water.fvec.Frame.replace()方法的使用及代码示例

x33g5p2x  于2022-01-19 转载在 其他  
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本文整理了Java中water.fvec.Frame.replace()方法的一些代码示例，展示了Frame.replace()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Frame.replace()方法的具体详情如下：
包路径：water.fvec.Frame
类名称：Frame
方法名：replace

Frame.replace介绍

暂无

代码示例

代码示例来源：origin: h2oai/h2o-2

public Vec factor(int col) {
 Vec nv = vecs()[col].toEnum();
 return replace(col, nv);
}

代码示例来源：origin: h2oai/h2o-3

public Vec setWeights(String name, Vec vec) {
 if(_weights)
  return _adaptedFrame.replace(weightChunkId(),vec);
 _adaptedFrame.insertVec(weightChunkId(),name,vec);
 _weights = true;
 return null;
}

代码示例来源：origin: h2oai/h2o-2

@Override
 protected Response serve() {
  try {
   if (column_index <= 0) throw new IllegalArgumentException("Column index is 1 based. Please supply a valid column index in the range [1,"+ src_key.numCols()+"]");
   Log.info("Factorizing column " + column_index);
   Vec nv =  src_key.vecs()[column_index - 1].toEnum();
   src_key.replace(column_index - 1, nv);
  } catch( Throwable e ) {
   return Response.error(e);
  }
  return Inspect2.redirect(this, src_key._key.toString());
 }
}

代码示例来源：origin: h2oai/h2o-3

private void convert2Enum(Frame f, int[] cols) {
 for (int col : cols) {
  f.replace(col, f.vec(col).toCategoricalVec()).remove();
 }
 DKV.put(f);
}

代码示例来源：origin: h2oai/h2o-2

@Override protected void execImpl() {
 Vec va = null;
 try {
  va = vactual.toEnum(); // always returns TransfVec
  actual_domain = va._domain;
  if (max_k > predict.numCols()-1) {
   Log.warn("Reducing Hitratio Top-K value to maximum value allowed: " + String.format("%,d", predict.numCols() - 1));
   max_k = predict.numCols() - 1;
  }
  final Frame actual_predict = new Frame(predict.names().clone(), predict.vecs().clone());
  actual_predict.replace(0, va); // place actual labels in first column
  hit_ratios = new HitRatioTask(max_k, seed).doAll(actual_predict).hit_ratios();
 } finally {       // Delete adaptation vectors
  if (va!=null) UKV.remove(va._key);
 }
}

代码示例来源：origin: h2oai/h2o-3

@Override
public String[] adaptTestForTrain(Frame test, boolean expensive, boolean computeMetrics) {
 boolean createStrataVec = _parms.isStratified() && (test.vec(_parms._strata_column) == null);
 if (createStrataVec) {
  Vec strataVec = test.anyVec().makeCon(Double.NaN);
  _toDelete.put(strataVec._key, "adapted missing strata vector");
  test.add(_parms._strata_column, strataVec);
 }
 String[] msgs = super.adaptTestForTrain(test, expensive, computeMetrics);
 if (createStrataVec) {
  Vec strataVec = CoxPH.StrataTask.makeStrataVec(test, _parms._stratify_by, _output._strataMap);
  _toDelete.put(strataVec._key, "adapted missing strata vector");
  test.replace(test.find(_parms._strata_column), strataVec);
  if (_output._strataOnlyCols != null)
   test.remove(_output._strataOnlyCols);
 }
 return msgs;
}

代码示例来源：origin: h2oai/h2o-3

int prep(Frame fr) {
  fr.remove("ID").remove(); // Remove not-predictive ID
  int ci = fr.find("RACE"); // Change RACE to categorical
  Scope.track(fr.replace(ci,fr.vecs()[ci].toCategoricalVec()));
  return fr.find("CAPSULE"); // Prostate: predict on CAPSULE
 }
}, false, DistributionFamily.bernoulli);

代码示例来源：origin: h2oai/h2o-2

@Override
 protected Response serve() {
  try {
   if (column_index <= 0 || column_index > src_key.numCols()) throw new IllegalArgumentException("Column index is 1 based. Please supply a valid column index in the range [1,"+ src_key.numCols()+"]");
   Log.info("Integerizing column " + column_index);
   Vec nv;
   if ((nv= src_key.vecs()[column_index-1].masterVec()) == null) {
    assert src_key.vecs()[column_index-1].isInt();
    nv = src_key.vecs()[column_index-1];
    nv._domain = null;
   } else {
    assert src_key.vecs()[column_index - 1].masterVec().isInt();
    nv = src_key.vecs()[column_index - 1].masterVec();
   }
   src_key.replace(column_index - 1, nv);

  } catch( Throwable e ) {
   return Response.error(e);
  }
  return Inspect2.redirect(this, src_key._key.toString());
 }
}

