@Override
protected void execImpl() {
Vec va = null, vp = null, avp = null;
try {
if (classification) {
// Create a new vectors - it is cheap since vector are only adaptation vectors
va = vactual.toEnum(); // always returns TransfVec
actual_domain = va._domain;
vp = vpredict.toEnum(); // always returns TransfVec
predicted_domain = vp._domain;
if (!Arrays.equals(actual_domain, predicted_domain)) {
domain = Utils.domainUnion(actual_domain, predicted_domain);
int[][] vamap = Model.getDomainMapping(domain, actual_domain, true);
va = TransfVec.compose((TransfVec) va, vamap, domain, false); // delete original va
int[][] vpmap = Model.getDomainMapping(domain, predicted_domain, true);
vp = TransfVec.compose((TransfVec) vp, vpmap, domain, false); // delete original vp
} else domain = actual_domain;
// The vectors are from different groups => align them, but properly delete it after
// computation
if (!va.group().equals(vp.group())) {
avp = vp;
vp = va.align(vp);
}
cm = new CM(domain.length).doAll(va, vp)._cm;
} else {
mse = new CM(1).doAll(vactual, vpredict).mse();
}
return;
} finally { // Delete adaptation vectors
if (va != null) UKV.remove(va._key);
if (vp != null) UKV.remove(vp._key);
if (avp != null) UKV.remove(avp._key);
}
}
// Adapt a trained model to a test dataset with different enums
/*@Test*/ public void testModelAdapt() {
File file1 = TestUtil.find_test_file("./smalldata/kaggle/KDDTrain.arff.gz");
Key fkey1 = NFSFileVec.make(file1);
Key dest1 = Key.make("KDDTrain.hex");
File file2 = TestUtil.find_test_file("./smalldata/kaggle/KDDTest.arff.gz");
Key fkey2 = NFSFileVec.make(file2);
Key dest2 = Key.make("KDDTest.hex");
GBM gbm = null;
Frame fr = null;
try {
gbm = new GBM();
gbm.source = ParseDataset2.parse(dest1, new Key[] {fkey1});
UKV.remove(fkey1);
gbm.response = gbm.source.remove(41); // Response is col 41
gbm.ntrees = 2;
gbm.max_depth = 8;
gbm.learn_rate = 0.2f;
gbm.min_rows = 10;
gbm.nbins = 50;
gbm.invoke();
// The test data set has a few more enums than the train
Frame ftest = ParseDataset2.parse(dest2, new Key[] {fkey2});
Frame preds = gbm.score(ftest);
} finally {
UKV.remove(dest1); // 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();
}
}
}
// 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);
}
@Test
public void testFullVectAssignment() {
Key k = loadAndParseKey("cars.hex", "smalldata/cars.csv");
Key k2 = executeExpression("cars.hex");
testDataFrameStructure(k2, 406, 8);
UKV.remove(k2);
k2 = executeExpression("a5 = cars.hex[2]");
testVectorExpression("a5", 8, 8, 8, 4, 6, 6);
UKV.remove(k2);
UKV.remove(k);
UKV.remove(Key.make("a5"));
}
@Test
public void testColumnSelectors() {
Key k = loadAndParseKey("cars.hex", "smalldata/cars.csv");
Key k2 = executeExpression("cars.hex[2]");
testDataFrameStructure(k2, 406, 1);
testKeyValues(k2, 8, 8, 8, 4, 6, 6);
UKV.remove(k2);
k2 = executeExpression("cars.hex$year");
testDataFrameStructure(k2, 406, 1);
testKeyValues(k2, 73, 70, 72, 76, 78, 81);
UKV.remove(k2);
UKV.remove(k);
}
/*@org.junit.Test*/ public void covtype() {
// Key okey = loadAndParseFile("covtype.hex", "smalldata/covtype/covtype.20k.data");
// Key okey = loadAndParseFile("covtype.hex", "../datasets/UCI/UCI-large/covtype/covtype.data");
// Key okey = loadAndParseFile("covtype.hex", "/home/0xdiag/datasets/standard/covtype.data");
Key okey = loadAndParseFile("mnist.hex", "smalldata/mnist/mnist8m.10k.csv.gz");
