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)
