''' 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