def check_dataset_update(args, dataset):
"""Checks if the dataset information must be updated.
"""
return (args.dataset_attributes or args.import_fields
or (args.shared_flag and shared_changed(args.shared, dataset)) or
(((hasattr(args, 'max_categories') and args.max_categories > 0) or
(hasattr(args, 'multi_label') and args.multi_label))
and args.objective_field))
def evaluate(time_series_set, datasets, api, args, resume,
session_file=None, path=None, log=None,
fields=None, dataset_fields=None):
"""Evaluates a list of time-series with the given dataset
"""
output = args.predictions
evaluations, resume = evaluations_process(
time_series_set, datasets, fields,
dataset_fields, api, args, resume,
session_file=session_file, path=path, log=log)
for evaluation in evaluations:
evaluation = r.get_evaluation(evaluation, api, args.verbosity,
session_file)
if shared_changed(args.shared, evaluation):
evaluation_args = {"shared": args.shared}
evaluation = r.update_evaluation(evaluation, evaluation_args,
args, api=api, path=path,
session_file=session_file)
file_name = output
r.save_evaluation(evaluation, file_name, api)
return resume
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`.
"""
deepnet = None
deepnets = None
# variables from command-line options
resume = args.resume_
deepnet_ids = args.deepnet_ids_
output = args.predictions
# there's only one deepnet to be generated at present
args.max_parallel_deepnets = 1
# deepnets cannot be published yet.
args.public_deepnet = False
# It is compulsory to have a description to publish either datasets or
# deepnet
if (not args.description_
and (args.public_deepnet 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 args.objective_field:
csv_properties.update({'objective_field': args.objective_field})
# 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 datasets:
# Now we have a dataset, let's check if there's an objective_field
# given by the user and update it in the fields structure
args.objective_id_ = get_objective_id(args, fields)
if args.deepnet_file:
# deepnet is retrieved from the contents of the given local
# JSON file
deepnet, csv_properties, fields = u.read_local_resource(
args.deepnet_file, csv_properties=csv_properties)
deepnets = [deepnet]
deepnet_ids = [deepnet['resource']]
else:
# deepnet is retrieved from the remote object
deepnets, deepnet_ids, resume = \
pdn.deepnets_processing( \
datasets, deepnets, deepnet_ids, \
api, args, resume, fields=fields, \
session_file=session_file, path=path, log=log)
if deepnets:
deepnet = deepnets[0]
# We update the deepnet's public state if needed
if deepnet:
if isinstance(deepnet, basestring) or \
api.status(deepnet) != bigml.api.FINISHED:
if not a.has_test(args):
query_string = MINIMUM_MODEL
elif args.export_fields:
query_string = r.ALL_FIELDS_QS
else:
query_string = ''
deepnet = u.check_resource(deepnet,
api.get_deepnet,
query_string=query_string)
deepnets[0] = deepnet
if (args.public_deepnet or
(args.shared_flag and r.shared_changed(args.shared, deepnet))):
deepnet_args = {}
if args.shared_flag and r.shared_changed(args.shared, deepnet):
deepnet_args.update(shared=args.shared)
if args.public_deepnet:
deepnet_args.update( \
set_publish_model_args(args))
if deepnet_args:
deepnet = update_deepnet( \
deepnet, deepnet_args, args,
api=api, path=path, \
session_file=session_file)
deepnet[0] = deepnet
# We get the fields of the deepnet if we haven't got
# them yet and need them
if deepnet and (args.test_set or args.export_fields):
fields = pdn.get_deepnet_fields( \
deepnet, csv_properties, args)
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
# If predicting
if deepnets and (a.has_test(args) or \
(test_dataset and args.remote)):
if test_dataset is None:
test_dataset = get_test_dataset(args)
# Remote predictions: predictions are computed as batch predictions
# in bigml.com except when --no-batch flag is set on
if args.remote and not args.no_batch:
# 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 = 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)
csv_properties.update(objective_field=None,
objective_field_present=False)
test_fields = pd.get_fields_structure(test_dataset, csv_properties)
batch_prediction_args = set_batch_prediction_args(
args, fields=fields, dataset_fields=test_fields)
remote_dn_prediction(deepnet, test_dataset, \
batch_prediction_args, args, \
api, resume, prediction_file=output, \
session_file=session_file, path=path, log=log)
else:
dn_prediction(deepnets, fields, args, session_file=session_file)
# If evaluate flag is on, create remote evaluation and save results in
# json and human-readable format.
if args.evaluate:
# When we resume evaluation and models were already completed, we
# should use the datasets array as test datasets
if args.has_test_datasets_:
test_dataset = get_test_dataset(args)
if args.dataset_off and not args.has_test_datasets_:
args.test_dataset_ids = datasets
if args.test_dataset_ids and args.dataset_off:
# Evaluate the models with the corresponding test datasets.
test_dataset_id = bigml.api.get_dataset_id( \
args.test_dataset_ids[0])
test_dataset = api.check_resource(test_dataset_id)
csv_properties.update(objective_field=None,
objective_field_present=False)
test_fields = pd.get_fields_structure(test_dataset, csv_properties)
resume = evaluate(deepnets,
args.test_dataset_ids,
api,
args,
resume,
fields=fields,
dataset_fields=test_fields,
session_file=session_file,
path=path,
log=log,
objective_field=args.objective_field)
else:
dataset = datasets[0]
if args.test_split > 0 or args.has_test_datasets_:
dataset = test_dataset
dataset = u.check_resource(dataset,
api=api,
query_string=r.ALL_FIELDS_QS)
dataset_fields = pd.get_fields_structure(dataset, None)
resume = evaluate(deepnets, [dataset],
api,
args,
resume,
fields=fields,
dataset_fields=dataset_fields,
session_file=session_file,
path=path,
log=log,
objective_field=args.objective_field)
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 dataset_processing(source,
api,
args,
resume,
fields=None,
csv_properties=None,
multi_label_data=None,
session_file=None,
path=None,
log=None):
"""Creating or retrieving dataset from input arguments
"""
datasets = []
dataset = None
if (args.training_set or args.source or
(hasattr(args, "evaluate") and args.evaluate and args.test_set)):
# if resuming, try to extract args.dataset form log files
if resume:
message = u.dated("Dataset not found. Resuming.\n")
resume, args.dataset = c.checkpoint(c.is_dataset_created,
path,
debug=args.debug,
message=message,
log_file=session_file,
console=args.verbosity)