// Key okey = loadAndParseFile("mnist.hex", "/home/0xdiag/datasets/mnist/mnist8m.csv");
ValueArray val = UKV.get(okey);
// setup default values for DRF
int ntrees = 8;
int depth = 999;
int gini = StatType.ENTROPY.ordinal();
int seed = 42;
StatType statType = StatType.values()[gini];
final int cols[] = new int[val.numCols()];
for (int i = 1; i < cols.length; i++) cols[i] = i - 1;
cols[cols.length - 1] = 0; // Class is in column 0 for mnist
// 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", 10, model.classes());
assertEquals("Number of trees", ntrees, model.size());
model.deleteKeys();
UKV.remove(modelKey);
UKV.remove(okey);
}
// Write-lock & delete 'k'. Will fail if 'k' is locked by anybody other than 'job_key'
public static void delete(Key k, Key job_key) {
if (k == null) return;
Value val = DKV.get(k);
if (val == null) return; // Or just nothing there to delete
if (!val.isLockable()) UKV.remove(k); // Simple things being deleted
else ((Lockable) val.get()).delete(job_key, 0.0f); // Lockable being deleted
}
@Test
public void testDifferentSizeOps() {
Key cars = loadAndParseKey("cars.hex", "smalldata/cars.csv");
Key poker = loadAndParseKey("p.hex", "smalldata/poker/poker-hand-testing.data");
testVectorExpression("cars.hex$year + p.hex[1]", 74, 82, 81, 84, 86, 81);
testVectorExpression("cars.hex$year - p.hex[1]", 72, 58, 63, 62, 64, 71);
testVectorExpression("cars.hex$year * p.hex[1]", 73, 840, 648, 803, 825, 380);
// testVectorExpression("cars.hex$year / p.hex[1]", 73, 70/12, 8, 76/11, 78/11, 15.2); // hard
// to get the numbers right + not needed no new coverage
testVectorExpression("p.hex[1] + cars.hex$year", 74, 82, 81, 84, 86, 81);
testVectorExpression("p.hex[1] - cars.hex$year", -72, -58, -63, -62, -64, -71);
testVectorExpression("p.hex[1] * cars.hex$year", 73, 840, 648, 803, 825, 380);
// testVectorExpression("p.hex[1] / cars.hex$year", 1/73, 12/70, 0.125, 11/76, 11/78, 5/81);
UKV.remove(poker);
UKV.remove(cars);
}
public static ValueArray loadAndParseKey(Key okey, String path) {
FileIntegrityChecker c = FileIntegrityChecker.check(new File(path),false);
Key k = c.syncDirectory(null,null,null,null);
ParseDataset.forkParseDataset(okey, new Key[] { k }, null).get();
UKV.remove(k);
ValueArray res = DKV.get(okey).get();
return res;
}
/** Actually remove/delete all Vecs from memory, not just from the Frame. */
public void remove(Futures fs) {
if (vecs().length > 0) {
for (Vec v : _vecs) UKV.remove(v._key, fs);
}
_names = new String[0];
_vecs = new Vec[0];
_keys = new Key[0];
}
protected void testScalarExpression(String expr, double result) {
Key key = executeExpression(expr);
ValueArray va = ValueArray.value(key);
assertEquals(va.numRows(), 1);
assertEquals(va.numCols(), 1);
assertEquals(result, va.datad(0, 0), 0.0);
UKV.remove(key);
}
@Test
public void testLargeDataOps() {
Key poker = loadAndParseKey("p.hex", "smalldata/poker/poker-hand-testing.data");
testVectorExpression("p.hex[1] + p.hex[2]", 2, 15, 13, 15, 12, 7);
testVectorExpression("p.hex[1] - p.hex[2]", 0, 9, 5, 7, 10, 3);
testVectorExpression("p.hex[1] * p.hex[2]", 1, 36, 36, 44, 11, 10);
testVectorExpression("p.hex[1] / p.hex[2]", 1.0, 4.0, 2.25, 2.75, 11.0, 2.5);
UKV.remove(poker);
}
@Test
public void testVectorOperators() {
Key k = loadAndParseKey("cars.hex", "smalldata/cars.csv");
