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