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,