testVectorExpression("cars.hex[2] + cars.hex$year", 81, 78, 80, 80, 84, 87);
testVectorExpression("cars.hex[2] - cars.hex$year", -65, -62, -64, -72, -72, -75);
testVectorExpression("cars.hex[2] * cars.hex$year", 584, 560, 576, 304, 468, 486);
testVectorExpression("cars.hex$year / cars.hex[2]", 9.125, 8.75, 9.0, 19.0, 13.0, 13.5);
UKV.remove(k);
}
/** 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];
}
// ==========================================================================
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();
}
}
}
public void onException(Throwable ex) {
UKV.remove(dest());
Value v = DKV.get(progressKey());
if( v != null ) {
ChunkProgress p = v.get();
p = p.error(ex.getMessage());
DKV.put(progressKey(), p);
}
cancel(ex);
}
protected String[] getVectorDomain(final Vec v) {
assert v==null || v.isInt() || v.isEnum() : "Cannot get vector domain!";
if (v==null) return null;
String[] r = null;
if (v.isEnum()) {
r = v.domain();
} else {
Vec tmp = v.toEnum();
r = tmp.domain();
UKV.remove(tmp._key);
}
return r;
}
@Test
public void testBigLargeExpression() {
Key poker = loadAndParseKey("p.hex", "smalldata/poker/poker-hand-testing.data");
testVectorExpression(
"p.hex[1] / p.hex[2] + p.hex[3] * p.hex[1] - p.hex[5] + (2* p.hex[1] - (p.hex[2]+3))",
8,
35,
63.25,
85.75,
116.0,
43.5);
UKV.remove(poker);
}
@Override
protected void execImpl() {
Vec va = null, vp;
try {
va = vactual.toEnum(); // always returns TransfVec
vp = vpredict;
// The vectors are from different groups => align them, but properly delete it after
// computation
if (!va.group().equals(vp.group())) {
vp = va.align(vp);
}
// compute thresholds, if not user-given
if (thresholds != null) {
sort(thresholds);
if (Utils.minValue(thresholds) < 0)
throw new IllegalArgumentException("Minimum threshold cannot be negative.");
if (Utils.maxValue(thresholds) > 1)
throw new IllegalArgumentException("Maximum threshold cannot be greater than 1.");
} else {
HashSet hs = new HashSet();
final int bins = (int) Math.min(vpredict.length(), 200l);
final long stride = Math.max(vpredict.length() / bins, 1);
for (int i = 0; i < bins; ++i)
hs.add(
new Float(
vpredict.at(i * stride))); // data-driven thresholds TODO: use percentiles (from
// Summary2?)
for (int i = 0; i < 51; ++i)
hs.add(new Float(i / 50.)); // always add 0.02-spaced thresholds from 0 to 1
// created sorted vector of unique thresholds
thresholds = new float[hs.size()];
int i = 0;
for (Object h : hs) {
thresholds[i++] = (Float) h;
}
sort(thresholds);
}
// compute CMs
aucdata =
new AUCData()
.compute(
new AUCTask(thresholds, va.mean()).doAll(va, vp).getCMs(),
thresholds,
va._domain,
threshold_criterion);
} finally { // Delete adaptation vectors
if (va != null) UKV.remove(va._key);
}
}
// ==========================================================================
/*@Test*/ public void testBasicCRUD() {
// Parse a file with many broken enum/string columns
Key k = Key.make("zip.hex");
try {
Frame fr = TestUtil.parseFrame(k, "smalldata/zip_code/zip_code_database.csv.gz");
System.out.println(fr);
StringBuilder sb = new StringBuilder();
String[] fs = fr.toStringHdr(sb);
int lim = Math.min(40, (int) fr.numRows());
for (int i = 0; i < lim; i++) fr.toString(sb, fs, i);
System.out.println(sb.toString());
} finally {
UKV.remove(k);
}
}
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;
}
