def create_ensembles(datasets, ensemble_ids, ensemble_args, args,
number_of_ensembles=1,
api=None, path=None, session_file=None, log=None):
"""Create ensembles from input data
"""
if api is None:
api = bigml.api.BigML()
ensembles = ensemble_ids[:]
existing_ensembles = len(ensembles)
model_ids = []
ensemble_args_list = []
if isinstance(ensemble_args, list):
ensemble_args_list = ensemble_args
if args.dataset_off and args.evaluate:
args.test_dataset_ids = datasets[:]
if not args.multi_label:
datasets = datasets[existing_ensembles:]
if number_of_ensembles > 0:
message = dated("Creating %s.\n" %
plural("ensemble", number_of_ensembles))
log_message(message, log_file=session_file,
console=args.verbosity)
inprogress = []
for i in range(0, number_of_ensembles):
wait_for_available_tasks(inprogress, args.max_parallel_ensembles,
api, "ensemble",
wait_step=args.number_of_models)
if ensemble_args_list:
ensemble_args = ensemble_args_list[i]
if args.dataset_off and args.evaluate:
multi_dataset = args.test_dataset_ids[:]
del multi_dataset[i + existing_ensembles]
ensemble = api.create_ensemble(multi_dataset,
ensemble_args,
retries=None)
else:
ensemble = api.create_ensemble(datasets, ensemble_args,
retries=None)
ensemble_id = check_resource_error(ensemble,
"Failed to create ensemble: ")
log_message("%s\n" % ensemble_id, log_file=log)
ensemble_ids.append(ensemble_id)
inprogress.append(ensemble_id)
ensembles.append(ensemble)
log_created_resources("ensembles", path, ensemble_id,
mode='a')
models, model_ids = retrieve_ensembles_models(ensembles, api, path)
if number_of_ensembles < 2 and args.verbosity:
message = dated("Ensemble created: %s\n" %
get_url(ensemble))
log_message(message, log_file=session_file,
console=args.verbosity)
if args.reports:
report(args.reports, path, ensemble)
return ensembles, ensemble_ids, models, model_ids
def create_samples(datasets,
sample_ids,
sample_args,
args,
api=None,
path=None,
session_file=None,
log=None):
"""Create remote samples
"""
if api is None:
api = bigml.api.BigML()
samples = sample_ids[:]
existing_samples = len(samples)
sample_args_list = []
datasets = datasets[existing_samples:]
# if resuming and all samples were created, there will be no datasets left
if datasets:
if isinstance(sample_args, list):
sample_args_list = sample_args
# Only one sample per command, at present
number_of_samples = 1
max_parallel_samples = 1
message = dated("Creating %s.\n" % plural("sample", number_of_samples))
log_message(message, log_file=session_file, console=args.verbosity)
inprogress = []
for i in range(0, number_of_samples):
wait_for_available_tasks(inprogress, max_parallel_samples, api,
"sample")
if sample_args_list:
sample_args = sample_args_list[i]
sample = api.create_sample(datasets[i], sample_args, retries=None)
sample_id = check_resource_error(sample,
"Failed to create sample: ")
log_message("%s\n" % sample_id, log_file=log)
sample_ids.append(sample_id)
inprogress.append(sample_id)
samples.append(sample)
log_created_resources("samples", path, sample_id, mode='a')
if args.verbosity:
if bigml.api.get_status(sample)['code'] != bigml.api.FINISHED:
try:
sample = check_resource(sample,
api.get_sample,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished sample: %s" %
str(exception))
samples[0] = sample
message = dated("Sample created: %s\n" % get_url(sample))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, sample)
def create_clusters(datasets, cluster_ids, cluster_args,
args, api=None, path=None,
session_file=None, log=None):
"""Create remote clusters
"""
if api is None:
api = bigml.api.BigML()
clusters = cluster_ids[:]
existing_clusters = len(clusters)
cluster_args_list = []
datasets = datasets[existing_clusters:]
# if resuming and all clusters were created, there will be no datasets left
if datasets:
if isinstance(cluster_args, list):
cluster_args_list = cluster_args