# If we have a source but no dataset or model has been provided, we
# create a new dataset if the no_dataset option isn't set up. Also
# if evaluate is set and test_set has been provided.
if ((source and not args.has_datasets_ and not args.has_models_
and not args.no_dataset)
or (hasattr(args, "evaluate") and args.evaluate and args.test_set
and not args.dataset)):
dataset_args = r.set_dataset_args(args,
fields,
multi_label_data=multi_label_data)
dataset = r.create_dataset(source, dataset_args, args, api, path,
session_file, log)
# If set of datasets is provided, let's check their ids.
elif args.dataset_ids:
for i in range(0, len(args.dataset_ids)):
dataset_id = args.dataset_ids[i]
if isinstance(dataset_id, dict) and "id" in dataset_id:
dataset_id = dataset_id["id"]
datasets.append(bigml.api.get_dataset_id(dataset_id))
dataset = datasets[0]
# If a dataset is provided, let's retrieve it.
elif args.dataset:
dataset = bigml.api.get_dataset_id(args.dataset)
# If we already have a dataset, we check the status and get the fields if
# we hadn't them yet.
if dataset:
dataset = r.get_dataset(dataset, api, args.verbosity, session_file)
if ('object' in dataset and 'objective_field' in dataset['object']
and 'column_number' in dataset['object']['objective_field']):
dataset_objective = dataset['object']['objective_field'][
'column_number']
csv_properties.update(objective_field=dataset_objective,
objective_field_present=True)
fields = get_fields_structure(dataset, csv_properties)
if args.public_dataset:
r.publish_dataset(dataset, args, api, session_file)
if hasattr(args, 'objective_field'):
new_objective = get_new_objective(fields, args.objective_field)
else:
new_objective = None
updated = False
# We'll update the dataset if
# the flag --dataset_attributes is used
# the --multi-label flag is used and there's an --objective-field
# the --max-categories flag is used and there's an --objective-field
# the --impor-fields flag is used
if check_dataset_update(args, dataset):
dataset_args = r.set_dataset_args(args, fields)
if args.shared_flag and shared_changed(args.shared, dataset):
dataset_args.update(shared=args.shared)
dataset = r.update_dataset(dataset,
dataset_args,
args,
api=api,
path=path,
session_file=session_file)
dataset = r.get_dataset(dataset, api, args.verbosity, session_file)
updated = True
if new_objective is not None:
csv_properties.update(objective_field=args.objective_field,
objective_field_present=True)
updated = True
if updated:
fields = Fields(dataset['object']['fields'], **csv_properties)
if not datasets:
datasets = [dataset]
else:
datasets[0] = dataset
return datasets, resume, csv_properties, fields
def evaluate(models_or_ensembles,
datasets,
api,
args,
resume,
session_file=None,
path=None,
log=None,
fields=None,
dataset_fields=None,
labels=None,
all_labels=None,
objective_field=None):
"""Evaluates a list of models or ensembles with the given dataset
"""
output = args.predictions
evaluation_files = []
evaluations, resume = evaluations_process(models_or_ensembles,
datasets,
fields,
dataset_fields,
api,
args,
resume,
session_file=session_file,
path=path,
log=log,
labels=labels,
all_labels=all_labels,
objective_field=objective_field)
if hasattr(args, 'multi_label') and args.multi_label:
file_labels = [
slugify(name)
for name in u.objective_field_names(models_or_ensembles, api)
]
for index, evaluation in enumerate(evaluations):
evaluation = r.get_evaluation(evaluation, api, args.verbosity,
session_file)
if shared_changed(args.shared, evaluation):
evaluation_args = {"shared": args.shared}
evaluation = r.update_evaluation(evaluation,
evaluation_args,
args,
api=api,
path=path,
session_file=session_file)
file_name = output
if hasattr(args, 'multi_label') and args.multi_label:
suffix = file_labels[index]
file_name += "_%s" % suffix
evaluation_files.append("%s.json" % file_name)
if args.test_datasets or args.dataset_off:
suffix = evaluation['resource'].replace('evaluation/', '_')
file_name += "_%s" % suffix
evaluation_files.append("%s.json" % file_name)
r.save_evaluation(evaluation, file_name, api)
if (hasattr(args, 'multi_label') and args.multi_label) or \
args.test_datasets or args.dataset_off:
mean_evaluation = average_evaluations(evaluation_files)
r.save_evaluation(mean_evaluation, output, api)
return resume
def compute_output(api, args):
""" Creates a fusion using the `models` list or uses the ids
of a previously created BigML fusion to make predictions for the `test_set`.
"""
fusion = None
# variables from command-line options
resume = args.resume_
fusion_ids = args.fusion_ids_
output = args.predictions
# there's only one fusion to be generated at present
args.max_parallel_fusions = 1
# fusion cannot be published yet.
args.public_fusion = False
# It is compulsory to have a description to publish either datasets or
# fusions
if (not args.description_ and args.public_fusion):
sys.exit("You should provide a description to publish.")