代码示例来源：origin: h2oai/h2o-3

private GBMModel trainGbm(final int ntrees) {
 Frame f = Scope.track(parse_test_file("smalldata/logreg/prostate.csv"));
 final String response = "CAPSULE";
 f.replace(f.find(response), f.vec(response).toCategoricalVec()).remove();
 DKV.put(f._key, f);
 GBMModel.GBMParameters gbmParams = new GBMModel.GBMParameters();
 gbmParams._seed = 123;
 gbmParams._train = f._key;
 gbmParams._ignored_columns = new String[]{"ID"};
 gbmParams._response_column = response;
 gbmParams._ntrees = ntrees;
 gbmParams._score_each_iteration = true;
 return(GBMModel) Scope.track_generic(new GBM(gbmParams).trainModel().get());
}

代码示例来源：origin: h2oai/h2o-3

@BeforeClass
public static void setup() {
 stall_till_cloudsize(1);
 _covtype = parse_test_file("smalldata/covtype/covtype.20k.data");
 _covtype.replace(_covtype.numCols()-1,_covtype.lastVec().toCategoricalVec()).remove();
 Key[] keys = new Key[]{Key.make("train"),Key.make("test")};
 H2O.submitTask(new FrameSplitter(_covtype, new double[]{.8},keys,null)).join();
 _train = DKV.getGet(keys[0]);
 _test = DKV.getGet(keys[1]);
}

代码示例来源：origin: h2oai/h2o-3

private GBMModel prepareGBMModel(String dataset, String[] ignoredColumns, String response, boolean classification, int ntrees) {
 Frame f = parse_test_file(dataset);
 try {
  if (classification && !f.vec(response).isCategorical()) {
   f.replace(f.find(response), f.vec(response).toCategoricalVec()).remove();
   DKV.put(f._key, f);
  }
  GBMModel.GBMParameters gbmParams = new GBMModel.GBMParameters();
  gbmParams._train = f._key;
  gbmParams._ignored_columns = ignoredColumns;
  gbmParams._response_column = response;
  gbmParams._ntrees = ntrees;
  gbmParams._score_each_iteration = true;
  return new GBM(gbmParams).trainModel().get();
 } finally {
  if (f!=null) f.delete();
 }
}

代码示例来源：origin: h2oai/h2o-3

private DRFModel prepareDRFModel(String dataset, String[] ignoredColumns, String response, boolean classification, int ntrees) {
 Frame f = parse_test_file(dataset);
 try {
  if (classification && !f.vec(response).isCategorical()) {
   f.replace(f.find(response), f.vec(response).toCategoricalVec()).remove();
   DKV.put(f._key, f);
  }
  DRFModel.DRFParameters drfParams = new DRFModel.DRFParameters();
  drfParams._train = f._key;
  drfParams._ignored_columns = ignoredColumns;
  drfParams._response_column = response;
  drfParams._ntrees = ntrees;
  drfParams._score_each_iteration = true;
  return new DRF(drfParams).trainModel().get();
 } finally {
  if (f!=null) f.delete();
 }
}

代码示例来源：origin: h2oai/h2o-3

@Test
public void testMonotoneConstraintsInverse() {
 Scope.enter();
 try {
  final String response = "power (hp)";
  Frame f = parse_test_file("smalldata/junit/cars.csv");
  f.replace(f.find(response), f.vecs()[f.find("cylinders")].toNumericVec()).remove();
  DKV.put(Scope.track(f));
  GBMModel.GBMParameters parms = new GBMModel.GBMParameters();
  parms._response_column = response;
  parms._train = f._key;
  parms._ignored_columns = new String[]{"name"};
  parms._seed = 42;
  GBMModel.GBMParameters noConstrParams = (GBMModel.GBMParameters) parms.clone();
  GBMModel noConstrModel = new GBM(noConstrParams).trainModel().get();
  Scope.track_generic(noConstrModel);
  assertTrue(noConstrModel._output._varimp.toMap().get("cylinders") > 0);
  GBMModel.GBMParameters constrParams = (GBMModel.GBMParameters) parms.clone();
  constrParams._monotone_constraints = new KeyValue[] {new KeyValue("cylinders", -1)};
  GBMModel constrModel = new GBM(constrParams).trainModel().get();
  Scope.track_generic(constrModel);
  // we essentially eliminated the effect of the feature by setting an inverted constraint
  assertEquals(constrModel._output._varimp.toMap().get("cylinders"), 0, 0);
 } finally {
  Scope.exit();
 }
}

代码示例来源：origin: h2oai/h2o-3

@Test public void testCovtype() throws InterruptedException, ExecutionException {
  NaiveBayesModel model = null;
  Frame train = null, score = null;

  try {
   Scope.enter();
   train = parse_test_file(Key.make("covtype.hex"), "smalldata/covtype/covtype.20k.data");
   Scope.track(train.replace(54, train.vecs()[54].toCategoricalVec()));   // Change response to categorical
   DKV.put(train);

   NaiveBayesParameters parms = new NaiveBayesParameters();
   parms._train = train._key;
   parms._laplace = 0;
   parms._response_column = train._names[54];
   parms._compute_metrics = false;

   model = new NaiveBayes(parms).trainModel().get();

   // Done building model; produce a score column with class assignments
   score = model.score(train);
   Assert.assertTrue(model.testJavaScoring(train,score,1e-6));
  } finally {
   if (train != null) train.delete();
   if (score != null) score.delete();
   if (model != null) model.delete();
   Scope.exit();
  }
 }
}

