init_UV = 'random' K_range = [1,2,3] no_folds = 10 restarts = 1 quality_metric = 'AIC' output_file = "./results.txt" alpha, beta = 1., 1. lambdaU = 1./10. lambdaV = 1./10. priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV } # Load in the CCLE EC50 dataset R,M = load_ccle(ic50=False) # Run the cross-validation framework #random.seed(42) #numpy.random.seed(9000) nested_crossval = LineSearchCrossValidation( classifier=bnmf_gibbs_optimised, R=R, M=M, values_K=K_range, folds=no_folds, priors=priors, init_UV=init_UV, iterations=iterations, restarts=restarts, quality_metric=quality_metric,
K_range = [4,5,6,7,8,9,10] L_range = [4,5,6,7,8,9,10] 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 CCLE IC50 dataset R,M = load_ccle(ic50=True) # Run the cross-validation framework #random.seed(42) #numpy.random.seed(9000) nested_crossval = GreedySearchCrossValidation( classifier=bnmtf_vb_optimised, R=R, 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,