def command_line_argument_wrapper(model, n_iterations, group_map_files,
start_level, mapping_file, prediction_field, include_only, negate,
n_maintain, n_generate, score_predictions_function, split_abun_coef,
split_score_coef, merge_abun_coef, merge_score_coef, delete_abun_coef,
delete_score_coef, split_proportion, merge_proportion,
delete_proportion, n_cross_folds, n_processes, output_dir, n_trials):
"""
Sets up and executes scope optimization on a given problem, runs testing, and writes to files.
Builds the data structures and objects that are used by fresco.scope_optimization from
command line friendly arguments. Then runs scope optimization, performs cross fold testing
on the results if instructed, and writes the results to the files.
Args:
model: A string representing a machine learning classification model to be used
both for testing in within the optimization process.
n_iterations: The number of iterations to be completed by the optimization process.
group_map_files: A list of open files containing tab-separated lines mapping from
groups to objects. For example:
Group1 Object1 Object2 Object3
Group2 Object4 Object5
Map files should be ordered by decreasing level of scope, i.e., most general to least
general map files.
start_level: The starting scope level (map file index) for the optimization process.
mapping_file: An open file with tab separated lines mapping from a sample ID to its
properties. The first line should be tab separated identifiers for the properties.
For example:
SAMPLE_ID COLOR TASTE
APPLE_g GREEN AMAZING
APPLE_r RED AWEFUL
n_maintain: The number of vectors to be kept after every iteration of optimization.
n_generate: The number of vectors to be generated for each input vector by the
vector generator.
score_predictions_function: A function which takes two lists of class predictions of
equal length and returns a numerical score. For example:
def score_predictions(real_classes, predicted_classes):
return sum([1 if read_classes[i] != predicted_classes[i] else 0
for i in range(len(real_classes))])
split_abun_coef: The abundance deviation coefficient for the splitting heuristic.
split_score_coef: The prediction score deviation coefficient for the splitting heuristic.
merge_abun_coef: The abundance deviation coefficient for the merging heuristic.
merge_score_coef: The prediction score deviation coefficient for the merging heuristic.
delete_abun_coef: The abundance deviation coefficient for the deletion heuristic.
delete_score_coef: The prediction score deviation coefficient for the deletion heuristic.
split_proportion: The proportion of total features to be split each iteration.
merge_proportion: The proportion of total features to be split each iteration.
delete_proportion: The proportion of total features to be split each iteration.
n_cross_folds: The number of cross folds to use in scoring the vectors for selection.
n_processes: The number of additional processes to spawn, at maximum.
output_dir: The directory that the output files will be put in.
n_trials: The number of cross folds to use in scoring the vectors returned by the
optimization process. If 0, no testing will be performed.
"""
simple_var_types = [
("n_iterations", types.IntType),
("n_maintain", types.IntType),
("n_generate", types.IntType),
("n_processes", types.IntType),
("n_cross_folds", types.IntType),
("n_trials", types.IntType),
("start_level", types.IntType),
("split_score_coef", (types.IntType, types.FloatType)),
("split_abun_coef", (types.IntType, types.FloatType)),
("merge_score_coef", (types.IntType, types.FloatType)),
("merge_abun_coef", (types.IntType, types.FloatType)),
("delete_score_coef", (types.IntType, types.FloatType)),
("delete_abun_coef", (types.IntType, types.FloatType)),
("split_proportion", (types.IntType, types.FloatType)),
("merge_proportion", (types.IntType, types.FloatType)),
("delete_proportion", (types.IntType, types.FloatType)),
("n_cross_folds", types.IntType),
("mapping_file", types.FileType),
("negate", types.BooleanType),
("output_dir", types.StringType),
("prediction_field", types.StringType),
("include_only", (types.NoneType, types.ListType,
types.TupleType)),
("group_map_files", types.ListType)
]
for var_name, var_type in simple_var_types:
check_input_type(var_name, locals()[var_name], var_type)
if len(inspect.getargspec(score_predictions_function)[0]) < 2:
raise InputTypeError("scope_predictions_function should take at least two parameters")
if not all([isinstance(f, types.FileType) for f in group_map_files]):
raise InputTypeError("group_map_files should be a list of open files")
if include_only != None:
if not isinstance(include_only[0], types.StringType):
raise InputTypeError("include_only[0] should be of type string")
if not isinstance(include_only[1], types.ListType) or \
not all([isinstance(value, types.StringType) for value in include_only[1]]):
raise InputTypeError("include_only[1] should be a list of strings")