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])
if args.fusion_file:
# fusion regression is retrieved from the contents of the given local
# JSON file
fusion, csv_properties, fields = u.read_local_resource(
args.fusion_file, csv_properties=csv_properties)
fusion_ids = [fusion]
else:
# fusion is retrieved from the remote object or created
fusion, resume = \
pf.fusion_processing( \
fusion, fusion_ids, \
api, args, resume, \
session_file=session_file, path=path, log=log)
# We update the fusion public state if needed
if fusion:
if isinstance(fusion, basestring):
if not a.has_test(args):
query_string = MINIMUM_MODEL
elif args.export_fields:
query_string = r.ALL_FIELDS_QS
else:
query_string = ''
fusion = u.check_resource(fusion,
api.get_fusion,
query_string=query_string)
if (args.public_fusion or
(args.shared_flag and r.shared_changed(args.shared, fusion))):
fusion_args = {}
if args.shared_flag and r.shared_changed(args.shared, fusion):
fusion_args.update(shared=args.shared)
if args.public_fusion:
fusion_args.update( \
rfus.set_publish_fusion_args(args))
if fusion_args:
fusion = rfus.update_fusion( \
fusion, fusion_args, args,
api=api, path=path, \
session_file=session_file)
# We get the fields of the fusion if we haven't got
# them yet and need them
if fusion and (args.test_set or args.evaluate):
fields = pf.get_fusion_fields( \
fusion, csv_properties, args)
# If predicting
if fusion and (a.has_test(args) or \
args.remote):
test_dataset = get_test_dataset(args)
# Remote predictions: predictions are computed as batch predictions
# in bigml.com except when --no-batch flag is set on
if args.remote and not args.no_batch:
# 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 = 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)
csv_properties.update(objective_field=None,
objective_field_present=False)
test_fields = pd.get_fields_structure(test_dataset, csv_properties)
if not args.evaluate:
batch_prediction_args = set_batch_prediction_args(
args, fields=fields, dataset_fields=test_fields)
remote_prediction(fusion, test_dataset, \
batch_prediction_args, args, \
api, resume, prediction_file=output, \
session_file=session_file, path=path, log=log)
else:
prediction([fusion], fields, args, session_file=session_file)
# If evaluate flag is on, create remote evaluation and save results in
# json and human-readable format.
if args.evaluate:
# When we resume evaluation and models were already completed, we
# should use the datasets array as test datasets
args.max_parallel_evaluations = 1 # only one evaluation at present
args.cross_validation_rate = 0 # no cross-validation
args.number_of_evaluations = 1 # only one evaluation
if args.has_test_datasets_:
test_dataset = get_test_dataset(args)
dataset = test_dataset
dataset = u.check_resource(dataset,
api=api,
query_string=r.ALL_FIELDS_QS)
dataset_fields = pd.get_fields_structure(dataset, None)
resume = evaluate([fusion], [dataset],
api,
args,
resume,
fields=fields,
dataset_fields=dataset_fields,
session_file=session_file,
path=path,
log=log,
objective_field=args.objective_field)
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 anomaly detectors using the `training_set`
or uses the ids of previously created BigML models to make
predictions for the `test_set`.
"""
anomaly = None
anomalies = None
# no multi-label support at present
# variables from command-line options
resume = args.resume_
anomaly_ids = args.anomaly_ids_
output = args.predictions
# there's only one anomaly detector to be generated at present
args.max_parallel_anomalies = 1
# anomalies cannot be published yet.
args.public_anomaly = False
# It is compulsory to have a description to publish either datasets or
# anomalies
if (not args.description_
and (args.public_anomaly 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.anomaly_file:
# anomaly is retrieved from the contents of the given local JSON file
anomaly, csv_properties, fields = u.read_local_resource(
args.anomaly_file, csv_properties=csv_properties)
anomalies = [anomaly]
anomaly_ids = [anomaly['resource']]
else:
# anomaly is retrieved from the remote object
anomalies, anomaly_ids, resume = pa.anomalies_processing(
datasets,
anomalies,
anomaly_ids,
api,
args,
resume,
fields=fields,
session_file=session_file,
path=path,
log=log)
if anomalies:
anomaly = anomalies[0]
# We update the anomaly's public state if needed
if anomaly:
if not a.has_test(args) and not args.anomalies_dataset:
query_string = MINIMUM_MODEL
elif not a.has_test(args):
query_string = ";".join([EXCLUDE_TREES, r.ALL_FIELDS_QS])
else:
query_string = r.ALL_FIELDS_QS
try:
anomaly_id = anomaly.get('resource', anomaly)
except AttributeError:
anomaly_id = anomaly
anomaly = u.check_resource(anomaly_id,
query_string=query_string,
api=api)
anomalies[0] = anomaly
if (args.public_anomaly or
(args.shared_flag and r.shared_changed(args.shared, anomaly))):
anomaly_args = {}
if args.shared_flag and r.shared_changed(args.shared, anomaly):
anomaly_args.update(shared=args.shared)
if args.public_anomaly:
anomaly_args.update(ra.set_publish_anomaly_args(args))
if anomaly_args:
anomaly = ra.update_anomaly(anomaly,
anomaly_args,
args,
api=api,
path=path,
session_file=session_file)
anomalies[0] = anomaly
# We get the fields of the anomaly detector if we haven't got
# them yet and need them
if anomaly and (args.test_set or args.export_fields):
fields = pa.get_anomaly_fields(anomaly, csv_properties, args)
# If creating a top anomalies excluded/included dataset
if args.anomalies_dataset and anomaly:
origin_dataset = anomaly['object'].get('dataset')
if origin_dataset is None:
sys.exit("The dataset used to generate the anomaly detector "
"cannot be found. Failed to generate the anomalies "
" dataset.")