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());
}
@Override
public void compute() {
String path = null; // getPathFromValue(val);
ValueArray ary = ValueArray.value(_arykey);
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);
}
@Override public void remove() {
super.remove();
UKV.remove(_progress);
}
/** Delete all vectors in given trash. */
private void cleanupTrash(HashSet<Key> trash, Futures fs) {
for (Key k : trash) UKV.remove(k, fs);
}
public Frame deepSlice(Object orows, Object ocols) {
// ocols is either a long[] or a Frame-of-1-Vec
long[] cols;
if (ocols == null) {
cols = (long[]) ocols;
assert cols == null;
} else {
if (ocols instanceof long[]) {
cols = (long[]) ocols;
} else if (ocols instanceof Frame) {
Frame fr = (Frame) ocols;
if (fr.numCols() != 1) {
throw new IllegalArgumentException(
"Columns Frame must have only one column (actually has "
+ fr.numCols()
+ " columns)");
}
long n = fr.anyVec().length();
if (n > MAX_EQ2_COLS) {
throw new IllegalArgumentException(
"Too many requested columns (requested " + n + ", max " + MAX_EQ2_COLS + ")");
}
cols = new long[(int) n];
Vec v = fr._vecs[0];
for (long i = 0; i < v.length(); i++) {
cols[(int) i] = v.at8(i);
}
} else {
throw new IllegalArgumentException(
"Columns is specified by an unsupported data type ("
+ ocols.getClass().getName()
+ ")");
}
}
// Since cols is probably short convert to a positive list.
int c2[] = null;
if (cols == null) {
c2 = new int[numCols()];
for (int i = 0; i < c2.length; i++) c2[i] = i;
} else if (cols.length == 0) {
c2 = new int[0];
} else if (cols[0] > 0) {
c2 = new int[cols.length];
for (int i = 0; i < cols.length; i++)
c2[i] = (int) cols[i] - 1; // Convert 1-based cols to zero-based
} else {
c2 = new int[numCols() - cols.length];
int j = 0;
for (int i = 0; i < numCols(); i++) {
if (j >= cols.length || i < (-cols[j] - 1)) c2[i - j] = i;
else j++;
}
}
for (int i = 0; i < c2.length; i++)
if (c2[i] >= numCols())
throw new IllegalArgumentException(
"Trying to select column " + c2[i] + " but only " + numCols() + " present.");
if (c2.length == 0)
throw new IllegalArgumentException(
"No columns selected (did you try to select column 0 instead of column 1?)");
// Do Da Slice
// orows is either a long[] or a Vec
if (orows == null)
return new DeepSlice((long[]) orows, c2)
.doAll(c2.length, this)
.outputFrame(names(c2), domains(c2));
else if (orows instanceof long[]) {
final long CHK_ROWS = 1000000;
long[] rows = (long[]) orows;
if (rows.length == 0)
return new DeepSlice(rows, c2).doAll(c2.length, this).outputFrame(names(c2), domains(c2));
if (rows[0] < 0)
return new DeepSlice(rows, c2).doAll(c2.length, this).outputFrame(names(c2), domains(c2));
// Vec'ize the index array
AppendableVec av = new AppendableVec("rownames");
int r = 0;
int c = 0;
while (r < rows.length) {
NewChunk nc = new NewChunk(av, c);
long end = Math.min(r + CHK_ROWS, rows.length);
for (; r < end; r++) {
nc.addNum(rows[r]);
}
nc.close(c++, null);
}
Vec c0 = av.close(null); // c0 is the row index vec
Frame fr2 =
new Slice(c2, this)
.doAll(c2.length, new Frame(new String[] {"rownames"}, new Vec[] {c0}))
.outputFrame(names(c2), domains(c2));
UKV.remove(c0._key); // Remove hidden vector
return fr2;
}
Frame frows = (Frame) orows;
Vec vrows = frows.anyVec();
// It's a compatible Vec; use it as boolean selector.
// Build column names for the result.
