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(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,
'init_UV': 'exponential',
'expo_prior': 0.1
}
K_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} for K in K_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=NMF,
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()