代码示例来源：origin: h2oai/h2o-3

@Test @Ignore public void testConstantColumns(){
 GLMModel model1 = null, model2 = null, model3 = null, model4 = null;
 Frame fr = parse_test_file(Key.make("Airlines"), "smalldata/airlines/allyears2k_headers.zip");
 Vec y = fr.vec("IsDepDelayed").makeCopy(null);
 fr.replace(fr.find("IsDepDelayed"),y).remove();
  Vec weights = fr.anyVec().makeZero();
  new MRTask(){
   @Override public void map(Chunk c){
    int i = 0;
    for(i = 0; i < c._len; ++i){
     long rid = c.start()+i;
     if(rid >= 1999) break;
     c.set(i,1);
    }
   }
  }.doAll(weights);
  fr.add("weights", weights);
  DKV.put(fr);
  GLMParameters parms = new GLMParameters(Family.gaussian);
  parms._train = fr._key;
  parms._weights_column = "weights";
  parms._lambda_search = true;
  parms._alpha = new double[]{0};
  parms._response_column = "IsDepDelayed";
  parms._ignored_columns = new String[]{"DepTime", "ArrTime", "Cancelled", "CancellationCode", "DepDelay", "Diverted", "CarrierDelay", "WeatherDelay", "NASDelay", "SecurityDelay", "LateAircraftDelay", "IsArrDelayed"};
  parms._standardize = true;
  model1 = new GLM(parms).trainModel().get();
  model1.delete();
 fr.delete();
}

代码示例来源：origin: h2oai/h2o-3

@Test
public void imputeWithMeanTest() {
 fr = new TestFrameBuilder()
     .withName("testFrame")
     .withColNames("ColA")
     .withVecTypes(Vec.T_STR)
     .withDataForCol(0, ar("1", "2", null))
     .build();
 String[] teColumns = {""};
 TargetEncoder tec = new TargetEncoder(teColumns);
 // We have to do this trick because we cant initialize array with `null` values.
 Vec strVec = fr.vec("ColA");
 Vec numericVec = strVec.toNumericVec();
 fr.replace(0, numericVec);
 Frame withImputed = tec.imputeWithMean(fr, 0, 1.5);
 Vec expected = dvec(1, 2, 1.5);
 Vec resultVec = withImputed.vec(0);
 assertVecEquals(expected, resultVec, 1e-5);
 expected.remove();
 strVec.remove();
 resultVec.remove();
 withImputed.delete();
 numericVec.remove();
}

代码示例来源：origin: h2oai/h2o-3

Scope.track(fr2.replace(ci, fr2.vecs()[ci].toCategoricalVec()));   // Convert response 'Angaus' to categorical
DKV.put(fr2);
parms._train = fr2._key;

代码示例来源：origin: h2oai/h2o-3

@Test public void testProstate() throws InterruptedException, ExecutionException {
 NaiveBayesModel model = null;
 Frame train = null, score = 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()));
  train.remove("ID").remove();
  DKV.put(train._key, train);
  NaiveBayesParameters parms = new NaiveBayesParameters();
  parms._train = train._key;
  parms._laplace = 0;
  parms._response_column = train._names[0];
  parms._compute_metrics = true;
  model = new NaiveBayes(parms).trainModel().get();
   
  // Done building model; produce a score column with class assignments
  score = model.score(train);
  Assert.assertTrue(model.testJavaScoring(train,score,1e-6));
 } finally {
  if (train != null) train.delete();
  if (score != null) score.delete();
  if (model != null) model.delete();
  Scope.exit();
 }
}

代码示例来源：origin: h2oai/h2o-3

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()));
train.remove("ID").remove();
DKV.put(train._key, train);

代码示例来源：origin: h2oai/h2o-3

@Test public void testGBMPredict() {
 GBMModel gbm = null;
 GBMModel.GBMParameters parms = new GBMModel.GBMParameters();
 Frame pred=null, res=null;
 Scope.enter();
 try {
  Frame train = parse_test_file("smalldata/gbm_test/ecology_model.csv");
  train.remove("Site").remove();     // Remove unique ID
  int ci = train.find("Angaus");
  Scope.track(train.replace(ci, train.vecs()[ci].toCategoricalVec()));   // Convert response 'Angaus' to categorical
  DKV.put(train);                    // Update frame after hacking it
  parms._train = train._key;
  parms._response_column = "Angaus"; // Train on the outcome
  parms._distribution = DistributionFamily.multinomial;
  gbm = new GBM(parms).trainModel().get();
  pred = parse_test_file("smalldata/gbm_test/ecology_eval.csv" );
  pred.remove("Angaus").remove();    // No response column during scoring
  res = gbm.score(pred);
  // Build a POJO, validate same results
  Assert.assertTrue(gbm.testJavaScoring(pred, res, 1e-15));
 } finally {
  parms._train.remove();
  if( gbm  != null ) gbm .delete();
  if( pred != null ) pred.remove();
  if( res  != null ) res .remove();
  Scope.exit();
 }
}

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