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(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,
restarts=restarts,
quality_metric=quality_metric,
file_performance=output_file
)
nested_crossval.run()
'lambdaG': lambdaG
}
# Load in the Sanger dataset
(_, X_min, M, _, _, _, _) = load_Sanger(standardised=standardised)
# Run the cross-validation framework
random.seed(42)
numpy.random.seed(9000)
nested_crossval = GreedySearchCrossValidation(classifier=bnmtf_vb_optimised,
R=X_min,
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()
"""
All model fits for fold 1, metric AIC: [(5, 5, 267436.21583785285), (6, 5, 268266.96019512357), (5, 6, 257633.99837328543), (6, 6, 258811.15017865735), (5, 7, 267685.94226889982), (6, 7, 259475.47788105684)].
Best K,L for fold 1: (5, 6).
Performance: {'R^2': 0.8054505750429645, 'MSE': 2.260784680365453, 'Rp': 0.89749750215470236}.
All model fits for fold 2, metric AIC: [(5, 5, 257397.3462916202), (6, 5, 258208.83672100294), (5, 6, 258507.40258252042), (6, 6, 268899.95363038778)].
Best K,L for fold 2: (5, 5).
Performance: {'R^2': 0.7932515856937303, 'MSE': 2.4062208604789466, 'Rp': 0.89070894807048406}.
output_file = "./results.txt"
alpha, beta = 1.0, 1.0
lambdaF = 1.0 / 10.0
lambdaS = 1.0 / 10.0
lambdaG = 1.0 / 10.0
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)
minimum_TN = 0.1
# Load in the Sanger dataset
(_,X_min,M,_,_,_,_) = load_gdsc(standardised=standardised,sep=',')
# Run the cross-validation framework
#random.seed(1)
#numpy.random.seed(1)
nested_crossval = GreedySearchCrossValidation(
classifier=nmtf_icm,
R=X_min,
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)
"""
all_MSE = [2.2020002331612534, 2.2364503149918011, 2.1611831576199534, 2.1569381861635395, 2.1530470452271864, 2.272519698528658, 2.1910498022580613, 2.2302383199950797, 2.1027416628364484, 2.283196008129782]
all_R2 = [0.8068027775294401, 0.8122652321538621, 0.8155286993833876, 0.8151068635575036, 0.8227521825461013, 0.8062086302462692, 0.8136429679161671, 0.8113058601446024, 0.8152542609952846, 0.8080593057170452]
Average MSE: 2.1989364428911764 +- 0.0029521290510586768
Average R^2: 0.81269267801896627 +- 2.2283761452627026e-05
