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 get_samples(sample_ids,
args,
api=None,
session_file=None,
query_string=''):
"""Retrieves remote samples in its actual status
"""
if api is None:
api = bigml.api.BigML()
sample_id = ""
samples = sample_ids
sample_id = sample_ids[0]
message = dated("Retrieving %s. %s\n" %
(plural("sample", len(sample_ids)), get_url(sample_id)))
log_message(message, log_file=session_file, console=args.verbosity)
# only one sample to predict at present
try:
sample = api.get_sample(sample_ids[0], query_string=query_string)
check_resource_error(
sample, "Failed to create sample: %s" % sample['resource'])
sample = check_resource(sample,
api=api,
query_string=query_string,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished sample: %s" % str(exception))
def create_evaluation(model_or_ensemble, dataset, evaluation_args, args,
api=None,
path=None, session_file=None, log=None, seed=SEED):
"""Create evaluation
``model_or_ensemble``: resource object or id for the model or ensemble
that should be evaluated
``dataset``: dataset object or id 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
``seed``: seed for the dataset sampling (when needed)
"""
if api is None:
api = bigml.api.BigML()
if args.cross_validation_rate > 0:
evaluation_args.update(seed=seed)
message = dated("Creating evaluation.\n")
log_message(message, log_file=session_file,
console=args.verbosity)
evaluation = api.create_evaluation(model_or_ensemble, dataset,
evaluation_args)
log_created_resources("evaluation", path,
bigml.api.get_evaluation_id(evaluation))
check_resource_error(evaluation, "Failed to create evaluation: ")
log_message("%s\n" % evaluation['resource'], log_file=log)
return evaluation
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 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_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_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 remote_predict_models(models, test_reader, prediction_file, api, args,
resume=False, output_path=None,
session_file=None, log=None, exclude=None):
"""Retrieve predictions remotely, combine them and save predictions to file
"""
predictions_files = []
prediction_args = {
"tags": args.tag
}
test_set_header = test_reader.has_headers()
if output_path is None:
output_path = u.check_dir(prediction_file)
message_logged = False
raw_input_data_list = []
for input_data in test_reader:
raw_input_data_list.append(input_data)
single_model = len(models) == 1
if single_model:
prediction_file = UnicodeWriter(prediction_file).open_writer()
for model in models:
model = bigml.api.get_model_id(model)
predictions_file = get_predictions_file_name(model,
output_path)
predictions_files.append(predictions_file)
if (not resume or
not c.checkpoint(c.are_predictions_created, predictions_file,
test_reader.number_of_tests(),
debug=args.debug)[0]):
if not message_logged:
message = u.dated("Creating remote predictions.\n")
u.log_message(message, log_file=session_file,
console=args.verbosity)
message_logged = True
with UnicodeWriter(predictions_file) as predictions_file:
for input_data in raw_input_data_list:
input_data_dict = test_reader.dict(input_data)
prediction = api.create_prediction(model, input_data_dict,
by_name=test_set_header,
wait_time=0,
args=prediction_args)
u.check_resource_error(prediction,
"Failed to create prediction: ")
u.log_message("%s\n" % prediction['resource'],
log_file=log)
prediction_row = prediction_to_row(prediction)
predictions_file.writerow(prediction_row)
if single_model:
write_prediction(prediction_row[0:2], prediction_file,
args.prediction_info,
input_data, exclude)
if single_model:
prediction_file.close_writer()
else:
combine_votes(predictions_files,
Model(models[0]).to_prediction,
prediction_file, args.method,
args.prediction_info, raw_input_data_list, exclude)
def create_models(dataset,
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)
last_model = None
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)
for i in range(0, args.number_of_models):
if i % args.max_parallel_models == 0 and i > 0:
try:
models[i - 1] = check_resource(models[i - 1],
api.get_model,
query_string=FIELDS_QS)
except ValueError, exception:
sys.exit("Failed to get a finished model: %s" %
str(exception))
if args.cross_validation_rate > 0:
new_seed = get_basic_seed(i + existing_models)
model_args.update(seed=new_seed)
model = api.create_model(dataset, model_args)
log_message("%s\n" % model['resource'], log_file=log)
model_ids.append(model['resource'])
models.append(model)
log_created_resources("models",
path,
bigml.api.get_model_id(model),
open_mode='a')
check_resource_error(
model, "Failed to create model %s:" % model['resource'])
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=FIELDS_QS)
except ValueError, 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)
def remote_predict(models,
test_reader,
prediction_file,
api,
resume=False,
verbosity=True,
output_path=None,
method=PLURALITY_CODE,
tags="",
session_file=None,
log=None,
debug=False,
prediction_info=None):
"""Retrieve predictions remotely, combine them and save predictions to file
"""
predictions_files = []
prediction_args = {"tags": tags}
test_set_header = test_reader.has_headers()
if output_path is None:
output_path = u.check_dir(prediction_file)
message_logged = False
raw_input_data_list = []
for model in models:
model = bigml.api.get_model_id(model)
predictions_file = get_predictions_file_name(model, output_path)
predictions_files.append(predictions_file)
if (not resume or not c.checkpoint(c.are_predictions_created,
predictions_file,
test_reader.number_of_tests(),
debug=debug)):
if not message_logged:
message = u.dated("Creating remote predictions.")
