water.Job.get()方法的使用及代码示例

x33g5p2x  于2022-01-22 转载在 其他  
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本文整理了Java中water.Job.get()方法的一些代码示例，展示了Job.get()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。Job.get()方法的具体详情如下：
包路径：water.Job
类名称：Job
方法名：get

Job.get介绍

[英]Blocks and get result of this job.

The call blocks on working task which was passed via #start(H2OCountedCompleter) method and returns the result which is fetched from UKV based on job destination key.
[中]块并获取此作业的结果。
调用阻塞通过#start（H2OCountedCompleter）方法传递的工作任务，并返回基于作业目标键从UKV获取的结果。

代码示例

代码示例来源：origin: h2oai/h2o-3

/**
 * Holds until AutoML's job is completed, if a job exists.
 */
public void get() {
 if (job != null) job.get();
}

代码示例来源：origin: h2oai/h2o-3

@Override
public void stop() {
 for (Frame f : tempFrames) f.delete();
 tempFrames = null;
 if (null == jobs) return; // already stopped
 for (Job j : jobs) j.stop();
 for (Job j : jobs) j.get(); // Hold until they all completely stop.
 jobs = null;
 // TODO: add a failsafe, if we haven't marked off as much work as we originally intended?
 // If we don't, we end up with an exceptional completion.
}

代码示例来源：origin: h2oai/h2o-3

private static KMeansModel doSeed( KMeansModel.KMeansParameters parms, long seed ) {
 parms._seed = seed;
 KMeans job = new KMeans(parms);
 KMeansModel kmm = job.trainModel().get();
 checkConsistency(kmm);
 for( int i=0; i<kmm._output._k[kmm._output._k.length-1]; i++ )
  Assert.assertTrue( "Seed: "+seed, kmm._output._size[i] != 0 );
 return kmm;
}

代码示例来源：origin: h2oai/h2o-3

private GLMModel prepareGLMModel(String dataset, String[] ignoredColumns, String response, GLMModel.GLMParameters.Family family) {
 Frame f = parse_test_file(dataset);
 try {
  GLMModel.GLMParameters params = new GLMModel.GLMParameters();
  params._train = f._key;
  params._ignored_columns = ignoredColumns;
  params._response_column = response;
  params._family = family;
  return new GLM(params).trainModel().get();
 } finally {
  if (f!=null) f.delete();
 }
}

代码示例来源：origin: h2oai/h2o-3

private IsolationForestModel prepareIsoForModel(String dataset, String[] ignoredColumns, int ntrees) {
 Frame f = parse_test_file(dataset);
 try {
  IsolationForestModel.IsolationForestParameters ifParams = new IsolationForestModel.IsolationForestParameters();
  ifParams._train = f._key;
  ifParams._ignored_columns = ignoredColumns;
  ifParams._ntrees = ntrees;
  ifParams._score_each_iteration = true;
  return new IsolationForest(ifParams).trainModel().get();
 } finally {
  if (f!=null) f.delete();
 }
}

代码示例来源：origin: h2oai/h2o-3

@Ignore
@Test public void testAirlines() {
 Frame frame = parse_test_file("smalldata/airlines/allyears2k_headers.zip");
 AggregatorModel.AggregatorParameters parms = new AggregatorModel.AggregatorParameters();
 parms._train = frame._key;
 parms._target_num_exemplars = 500;
 parms._rel_tol_num_exemplars = 0.05;
 long start = System.currentTimeMillis();
 AggregatorModel agg = new Aggregator(parms).trainModel().get();  // 0.179
 System.out.println("AggregatorModel finished in: " + (System.currentTimeMillis() - start)/1000. + " seconds");    agg.checkConsistency();
 frame.delete();
 Frame output = agg._output._output_frame.get();
 output.remove();
 checkNumExemplars(agg);
 agg.remove();
}