Vec[] vecs = new Vec[c2.length + 1];
String[] names = new String[c2.length + 1];
for (int i = 0; i < c2.length; ++i) {
vecs[i] = _vecs[c2[i]];
names[i] = _names[c2[i]];
}
vecs[c2.length] = vrows;
names[c2.length] = "predicate";
return new DeepSelect()
.doAll(c2.length, new Frame(names, vecs))
.outputFrame(names(c2), domains(c2));
}
@Test
public void testBasicExpr1() {
Key dest = Key.make("h.hex");
try {
File file = TestUtil.find_test_file("smalldata/tnc3_10.csv");
// File file = TestUtil.find_test_file("smalldata/iris/iris_wheader.csv");
// File file = TestUtil.find_test_file("smalldata/cars.csv");
Key fkey = NFSFileVec.make(file);
ParseDataset2.parse(dest, new Key[] {fkey});
UKV.remove(fkey);
checkStr("1.23"); // 1.23
checkStr(" 1.23 + 2.34"); // 3.57
checkStr(" 1.23 + 2.34 * 3"); // 10.71, L2R eval order
checkStr(" 1.23 2.34"); // Syntax error
checkStr("1.23 < 2.34"); // 1
checkStr("1.23 <=2.34"); // 1
checkStr("1.23 > 2.34"); // 0
checkStr("1.23 >=2.34"); // 0
checkStr("1.23 ==2.34"); // 0
checkStr("1.23 !=2.34"); // 1
checkStr("h.hex"); // Simple ref
checkStr("+(1.23,2.34)"); // prefix 3.57
checkStr("+(1.23)"); // Syntax error, not enuf args
checkStr("+(1.23,2,3)"); // Syntax error, too many args
checkStr("h.hex[2,3]"); // Scalar selection
checkStr("h.hex[2,+]"); // Function not allowed
checkStr("h.hex[2+4,-4]"); // Select row 6, all-cols but 4
checkStr("h.hex[1,-1]; h.hex[2,-2]; h.hex[3,-3]"); // Partial results are freed
checkStr("h.hex[2+3,h.hex]"); // Error: col selector has too many columns
checkStr("h.hex[2,]"); // Row 2 all cols
checkStr("h.hex[,3]"); // Col 3 all rows
checkStr("h.hex+1"); // Broadcast scalar over ary
checkStr("h.hex-h.hex");
checkStr("1.23+(h.hex-h.hex)");
checkStr("(1.23+h.hex)-h.hex");
checkStr("min(h.hex,1+2)");
checkStr("max(h.hex,1+2)");
checkStr("is.na(h.hex)");
checkStr("nrow(h.hex)*3");
checkStr("h.hex[nrow(h.hex)-1,ncol(h.hex)-1]");
checkStr("1=2");
checkStr("x");
checkStr("x+2");
checkStr("2+x");
checkStr("x=1");
checkStr("x<-1"); // Alternative R assignment syntax
checkStr("x=1;x=h.hex"); // Allowed to change types via shadowing at REPL level
checkStr("a=h.hex"); // Top-level assignment back to H2O.STORE
checkStr("x<-+");
checkStr("(h.hex+1)<-2");
checkStr("h.hex[nrow(h.hex=1),]");
checkStr("h.hex[2,3]<-4;");
checkStr("c(1,3,5)");
checkStr("function(=){x+1}(2)");
checkStr("function(x,=){x+1}(2)");
checkStr("function(x,<-){x+1}(2)");
checkStr("function(x,x){x+1}(2)");
checkStr("function(x,y,z){x[]}(h.hex,1,2)");
checkStr("function(x){x[]}(2)");
checkStr("function(x){x+1}(2)");
checkStr("function(x){y=x+y}(2)");
checkStr("function(x){}(2)");
checkStr("function(x){y=x*2; y+1}(2)");
checkStr("function(x){y=1+2}(2)");
checkStr("function(x){y=1+2;y=c(1,2)}"); // Not allowed to change types in inner scopes
checkStr("sum(1,2,3)");
checkStr("sum(c(1,3,5))");
checkStr("sum(4,c(1,3,5),2,6)");