local_anomaly = Anomaly(anomaly)
include = args.anomalies_dataset == ANOMALIES_IN
args.anomaly_filter_ = local_anomaly.anomalies_filter(include=include)
_, resume = pd.create_new_dataset(origin_dataset,
api,
args,
resume,
fields=fields,
session_file=session_file,
path=path,
log=log)
# If predicting
if anomaly and args.score:
args.test_dataset = anomaly['object']['dataset']
if anomalies and (a.has_test(args) or (test_dataset and args.remote)):
# test dataset can be defined by --test-split or --test-dataset or
# --test-datasets
if test_dataset is None:
test_dataset = get_test_dataset(args)
# Remote anomaly scores: scores are computed as batch anomaly scores
# in bigml.com except when --no-batch flag is set on
if args.remote and not args.no_batch:
# 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 = 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_anomaly_score_args = set_batch_anomaly_score_args(
args, fields=fields, dataset_fields=test_fields)
remote_anomaly_score(anomaly,
test_dataset,
batch_anomaly_score_args,
args,
api,
resume,
prediction_file=output,
session_file=session_file,
path=path,
log=log)
else:
anomaly_score(anomalies, fields, args, session_file=session_file)
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
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`.
"""
pca = None
# variables from command-line options
resume = args.resume_
pca_ids = args.pca_ids_
output = args.projections
# there's only one pca to be generated at present
args.max_parallel_pcas = 1
# pca cannot be published yet.
args.public_pca = False
# It is compulsory to have a description to publish either datasets or
# pcas
if (not args.description_ and (args.public_pca 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 args.objective_field:
csv_properties.update({'objective_field': args.objective_field})
# 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.pca_file:
# pca regression is retrieved from the contents of the given local
# JSON file
pca, csv_properties, fields = u.read_local_resource(
args.pca_file, csv_properties=csv_properties)
pca_ids = [pca]
else:
# pca is retrieved from the remote object or created
pca, resume = \
pc.pca_processing( \
datasets, pca, pca_ids, \
api, args, resume, fields=fields, \
session_file=session_file, path=path, log=log)
# We update the pca public state if needed
if pca:
if isinstance(pca, basestring):
if not a.has_test(args):
query_string = MINIMUM_MODEL
elif args.export_fields:
query_string = r.ALL_FIELDS_QS
else:
query_string = ''
pca = u.check_resource(pca, api.get_pca, query_string=query_string)
if (args.public_pca
or (args.shared_flag and shared_changed(args.shared, pca))):
pca_args = {}
if args.shared_flag and shared_changed(args.shared, pca):
pca_args.update(shared=args.shared)
if args.public_pca:
pca_args.update( \
r.set_publish_pca_args(args))
if pca_args:
pca = r.update_pca( \
pca, pca_args, args,
api=api, path=path, \
session_file=session_file)
# We get the fields of the pca if we haven't got
# them yet and need them
if pca and (args.test_set or args.export_fields):
fields = pc.get_pca_fields( \
pca, csv_properties, args)
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
# If predicting
if pca and (a.has_test(args) or \
(test_dataset and args.remote)):
if test_dataset is None:
test_dataset = get_test_dataset(args)
# Remote projections: projections are computed as batch projections
# in bigml.com except when --no-batch flag is set on
if args.remote and not args.no_batch:
# 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 = 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)
csv_properties.update(objective_field=None,
objective_field_present=False)
test_fields = pd.get_fields_structure(test_dataset, csv_properties)
batch_projection_args = set_batch_projection_args(
args, fields=fields, dataset_fields=test_fields)
remote_projection(pca, test_dataset, \
batch_projection_args, args, \
api, resume, projection_file=output, \
session_file=session_file, path=path, log=log)
else:
projection(pca, 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)
def compute_output(api, args):
""" Creates a sample based on a `train_set`, source or dataset.
"""
samples = None
# variables from command-line options
resume = args.resume_
sample_ids = args.sample_ids_
output = args.predictions
# there's only one sample to be generated at present
args.max_parallel_clusters = 1
# sample cannot be published yet.
args.public_sample = False
# It is compulsory to have a description to publish either datasets or
# clusters
if (not args.description_ and (args.public_sample 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-sample 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-sample 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, _, resume, csv_properties, fields) = dataset_properties
if args.sample_file:
# sample is retrieved from the contents of the given local JSON file
sample, csv_properties, fields = u.read_local_resource(
args.sample_file, csv_properties=csv_properties)
samples = [sample]
sample_ids = [sample['resource']]
else:
# sample is retrieved from the remote object
samples, sample_ids, resume = psa.samples_processing(
datasets,
samples,
sample_ids,
api,
args,
resume,
session_file=session_file,
path=path,
log=log)
if samples:
sample = samples[0]
# We update the sample's public state if needed
if sample:
if isinstance(sample, basestring):
# build the query string from the sample options
sample = u.check_resource(sample, api.get_sample)
samples[0] = sample
if (args.public_sample
or (args.shared_flag and shared_changed(args.shared, sample))):
sample_args = {}
if args.shared_flag and shared_changed(args.shared, sample):
sample_args.update(shared=args.shared)
if args.public_sample:
sample_args.update(r.set_publish_sample_args(args))
if sample_args:
sample = r.update_sample(sample,
sample_args,
args,
api=api,
path=path,
session_file=session_file)
samples[0] = sample
# We get the fields of the sample if we haven't got
# them yet and need them
if sample and psa.needs_sample_fields(args):
fields = psa.get_sample_fields(sample, csv_properties, args)
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
sample_file(samples[0],
fields,
args,
api,
path=path,
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)
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`.