代码示例来源：origin: h2oai/h2o-3

@Test public void testCovtype() {
 Frame frame = parse_test_file("smalldata/covtype/covtype.20k.data");
 AggregatorModel.AggregatorParameters parms = new AggregatorModel.AggregatorParameters();
 parms._train = frame._key;
 parms._target_num_exemplars = 500;
 parms._rel_tol_num_exemplars = 0.05;
 long start = System.currentTimeMillis();
 AggregatorModel agg = new Aggregator(parms).trainModel().get();  // 0.179
 System.out.println("AggregatorModel finished in: " + (System.currentTimeMillis() - start)/1000. + " seconds");    agg.checkConsistency();
 frame.delete();
 Frame output = agg._output._output_frame.get();
 Log.info("Exemplars: " + output.toString());
 output.remove();
 checkNumExemplars(agg);
 agg.remove();
}

代码示例来源：origin: h2oai/h2o-3

@Ignore
 @Test public void testMNIST() {
  Frame frame = parse_test_file("bigdata/laptop/mnist/train.csv.gz");

  AggregatorModel.AggregatorParameters parms = new AggregatorModel.AggregatorParameters();
  parms._train = frame._key;
  long start = System.currentTimeMillis();
  AggregatorModel agg = new Aggregator(parms).trainModel().get();
  System.out.println("AggregatorModel finished in: " + (System.currentTimeMillis() - start)/1000. + " seconds");    agg.checkConsistency();
  frame.delete();
  Frame output = agg._output._output_frame.get();
//    Log.info("Exemplars: " + output);
  output.remove();
  Log.info("Number of exemplars: " + agg._exemplars.length);
  checkNumExemplars(agg);
  agg.remove();
 }

代码示例来源：origin: h2oai/h2o-3

@Test public void testInts() {
 QuantileModel kmm = null;
 Frame fr = null;
 try {
  fr = ArrayUtils.frame(new double[][]{{0}, {0}, {0}, {0}, {0}, {0}, {0}, {0}, {0}, {0},
                     {1}, {1}, {1}, {1}, {1}, {1}, {1}, {1}, {1}, {1},
                     {2}, {2}, {2}, {2}, {2}, {2}, {2}, {2}, {2}, {2}});
  QuantileModel.QuantileParameters parms = new QuantileModel.QuantileParameters();
  parms._train = fr._key;
  Job<QuantileModel> job = new Quantile(parms).trainModel();
  kmm = job.get();
  job.remove();
 } finally {
  if( fr  != null ) fr .remove();
  if( kmm != null ) kmm.delete();
 }
}

代码示例来源：origin: h2oai/h2o-3

void trainSamplesPerIteration(int samples, int expected) {
 DeepWaterModel m = null;
 Frame tr = null;
 try {
  DeepWaterParameters p = new DeepWaterParameters();
  p._backend = getBackend();
  p._train = (tr=parse_test_file("bigdata/laptop/deepwater/imagenet/cat_dog_mouse.csv"))._key;
  p._response_column = "C2";
  p._learning_rate = 1e-3;
  p._epochs = 3;
  p._train_samples_per_iteration = samples;
  m = new DeepWater(p).trainModel().get();
  Assert.assertEquals(expected,m.iterations);
 } finally {
  if (m!=null) m.delete();
  if (tr!=null) tr.remove();
 }
}

代码示例来源：origin: h2oai/h2o-3

@Test public void test50pct() {
 QuantileModel kmm = null;
 Frame fr = null;
 try {
  double[][] d = new double[][]{{1.56386606237}, {0.812834256224}, {3.68417563302}, {3.12702210880}, {5.51277746586}};
  fr = ArrayUtils.frame(d);
  QuantileModel.QuantileParameters parms = new QuantileModel.QuantileParameters();
  parms._train = fr._key;
  Job<QuantileModel> job = new Quantile(parms).trainModel();
  kmm = job.get();
  job.remove();
  Assert.assertTrue(kmm._output._quantiles[0][5] == d[3][0]);
 } finally {
  if( fr  != null ) fr .remove();
  if( kmm != null ) kmm.delete();
 }
}

代码示例来源：origin: h2oai/h2o-3

@Test public void testInterpolate1() {
 QuantileModel kmm = null;
 Frame fr = null;
 try {
  double[][] d = new double[][]{{1}, {1}, {2}, {2}};
  fr = ArrayUtils.frame(d);
  QuantileModel.QuantileParameters parms = new QuantileModel.QuantileParameters();
  parms._train = fr._key;
  parms._probs = new double[]{0.5};
  Job<QuantileModel> job = new Quantile(parms).trainModel();
  kmm = job.get();
  job.remove();
  Assert.assertTrue(kmm._output._quantiles[0][0] == 1.5);
 } finally {
  if( fr  != null ) fr .remove();
  if( kmm != null ) kmm.delete();
 }
}
@Test public void testInterpolate2() {

