'alphatau': alphatau,
'betatau': betatau,
'alpha0': alpha0,
'beta0': beta0,
'lambdaU': lambdaU,
'lambdaV': lambdaV
}
''' Load in data. '''
R, M = load_gdsc_ic50()
I, J = M.shape
''' Generate matrices M - one list of M's for each value of K. '''
M_attempts = 1000
all_Ms_training_and_test = [
compute_folds_attempts(I=I,
J=J,
no_folds=no_folds,
attempts=M_attempts,
M=M) for K in values_K
]
''' We now run the Gibbs sampler on each of the M's for each fraction. '''
all_performances = {metric: [] for metric in metrics}
average_performances = {metric: []
for metric in metrics} # averaged over repeats
for K, (Ms_train, Ms_test) in zip(values_K, all_Ms_training_and_test):
print "Trying K=%s." % K
# Run the algorithm <repeats> times and store all the performances
for metric in metrics:
all_performances[metric].append([])
for fold, (M_train, M_test) in enumerate(zip(Ms_train, Ms_test)):
print "Fold %s of K=%s." % (fold + 1, K)
lambdaF, lambdaS, lambdaG = 0.1, 0.1, 0.1
alphatau, betatau = 1., 1.
alpha0, beta0 = 1., 1.
hyperparams = { 'alphatau':alphatau, 'betatau':betatau, 'alpha0':alpha0, 'beta0':beta0, 'lambdaF':lambdaF, 'lambdaS':lambdaS, 'lambdaG':lambdaG }
''' Load in data. '''
R, M = load_ccle_ec50()
I, J = M.shape
''' Generate matrices M - one list of M's for each value of K. '''
M_attempts = 1000
all_Ms_training_and_test = [
compute_folds_attempts(I=I,J=J,no_folds=no_folds,attempts=M_attempts,M=M)
for KL in values_KL
]
''' We now run the Gibbs sampler on each of the M's for each fraction. '''
all_performances = {metric:[] for metric in metrics}
average_performances = {metric:[] for metric in metrics} # averaged over repeats
for KL,(Ms_train,Ms_test) in zip(values_KL,all_Ms_training_and_test):
print "Trying K,L=%s." % KL
# Run the algorithm <repeats> times and store all the performances
for metric in metrics:
all_performances[metric].append([])
for fold,(M_train,M_test) in enumerate(zip(Ms_train,Ms_test)):
print "Fold %s of K,L=%s." % (fold+1, KL)