"""
source = None
dataset = None
model = None
models = None
fields = None
other_label = OTHER
ensemble_ids = []
multi_label_data = None
multi_label_fields = []
#local_ensemble = None
test_dataset = None
datasets = None
# variables from command-line options
resume = args.resume_
model_ids = args.model_ids_
output = args.output
dataset_fields = args.dataset_fields_
check_args_coherence(args)
path = u.check_dir(output)
session_file = "%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])
# labels to be used in multi-label expansion
labels = (None if args.labels is None else [
label.strip() for label in args.labels.split(args.args_separator)
])
if labels is not None:
labels = sorted([label for label in labels])
# multi_label file must be preprocessed to obtain a new extended file
if args.multi_label and args.training_set is not None:
(args.training_set, multi_label_data) = ps.multi_label_expansion(
args.training_set,
args.train_header,
args,
path,
labels=labels,
session_file=session_file)
args.train_header = True
args.objective_field = multi_label_data["objective_name"]
all_labels = l.get_all_labels(multi_label_data)
if not labels:
labels = all_labels
else:
all_labels = labels
if args.objective_field:
csv_properties.update({'objective_field': args.objective_field})
if args.source_file:
# source is retrieved from the contents of the given local JSON file
source, csv_properties, fields = u.read_local_resource(
args.source_file, csv_properties=csv_properties)
else:
# source is retrieved from the remote object
source, resume, csv_properties, fields = ps.source_processing(
api,
args,
resume,
csv_properties=csv_properties,
multi_label_data=multi_label_data,
session_file=session_file,
path=path,
log=log)
if source is not None:
args.source = bigml.api.get_source_id(source)
if args.multi_label and source:
multi_label_data = l.get_multi_label_data(source)
(args.objective_field, labels, all_labels,
multi_label_fields) = l.multi_label_sync(args.objective_field, labels,
multi_label_data, fields,
multi_label_fields)
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
if args.dataset_file:
# dataset is retrieved from the contents of the given local JSON file
model_dataset, csv_properties, fields = u.read_local_resource(
args.dataset_file, csv_properties=csv_properties)
if not args.datasets:
datasets = [model_dataset]
dataset = model_dataset
else:
datasets = u.read_datasets(args.datasets)
if not datasets:
# dataset is retrieved from the remote object
datasets, resume, csv_properties, fields = pd.dataset_processing(
source,
api,
args,
resume,
fields=fields,
csv_properties=csv_properties,
multi_label_data=multi_label_data,
session_file=session_file,
path=path,
log=log)
if datasets:
dataset = datasets[0]
if args.to_csv is not None:
resume = pd.export_dataset(dataset,
api,
args,
resume,
session_file=session_file,
path=path)
# Now we have a dataset, let's check if there's an objective_field
# given by the user and update it in the fields structure
args.objective_id_ = get_objective_id(args, fields)
# If test_split is used, split the dataset in a training and a test dataset
# according to the given split
if args.test_split > 0:
dataset, test_dataset, resume = pd.split_processing(
dataset,
api,
args,
resume,
multi_label_data=multi_label_data,
session_file=session_file,
path=path,
log=log)
datasets[0] = dataset
# Check if the dataset has a categorical objective field and it
# has a max_categories limit for categories
if args.max_categories > 0 and len(datasets) == 1:
if pd.check_max_categories(fields.fields[args.objective_id_]):
distribution = pd.get_categories_distribution(
dataset, args.objective_id_)
if distribution and len(distribution) > args.max_categories:
categories = [element[0] for element in distribution]
other_label = pd.create_other_label(categories, other_label)
datasets, resume = pd.create_categories_datasets(
dataset,
distribution,
fields,
args,
api,
resume,
session_file=session_file,
path=path,
log=log,
other_label=other_label)
else:
sys.exit("The provided objective field is not categorical nor "
"a full terms only text field. "
"Only these fields can be used with"
" --max-categories")
# If multi-dataset flag is on, generate a new dataset from the given
# list of datasets
if args.multi_dataset:
dataset, resume = pd.create_new_dataset(datasets,
api,
args,
resume,
fields=fields,
session_file=session_file,
path=path,
log=log)
datasets = [dataset]
# Check if the dataset has a generators file associated with it, and
# generate a new dataset with the specified field structure. Also
# if the --to-dataset flag is used to clone or sample the original dataset
if args.new_fields or (args.sample_rate != 1 and args.no_model) or \
(args.lisp_filter or args.json_filter) and not has_source(args):
if fields is None:
if isinstance(dataset, basestring):
dataset = u.check_resource(dataset, api=api)
fields = Fields(dataset, csv_properties)
args.objective_id_ = get_objective_id(args, fields)
args.objective_name_ = fields.field_name(args.objective_id_)
dataset, resume = pd.create_new_dataset(dataset,
api,
args,
resume,
fields=fields,
session_file=session_file,
path=path,
log=log)
datasets[0] = dataset
# rebuild fields structure for new ids and fields
csv_properties.update({
'objective_field': args.objective_name_,
'objective_field_present': True
})
fields = pd.get_fields_structure(dataset, csv_properties)
args.objective_id_ = get_objective_id(args, fields)
if args.multi_label and dataset and multi_label_data is None:
multi_label_data = l.get_multi_label_data(dataset)
(args.objective_field, labels, all_labels,
multi_label_fields) = l.multi_label_sync(args.objective_field, labels,
multi_label_data, fields,
multi_label_fields)
if dataset:
# retrieves max_categories data, if any
args.max_categories = get_metadata(dataset, 'max_categories',
args.max_categories)
other_label = get_metadata(dataset, 'other_label', other_label)
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
if args.model_file:
# model is retrieved from the contents of the given local JSON file
model, csv_properties, fields = u.read_local_resource(
args.model_file, csv_properties=csv_properties)
models = [model]
model_ids = [model['resource']]
ensemble_ids = []
elif args.ensemble_file:
# model is retrieved from the contents of the given local JSON file
ensemble, csv_properties, fields = u.read_local_resource(
args.ensemble_file, csv_properties=csv_properties)
model_ids = ensemble['object']['models'][:]
ensemble_ids = [ensemble['resource']]
models = model_ids[:]
model = retrieve_resource(args.retrieve_api_,
models[0],
query_string=r.ALL_FIELDS_QS)
models[0] = model
else:
# model is retrieved from the remote object
models, model_ids, ensemble_ids, resume = pm.models_processing(
datasets,
models,
model_ids,
api,
args,
resume,
fields=fields,
session_file=session_file,
path=path,
log=log,
labels=labels,
multi_label_data=multi_label_data,
other_label=other_label)
if models:
model = models[0]
