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(r.set_publish_cluster_args(args))
if cluster_args:
cluster = r.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 = 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_centroid_args = r.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}
r.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}
r.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`.
"""
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(r.set_publish_cluster_args(args))
if cluster_args:
cluster = r.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:
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 = 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_centroid_args = r.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:
centroids_info = cluster['object']['clusters']['clusters']
centroids = {centroid['name']: centroid['id']
for centroid in centroids_info}
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 datasets[centroids[cluster_name]] == '']
for centroid_id in centroid_ids:
dataset_args = {'centroid': centroid_id}
r.create_dataset(cluster, dataset_args, args, api=api, path=path,
session_file=session_file, log=log,
dataset_type='cluster')
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)