u.log_message(message,
log_file=session_file,
console=verbosity)
message_logged = True
predictions_file = csv.writer(open(predictions_file, 'w', 0),
lineterminator="\n")
for input_data in test_reader:
raw_input_data_list.append(input_data)
input_data_dict = test_reader.dict(input_data)
prediction = api.create_prediction(model,
input_data_dict,
by_name=test_set_header,
wait_time=0,
args=prediction_args)
u.check_resource_error(prediction,
"Failed to create prediction: ")
u.log_message("%s\n" % prediction['resource'], log_file=log)
prediction_row = prediction_to_row(prediction)
predictions_file.writerow(prediction_row)
combine_votes(predictions_files,
Model(models[0]).to_prediction, prediction_file, method,
prediction_info, raw_input_data_list)
def update_source(source, source_args, args, api=None, session_file=None):
"""Updates source properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating source. %s\n" % get_url(source))
log_message(message, log_file=session_file, console=args.verbosity)
source = api.update_source(source, source_args)
check_resource_error(source, "Failed to update source: ")
source = check_resource(source, api.get_source)
return source
def create_models(dataset, 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)
last_model = None
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)
for i in range(0, args.number_of_models):
if i % args.max_parallel_models == 0 and i > 0:
try:
models[i - 1] = check_resource(
models[i - 1], api.get_model, query_string=FIELDS_QS)
except ValueError, exception:
sys.exit("Failed to get a finished model: %s" %
str(exception))
if args.cross_validation_rate > 0:
new_seed = get_basic_seed(i + existing_models)
model_args.update(seed=new_seed)
model = api.create_model(dataset, model_args)
log_message("%s\n" % model['resource'], log_file=log)
model_ids.append(model['resource'])
models.append(model)
log_created_resources("models", path,
bigml.api.get_model_id(model), open_mode='a')
check_resource_error(model, "Failed to create model %s:" %
model['resource'])
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=FIELDS_QS)
except ValueError, 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)
def remote_predict(models, test_reader, prediction_file, api,
resume=False,
verbosity=True, output_path=None,
method=PLURALITY_CODE, tags="",
session_file=None, log=None, debug=False,
prediction_info=None):
"""Retrieve predictions remotely, combine them and save predictions to file
"""
predictions_files = []
prediction_args = {
"tags": tags
}
test_set_header = test_reader.has_headers()
if output_path is None:
output_path = u.check_dir(prediction_file)
message_logged = False
raw_input_data_list = []
for model in models:
model = bigml.api.get_model_id(model)
predictions_file = get_predictions_file_name(model,
output_path)
predictions_files.append(predictions_file)
if (not resume or
not c.checkpoint(c.are_predictions_created, predictions_file,
test_reader.number_of_tests(), debug=debug)):
if not message_logged:
message = u.dated("Creating remote predictions.")
