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 EC50 dataset R,M = load_ccle(ic50=False) # 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, restarts=restarts, quality_metric=quality_metric, file_performance=output_file ) nested_crossval.run()
'lambdaG': lambdaG } # Load in the Sanger dataset (_, X_min, M, _, _, _, _) = load_Sanger(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 model fits for fold 1, metric AIC: [(5, 5, 267436.21583785285), (6, 5, 268266.96019512357), (5, 6, 257633.99837328543), (6, 6, 258811.15017865735), (5, 7, 267685.94226889982), (6, 7, 259475.47788105684)]. Best K,L for fold 1: (5, 6). Performance: {'R^2': 0.8054505750429645, 'MSE': 2.260784680365453, 'Rp': 0.89749750215470236}. All model fits for fold 2, metric AIC: [(5, 5, 257397.3462916202), (6, 5, 258208.83672100294), (5, 6, 258507.40258252042), (6, 6, 268899.95363038778)]. Best K,L for fold 2: (5, 5). Performance: {'R^2': 0.7932515856937303, 'MSE': 2.4062208604789466, 'Rp': 0.89070894807048406}.
output_file = "./results.txt" alpha, beta = 1.0, 1.0 lambdaF = 1.0 / 10.0 lambdaS = 1.0 / 10.0 lambdaG = 1.0 / 10.0 priors = {"alpha": alpha, "beta": beta, "lambdaF": lambdaF, "lambdaS": lambdaS, "lambdaG": lambdaG} # Load in the CCLE EC50 dataset R, M = load_ccle(ic50=False) # Run the cross-validation framework # random.seed(1) # numpy.random.seed(1) nested_crossval = GreedySearchCrossValidation( classifier=bnmtf_gibbs_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, restarts=restarts, quality_metric=quality_metric, file_performance=output_file, ) nested_crossval.run(burn_in=burn_in, thinning=thinning)
minimum_TN = 0.1 # Load in the Sanger dataset (_,X_min,M,_,_,_,_) = load_gdsc(standardised=standardised,sep=',') # Run the cross-validation framework #random.seed(1) #numpy.random.seed(1) nested_crossval = GreedySearchCrossValidation( classifier=nmtf_icm, 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(minimum_TN=minimum_TN) """ all_MSE = [2.2020002331612534, 2.2364503149918011, 2.1611831576199534, 2.1569381861635395, 2.1530470452271864, 2.272519698528658, 2.1910498022580613, 2.2302383199950797, 2.1027416628364484, 2.283196008129782] all_R2 = [0.8068027775294401, 0.8122652321538621, 0.8155286993833876, 0.8151068635575036, 0.8227521825461013, 0.8062086302462692, 0.8136429679161671, 0.8113058601446024, 0.8152542609952846, 0.8080593057170452] Average MSE: 2.1989364428911764 +- 0.0029521290510586768 Average R^2: 0.81269267801896627 +- 2.2283761452627026e-05