def update_sample(sample,
sample_args,
args,
api=None,
path=None,
session_file=None):
"""Updates sample properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating sample. %s\n" % get_url(sample))
log_message(message, log_file=session_file, console=args.verbosity)
sample = api.update_sample(sample, sample_args)
check_resource_error(sample,
"Failed to update sample: %s" % sample['resource'])
sample = check_resource(sample, api.get_sample, raise_on_error=True)
if is_shared(sample):
message = dated("Shared sample link. %s\n" %
get_url(sample, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, sample)
return sample
def update_anomaly(anomaly,
anomaly_args,
args,
api=None,
path=None,
session_file=None):
"""Updates anomaly properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating anomaly detector. %s\n" % get_url(anomaly))
log_message(message, log_file=session_file, console=args.verbosity)
anomaly = api.update_anomaly(anomaly, anomaly_args)
check_resource_error(anomaly,
"Failed to update anomaly: %s" % anomaly['resource'])
anomaly = check_resource(anomaly,
api.get_anomaly,
query_string=FIELDS_QS,
raise_on_error=True)
if is_shared(anomaly):
message = dated("Shared anomaly link. %s\n" %
get_url(anomaly, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, anomaly)
return anomaly
def update_logistic_regression(logistic_regression,
logistic_regression_args,
args,
api=None,
path=None,
session_file=None):
"""Updates logistic regression properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating logistic regression. %s\n" %
get_url(logistic_regression))
log_message(message, log_file=session_file, console=args.verbosity)
logistic_regression = api.update_logistic_regression(logistic_regression, \
logistic_regression_args)
check_resource_error(
logistic_regression, "Failed to update logistic regression: %s" %
logistic_regression['resource'])
logistic_regression = check_resource(logistic_regression,
api.get_logistic_regression,
query_string=FIELDS_QS,
raise_on_error=True)
if is_shared(logistic_regression):
message = dated("Shared logistic regression link. %s\n" %
get_url(logistic_regression, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, logistic_regression)
return logistic_regression
def update_time_series(time_series,
time_series_args,
args,
api=None,
path=None,
session_file=None):
"""Updates time-series properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating time-series. %s\n" % get_url(time_series))
log_message(message, log_file=session_file, console=args.verbosity)
time_series = api.update_time_series(time_series, \
time_series_args)
check_resource_error(
time_series,
"Failed to update time-series: %s" % time_series['resource'])
time_series = check_resource(time_series,
api.get_time_series,
query_string=FIELDS_QS,
raise_on_error=True)
if is_shared(time_series):
message = dated("Shared time-series link. %s\n" %
get_url(time_series, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, time_series)
return time_series
def report(report_types_list, output_dir=None, resource=None):
"""Generate the requested reports
"""
shared = is_shared(resource)
for report_type in report_types_list:
REPORTS[report_type](resource, output_dir, shared)
def report(report_types_list, output_dir=None, resource=None):
"""Generate the requested reports
"""
shared = is_shared(resource)
for report_type in report_types_list:
REPORTS[report_type](resource, output_dir, shared)
def update_deepnet(deepnet,
deepnet_args,
args,
api=None,
path=None,
session_file=None):
"""Updates deepnet properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating deepnet. %s\n" % get_url(deepnet))
log_message(message, log_file=session_file, console=args.verbosity)
deepnet = api.update_deepnet(deepnet, deepnet_args)
check_resource_error(deepnet,
"Failed to update deepnet: %s" % deepnet['resource'])
deepnet = check_resource(deepnet,
api.get_deepnet,
query_string=FIELDS_QS,
raise_on_error=True)
if is_shared(deepnet):
message = dated("Shared deepnet link. %s\n" %
get_url(deepnet, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, deepnet)
return deepnet
def update_topic_model(topic_model,
topic_model_args,
args,
api=None,
path=None,
session_file=None):
"""Updates topic model properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating topic model. %s\n" % get_url(topic_model))
log_message(message, log_file=session_file, console=args.verbosity)
topic_model = api.update_topic_model(topic_model, \
topic_model_args)
check_resource_error(
topic_model,
"Failed to update topic model: %s" % topic_model['resource'])
topic_model = check_resource(topic_model,
api.get_topic_model,
query_string=FIELDS_QS,
raise_on_error=True)
if is_shared(topic_model):
message = dated("Shared topic model link. %s\n" %
get_url(topic_model, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, topic_model)
return topic_model
def share_resource(api, resource):
"""Creates a secret link to share the resource.
"""
resource_type = get_resource_type(resource)
resource = get_updater(api, resource_type)(resource, {"shared": True})
if api.ok(resource) and is_shared(resource):
return ("https://bigml.com/shared/%s/%s" %
(resource_type, resource['object']['shared_hash']))
else:
sys.exit("Failed to share the resource: %s" % resource['resource'])
def share_resource(api, resource):
"""Creates a secret link to share the resource.
