def compute_output(api, args):
""" Creates one or more models using the `training_set` or uses the ids
of previously created BigML models to make predictions for the `test_set`.
"""
association = None
associations = None
# no multi-label support at present
# variables from command-line options
resume = args.resume_
association_ids = args.association_ids_
output = args.predictions
# there's only one association resource to be generated at present
args.max_parallel_associations = 1
# associations cannot be published yet.
args.public_association = False
# It is compulsory to have a description to publish either datasets or
# associations
if (not args.description_ and (args.public_association or
args.public_dataset)):
sys.exit("You should provide a description to publish.")
# When using --new-fields, it is compulsory to specify also a dataset
# id
if args.new_fields and not args.dataset:
sys.exit("To use --new-fields you must also provide a dataset id"
" to generate the new dataset from it.")
path = u.check_dir(output)
session_file = u"%s%s%s" % (path, os.sep, SESSIONS_LOG)
csv_properties = {}
# If logging is required set the file for logging
log = None
if args.log_file:
u.check_dir(args.log_file)
log = args.log_file
# If --clear_logs the log files are cleared
clear_log_files([log])
# basic pre-model step: creating or retrieving the source related info
source, resume, csv_properties, fields = pms.get_source_info(
api, args, resume, csv_properties, session_file, path, log)
# basic pre-model step: creating or retrieving the dataset related info
dataset_properties = pms.get_dataset_info(
api, args, resume, source,
csv_properties, fields, session_file, path, log)
(_, datasets, test_dataset,
resume, csv_properties, fields) = dataset_properties
if args.association_file:
# association is retrieved from the contents of the given local JSON
# file
association, csv_properties, fields = u.read_local_resource(
args.association_file,
csv_properties=csv_properties)
associations = [association]
association_ids = [association['resource']]
else:
# association is retrieved from the remote object
associations, association_ids, resume = pa.associations_processing(
datasets, associations, association_ids, api, args, resume,
fields=fields,
session_file=session_file, path=path, log=log)
if associations:
association = associations[0]
# We update the association's public state if needed
if association:
if isinstance(association, basestring):
if not a.has_test(args):
query_string = MINIMUM_MODEL
else:
query_string = ''
association = u.check_resource(association, api.get_association,
query_string=query_string)
associations[0] = association
if (args.public_association or
(args.shared_flag and
r.shared_changed(args.shared, association))):
association_args = {}
if args.shared_flag and \
r.shared_changed(args.shared, association):
association_args.update(shared=args.shared)
if args.public_association:
association_args.update(ras.set_publish_association_args(args))
if association_args:
association = ras.update_association( \
association, association_args, args,
api=api, path=path,
session_file=session_file)
associations[0] = association
# We get the fields of the association if we haven't got
# them yet and need them
if association and args.test_set:
fields = pa.get_association_fields(association, csv_properties, args)
# If predicting
if associations and (a.has_test(args) or (test_dataset and args.remote)):
if test_dataset is None:
test_dataset = get_test_dataset(args)
# Remote association sets: association sets are computed as
# batch association sets
# in bigml.com except when --no-batch flag is set. They are currently
# not supported yet
if args.remote and not args.no_batch:
sys.exit("Batch association sets are currently not supported.")
else:
sys.exit("Local prediction of association sets is currently"
" not supported.")
u.print_generated_files(path, log_file=session_file,
verbosity=args.verbosity)
if args.reports:
clear_reports(path)
if args.upload:
upload_reports(args.reports, path)
def compute_output(api, args):
""" Creates one or more models using the `training_set` or uses the ids
of previously created BigML models to make predictions for the `test_set`.
"""
association = None
associations = None
# no multi-label support at present
# variables from command-line options
resume = args.resume_
association_ids = args.association_ids_
output = args.predictions
# there's only one association resource to be generated at present
args.max_parallel_associations = 1
# associations cannot be published yet.
args.public_association = False
# It is compulsory to have a description to publish either datasets or
# associations
if (not args.description_ and (args.public_association or
args.public_dataset)):
sys.exit("You should provide a description to publish.")