u.log_message(message, log_file=session_file,
console=verbosity)
message_logged = True
predictions_file = csv.writer(open(predictions_file, 'w', 0),
lineterminator="\n")
for input_data in test_reader:
raw_input_data_list.append(input_data)
input_data_dict = test_reader.dict(input_data)
prediction = api.create_prediction(model, input_data_dict,
by_name=test_set_header,
wait_time=0,
args=prediction_args)
u.check_resource_error(prediction,
"Failed to create prediction: ")
u.log_message("%s\n" % prediction['resource'], log_file=log)
prediction_row = prediction_to_row(prediction)
predictions_file.writerow(prediction_row)
combine_votes(predictions_files,
Model(models[0]).to_prediction,
prediction_file, method,
prediction_info, raw_input_data_list)
def get_ensemble(ensemble, api=None, verbosity=True, session_file=None):
"""Retrieves remote ensemble in its actual status
"""
if api is None:
api = bigml.api.BigML()
if (isinstance(ensemble, basestring)
or bigml.api.get_status(ensemble)['code'] != bigml.api.FINISHED):
message = dated("Retrieving ensemble. %s\n" % get_url(ensemble))
log_message(message, log_file=session_file, console=verbosity)
ensemble = check_resource(ensemble, api.get_ensemble)
check_resource_error(ensemble, "Failed to get ensemble: ")
return ensemble
def get_dataset(dataset, api=None, verbosity=True, session_file=None):
"""Retrieves the dataset in its actual state
"""
if api is None:
api = bigml.api.BigML()
if (isinstance(dataset, basestring)
or bigml.api.get_status(dataset)['code'] != bigml.api.FINISHED):
message = dated("Retrieving dataset. %s\n" % get_url(dataset))
log_message(message, log_file=session_file, console=verbosity)
dataset = check_resource(dataset, api.get_dataset)
check_resource_error(dataset, "Failed to get dataset: ")
return dataset
def update_dataset(dataset, dataset_args, verbosity,
api=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=verbosity)
dataset = api.update_dataset(dataset, dataset_args)
check_resource_error(dataset, "Failed to update dataset: ")
return dataset
def get_dataset(dataset, api=None, verbosity=True, session_file=None):
"""Retrieves the dataset in its actual state
"""
if api is None:
api = bigml.api.BigML()
if (isinstance(dataset, basestring) or
bigml.api.get_status(dataset)['code'] != bigml.api.FINISHED):
message = dated("Retrieving dataset. %s\n" %
get_url(dataset))
log_message(message, log_file=session_file,
console=verbosity)
dataset = check_resource(dataset, api.get_dataset)
check_resource_error(dataset, "Failed to get dataset: ")
return dataset
def update_project(project_args, args, api=None, session_file=None, log=None):
"""Updates project properties
"""
if api is None:
api = bigml.api.BigML()
message = dated("Updating project attributes.\n")
log_message(message, log_file=session_file, console=args.verbosity)
project = api.update_project(args.project_id, project_args)
check_resource_error(project,
"Failed to update project: %s" % project['resource'])
message = dated("Project \"%s\" has been updated.\n" % project['resource'])
log_message(message, log_file=session_file, console=args.verbosity)
log_message("%s\n" % args.project_id, log_file=log)
return project
def get_ensemble(ensemble, api=None, verbosity=True, session_file=None):
"""Retrieves remote ensemble in its actual status
"""
if api is None:
api = bigml.api.BigML()
if (isinstance(ensemble, basestring) or
bigml.api.get_status(ensemble)['code'] != bigml.api.FINISHED):
message = dated("Retrieving ensemble. %s\n" %
get_url(ensemble))
log_message(message, log_file=session_file,
console=verbosity)
ensemble = check_resource(ensemble, api.get_ensemble)
check_resource_error(ensemble, "Failed to get ensemble: ")
return ensemble
def create_evaluations(model_ids,
dataset,
evaluation_args,
args,
api=None,
path=None,
session_file=None,
log=None,
existing_evaluations=0):
"""Create evaluations for a list of models
``model_ids``: list of model ids to create an evaluation of
``dataset``: dataset object or id 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
``seed``: seed for the dataset sampling (when needed)
"""
evaluations = []
if api is None:
api = bigml.api.BigML()
number_of_evaluations = len(model_ids)
message = dated("Creating evaluations.\n")
log_message(message, log_file=session_file, console=args.verbosity)
for i in range(0, number_of_evaluations):
model = model_ids[i]
if i % args.max_parallel_evaluations == 0 and i > 0:
try:
evaluations[i - 1] = check_resource(evaluations[i - 1],
api.get_evaluation)
except ValueError, exception:
sys.exit("Failed to get a finished evaluation: %s" %
str(exception))
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)
log_created_resources("evaluations",
path,
bigml.api.get_evaluation_id(evaluation),
open_mode='a')
