''' Settings nested cross-validation. '''
K_range = [3,4,5,6,7]
no_folds = 5
no_threads = 5
stratify_rows = False
parallel = False
folder_results = './results/gaussian_exponential_ard/'
output_file = folder_results+'results.txt'
files_nested_performances = [folder_results+'fold_%s.txt'%(fold+1) for fold in range(no_folds)]
''' Construct the parameter search. '''
parameter_search = [{'K':K, 'hyperparameters':hyperparameters} for K in K_range]
''' Run the cross-validation framework. '''
nested_crossval = MatrixNestedCrossValidation(
method=method,
R=R,
M=M,
K=no_folds,
P=no_threads,
parameter_search=parameter_search,
train_config=train_config,
predict_config=predict_config,
file_performance=output_file,
files_nested_performances=files_nested_performances,
)
nested_crossval.run(parallel=parallel, stratify_rows=stratify_rows)
}
''' 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_gaussian_ard/'
output_file = folder_results+'results.txt'
files_nested_performances = [folder_results+'fold_%s.txt'%(fold+1) for fold in range(no_folds)]
''' Construct the parameter search. '''
parameter_search = [{'K':K, 'hyperparameters':hyperparameters} for K in K_range]
''' Run the cross-validation framework. '''
nested_crossval = MatrixNestedCrossValidation(
method=method,
R=R,
M=M,
K=no_folds,
P=no_threads,
parameter_search=parameter_search,
train_config=train_config,
predict_config=predict_config,
file_performance=output_file,
files_nested_performances=files_nested_performances,
)
nested_crossval.run(parallel=parallel)
''' Settings nested cross-validation. '''
K_range = [2,3,4]
no_folds = 5
no_threads = 5
parallel = False
folder_results = './results/baseline_mf_nonprobabilistic/'
output_file = folder_results+'results.txt'
files_nested_performances = [folder_results+'fold_%s.txt'%(fold+1) for fold in range(no_folds)]
''' Construct the parameter search. '''
parameter_search = [{'K':K, 'hyperparameters':hyperparameters} for K in K_range]
''' Run the cross-validation framework. '''
nested_crossval = MatrixNestedCrossValidation(
method=method,
R=R,
M=M,
K=no_folds,
P=no_threads,
parameter_search=parameter_search,
train_config=train_config,
predict_config=predict_config,
file_performance=output_file,
files_nested_performances=files_nested_performances,
)
nested_crossval.run(parallel=parallel)