# When using --new-fields, it is compulsory to specify also a dataset
# id
if args.new_fields and not args.dataset:
sys.exit("To use --new-fields you must also provide a dataset id"
" to generate the new dataset from it.")
path = u.check_dir(output)
session_file = u"%s%s%s" % (path, os.sep, SESSIONS_LOG)
csv_properties = {}
# If logging is required set the file for logging
log = None
if args.log_file:
u.check_dir(args.log_file)
log = args.log_file
# If --clear_logs the log files are cleared
clear_log_files([log])
# basic pre-model step: creating or retrieving the source related info
source, resume, csv_properties, fields = pms.get_source_info(
api, args, resume, csv_properties, session_file, path, log)
# basic pre-model step: creating or retrieving the dataset related info
dataset_properties = pms.get_dataset_info(
api, args, resume, source,
csv_properties, fields, session_file, path, log)
(_, datasets, test_dataset,
resume, csv_properties, fields) = dataset_properties
if args.association_file:
# association is retrieved from the contents of the given local JSON
# file
association, csv_properties, fields = u.read_local_resource(
args.association_file,
csv_properties=csv_properties)
associations = [association]
association_ids = [association['resource']]
else:
# association is retrieved from the remote object
associations, association_ids, resume = pa.associations_processing(
datasets, associations, association_ids, api, args, resume,
fields=fields,
session_file=session_file, path=path, log=log)
if associations:
association = associations[0]
# We update the association's public state if needed
if association:
if isinstance(association, basestring):
if not a.has_test(args):
query_string = MINIMUM_MODEL
else:
query_string = ''
association = u.check_resource(association, api.get_association,
query_string=query_string)
associations[0] = association
if (args.public_association or
(args.shared_flag and
r.shared_changed(args.shared, association))):
association_args = {}
if args.shared_flag and \
r.shared_changed(args.shared, association):
association_args.update(shared=args.shared)
if args.public_association:
association_args.update(r.set_publish_association_args(args))
if association_args:
association = r.update_association( \
association, association_args, args,
api=api, path=path,
session_file=session_file)
associations[0] = association
# We get the fields of the association if we haven't got
# them yet and need them
if association and args.test_set:
fields = pa.get_association_fields(association, csv_properties, args)
# If predicting
if associations and (a.has_test(args) or (test_dataset and args.remote)):
if test_dataset is None:
test_dataset = get_test_dataset(args)
# Remote association sets: association sets are computed as
# batch association sets
# in bigml.com except when --no-batch flag is set. They are currently
# not supported yet
if args.remote and not args.no_batch:
sys.exit("Batch association sets are currently not supported.")
"""
# create test source from file
test_name = "%s - test" % args.name
if args.test_source is None:
test_properties = ps.test_source_processing(
api, args, resume, name=test_name,
session_file=session_file, path=path, log=log)
(test_source, resume,
csv_properties, test_fields) = test_properties
else:
test_source_id = bigml.api.get_source_id(args.test_source)
test_source = api.check_resource(test_source_id)
if test_dataset is None:
# create test dataset from test source
dataset_args = r.set_basic_dataset_args(args, name=test_name)
test_dataset, resume = pd.alternative_dataset_processing(
test_source, "test", dataset_args, api, args,
resume, session_file=session_file, path=path, log=log)
else:
test_dataset_id = bigml.api.get_dataset_id(test_dataset)
test_dataset = api.check_resource(test_dataset_id)
test_fields = pd.get_fields_structure(test_dataset,
csv_properties)
batch_association_args = r.set_batch_association_args(
args, fields=fields,
dataset_fields=test_fields)
remote_association( \
association, test_dataset, batch_association_args,
args, api, resume, prediction_file=output,
session_file=session_file, path=path, log=log)
"""
else:
sys.exit("Local prediction of association sets is currently"
" not supported.")
"""
association_set(associations, fields, args,
session_file=session_file)
"""
u.print_generated_files(path, log_file=session_file,
verbosity=args.verbosity)
if args.reports:
clear_reports(path)
if args.upload:
upload_reports(args.reports, path)