# Only one cluster per command, at present
number_of_clusters = 1
message = dated("Creating %s.\n" %
plural("cluster", number_of_clusters))
log_message(message, log_file=session_file,
console=args.verbosity)
query_string = FIELDS_QS
inprogress = []
for i in range(0, number_of_clusters):
wait_for_available_tasks(inprogress, args.max_parallel_clusters,
api, "cluster")
if cluster_args_list:
cluster_args = cluster_args_list[i]
cluster = api.create_cluster(datasets, cluster_args, retries=None)
cluster_id = check_resource_error(cluster,
"Failed to create cluster: ")
log_message("%s\n" % cluster_id, log_file=log)
cluster_ids.append(cluster_id)
inprogress.append(cluster_id)
clusters.append(cluster)
log_created_resources("clusters", path, cluster_id, mode='a')
if args.verbosity:
if bigml.api.get_status(cluster)['code'] != bigml.api.FINISHED:
try:
cluster = check_resource(cluster, api.get_cluster,
query_string=query_string,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished cluster: %s" %
str(exception))
clusters[0] = cluster
message = dated("Cluster created: %s\n" %
get_url(cluster))
log_message(message, log_file=session_file,
console=args.verbosity)
if args.reports:
report(args.reports, path, cluster)
def create_fusion(models,
fusion,
fusion_args,
args,
api=None,
path=None,
session_file=None,
log=None):
"""Create remote fusion
"""
if api is None:
api = bigml.api.BigML()
fusions = []
fusion_ids = []
if fusion is not None:
fusions = [fusion]
fusion_ids = [fusion]
# if resuming and all fusions were created
if models:
# Only one fusion per command, at present
message = dated("Creating fusion.\n")
log_message(message, log_file=session_file, console=args.verbosity)
query_string = FIELDS_QS
inprogress = []
wait_for_available_tasks(inprogress, args.max_parallel_fusions, api,
"fusion")
fusion = api.create_fusion(models, fusion_args, retries=None)
fusion_id = check_resource_error( \
fusion,
"Failed to create fusion: ")
log_message("%s\n" % fusion_id, log_file=log)
fusion_ids.append(fusion_id)
inprogress.append(fusion_id)
fusions.append(fusion)
log_created_resources("fusions", path, fusion_id, mode='a')
if args.verbosity:
if bigml.api.get_status(fusion)['code'] != bigml.api.FINISHED:
try:
fusion = check_resource( \
fusion, api.get_fusion,
query_string=query_string,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished fusion: %s" %
str(exception))
fusions[0] = fusion
message = dated("Fusion created: %s\n" % get_url(fusion))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, fusion)
def create_anomalies(datasets,
anomaly_ids,
anomaly_args,
args,
api=None,
path=None,
session_file=None,
log=None):
"""Create remote anomalies
"""
if api is None:
api = bigml.api.BigML()
anomalies = anomaly_ids[:]
existing_anomalies = len(anomalies)
anomaly_args_list = []
datasets = datasets[existing_anomalies:]
# if resuming and all anomalies were created,
# there will be no datasets left
if datasets:
if isinstance(anomaly_args, list):
anomaly_args_list = anomaly_args
# Only one anomaly per command, at present
number_of_anomalies = 1
message = dated("Creating %s.\n" %
plural("anomaly detector", number_of_anomalies))
log_message(message, log_file=session_file, console=args.verbosity)
query_string = FIELDS_QS
inprogress = []
for i in range(0, number_of_anomalies):
wait_for_available_tasks(inprogress, args.max_parallel_anomalies,
api, "anomaly")
if anomaly_args_list:
anomaly_args = anomaly_args_list[i]
anomaly = api.create_anomaly(datasets, anomaly_args, retries=None)
anomaly_id = check_resource_error(anomaly,
"Failed to create anomaly: ")
log_message("%s\n" % anomaly_id, log_file=log)
anomaly_ids.append(anomaly_id)
inprogress.append(anomaly_id)
anomalies.append(anomaly)
log_created_resources("anomalies", path, anomaly_id, mode='a')
if args.verbosity:
if bigml.api.get_status(anomaly)['code'] != bigml.api.FINISHED:
