code: def pca_wideDataset_rotterdam_pcapower():
      tol = 2e-5
      h2o.remove_all()
      print("Importing Rotterdam.csv data...")
      rotterdamH2O = h2o.upload_file(
          pyunit_utils.locate("bigdata/laptop/jira/rotterdam.csv.zip")
      )
      y = set(["relapse"])
      x = list(set(rotterdamH2O.names) - y)
  
      print("------  Testing Power PCA --------")
      gramSVD = H2OPCA(
          k=8, impute_missing=True, transform="STANDARDIZE", seed=12345
      )
      gramSVD.train(x=x, training_frame=rotterdamH2O)
      powerPCA = H2OPCA(
          k=8,
          impute_missing=True,
          transform="STANDARDIZE",
          pca_method="Power",
          seed=12345,
      )  # power
      powerPCA.train(x=x, training_frame=rotterdamH2O)
      # compare singular values and stuff with GramSVD
      print(
          "@@@@@@  Comparing eigenvalues between GramSVD and Power...\n"
      )
      pyunit_utils.assert_H2OTwoDimTable_equal(
          gramSVD._model_json["output"]["importance"],
          powerPCA._model_json["output"]["importance"],
          [
              "Standard deviation",
              "Cumulative Proportion",
              "Cumulative Proportion",
          ],
          tolerance=1e-6,
          check_all=False,
      )
      print(
          "@@@@@@  Comparing eigenvectors between GramSVD and Power...\n"
      )
      # compare singular vectors
  
      pyunit_utils.assert_H2OTwoDimTable_equal(
          gramSVD._model_json["output"]["eigenvectors"],
          powerPCA._model_json["output"]["eigenvectors"],
          powerPCA._model_json["output"]["names"],
          tolerance=tol,
          check_sign=True,
          check_all=False,
      )
  
imports: import sys
  import h2o
  from random import randint
  from tests import pyunit_utils
  from h2o.estimators.pca import H2OPrincipalComponentAnalysisEstimator
  from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: pca_wideDataset_rotterdam_pcapower() without return types