code: def pca_scoring():
      print("Importing arrests.csv data...")
      arrestsH2O = h2o.upload_file(
          pyunit_utils.locate("smalldata/pca_test/USArrests.csv")
      )
  
      print("Run PCA with transform = 'DEMEAN'")
  
      fitH2O = H2OPCA(k=4, transform="DEMEAN")
      fitH2O.train(x=list(range(4)), training_frame=arrestsH2O)
      # TODO: fitH2O.show()
  
      print("Project training data into eigenvector subspace")
      predH2O = fitH2O.predict(arrestsH2O)
      print("H2O Projection:")
      predH2O.head()
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.pca import H2OPrincipalComponentAnalysisEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: pca_scoring() without return types