L_range = [1, 2, 3]
no_threads = 2
no_folds = 10
output_file = "./results.txt"
files_nested_performances = [
"./fold_%s.txt" % fold for fold in range(1, no_folds + 1)
]
# Construct the parameter search
parameter_search = [{
'K': K,
'L': L
} for (K, L) in itertools.product(K_range, L_range)]
# 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 = MatrixNestedCrossValidation(
method=NMTF,
X=R,
M=M,
K=no_folds,
P=no_threads,
parameter_search=parameter_search,
train_config=train_config,
file_performance=output_file,
files_nested_performances=files_nested_performances)
nested_crossval.run()
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_vb_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,
file_performance=output_file)