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(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)
priors = { 'alpha': alpha, 'beta': beta, 'lambdaF': lambdaF, 'lambdaS': lambdaS, 'lambdaG': lambdaG } minimum_TN = 0.01 # Load in the CCLE IC50 dataset R, M = load_ccle(ic50=True) # Run the cross-validation framework #random.seed(1) #numpy.random.seed(1) nested_crossval = GreedySearchCrossValidation(classifier=nmtf_icm, 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(minimum_TN=minimum_TN)