single_model = len(models) == 1
# If multi-label flag is set and no training_set was provided, label
# info is extracted from the user_metadata. If models belong to an
# ensemble, the ensemble must be retrieved to get the user_metadata.
if model and args.multi_label and multi_label_data is None:
if ensemble_ids and isinstance(ensemble_ids[0], dict):
resource = ensemble_ids[0]
elif belongs_to_ensemble(model):
ensemble_id = get_ensemble_id(model)
resource = rens.get_ensemble(ensemble_id,
api=api,
verbosity=args.verbosity,
session_file=session_file)
else:
resource = model
multi_label_data = l.get_multi_label_data(resource)
# We update the model's public state if needed
if model:
if (isinstance(model, basestring)
or bigml.api.get_status(model)['code'] != bigml.api.FINISHED):
if not args.evaluate and not a.has_train(args) and \
not a.has_test(args):
query_string = MINIMUM_MODEL
elif not args.test_header:
query_string = r.ALL_FIELDS_QS
else:
query_string = "%s;%s" % (r.ALL_FIELDS_QS, r.FIELDS_QS)
model = u.check_resource(model,
api.get_model,
query_string=query_string)
models[0] = model
if (args.black_box or args.white_box or
(args.shared_flag and r.shared_changed(args.shared, model))):
model_args = {}
if args.shared_flag and r.shared_changed(args.shared, model):
model_args.update(shared=args.shared)
if args.black_box or args.white_box:
model_args.update(rmod.set_publish_model_args(args))
if model_args:
model = rmod.update_model(model,
model_args,
args,
api=api,
path=path,
session_file=session_file)
models[0] = model
# We get the fields of the model if we haven't got
# them yet and need them
if model and not args.evaluate and (a.has_test(args)
or args.export_fields):
# if we are using boosted ensembles to predict, activate boosting
if model['object'].get('boosted_ensemble'):
args.boosting = True
# If more than one model, use the full field structure
if (not single_model and not args.multi_label
and belongs_to_ensemble(model)):
if ensemble_ids:
ensemble_id = ensemble_ids[0]
args.ensemble_ids_ = ensemble_ids
else:
ensemble_id = get_ensemble_id(model)
fields = pm.get_model_fields(model,
csv_properties,
args,
single_model=single_model,
multi_label_data=multi_label_data)
# Free memory after getting fields
# local_ensemble = None
gc.collect()
# Fills in all_labels from user_metadata
if args.multi_label and not all_labels:
(args.objective_field, labels, all_labels,
multi_label_fields) = l.multi_label_sync(args.objective_field, labels,
multi_label_data, fields,
multi_label_fields)
if model:
# retrieves max_categories data, if any
args.max_categories = get_metadata(model, 'max_categories',
args.max_categories)
other_label = get_metadata(model, 'other_label', other_label)
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
# If predicting
if (models and (a.has_test(args) or (test_dataset and args.remote))
and not args.evaluate):
models_per_label = 1
if test_dataset is None:
test_dataset = get_test_dataset(args)
if args.multi_label:
# When prediction starts from existing models, the
# multi_label_fields can be retrieved from the user_metadata
# in the models
if args.multi_label_fields is None and multi_label_fields:
multi_label_field_names = [
field[1] for field in multi_label_fields
]
args.multi_label_fields = ",".join(multi_label_field_names)
test_set = ps.multi_label_expansion(args.test_set,
args.test_header,
args,
path,
labels=labels,
session_file=session_file,
input_flag=True)[0]
test_set_header = True
# Remote predictions: predictions are computed as batch predictions
# in bigml.com except when --no-batch flag is set on or multi-label
# or max-categories are used
if (args.remote and not args.no_batch and not args.multi_label
and not args.method == COMBINATION):
# 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,
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 = rds.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)
csv_properties.update(objective_field=None,
objective_field_present=False)
test_fields = pd.get_fields_structure(test_dataset, csv_properties)
if args.to_dataset and args.dataset_off:
model = api.check_resource(model['resource'],
query_string=r.ALL_FIELDS_QS)
model_fields = Fields(model)
objective_field_name = model_fields.field_name( \
model_fields.objective_field)
if objective_field_name in test_fields.fields_by_name.keys():
args.prediction_name = "%s (predicted)" % \
objective_field_name
batch_prediction_args = rbp.set_batch_prediction_args(
args, fields=fields, dataset_fields=test_fields)
remote_predict(model,
test_dataset,
batch_prediction_args,
args,
api,
resume,
prediction_file=output,
session_file=session_file,
path=path,
log=log)
else:
models_per_label = args.number_of_models
if (args.multi_label and ensemble_ids
and args.number_of_models == 1):
# use case where ensembles are read from a file
models_per_label = len(models) / len(ensemble_ids)
predict(models,
fields,
args,
api=api,
log=log,
resume=resume,
session_file=session_file,
labels=labels,
models_per_label=models_per_label,
other_label=other_label,
multi_label_data=multi_label_data)
# When combine_votes flag is used, retrieve the predictions files saved
# in the comma separated list of directories and combine them
if args.votes_files_:
model_id = re.sub(r'.*(model_[a-f0-9]{24})__predictions\.csv$', r'\1',
args.votes_files_[0]).replace("_", "/")
try:
model = u.check_resource(model_id, api.get_model)
except ValueError, exception:
sys.exit("Failed to get model %s: %s" % (model_id, str(exception)))
local_model = Model(model)
message = u.dated("Combining votes.\n")
u.log_message(message, log_file=session_file, console=args.verbosity)
combine_votes(args.votes_files_,
local_model.to_prediction,
output,
method=args.method)
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`.
