def scope_optimization_cross_validation(scope_optimization, initial_feature_vector, problem_data, vector_model, prediction_scoring_function, n_cross_folds, n_processes):
masks = [(train, test) for train, test in KFold(problem_data.get_n_unmasked_samples(), n_folds=n_cross_folds)]
#Perform the optimization process for each fold.
n_iterations = scope_optimization.n_iterations
training_outcomes = [[] for i in range(n_iterations)]
for train_mask, test_mask in masks:
problem_data.push_mask(test_mask)
iteration_outcomes = scope_optimization.optimize_vector(initial_feature_vector, problem_data, True)
problem_data.pop_mask()
for iteration in range(n_iterations):
training_outcomes[iteration].append(iteration_outcomes[iteration])
#Score the resulting feature vectors for each fold in parallel.
process_definitions = []
for iteration in range(n_iterations):
for mask_index in range(len(masks)):
X = build_sample_matrix(problem_data, training_outcomes[iteration][mask_index].feature_vector)
y = problem_data.get_response_variables()
train_mask, test_mask = masks[mask_index]
process_definitions.append(ProcessDefinition(build_model_outcome_with_matrices, positional_arguments=
(training_outcomes[iteration][mask_index].feature_vector, vector_model,
prediction_scoring_function, X[train_mask], y[train_mask], X[test_mask], y[test_mask]),
tag=(iteration, mask_index)))
test_results = []
multiprocess_functions(process_definitions, test_results.append, n_processes)
test_outcomes = [[None] * len(masks) for i in range(scope_optimization.n_iterations)]
for tag, mask_result in test_results:
iteration, mask_index = tag
test_outcomes[iteration][mask_index] = mask_result
return test_outcomes
def score_feature_vector_set(self, problem_data, feature_vector_set, n_processes):
outcome_set = []
testing_functions = []
for new_vector in feature_vector_set:
X = build_sample_matrix(problem_data, new_vector)
y = problem_data.get_response_variables()
testing_functions.append(ProcessDefinition(self.score_feature_vector, positional_arguments=(None, new_vector, X, y)))
result_handler = outcome_set.append
multiprocess_functions(testing_functions, result_handler, n_processes)
return outcome_set
