'''
Run nested cross-validation experiment on the methylation PM dataset, with
the Gaussian + Exponential model.
'''
project_location = "/Users/thomasbrouwer/Documents/Projects/libraries/"
import sys
sys.path.append(project_location)
from BMF_Priors.code.models.bmf_gaussian_exponential import BMF_Gaussian_Exponential
from BMF_Priors.code.cross_validation.nested_matrix_cross_validation import MatrixNestedCrossValidation
from BMF_Priors.data.methylation.load_data import load_promoter_methylation_integer
''' Settings BMF model. '''
method = BMF_Gaussian_Exponential
R, M = load_promoter_methylation_integer()
hyperparameters = {'alpha': 1., 'beta': 1., 'lamb': 0.1}
train_config = {
'iterations': 220,
'init': 'random',
}
predict_config = {
'burn_in': 200,
'thinning': 1,
}
''' Settings nested cross-validation. '''
K_range = [1, 2, 3, 4, 5, 6, 7]
no_folds = 5
no_threads = 5
parallel = False
folder_results = './results/gaussian_exponential/'
output_file = folder_results + 'results.txt'
Run nested cross-validation experiment on the methylation PM dataset, with
the All Gaussian model (univariate posterior).
'''
import sys, os
project_location = os.path.dirname(__file__)+"/../../../../"
sys.path.append(project_location)
from BMF_Priors.code.models.bmf_gaussian_gaussian_univariate import BMF_Gaussian_Gaussian_univariate
from BMF_Priors.code.cross_validation.nested_matrix_cross_validation import MatrixNestedCrossValidation
from BMF_Priors.data.methylation.load_data import load_promoter_methylation_integer
''' Settings BMF model. '''
method = BMF_Gaussian_Gaussian_univariate
R, M = load_promoter_methylation_integer()
hyperparameters = { 'alpha':1., 'beta':1., 'lamb':0.1 }
train_config = {
'iterations' : 220,
'init' : 'random',
}
predict_config = {
'burn_in' : 200,
'thinning' : 1,
}
''' Settings nested cross-validation. '''
K_range = [1,2,3,4,5,6,7]
no_folds = 5
no_threads = 5
