code: def wide_dataset_large():
      print("Reading in Arcene training data for binomial modeling.")
      trainDataResponse = np.genfromtxt(
          pyunit_utils.locate(
              "smalldata/arcene/arcene_train_labels.labels"
          ),
          delimiter=" ",
      )
      trainDataResponse = np.where(trainDataResponse == -1, 0, 1)
      trainDataFeatures = np.genfromtxt(
          pyunit_utils.locate("smalldata/arcene/arcene_train.data"),
          delimiter=" ",
      )
      xtrain = np.transpose(trainDataFeatures).tolist()
      ytrain = trainDataResponse.tolist()
      trainData = h2o.H2OFrame(list(zip(*[ytrain] + xtrain)))
  
      trainData[0] = trainData[0].asfactor()
  
      print(
          "Run model on 3250 columns of Arcene with strong rules off."
      )
      model = H2OGeneralizedLinearEstimator(
          family="binomial", lambda_search=False, alpha=1
      )
      model.train(x=list(range(1, 3250)), y=0, training_frame=trainData)
  
      print("Test model on validation set.")
      validDataResponse = np.genfromtxt(
          pyunit_utils.locate(
              "smalldata/arcene/arcene_valid_labels.labels"
          ),
          delimiter=" ",
      )
      validDataResponse = np.where(validDataResponse == -1, 0, 1)
      validDataFeatures = np.genfromtxt(
          pyunit_utils.locate("smalldata/arcene/arcene_valid.data"),
          delimiter=" ",
      )
      xvalid = np.transpose(validDataFeatures).tolist()
      yvalid = validDataResponse.tolist()
      validData = h2o.H2OFrame(list(zip(*[yvalid] + xvalid)))
      prediction = model.predict(validData)
  
      print("Check performance of predictions.")
      performance = model.model_performance(validData)
  
      print("Check that prediction AUC better than guessing (0.5).")
      assert (
          performance.auc() > 0.5
      ), "predictions should be better then pure chance"
  
imports: from builtins import range
  import sys
  import h2o
  from tests import pyunit_utils
  import numpy
  from h2o.estimators.glm import H2OGeneralizedLinearEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: wide_dataset_large() without return types