"""
resource_type = get_resource_type(resource)
resource = get_updater(api, resource_type)(resource, {"shared": True})
if api.ok(resource) and is_shared(resource):
return ("https://bigml.com/shared/%s/%s" %
(resource_type, resource['object']['shared_hash']))
else:
sys.exit("Failed to share the resource: %s" % resource['resource'])
def update_dataset(dataset, dataset_args, args,
api=None, path=None, session_file=None):
"""Updates dataset properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating dataset. %s\n" %
get_url(dataset))
log_message(message, log_file=session_file,
console=args.verbosity)
dataset = api.update_dataset(dataset, dataset_args)
if is_shared(dataset):
message = dated("Shared dataset link. %s\n" %
get_url(dataset, shared=True))
log_message(message, log_file=session_file,
console=args.verbosity)
if args.reports:
report(args.reports, path, dataset)
check_resource_error(dataset, "Failed to update dataset: ")
return dataset
def update_dataset(dataset,
dataset_args,
args,
api=None,
path=None,
session_file=None):
"""Updates dataset properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating dataset. %s\n" % get_url(dataset))
log_message(message, log_file=session_file, console=args.verbosity)
dataset = api.update_dataset(dataset, dataset_args)
if is_shared(dataset):
message = dated("Shared dataset link. %s\n" %
get_url(dataset, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, dataset)
check_resource_error(dataset, "Failed to update dataset: ")
return dataset
def update_pca(pca, pca_args, args, api=None, path=None, session_file=None):
"""Updates pca properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating PCA. %s\n" % get_url(pca))
log_message(message, log_file=session_file, console=args.verbosity)
pca = api.update_pca(pca, pca_args)
check_resource_error(pca, "Failed to update PCA: %s" % pca['resource'])
pca = check_resource(pca,
api.get_pca,
query_string=FIELDS_QS,
raise_on_error=True)
if is_shared(pca):
message = dated("Shared PCA link. %s\n" % get_url(pca, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, pca)
return pca
def update_evaluation(evaluation, evaluation_args, args,
api=None, path=None, session_file=None):
"""Updates evaluation properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating evaluation. %s\n" %
get_url(evaluation))
log_message(message, log_file=session_file,
console=args.verbosity)
evaluation = api.update_evaluation(evaluation, evaluation_args)
check_resource_error(evaluation, "Failed to update evaluation: %s"
% evaluation['resource'])
if is_shared(evaluation):
message = dated("Shared evaluation link. %s\n" %
get_url(evaluation, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, evaluation)
return evaluation
def update_model(model, model_args, args,
api=None, path=None, session_file=None):
"""Updates model properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating model. %s\n" %
get_url(model))
log_message(message, log_file=session_file,
console=args.verbosity)
model = api.update_model(model, model_args)
check_resource_error(model, "Failed to update model: %s"
% model['resource'])
if is_shared(model):
message = dated("Shared model link. %s\n" %
get_url(model, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, model)
return model
def update_cluster(cluster, cluster_args, args,
api=None, path=None, session_file=None):
"""Updates cluster properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating cluster. %s\n" %
get_url(cluster))
log_message(message, log_file=session_file,
console=args.verbosity)
cluster = api.update_cluster(cluster, cluster_args)
check_resource_error(cluster, "Failed to update cluster: %s"
% cluster['resource'])
cluster = check_resource(cluster, api.get_cluster, query_string=FIELDS_QS,
raise_on_error=True)
if is_shared(cluster):
message = dated("Shared cluster link. %s\n" %
get_url(cluster, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, cluster)
return cluster
def update_evaluation(evaluation,
evaluation_args,
args,
api=None,
path=None,
session_file=None):
"""Updates evaluation properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating evaluation. %s\n" % get_url(evaluation))
log_message(message, log_file=session_file, console=args.verbosity)
evaluation = api.update_evaluation(evaluation, evaluation_args)
check_resource_error(
evaluation, "Failed to update evaluation: %s" % evaluation['resource'])
if is_shared(evaluation):
message = dated("Shared evaluation link. %s\n" %
get_url(evaluation, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, evaluation)
return evaluation
def update_model(model,
model_args,
args,
api=None,
path=None,
session_file=None):
"""Updates model properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating model. %s\n" % get_url(model))
log_message(message, log_file=session_file, console=args.verbosity)
model = api.update_model(model, model_args)
check_resource_error(model,
"Failed to update model: %s" % model['resource'])
if is_shared(model):
message = dated("Shared model link. %s\n" %
get_url(model, shared=True))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, model)
return model
def shared_changed(shared, resource):
"""Returns True if the shared status of the resource differs from the user
given value
"""
return is_shared(resource) != shared
def shared_changed(shared, resource):
"""Returns True if the shared status of the resource differs from the user
given value
"""
return is_shared(resource) != shared