checkStr("sum(1,h.hex,3)");
checkStr("h.hex[,c(1,3,5)]");
checkStr("h.hex[c(1,3,5),]");
checkStr("a=c(11,22,33,44,55,66); a[c(2,6,1),]");
checkStr("function(a){a[];a=1}");
checkStr("a=1;a=2;function(x){x=a;a=3}");
checkStr("a=h.hex;function(x){x=a;a=3;nrow(x)*a}(a)");
checkStr("a=h.hex;a[,1]=(a[,1]==8)");
// Higher-order function typing: fun is typed in the body of function(x)
checkStr("function(funy){function(x){funy(x)*funy(x)}}(sgn)(-2)");
// Filter/selection
checkStr("h.hex[h.hex[,2]>4,]");
checkStr("a=c(1,2,3);a[a[,1]>10,1]");
checkStr("apply(h.hex,2,sum)");
checkStr("y=5;apply(h.hex,2,function(x){x[]+y})");
checkStr("apply(h.hex,2,function(x){x=1;h.hex})");
checkStr("apply(h.hex,2,function(x){h.hex})");
checkStr("mean=function(x){apply(x,2,sum)/nrow(x)};mean(h.hex)");
// Conditional selection;
checkStr("ifelse(0,1,2)");
checkStr("ifelse(0,h.hex+1,h.hex+2)");
checkStr("ifelse(h.hex>3,99,h.hex)"); // Broadcast selection
checkStr("ifelse(0,+,*)(1,2)"); // Select functions
checkStr("(0 ? + : *)(1,2)"); // Trinary select
checkStr("(1? h.hex : (h.hex+1))[1,2]"); // True (vs false) test
// Impute the mean
checkStr(
"apply(h.hex,2,function(x){total=sum(ifelse(is.na(x),0,x)); rcnt=nrow(x)-sum(is.na(x)); mean=total / rcnt; ifelse(is.na(x),mean,x)})");
checkStr("factor(h.hex[,5])");
// Slice assignment & map
checkStr("h.hex[,2]");
checkStr("h.hex[,2]+1");
checkStr("h.hex[,3]=3.3;h.hex"); // Replace a col with a constant
checkStr("h.hex[,3]=h.hex[,2]+1"); // Replace a col
checkStr("h.hex[,ncol(h.hex)+1]=4"); // Extend a col
checkStr("a=ncol(h.hex);h.hex[,c(a+1,a+2)]=5"); // Extend two cols
checkStr("table(h.hex)");
checkStr("table(h.hex[,3])");
checkStr("h.hex[,4] != 29 || h.hex[,2] < 305 && h.hex[,2] < 81");
checkStr("a=cbind(c(1,2,3), c(4,5,6))");
// checkStr("h.hex[h.hex[,2]>4,]=-99");
// checkStr("h.hex[2,]=h.hex[7,]");
// checkStr("h.hex[c(1,3,5),1] = h.hex[c(2,4,6),2]");
// checkStr("h.hex[c(1,3,5),1] = h.hex[c(2,4),2]");
// checkStr("map()");
// checkStr("map(1)");
// checkStr("map(+,h.hex,1)");
// checkStr("map(+,1,2)");
// checkStr("map(function(x){x[];1},h.hex)");
// checkStr("map(function(a,b,d){a+b+d},h.hex,h.hex,1)");
// checkStr("map(function(a,b){a+ncol(b)},h.hex,h.hex)");
checkStr("a=0;x=0"); // Delete keys from global scope
} finally {
UKV.remove(dest); // Remove original hex frame key
}
}
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();
}
}
// ---
// Test some basic expressions on "cars.csv"
@Test
public void testBasicCrud() {
Key k = loadAndParseKey("cars.hex", "smalldata/cars.csv");
testVectorExpression("cars.hex[1] + cars.hex$cylinders", 21, 23, 25, 24, 23, 36.7);
UKV.remove(k);
}
public void testVectorExpression(
String expr, double n1, double n2, double n3, double nx3, double nx2, double nx1) {
Key key = executeExpression(expr);
testKeyValues(key, n1, n2, n3, nx3, nx2, nx1);
UKV.remove(key);
}