"""
cluster = None
clusters = None
# no multi-label support at present
# variables from command-line options
resume = args.resume_
cluster_ids = args.cluster_ids_
output = args.predictions
# there's only one cluster to be generated at present
args.max_parallel_clusters = 1
# clusters cannot be published yet.
args.public_cluster = False
# It is compulsory to have a description to publish either datasets or
# clusters
if (not args.description_
and (args.public_cluster 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.cluster_file:
# cluster is retrieved from the contents of the given local JSON file
cluster, csv_properties, fields = u.read_local_resource(
args.cluster_file, csv_properties=csv_properties)
clusters = [cluster]
cluster_ids = [cluster['resource']]
else:
# cluster is retrieved from the remote object
clusters, cluster_ids, resume = pc.clusters_processing(
datasets,
clusters,
cluster_ids,
api,
args,
resume,
fields=fields,
session_file=session_file,
path=path,
log=log)
if clusters:
cluster = clusters[0]
# We update the cluster's public state if needed
if cluster:
if isinstance(cluster, basestring):
if args.cluster_datasets is None and not a.has_test(args):
query_string = MINIMUM_MODEL
else:
query_string = ''
cluster = u.check_resource(cluster,
api.get_cluster,
query_string=query_string)
clusters[0] = cluster
if (args.public_cluster or
(args.shared_flag and r.shared_changed(args.shared, cluster))):
cluster_args = {}
if args.shared_flag and r.shared_changed(args.shared, cluster):
cluster_args.update(shared=args.shared)
if args.public_cluster:
cluster_args.update(rcl.set_publish_cluster_args(args))
if cluster_args:
cluster = rcl.update_cluster(cluster,
cluster_args,
args,
api=api,
path=path,
session_file=session_file)
clusters[0] = cluster
# We get the fields of the cluster if we haven't got
# them yet and need them
if cluster and (args.test_set or args.export_fields):
if isinstance(cluster, dict):
cluster = cluster['resource']
cluster = u.check_resource(cluster,
api.get_cluster,
query_string=r.ALL_FIELDS_QS)
fields = pc.get_cluster_fields(cluster, csv_properties, args)
# If predicting
if clusters and (a.has_test(args) or (test_dataset and args.remote)):
if test_dataset is None:
test_dataset = get_test_dataset(args)
# Remote centroids: centroids are computed as batch centroids
# in bigml.com except when --no-batch flag is set on
if args.remote and not args.no_batch:
# 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 = 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_centroid_args = rbc.set_batch_centroid_args(
args, fields=fields, dataset_fields=test_fields)
remote_centroid(cluster,
test_dataset,
batch_centroid_args,
args,
api,
resume,
prediction_file=output,
session_file=session_file,
path=path,
log=log)
else:
centroid(clusters, fields, args, session_file=session_file)
if cluster and args.cluster_datasets is not None:
cluster = api.check_resource(cluster)
centroids_info = cluster['object']['clusters']['clusters']
centroids = {
centroid['name']: centroid['id']
for centroid in centroids_info
}
cluster_datasets = cluster['object']['cluster_datasets']
if args.cluster_datasets == '':
centroid_ids = centroids.values()
else:
centroid_ids = [
centroids[cluster_name]
for cluster_name in args.cluster_datasets_
if cluster_datasets.get(centroids[cluster_name], '') == ''
]
for centroid_id in centroid_ids:
dataset_args = {'centroid': centroid_id}
create_dataset(cluster,
dataset_args,
args,
api=api,
path=path,
session_file=session_file,
log=log,
dataset_type='cluster')
if cluster and args.cluster_models is not None:
cluster = api.check_resource(cluster)
centroids_info = cluster['object']['clusters']['clusters']
centroids = {
centroid['name']: centroid['id']
for centroid in centroids_info
}
models = cluster['object']['cluster_models']
if args.cluster_models == '':
centroid_ids = centroids.values()
else:
centroid_ids = [
centroids[cluster_name]
for cluster_name in args.cluster_models_
if models.get(centroids[cluster_name], '') == ''
]
for centroid_id in centroid_ids:
model_args = {'centroid': centroid_id}
create_model(cluster,
model_args,
args,
api=api,
path=path,
session_file=session_file,
log=log,
model_type='cluster')
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
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`.
"""
topic_model = None
topic_models = None
# no multi-label support at present
# variables from command-line options
resume = args.resume_
topic_model_ids = args.topic_model_ids_
output = args.predictions
# there's only one topic model resource to be generated at present
args.max_parallel_topic_models = 1
# topic models cannot be published yet.
args.public_topic_model = False
# It is compulsory to have a description to publish either datasets or
# topic models
if (not args.description_
and (args.public_topic_model 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.topic_model_file:
# topic model is retrieved from the contents of the given local JSON
# file
topic_model, csv_properties, fields = u.read_local_resource(
args.topic_model_file, csv_properties=csv_properties)
topic_models = [topic_model]
topic_model_ids = [topic_model['resource']]
else:
# topic model is retrieved from the remote object
topic_models, topic_model_ids, resume = pt.topic_model_processing(
datasets,
topic_models,
topic_model_ids,
api,
args,
resume,
fields=fields,
session_file=session_file,
path=path,
log=log)
if topic_models:
topic_model = topic_models[0]
# We update the topic model's public state if needed
if topic_model:
if isinstance(topic_model, basestring):
if not a.has_test(args):
query_string = MINIMUM_MODEL
else:
query_string = ''
topic_model = u.check_resource(topic_model,
api.topic_model,
query_string=query_string)
topic_models[0] = topic_model
if (args.public_topic_model or
(args.shared_flag and r.shared_changed(args.shared, topic_model))):
topic_model_args = {}
if args.shared_flag and \
r.shared_changed(args.shared, topic_model):
topic_model_args.update(shared=args.shared)
if args.public_topic_model:
topic_model_args.update(rtm.set_publish_topic_model_args(args))
if topic_model_args:
topic_model = rtm.update_topic_model( \
topic_model, topic_model_args, args,
api=api, path=path,
session_file=session_file)
topic_models[0] = topic_model
# We get the fields of the topic model if we haven't got
# them yet and need them
if topic_model and args.test_set:
csv_properties.update({
'objective_field_present': False,
'objective_field': None
})
fields = pt.get_topic_model_fields(topic_model, csv_properties, args)
# If predicting
if topic_models and (a.has_test(args) or (test_dataset and args.remote)):
if test_dataset is None:
test_dataset = get_test_dataset(args)