if not exists(output_dir):
makedirs(output_dir)
log_fp = join(output_dir, 'info.log')
logging.basicConfig(filename=log_fp, filemode='w', level=logging.DEBUG,
format='%(asctime)s\t%(levelname)s\t%(message)s')
logging.info('Started feature vector optimization process for \'%s\' '
'model' % model)
start_time = time()
vector_model = GroupVectorModel(parse_model_string(model))
group_vector_scorer = CrossValidationGroupVectorScorer(score_predictions_function, vector_model, n_cross_folds)
problem_data = build_problem_data(group_map_files, mapping_file, prediction_field, start_level, include_only, negate, n_processes)
assert isinstance(problem_data, ProblemData),\
"build_problem_data did not return a GroupProblemData"
initial_feature_ids = problem_data.get_group_ids(start_level)
initial_feature_vector = FeatureVector([problem_data.get_feature_record(start_level, feature_id) for feature_id in initial_feature_ids])
assert isinstance(initial_feature_vector, FeatureVector),\
"build_problem_data did not return a FeatureVector"
group_actions = [SplitAction(problem_data, split_proportion, split_abun_coef, split_score_coef),
MergeAction(problem_data, merge_proportion, merge_abun_coef, merge_score_coef),
DeleteAction(problem_data, delete_proportion, delete_abun_coef, delete_score_coef)]
vector_generator = ActionVectorGenerator(group_actions, n_generate, problem_data)
scope_optimization = ScopeOptimization(group_vector_scorer, vector_generator, n_processes, n_iterations, n_maintain)
logging.info('Completed optimization initialization')
if n_trials > 0:
test_outcomes = scope_optimization_cross_validation(scope_optimization, initial_feature_vector, problem_data, vector_model, score_predictions_function, n_trials, n_processes)
assert len(test_outcomes) == n_iterations, "test outcomes isn't returning an entry for each iteration"
assert all([len(mask_outcomes) == n_trials for mask_outcomes in test_outcomes]), \
"test outcomes isn't returning an outcome for each mask for each iteration"
assert all([all([isinstance(outcome, ModelOutcome) for outcome in mask_outcomes])
for mask_outcomes in test_outcomes]), \
"test outcomes are not all of type ModelOutcome"
prediction_testing_output_fp = join(output_dir,
'prediction_testing_output.txt')
write_to_file(testing_output_lines(test_outcomes),
prediction_testing_output_fp)
assert isfile(prediction_testing_output_fp), \
"Output file \"%s\"was not created" %prediction_testing_output_fp
feature_vector_output_fp = join(output_dir,
'fold_feature_vectors_output.txt')
write_to_file(fold_features_output_lines(test_outcomes[-1], problem_data),
feature_vector_output_fp)
assert isfile(feature_vector_output_fp), \
"Output file \"%s\"was not created" %feature_vector_output_fp
else:
outcome = scope_optimization.optimize_vector(initial_feature_vector, problem_data, False)
assert isinstance(outcome, ModelOutcome), "scope_optimization outcome is not a ModelOutcome"
feature_vector_output_fp = join(output_dir,
'feature_vector_output.txt')
write_to_file(feature_output_lines(outcome, problem_data), feature_vector_output_fp)
assert isfile(feature_vector_output_fp), \
"Output file \"%s\"was not created" %feature_vector_output_fp
end_time = time()
elapsed_time = end_time - start_time
logging.info('Finished feature vector optimization process for \'%s\' '
'model' % model)
logging.info('Total elapsed time (in seconds): %d' % elapsed_time)
def command_line_argument_wrapper(model, n_iterations, group_map_files,
start_level, mapping_file, prediction_field, include_only, negate,
n_maintain, n_generate, score_predictions_function, split_abun_coef,
split_score_coef, merge_abun_coef, merge_score_coef, delete_abun_coef,
delete_score_coef, split_proportion, merge_proportion,
delete_proportion, n_cross_folds, n_processes, output_dir, n_trials):
"""
Sets up and executes scope optimization on a given problem, runs testing, and writes to files.
Builds the data structures and objects that are used by fresco.scope_optimization from
command line friendly arguments. Then runs scope optimization, performs cross fold testing
on the results if instructed, and writes the results to the files.
Args:
model: A string representing a machine learning classification model to be used
both for testing in within the optimization process.
n_iterations: The number of iterations to be completed by the optimization process.
group_map_files: A list of open files containing tab-separated lines mapping from
groups to objects. For example:
Group1 Object1 Object2 Object3
Group2 Object4 Object5
Map files should be ordered by decreasing level of scope, i.e., most general to least
general map files.
start_level: The starting scope level (map file index) for the optimization process.
mapping_file: An open file with tab separated lines mapping from a sample ID to its
properties. The first line should be tab separated identifiers for the properties.
