def i_create_cluster_resources_from_local_cluster(step, directory=None, test=None, output=None): ok_(test is not None and output is not None and directory is not None) test = res_filename(test) with open(os.path.join(directory, "clusters")) as handler: cluster_id = handler.readline().strip() command = ("bigmler cluster --cluster-file " + storage_file_name(directory, cluster_id) + " --test " + test + " --store --output " + output) shell_execute(command, output, test=test)
def i_create_anomaly_resources_from_local_anomaly_detector(step, directory=None, test=None, output=None): if test is None or output is None or directory is None: assert False with open(os.path.join(directory, "anomalies")) as handler: anomaly_id = handler.readline().strip() command = ("bigmler anomaly --anomaly-file " + storage_file_name(directory, anomaly_id) + " --test " + test + " --store --output " + output) shell_execute(command, output, test=test)
def i_create_anomaly_resources_from_local_anomaly_detector( step, directory=None, test=None, output=None): if test is None or output is None or directory is None: assert False with open(os.path.join(directory, "anomalies")) as handler: anomaly_id = handler.readline().strip() command = ("bigmler anomaly --anomaly-file " + storage_file_name(directory, anomaly_id) + " --test " + test + " --store --output " + output) shell_execute(command, output, test=test)
def best_first_search(datasets_file, api, args, common_options, staleness=None, penalty=None, objective_name=None, resume=False): """Selecting the fields to be used in the model construction """ counter = 0 loop_counter = 0 features_file = os.path.normpath( os.path.join(args.output_dir, FEATURES_LOG)) with open(features_file, u.open_mode("w")) as features_handler: features_writer = csv.writer(features_handler, lineterminator="\n") features_writer.writerow( ["step", "state", "score", "metric_value", "best_score"]) features_handler.flush() if staleness is None: staleness = DEFAULT_STALENESS if penalty is None: penalty = DEFAULT_PENALTY # retrieving the first dataset in the file try: with open(datasets_file, u.open_mode("r")) as datasets_handler: dataset_id = datasets_handler.readline().strip() except IOError, exc: sys.exit("Could not read the generated datasets file: %s" % str(exc)) try: stored_dataset = u.storage_file_name(args.output_dir, dataset_id) with open(stored_dataset, u.open_mode("r")) as dataset_handler: dataset = json.loads(dataset_handler.read()) except IOError: dataset = api.check_resource(dataset_id, query_string=ALL_FIELDS_QS) # initial feature set fields = Fields(dataset) excluded_features = ([] if args.exclude_features is None else args.exclude_features.split(args.args_separator)) try: excluded_ids = [ fields.field_id(feature) for feature in excluded_features ] objective_id = fields.field_id(objective_name) except ValueError, exc: sys.exit(exc)
def best_first_search(datasets_file, api, args, common_options, staleness=None, penalty=None, objective_name=None, resume=False): """Selecting the fields to be used in the model construction """ counter = 0 loop_counter = 0 features_file = os.path.normpath(os.path.join(args.output_dir, FEATURES_LOG)) with open(features_file, u.open_mode("w")) as features_handler: features_writer = csv.writer(features_handler, lineterminator="\n") features_writer.writerow([ "step", "state", "score", "metric_value", "best_score"]) features_handler.flush() if staleness is None: staleness = DEFAULT_STALENESS if penalty is None: penalty = DEFAULT_PENALTY # retrieving the first dataset in the file try: with open(datasets_file, u.open_mode("r")) as datasets_handler: dataset_id = datasets_handler.readline().strip() except IOError, exc: sys.exit("Could not read the generated datasets file: %s" % str(exc)) try: stored_dataset = u.storage_file_name(args.output_dir, dataset_id) with open(stored_dataset, u.open_mode("r")) as dataset_handler: dataset = json.loads(dataset_handler.read()) except IOError: dataset = api.check_resource(dataset_id, query_string=ALL_FIELDS_QS) # initial feature set fields = Fields(dataset) excluded_features = ([] if args.exclude_features is None else args.exclude_features.split( args.args_separator)) try: excluded_ids = [fields.field_id(feature) for feature in excluded_features] objective_id = fields.field_id(objective_name) except ValueError, exc: sys.exit(exc)
FEATURES_LOG)) features_writer = UnicodeWriter(features_file).open_writer() features_header = FEATURES_HEADER if staleness is None: staleness = DEFAULT_STALENESS if penalty is None: penalty = DEFAULT_PENALTY # retrieving the first dataset in the file try: with open(datasets_file, u.open_mode("r")) as datasets_handler: dataset_id = datasets_handler.readline().strip() except IOError, exc: sys.exit("Could not read the generated datasets file: %s" % str(exc)) try: stored_dataset = u.storage_file_name(args.output_dir, dataset_id) with open(stored_dataset, u.open_mode("r")) as dataset_handler: dataset = json.loads(dataset_handler.read()) except IOError: dataset = api.check_resource(dataset_id, query_string=ALL_FIELDS_QS) # initial feature set fields = Fields(dataset) excluded_features = ([] if args.exclude_features is None else args.exclude_features.split( args.args_separator)) try: excluded_ids = [fields.field_id(feature) for feature in excluded_features] objective_id = fields.field_id(objective_name) except ValueError, exc:
features_file = os.path.normpath( os.path.join(args.output_dir, FEATURES_LOG)) features_writer = UnicodeWriter(features_file).open_writer() features_header = FEATURES_HEADER if staleness is None: staleness = DEFAULT_STALENESS if penalty is None: penalty = DEFAULT_PENALTY # retrieving the first dataset in the file try: with open(datasets_file, u.open_mode("r")) as datasets_handler: dataset_id = datasets_handler.readline().strip() except IOError, exc: sys.exit("Could not read the generated datasets file: %s" % str(exc)) try: stored_dataset = u.storage_file_name(args.output_dir, dataset_id) with open(stored_dataset, u.open_mode("r")) as dataset_handler: dataset = json.loads(dataset_handler.read()) except IOError: dataset = api.check_resource(dataset_id, query_string=ALL_FIELDS_QS) # initial feature set fields = Fields(dataset) excluded_features = ([] if args.exclude_features is None else args.exclude_features.split(args.args_separator)) try: excluded_ids = [ fields.field_id(feature) for feature in excluded_features ] objective_id = fields.field_id(objective_name) except ValueError, exc: sys.exit(exc)