text: """
  Run the cross validation with greedy search for model selection using Gibbs-NMTF 
  on the Sanger dataset.
  """
  
  import sys, os
  project_location = os.path.dirname(__file__)+"/../../../../../"
  sys.path.append(project_location)
  
  import numpy, random
  from BNMTF.code.models.bnmtf_vb_optimised import bnmtf_vb_optimised
  from BNMTF.code.cross_validation.greedy_search_cross_validation import GreedySearchCrossValidation
  from BNMTF.data_drug_sensitivity.gdsc.load_data import load_gdsc
  
  
  # Settings
  standardised = False
  iterations = 1000
  
  init_S = 'random' #'exp' #
  init_FG = 'kmeans' #'exp' #
  
  K_range = [5,6,7,8,9,10]
  L_range = [5,6,7,8,9,10]
  no_folds = 10
  restarts = 1
  
  quality_metric = 'AIC'
  output_file = "./results.txt"
  
  alpha, beta = 1., 1.
  lambdaF = 1./10.
  lambdaS = 1./10.
  lambdaG = 1./10.
  priors = { 'alpha':alpha, 'beta':beta, 'lambdaF':lambdaF, 'lambdaS':lambdaS, 'lambdaG':lambdaG }
  
  # Load in the Sanger dataset
  (_,X_min,M,_,_,_,_) = load_gdsc(standardised=standardised)
  
  # Run the cross-validation framework
  #random.seed(42)
  #numpy.random.seed(9000)
  nested_crossval = GreedySearchCrossValidation(
      classifier=bnmtf_vb_optimised,
      R=X_min,
      M=M,
      values_K=K_range,
      values_L=L_range,
      folds=no_folds,
      priors=priors,
      init_S=init_S,
      init_FG=init_FG,
      iterations=iterations,
      restarts=restarts,
      quality_metric=quality_metric,
      file_performance=output_file
  )
  nested_crossval.run()
  
  """
  all_MSE = [2.2811777476249415, 2.1782935772707153, 2.3760214934948851, 2.4070138866182651, 2.1679193763392863, 2.4351661211853344, 2.3531667160686407, 2.4375820084579578, 2.1737221434522502, 2.3957602752026799]
  all_R2 = [0.8004514561880776, 0.8095655871226215, 0.7982332012844026, 0.7939011733335062, 0.8135460410954071, 0.7914028391107459, 0.8050979272119902, 0.7964032435159856, 0.8102340265362746, 0.805071751458151]
  
  Average MSE: 2.3205823345714953 +- 0.011074845252916733
  Average R^2: 0.80239072468571615 +- 5.0165577464731684e-05
  """