Python MatrixSingleCrossValidation 예제들

예제 #1

project_location = "/Users/thomasbrouwer/Documents/Projects/libraries/"
import sys
sys.path.append(project_location)

from BMF_Priors.code.models.baseline_average_row import RowAverage
from BMF_Priors.code.cross_validation.matrix_single_cross_validation import MatrixSingleCrossValidation
from BMF_Priors.data.methylation.load_data import load_gene_body_methylation_integer
''' Settings BMF model. '''
method = RowAverage
R, M = load_gene_body_methylation_integer()
hyperparameters = {}
train_config = {'iterations': 0, 'init': ''}
predict_config = {'burn_in': 0, 'thinning': 0}
parameters = {'K': 0, 'hyperparameters': hyperparameters}
''' Settings nested cross-validation. '''
no_folds = 5
folder_results = './results/baseline_average_row/'
output_file = folder_results + 'results.txt'
''' Run the cross-validation framework. '''
crossval = MatrixSingleCrossValidation(
    method=method,
    R=R,
    M=M,
    K=no_folds,
    parameters=parameters,
    train_config=train_config,
    predict_config=predict_config,
    file_performance=output_file,
)
crossval.run()

예제 #2

파일: baseline_average_column.py 프로젝트: changchunli/BMF_Priors

from BMF_Priors.code.cross_validation.matrix_single_cross_validation import MatrixSingleCrossValidation
from BMF_Priors.data.drug_sensitivity.load_data import load_ccle_ic50_integer


''' Settings BMF model. '''
method = ColumnAverage
R, M = load_ccle_ic50_integer()
hyperparameters = {}
train_config = { 'iterations': 0, 'init': '' }
predict_config = { 'burn_in': 0, 'thinning': 0 }
parameters = {'K':0, 'hyperparameters':hyperparameters}


''' Settings nested cross-validation. '''
no_folds = 5
folder_results = './results/baseline_average_column/'
output_file = folder_results+'results.txt'


''' Run the cross-validation framework. '''
crossval = MatrixSingleCrossValidation(
    method=method,
    R=R,
    M=M,
    K=no_folds,
    parameters=parameters,
    train_config=train_config,
    predict_config=predict_config,
    file_performance=output_file,
)
crossval.run()