check_resource_error(evaluation, "Failed to create evaluation: ")
evaluations.append(evaluation)
log_message("%s\n" % evaluation['resource'], log_file=log)
def create_library(source_code,
library_args,
args,
api=None,
path=None,
session_file=None,
log=None):
"""Creates remote library
"""
if api is None:
api = bigml.api.BigML()
message = dated("Creating library \"%s\".\n" % library_args["name"])
log_message(message, log_file=session_file, console=args.verbosity)
library = api.create_library(source_code, library_args)
log_created_resources("library",
path,
bigml.api.get_library_id(library),
mode='a')
library_id = check_resource_error(library, "Failed to create library: ")
try:
library = check_resource(library, api.get_library, raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a compiled library: %s" % str(exception))
def create_dataset(source_or_dataset,
dataset_args,
args,
api=None,
path=None,
session_file=None,
log=None,
dataset_type=None):
"""Creates remote dataset from source, dataset or datasets list
"""
if api is None:
api = bigml.api.BigML()
message = dated("Creating dataset.\n")
log_message(message, log_file=session_file, console=args.verbosity)
dataset = api.create_dataset(source_or_dataset, dataset_args)
suffix = "_" + dataset_type if dataset_type else ""
log_created_resources("dataset%s" % suffix,
path,
bigml.api.get_dataset_id(dataset),
open_mode='a')
dataset_id = check_resource_error(dataset, "Failed to create dataset: ")
try:
dataset = check_resource(dataset,
api.get_dataset,
query_string=ALL_FIELDS_QS)
except ValueError, exception:
sys.exit("Failed to get a finished dataset: %s" % str(exception))
def create_forecast(time_series,
input_data,
forecast_args,
args,
api=None,
session_file=None,
path=None,
log=None):
"""Creates remote forecast
"""
if api is None:
api = bigml.api.BigML()
message = dated("Creating remote forecast.\n")
log_message(message, log_file=session_file, console=args.verbosity)
forecast = api.create_forecast(time_series,
input_data,
forecast_args,
retries=None)
log_created_resources("forecast",
path,
bigml.api.get_forecast_id(forecast),
mode='a')
forecast_id = check_resource_error(forecast, "Failed to create forecast: ")
try:
forecast = check_resource(forecast,
api.get_forecast,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished forecast: %s" % str(exception))
def create_external_connector(external_connector_args,
args,
api=None,
session_file=None,
path=None,
log=None):
"""Creates remote external connector
"""
if api is None:
api = bigml.api.BigML()
message = dated("Creating external connector.\n")
log_message(message, log_file=session_file, console=args.verbosity)
external_connector = api.create_external_connector( \
args.connection_info, external_connector_args)
log_created_resources( \
"external_connector", path,
bigml.api.get_external_connector_id(external_connector),
mode='a')
external_connector_id = check_resource_error( \
external_connector,
"Failed to create external connector: ")
try:
external_connector = check_resource( \
external_connector, api=api, raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished external connector: %s" % \
str(exception))
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_batch_prediction(model_or_ensemble, test_dataset,
batch_prediction_args, verbosity,
api=None, session_file=None,
path=None, log=None):
"""Creates remote batch_prediction
"""
if api is None:
api = bigml.api.BigML()
message = dated("Creating batch prediction.\n")
log_message(message, log_file=session_file, console=verbosity)
batch_prediction = api.create_batch_prediction(model_or_ensemble,
test_dataset,
batch_prediction_args)
log_created_resources("batch_prediction", path,
bigml.api.get_batch_prediction_id(batch_prediction),
open_mode='a')
batch_prediction_id = check_resource_error(
batch_prediction, "Failed to create batch prediction: ")
try:
batch_prediction = check_resource(batch_prediction,
api.get_batch_prediction)
except ValueError, exception:
sys.exit("Failed to get a finished batch prediction: %s"
% str(exception))
def create_source(data_set,
source_args,
args,
api=None,
path=None,
session_file=None,
log=None,
source_type=None):
"""Creates remote source
"""
if api is None:
api = bigml.api.BigML()
suffix = "" if source_type is None else "%s " % source_type
message = dated("Creating %ssource.\n" % suffix)
log_message(message, log_file=session_file, console=args.verbosity)
check_fields_struct(source_args, "source")
source = api.create_source(data_set,
source_args,
progress_bar=args.progress_bar)
if path is not None:
suffix = "_" + source_type if source_type else ""
log_created_resources("source%s" % suffix,
path,
source['resource'],
mode='ab',
comment=(u"%s\n" % source['object']['name']))