try:
anomaly = check_resource(anomaly,
api.get_anomaly,
query_string=query_string,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished anomaly: %s" %
str(exception))
anomalies[0] = anomaly
message = dated("Anomaly created: %s\n" % get_url(anomaly))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, anomaly)
def create_logistic_regressions(datasets,
logistic_regression_ids,
logistic_regression_args,
args,
api=None,
path=None,
session_file=None,
log=None):
"""Create remote logistic regressions
"""
if api is None:
api = bigml.api.BigML()
logistic_regressions = logistic_regression_ids[:]
existing_logistic_regressions = len(logistic_regressions)
logistic_regression_args_list = []
datasets = datasets[existing_logistic_regressions:]
# if resuming and all logistic regressions were created,
# there will be no datasets left
if datasets:
if isinstance(logistic_regression_args, list):
logistic_regression_args_list = logistic_regression_args
# Only one logistic regression per command, at present
number_of_logistic_regressions = 1
message = dated(
"Creating %s.\n" %
plural("logistic regression", number_of_logistic_regressions))
log_message(message, log_file=session_file, console=args.verbosity)
query_string = FIELDS_QS
inprogress = []
for i in range(0, number_of_logistic_regressions):
wait_for_available_tasks(inprogress,
args.max_parallel_logistic_regressions,
api, "logisticregression")
if logistic_regression_args_list:
logistic_regression_args = logistic_regression_args_list[i]
if args.cross_validation_rate > 0:
new_seed = get_basic_seed(i + existing_logistic_regressions)
logistic_regression_args.update(seed=new_seed)
if (args.test_datasets and args.evaluate):
dataset = datasets[i]
logistic_regression = api.create_logistic_regression( \
dataset, logistic_regression_args, retries=None)
elif args.dataset_off and args.evaluate:
multi_dataset = args.test_dataset_ids[:]
del multi_dataset[i + existing_logistic_regressions]
logistic_regression = api.create_logistic_regression( \
multi_dataset, logistic_regression_args, retries=None)
else:
logistic_regression = api.create_logistic_regression( \
datasets, logistic_regression_args, retries=None)
logistic_regression_id = check_resource_error( \
logistic_regression, "Failed to create logistic regression: ")
log_message("%s\n" % logistic_regression_id, log_file=log)
logistic_regression_ids.append(logistic_regression_id)
inprogress.append(logistic_regression_id)
logistic_regressions.append(logistic_regression)
log_created_resources("logistic_regressions",
path,
logistic_regression_id,
mode='a')
if args.verbosity:
if bigml.api.get_status(logistic_regression)['code'] != \
bigml.api.FINISHED:
try:
logistic_regression = check_resource( \
logistic_regression, api.get_logistic_regression,
query_string=query_string, raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished logistic regression:"
" %s" % str(exception))
logistic_regressions[0] = logistic_regression
message = dated("Logistic regression created: %s\n" %
get_url(logistic_regression))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, logistic_regression)
def create_models(datasets, model_ids, model_args,
args, api=None, path=None,
session_file=None, log=None):
"""Create remote models
"""
if api is None:
api = bigml.api.BigML()
models = model_ids[:]
existing_models = len(models)
model_args_list = []
if args.dataset_off and args.evaluate:
args.test_dataset_ids = datasets[:]
if not args.multi_label:
datasets = datasets[existing_models:]
# if resuming and all models were created, there will be no datasets left
if datasets:
dataset = datasets[0]
if isinstance(model_args, list):
model_args_list = model_args
if args.number_of_models > 0:
message = dated("Creating %s.\n" %
plural("model", args.number_of_models))
log_message(message, log_file=session_file,
console=args.verbosity)
