[_cols.length - 1];
ClusterDist cd = new ClusterDist();
for (int row = 0; row < rows; row++) {
KMeans.datad(va, bits, row, _cols, _normalized, values);
KMeans.closest(_clusters, values, cd);
_rows[cd._cluster]++;
_dist[cd._cluster] += cd._dist;
}
_arykey = null;
_cols = null;
_clusters = null;
}
@Override
public void parse(Key key) throws IOException {
_firstRow = true;
InputStream is = DKV.get(key).openStream();
try {
_fs = new POIFSFileSystem(is);
MissingRecordAwareHSSFListener listener = new MissingRecordAwareHSSFListener(this);
_formatListener = new FormatTrackingHSSFListener(listener);
HSSFEventFactory factory = new HSSFEventFactory();
HSSFRequest request = new HSSFRequest();
request.addListenerForAllRecords(_formatListener);
factory.processWorkbookEvents(request, _fs);
} finally {
try {
is.close();
} catch (IOException e) {
}
}
}
@SuppressWarnings("unused") // called through reflection by RequestServer
public JobsV3 fetch(int version, JobsV3 s) {
Key key = s.job_id.key();
Value val = DKV.get(key);
if (null == val) throw new IllegalArgumentException("Job is missing");
Iced ice = val.get();
if (!(ice instanceof Job))
throw new IllegalArgumentException("Must be a Job not a " + ice.getClass());
Job j = (Job) ice;
s.jobs = new JobV3[1];
// s.fillFromImpl(jobs);
try {
s.jobs[0] = (JobV3) Schema.schema(version, j).fillFromImpl(j);
}
// no special schema for this job subclass, so fall back to JobV3
catch (H2ONotFoundArgumentException e) {
s.jobs[0] = new JobV3().fillFromImpl(j);
}
return s;
}
// 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 ? 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.C1NCHUNK, _be);
val2.setdsk(); // It is already on disk.
// If not-home, then block till the Key is everywhere. Most calls here are
// from the parser loading a text file, and the parser splits the work such
// that most puts here are on home - so this is a simple speed optimization:
// do not make a Futures nor block on it on home.
Futures fs = dkey.home() ? null : new Futures();
// Atomically insert: fails on a race, but then return the old version
Value val3 = DKV.DputIfMatch(dkey, val2, null, fs);
if (!dkey.home() && fs != null) fs.blockForPending();
return val3 == null ? val2 : val3;
}
static boolean checkSaneFrame_impl() {
for (Key k : H2O.localKeySet()) {
Value val = H2O.raw_get(k);
if (val.isFrame()) {
Frame fr = val.get();
Vec vecs[] = fr.vecs();
for (int i = 0; i < vecs.length; i++) {
Vec v = vecs[i];
if (DKV.get(v._key) == null) {
System.err.println(
"Frame "
+ fr._key
+ " in the DKV, is missing Vec "
+ v._key
+ ", name="
+ fr._names[i]);
return false;
}
}
}
}
return true;
}