代码示例来源：origin: h2oai/h2o-3

@Test public void testInterpolate2() {
 QuantileModel kmm = null;
 Frame fr = null;
 try {
  double[][] d = new double[][]{{1}, {1}, {3}, {2}};
  fr = ArrayUtils.frame(d);
  QuantileModel.QuantileParameters parms = new QuantileModel.QuantileParameters();
  parms._train = fr._key;
  parms._probs = new double[]{0.5};
  Job<QuantileModel> job = new Quantile(parms).trainModel();
  kmm = job.get();
  job.remove();
  Assert.assertTrue(kmm._output._quantiles[0][0] == 1.5);
 } finally {
  if( fr  != null ) fr .remove();
  if( kmm != null ) kmm.delete();
 }
}
@Test public void testInterpolateLow() {

代码示例来源：origin: h2oai/h2o-3

@Test public void testInterpolateHigh() {
 QuantileModel kmm = null;
 Frame fr = null;
 try {
  double[][] d = new double[][]{{1}, {2}, {3}};
  fr = ArrayUtils.frame(d);
  QuantileModel.QuantileParameters parms = new QuantileModel.QuantileParameters();
  parms._train = fr._key;
  parms._probs = new double[]{0.51};
  Job<QuantileModel> job = new Quantile(parms).trainModel();
  kmm = job.get();
  job.remove();
  Assert.assertTrue(kmm._output._quantiles[0][0] == 2.02);
 } finally {
  if( fr  != null ) fr .remove();
  if( kmm != null ) kmm.delete();
 }
}
@Test public void testInterpolateHighWeighted() {

代码示例来源：origin: h2oai/h2o-3

@Test public void testDirectMatch() {
 QuantileModel kmm = null;
 Frame fr = null;
 try {
  double[][] d = new double[][]{{1}, {1}, {1}, {2}};
  fr = ArrayUtils.frame(d);
  QuantileModel.QuantileParameters parms = new QuantileModel.QuantileParameters();
  parms._train = fr._key;
  parms._probs = new double[]{0.5};
  Job<QuantileModel> job = new Quantile(parms).trainModel();
  kmm = job.get();
  job.remove();
  Assert.assertTrue(kmm._output._quantiles[0][0] == 1);
 } finally {
  if( fr  != null ) fr .remove();
  if( kmm != null ) kmm.delete();
 }
}
@Test public void testInterpolate1() {

代码示例来源：origin: h2oai/h2o-3

@Test public void testInterpolateLow() {
 QuantileModel kmm = null;
 Frame fr = null;
 try {
  double[][] d = new double[][]{{1}, {2}, {3}};
  fr = ArrayUtils.frame(d);
  QuantileModel.QuantileParameters parms = new QuantileModel.QuantileParameters();
  parms._train = fr._key;
  parms._probs = new double[]{0.49};
  Job<QuantileModel> job = new Quantile(parms).trainModel();
  kmm = job.get();
  job.remove();
  Assert.assertTrue(kmm._output._quantiles[0][0] == 1.98);
 } finally {
  if( fr  != null ) fr .remove();
  if( kmm != null ) kmm.delete();
 }
}
@Test public void testInterpolateHigh() {

代码示例来源：origin: h2oai/h2o-3

@Test public void testAbalone() {
 Scope.enter();
 GLMModel model = null;
 try {
  Frame fr = parse_test_file("smalldata/glm_test/Abalone.gz");
  Scope.track(fr);
  GLMParameters params = new GLMParameters(Family.gaussian);
  params._train = fr._key;
  params._response_column = fr._names[8];
  params._alpha = new double[]{1.0};
  params._lambda_search = true;
  GLM glm = new GLM(params);
  model = glm.trainModel().get();
  testScoring(model,fr);
 } finally {
  if( model != null ) model.delete();
  Scope.exit();
 }
}

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

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();
 }
}

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