single_model = args.number_of_models == 1 and existing_models == 0
# if there's more than one model the first one must contain
# the entire field structure to be used as reference.
query_string = (FIELDS_QS if single_model and (args.test_header \
and not args.export_fields) else ALL_FIELDS_QS)
inprogress = []
for i in range(0, args.number_of_models):
wait_for_available_tasks(inprogress, args.max_parallel_models,
api, "model")
if model_args_list:
model_args = model_args_list[i]
if args.cross_validation_rate > 0:
new_seed = get_basic_seed(i + existing_models)
model_args.update(seed=new_seed)
# one model per dataset (--max-categories or single model)
if (args.max_categories > 0 or
(args.test_datasets and args.evaluate)):
dataset = datasets[i]
model = api.create_model(dataset, model_args, retries=None)
elif args.dataset_off and args.evaluate:
multi_dataset = args.test_dataset_ids[:]
del multi_dataset[i + existing_models]
model = api.create_model(multi_dataset, model_args,
retries=None)
else:
model = api.create_model(datasets, model_args,
retries=None)
model_id = check_resource_error(model,
"Failed to create model: ")
log_message("%s\n" % model_id, log_file=log)
model_ids.append(model_id)
inprogress.append(model_id)
models.append(model)
log_created_resources("models", path, model_id, mode='a')
if args.number_of_models < 2 and args.verbosity:
if bigml.api.get_status(model)['code'] != bigml.api.FINISHED:
try:
model = check_resource(model, api.get_model,
query_string=query_string,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished model: %s" %
str(exception))
models[0] = model
message = dated("Model created: %s\n" %
get_url(model))
log_message(message, log_file=session_file,
console=args.verbosity)
if args.reports:
report(args.reports, path, model)
def create_time_series(datasets,
time_series_ids,
time_series_args,
args,
api=None,
path=None,
session_file=None,
log=None):
"""Create remote time-series
"""
if api is None:
api = bigml.api.BigML()
time_series_set = time_series_ids[:]
existing_time_series = len(time_series_set)
time_series_args_list = []
datasets = datasets[existing_time_series:]
# if resuming and all time-series were created,
# there will be no datasets left
if datasets:
if isinstance(time_series_args, list):
time_series_args_list = time_series_args
# Only one time-series per command, at present
number_of_time_series = 1
message = dated("Creating %s time-series.\n" % number_of_time_series)
log_message(message, log_file=session_file, console=args.verbosity)
query_string = FIELDS_QS
inprogress = []
for i in range(0, number_of_time_series):
wait_for_available_tasks(inprogress, args.max_parallel_time_series,
api, "timeseries")
if time_series_args_list:
time_series_args = time_series_args_list[i]
if (args.test_datasets and args.evaluate):
dataset = datasets[i]
time_series = api.create_time_series( \
dataset, time_series_args, retries=None)
else:
time_series = api.create_time_series( \
datasets, time_series_args, retries=None)
time_series_id = check_resource_error( \
time_series, "Failed to create time-series: ")
log_message("%s\n" % time_series_id, log_file=log)
time_series_ids.append(time_series_id)
inprogress.append(time_series_id)
time_series_set.append(time_series)
log_created_resources("time_series",
path,
time_series_id,
mode='a')
if args.verbosity:
if bigml.api.get_status(time_series)['code'] != \
bigml.api.FINISHED:
try:
time_series = check_resource( \
time_series, api.get_time_series,
query_string=query_string,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished time-series:"
" %s" % str(exception))
time_series_set[0] = time_series
message = dated("Time-series created: %s\n" % get_url(time_series))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, time_series)
def create_pca(datasets,
pca,
pca_args,
args,
api=None,
path=None,
session_file=None,
log=None):
"""Create remote pcas
"""
if api is None:
api = bigml.api.BigML()
pcas = []
pca_ids = []
if pca is not None:
pcas = [pca]
pca_ids = [pca]
existing_pcas = len(pcas)
pca_args_list = []
datasets = datasets[existing_pcas:]
# if resuming and all pcas were created, there will
# be no datasets left
if datasets:
if isinstance(pca_args, list):
pca_args_list = pca_args
# Only one pca per command, at present
number_of_pcas = 1
message = dated("Creating %s.\n" % plural("pca", number_of_pcas))
log_message(message, log_file=session_file, console=args.verbosity)
query_string = FIELDS_QS
inprogress = []
for i in range(0, number_of_pcas):
wait_for_available_tasks(inprogress, args.max_parallel_pcas, api,
"pca")
if pca_args_list:
pca_args = pca_args_list[i]
pca = api.create_pca(datasets, pca_args, retries=None)
pca_id = check_resource_error( \
pca,
"Failed to create pca: ")
log_message("%s\n" % pca_id, log_file=log)
pca_ids.append(pca_id)
inprogress.append(pca_id)
pcas.append(pca)