# Remote topic distributions:topic distributions are computed as
# batch topic distributions
# in bigml.com except when --no-batch flag is set.
if args.remote and not args.no_batch:
# 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 = 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_topic_distribution_args = \
rtd.set_batch_topic_distribution_args( \
args, fields=fields, \
dataset_fields=test_fields)
remote_topic_distribution( \
topic_model, test_dataset, batch_topic_distribution_args,
args, api, resume, prediction_file=output,
session_file=session_file, path=path, log=log)
else:
topic_distribution(topic_models,
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)
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`.
"""
time_series = None
time_series_set = None
# variables from command-line options
resume = args.resume_
time_series_ids = args.time_series_ids_
output = args.predictions
# there's only one time_series to be generated at present
args.max_parallel_time_series = 1
args.max_parallel_evaluations = 1
# time_series cannot be published yet.
args.public_time_series = False
# no cross-validations
args.dataset_off = False
args.cross_validation_rate = 0
args.number_of_evaluations = 1
# It is compulsory to have a description to publish either datasets or
# time_series
if (not args.description_
and (args.public_time_series 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 args.objective_field:
csv_properties.update({'objective_field': args.objective_field})
# 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 datasets:
# Now we have a dataset, let's check if there's an objective_field
# given by the user and update it in the fields structure
args.objective_id_ = get_objective_id(args, fields)
# if the time series is going to be evaluated, and we don't have
# test data, we need to divide the rows using ranges, so we'll need
# max rows
args.max_rows = datasets[0]["object"]["rows"]
if args.time_series_file:
# time-series is retrieved from the contents of the given local
# JSON file
time_series, csv_properties, fields = u.read_local_resource(
args.time_series_file, csv_properties=csv_properties)
time_series_set = [time_series]
time_series_ids = [time_series['resource']]
else:
# time-series is retrieved from the remote object
time_series_set, time_series_ids, resume = \
pts.time_series_processing( \
datasets, time_series_set, time_series_ids, \
api, args, resume, fields=fields, \
session_file=session_file, path=path, log=log)
if time_series_set:
time_series = time_series_set[0]
# We update the time-series' public state if needed
if time_series:
if isinstance(time_series, basestring):
query_string = r.ALL_FIELDS_QS
time_series = u.check_resource(time_series,
api.get_time_series,
query_string=query_string)
time_series_set[0] = time_series
if (args.public_time_series or
(args.shared_flag and r.shared_changed(args.shared, time_series))):
time_series_args = {}
if args.shared_flag and r.shared_changed(args.shared, time_series):
time_series_args.update(shared=args.shared)
if args.public_time_series:
time_series_args.update( \
rts.set_publish_time_series_args(args))
if time_series_args:
time_series = rts.update_time_series( \
time_series, time_series_args, args,
api=api, path=path, \
session_file=session_file)
time_series_set[0] = time_series
"""
# We get the fields of the time-series if we haven't got
# them yet and need them
if time_series and (args.test_set or args.export_fields):
fields = pts.get_time_series_fields( \
time_series, csv_properties, args)
"""
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
# If forecasting
if time_series_set and a.has_ts_test(args):
if args.remote:
forecast_args = set_forecast_args(args, fields=fields)
remote_forecast(time_series, forecast_args, args, \
api, resume, \
session_file=session_file, path=path, log=log)
else:
forecast(time_series, args, session_file=session_file)
# If evaluate flag is on, create remote evaluation and save results in
# json and human-readable format.
if args.evaluate:
# When we resume evaluation and models were already completed, we
# should use the datasets array as test datasets
if args.has_test_datasets_:
test_dataset = get_test_dataset(args)
if args.dataset_off and not args.has_test_datasets_:
args.test_dataset_ids = datasets
if args.test_dataset_ids and args.dataset_off:
# Evaluate the models with the corresponding test datasets.
test_dataset_id = bigml.api.get_dataset_id( \
args.test_dataset_ids[0])
test_dataset = api.check_resource(test_dataset_id)
csv_properties.update(objective_field=None,
objective_field_present=False)
test_fields = pd.get_fields_structure(test_dataset, csv_properties)
resume = evaluate(time_series_set,
args.test_dataset_ids,
api,
args,
resume,
fields=fields,
dataset_fields=test_fields,
session_file=session_file,
path=path,
log=log,
objective_field=args.objective_field)
else:
dataset = datasets[0]
if args.test_split > 0 or args.has_test_datasets_:
dataset = test_dataset
else:
args.range_ = [
int(args.max_rows * r.EVALUATE_SAMPLE_RATE), args.max_rows
]
dataset = u.check_resource(dataset,
api=api,
query_string=r.ALL_FIELDS_QS)
dataset_fields = pd.get_fields_structure(dataset, None)
resume = evaluate(time_series_set, [dataset],
api,
args,
resume,
fields=fields,
dataset_fields=dataset_fields,
session_file=session_file,
path=path,
log=log)
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)