For example:
SAMPLE_ID COLOR TASTE
APPLE_g GREEN AMAZING
APPLE_r RED AWEFUL
n_maintain: The number of vectors to be kept after every iteration of optimization.
n_generate: The number of vectors to be generated for each input vector by the
vector generator.
score_predictions_function: A function which takes two lists of class predictions of
equal length and returns a numerical score. For example:
def score_predictions(real_classes, predicted_classes):
return sum([1 if read_classes[i] != predicted_classes[i] else 0
for i in range(len(real_classes))])
split_abun_coef: The abundance deviation coefficient for the splitting heuristic.
split_score_coef: The prediction score deviation coefficient for the splitting heuristic.
merge_abun_coef: The abundance deviation coefficient for the merging heuristic.
merge_score_coef: The prediction score deviation coefficient for the merging heuristic.
delete_abun_coef: The abundance deviation coefficient for the deletion heuristic.
delete_score_coef: The prediction score deviation coefficient for the deletion heuristic.
split_proportion: The proportion of total features to be split each iteration.
merge_proportion: The proportion of total features to be split each iteration.
delete_proportion: The proportion of total features to be split each iteration.
n_cross_folds: The number of cross folds to use in scoring the vectors for selection.
n_processes: The number of additional processes to spawn, at maximum.
output_dir: The directory that the output files will be put in.
n_trials: The number of cross folds to use in scoring the vectors returned by the
optimization process. If 0, no testing will be performed.
"""
if not exists(output_dir):
makedirs(output_dir)
log_fp = join(output_dir, 'info.log')
logging.basicConfig(filename=log_fp, filemode='w', level=logging.DEBUG,
format='%(asctime)s\t%(levelname)s\t%(message)s')
logging.info('Started feature vector optimization process for \'%s\' '
'model' % model)
start_time = time()
feature_vector_output_fp = join(output_dir,
'feature_vector_output.txt')
vector_model = GroupVectorModel(parse_model_string(model))
group_vector_scorer = CrossValidationGroupVectorScorer(score_predictions_function, vector_model, n_cross_folds)
problem_data, initial_feature_vector = build_problem_data(group_map_files, mapping_file, prediction_field, start_level, include_only, negate, n_processes)
group_actions = [SplitAction(problem_data, split_proportion, split_abun_coef, split_score_coef),
MergeAction(problem_data, merge_proportion, merge_abun_coef, merge_score_coef),
DeleteAction(problem_data, delete_proportion, delete_abun_coef, delete_score_coef)]
vector_generator = ActionVectorGenerator(group_actions, n_generate)
if n_trials > 0:
xfold_feature_vectors = [[] for i in range(n_iterations)]
masks = [(train, test) for train, test in KFold(problem_data.get_n_samples(), n_folds=n_trials, indices=False)]
for train_mask, test_mask in masks:
problem_data.set_mask(train_mask)
iteration_outcomes = scope_optimization(initial_feature_vector, problem_data, group_vector_scorer, vector_generator, n_iterations, n_processes, n_maintain, True)
for iteration in range(len(iteration_outcomes)):
xfold_feature_vectors[iteration].append(iteration_outcomes[iteration].feature_vector)
functions = []
mask_results = []
for iteration in range(len(iteration_outcomes)):
for mask_index in range(len(masks)):
functions.append( (mask_testing, (problem_data, masks[mask_index], vector_model, score_predictions_function, xfold_feature_vectors[iteration][mask_index], (iteration, mask_index))) )
multiprocess_functions(functions, mask_results.append, n_processes)
test_outcomes = [[None for x in range(len(masks))] for i in range(n_iterations)]
for tag, mask_result in mask_results:
iteration, mask_index = tag
test_outcomes[iteration][mask_index] = mask_result
prediction_testing_output_fp = join(output_dir,
'prediction_testing_output.txt')
write_to_file(testing_output_lines(test_outcomes),
prediction_testing_output_fp)
avg_outcome = stitch_avg_outcome(test_outcomes[-1], masks)
write_to_file(feature_output_lines(avg_outcome),
feature_vector_output_fp)
else:
outcome = scope_optimization(initial_feature_vector, problem_data, group_vector_scorer, vector_generator, n_iterations, n_processes, n_maintain, False)
write_to_file(feature_output_lines(outcome), feature_vector_output_fp)
end_time = time()
elapsed_time = end_time - start_time
logging.info('Finished feature vector optimization process for \'%s\' '
'model' % model)
logging.info('Total elapsed time (in seconds): %d' % elapsed_time)