log_created_resources("pcas", path, pca_id, mode='a')
if args.verbosity:
if bigml.api.get_status(pca)['code'] != bigml.api.FINISHED:
try:
pca = check_resource( \
pca, api.get_pca,
query_string=query_string,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished pca: %s" %
str(exception))
pcas[0] = pca
message = dated("PCA created: %s\n" % get_url(pca))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, pca)
def create_topic_models(datasets,
topic_model_ids,
topic_model_args,
args,
api=None,
path=None,
session_file=None,
log=None):
"""Create remote topic models
"""
if api is None:
api = bigml.api.BigML()
topic_models = topic_model_ids[:]
existing_topic_models = len(topic_models)
topic_model_args_list = []
datasets = datasets[existing_topic_models:]
# if resuming and all topic models were created, there will
# be no datasets left
if datasets:
if isinstance(topic_model_args, list):
topic_model_args_list = topic_model_args
# Only one topic model per command, at present
number_of_topic_models = 1
message = dated("Creating %s.\n" %
plural("topic model", number_of_topic_models))
log_message(message, log_file=session_file, console=args.verbosity)
query_string = FIELDS_QS
inprogress = []
for i in range(0, number_of_topic_models):
wait_for_available_tasks(inprogress,
args.max_parallel_topic_models, api,
"topicmodel")
if topic_model_args_list:
topic_model_args = topic_model_args_list[i]
topic_model = api.create_topic_model(datasets,
topic_model_args,
retries=None)
topic_model_id = check_resource_error( \
topic_model,
"Failed to create topic model: ")
log_message("%s\n" % topic_model_id, log_file=log)
topic_model_ids.append(topic_model_id)
inprogress.append(topic_model_id)
topic_models.append(topic_model)
log_created_resources("topic_models",
path,
topic_model_id,
mode='a')
if args.verbosity:
if bigml.api.get_status(topic_model)['code'] != bigml.api.FINISHED:
try:
topic_model = check_resource( \
topic_model, api.get_topic_model,
query_string=query_string,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished topic model: %s" %
str(exception))
topic_models[0] = topic_model
message = dated("Topic model created: %s\n" % get_url(topic_model))
log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
report(args.reports, path, topic_model)
def create_evaluations(model_or_ensemble_ids, datasets, evaluation_args,
args, api=None,
path=None, session_file=None, log=None,
existing_evaluations=0):
"""Create evaluations for a list of models
``model_or_ensemble_ids``: list of model or ensemble ids to create
an evaluation of
``datasets``: dataset objects or ids to evaluate with
``evaluation_args``: arguments for the ``create_evaluation`` call
``args``: input values for bigmler flags
``api``: api to remote objects in BigML
``path``: directory to store the BigMLer generated files in
``session_file``: file to store the messages of that session
``log``: user provided log file
``existing_evaluations``: evaluations found when attempting resume
"""
evaluations = []
dataset = datasets[0]
evaluation_args_list = []
if isinstance(evaluation_args, list):
evaluation_args_list = evaluation_args
if api is None:
api = bigml.api.BigML()
remaining_ids = model_or_ensemble_ids[existing_evaluations:]
if args.test_dataset_ids or args.dataset_off:
remaining_datasets = datasets[existing_evaluations:]
number_of_evaluations = len(remaining_ids)
message = dated("Creating evaluations.\n")
log_message(message, log_file=session_file,
console=args.verbosity)
inprogress = []
for i in range(0, number_of_evaluations):
model = remaining_ids[i]
if args.test_dataset_ids or args.dataset_off:
dataset = remaining_datasets[i]
wait_for_available_tasks(inprogress, args.max_parallel_evaluations,
api, "evaluation")
if evaluation_args_list != []:
evaluation_args = evaluation_args_list[i]
if args.cross_validation_rate > 0:
new_seed = get_basic_seed(i + existing_evaluations)
evaluation_args.update(seed=new_seed)
evaluation = api.create_evaluation(model, dataset, evaluation_args,
retries=None)
evaluation_id = check_resource_error(evaluation,
"Failed to create evaluation: ")
inprogress.append(evaluation_id)
log_created_resources("evaluations", path, evaluation_id,
mode='a')
evaluations.append(evaluation)
log_message("%s\n" % evaluation['resource'], log_file=log)
if (args.number_of_evaluations < 2 and len(evaluations) == 1
and args.verbosity):
evaluation = evaluations[0]
if bigml.api.get_status(evaluation)['code'] != bigml.api.FINISHED:
try:
evaluation = check_resource(evaluation, api.get_evaluation,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished evaluation: %s" %
str(exception))
evaluations[0] = evaluation
message = dated("Evaluation created: %s\n" %
get_url(evaluation))
log_message(message, log_file=session_file,
console=args.verbosity)
if args.reports:
report(args.reports, path, evaluation)
