code: def generate_dataset(
      family,
      nrow,
      ncol,
      realFrac,
      intFrac,
      enumFrac,
      missingFrac,
      factorRange,
      numericRange,
      targetFactor,
      numGamCols,
      seed,
  ):
      if family == "binomial":
          responseFactor = 2
      elif family == "gaussian":
          responseFactor = 1
      else:
          responseFactor = targetFactor
  
      trainData = random_dataset(
          nrow,
          ncol,
          realFrac=realFrac,
          intFrac=intFrac,
          enumFrac=enumFrac,
          factorR=factorRange,
          integerR=numericRange,
          responseFactor=responseFactor,
          misFrac=missingFrac,
          randSeed=seed,
      )
      myX = trainData.names
      myY = "response"
      myX.remove(myY)
  
      colNames = trainData.names
      colNames.remove("response")
      print("gam columns: {0}".format(colNames[0:numGamCols]))
      # train model to know the coefficient size
      m = gam(
          family=family,
          gam_columns=colNames[0:numGamCols],
          max_iterations=2,
      )
      m.train(training_frame=trainData, x=myX, y=myY)
      coef = m.coef()
      coefLen = len(coef)
      if family == "multinomial":
          coefLen = len(coef["coefficients"])
      randCoeff = [random() for ind in range(coefLen)]
      # train model again with random coefficients to generate the synthetic datasets
      m = gam(
          family=family,
          gam_columns=colNames[0:numGamCols],
          max_iterations=1,
          startval=randCoeff,
      )
      m.train(training_frame=trainData, x=myX, y=myY)
      f2 = m.predict(trainData)
      # to see coefficient, do m.coef()
      finalDataset = trainData[myX]
      finalDataset = finalDataset.cbind(f2[0])
      finalDataset.set_name(col=finalDataset.ncols - 1, name="response")
      print(finalDataset.types)
      return finalDataset
  
file_path: h2o-3/h2o-py/tests/testdir_algos/gam/pyunit_generate_synthetic_GAM_data.py
imports: import sys
  import h2o
  from tests import pyunit_utils
  from h2o.estimators.gam import H2OGeneralizedAdditiveEstimator
  from random import random
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: generate_dataset(family, nrow, ncol, realFrac, intFrac, enumFrac, missingFrac, factorRange, numericRange, targetFactor, numGamCols, seed) and returns: finalDataset