How to use

Rapids

in

water.rapids

private Frame execRapidsAndGetFrame(String astTree) {
  Val val = Rapids.exec(astTree);
  return register(val.getFrame());
}

public long rowsWithNa() {
 if( _rowsWithNa!=-1 ) return _rowsWithNa;
 String x = String.format("(na.omit %s)", _fr._key);
 Val res = Rapids.exec(x);
 Frame f = res.getFrame();
 long cnt = _fr.numRows()  -  f.numRows();
 f.delete();
 return (_rowsWithNa=cnt);
}

_train = Rapids.exec(String.format("(na.omit %s)", tranRebalanced._key)).getFrame(); // remove NA rows
DKV.remove(tranRebalanced._key);
checkMemoryFootPrint();

@Test
public void calculateSingleNumberResultTest() {
  fr = new TestFrameBuilder()
      .withName("testFrame")
      .withColNames("ColA")
      .withVecTypes(Vec.T_NUM)
      .withDataForCol(0, ard(1, 2, 3))
      .build();
  String tree = "(sum (cols testFrame [0.0] ))";
  Val val = Rapids.exec(tree);
  assertEquals(val.getNum(), 6.0, 1e-5);
}

@Test
public void changeKeyFrameTest() {
 Frame res = null;
 try {
  fr = new TestFrameBuilder()
      .withName("testFrame")
      .withColNames("ColA")
      .withVecTypes(Vec.T_NUM)
      .withDataForCol(0, ard(1, 2))
      .build();
  String tree = "( append testFrame 42 'appended' )";
  Val val = Rapids.exec(tree);
  res = val.getFrame();
  res._key = fr._key;
  DKV.put(fr._key, res);
 } finally {
  res.delete();
 }
}

 model = job.trainModel().get();
 String s = "(tmp= py_4 (rows (cols_py " + model._output._representation_key + " [0 1]) (tmp= py_3 (| (| (| (| (| (== (tmp= py_2 " + acs_zcta_fr._key + ") \"10065\") (== py_2 \"11219\")) (== py_2 \"66753\")) (== py_2 \"84104\")) (== py_2 \"94086\")) (== py_2 \"95014\")))))";
 Val val = Rapids.exec(s);
} catch (Throwable t) {
 t.printStackTrace();

Frame withAppendedFrame = Rapids.exec(tree).getFrame();
withAppendedFrame._key = Key.make();
DKV.put(withAppendedFrame);

_train = Rapids.exec(String.format("(na.omit %s)", tranRebalanced._key)).getFrame(); // remove NA rows
DKV.remove(tranRebalanced._key);

Frame tmp = Rapids.exec(ast).getFrame();
pred2labels = tmp.vecs()[0];
cm = ConfusionMatrixTest.buildCM(labels, pred2labels);

private Frame execRapidsAndGetFrame(String astTree) {
  Val val = Rapids.exec(astTree);
  return register(val.getFrame());
}

public long rowsWithNa() {
 if( _rowsWithNa!=-1 ) return _rowsWithNa;
 String x = String.format("(na.omit %s)", _fr._key);
 Val res = Rapids.exec(x);
 Frame f = res.getFrame();
 long cnt = _fr.numRows()  -  f.numRows();
 f.delete();
 return (_rowsWithNa=cnt);
}

_train = Rapids.exec(String.format("(na.omit %s)", tranRebalanced._key)).getFrame(); // remove NA rows
DKV.remove(tranRebalanced._key);
checkMemoryFootPrint();

_train = Rapids.exec(String.format("(na.omit %s)", tranRebalanced._key)).getFrame(); // remove NA rows
DKV.remove(tranRebalanced._key);