source_id = check_resource_error(source, "Failed to create source: ")
try:
source = check_resource(source,
api.get_source,
query_string=ALL_FIELDS_QS,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished source: %s" % str(exception))
def publish_dataset(dataset, args, api=None, session_file=None):
"""Publishes dataset and sets its price (if any)
"""
if api is None:
api = bigml.api.BigML()
public_dataset = {"private": False}
if args.dataset_price:
message = dated("Updating dataset. %s\n" % get_url(dataset))
log_message(message, log_file=session_file, console=args.verbosity)
public_dataset.update(price=args.dataset_price)
message = dated("Updating dataset. %s\n" % get_url(dataset))
log_message(message, log_file=session_file, console=args.verbosity)
dataset = api.update_dataset(dataset, public_dataset)
check_resource_error(dataset, "Failed to update dataset: ")
return dataset
def create_batch_prediction(model_or_ensemble,
test_dataset,
batch_prediction_args,
args,
api=None,
session_file=None,
path=None,
log=None):
"""Creates remote batch_prediction
"""
if api is None:
api = bigml.api.BigML()
message = dated("Creating batch prediction.\n")
log_message(message, log_file=session_file, console=args.verbosity)
batch_prediction = api.create_batch_prediction(model_or_ensemble,
test_dataset,
batch_prediction_args,
retries=None)
log_created_resources("batch_prediction",
path,
bigml.api.get_batch_prediction_id(batch_prediction),
mode='a')
batch_prediction_id = check_resource_error(
batch_prediction, "Failed to create batch prediction: ")
try:
batch_prediction = check_resource(batch_prediction,
api.get_batch_prediction,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished batch prediction: %s" %
str(exception))
def create_execution(execution_args,
args,
api=None,
path=None,
session_file=None,
log=None):
"""Creates remote execution
"""
message = dated("Creating execution.\n")
log_message(message, log_file=session_file, console=args.verbosity)
scripts = args.script_ids if args.script_ids else args.script
execution = api.create_execution(scripts, execution_args)
log_created_resources("execution",
path,
bigml.api.get_execution_id(execution),
mode='a')
execution_id = check_resource_error(execution,
"Failed to create execution: ")
try:
execution = check_resource(execution,
api.get_execution,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished execution: %s" % str(exception))
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_models(dataset, 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 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
else ALL_FIELDS_QS)
for i in range(0, args.number_of_models):
if i % args.max_parallel_models == 0 and i > 0:
try:
models[i - 1] = check_resource(
models[i - 1], api.get_model,
query_string=query_string)
except ValueError, exception:
sys.exit("Failed to get a finished model: %s" %
str(exception))
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)
model = api.create_model(dataset, model_args)
model_id = check_resource_error(model, "Failed to create model: ")
log_message("%s\n" % model_id, log_file=log)
model_ids.append(model_id)
models.append(model)
log_created_resources("models", path, model_id, open_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)
except ValueError, 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)
def create_ensemble(dataset, ensemble_args, args, api=None, path=None,
session_file=None, log=None):
"""Create ensemble from input data
"""
if api is None:
api = bigml.api.BigML()
message = dated("Creating ensemble.\n")
log_message(message, log_file=session_file,
console=args.verbosity)
ensemble = api.create_ensemble(dataset, ensemble_args)
log_created_resources("ensemble", path,
bigml.api.get_ensemble_id(ensemble))
check_resource_error(ensemble, "Failed to create ensemble: ")
log_message("%s\n" % ensemble['resource'], log_file=log)
return ensemble
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_dataset(source_or_dataset, dataset_args, args, api=None, path=None,
session_file=None, log=None, dataset_type=None):
"""Creates remote dataset
"""
if api is None:
api = bigml.api.BigML()
message = dated("Creating dataset.\n")
log_message(message, log_file=session_file, console=args.verbosity)
dataset = api.create_dataset(source_or_dataset, dataset_args)
suffix = "_" + dataset_type if dataset_type else ""
log_created_resources("dataset%s" % suffix, path,
bigml.api.get_dataset_id(dataset))
check_resource_error(dataset, "Failed to create dataset: ")
try:
dataset = check_resource(dataset, api.get_dataset)
except ValueError, exception:
sys.exit("Failed to get a finished dataset: %s" % str(exception))
def update_source_fields(source, updated_values, fields, api=None,
verbosity=True, session_file=None):
"""Update remote source with new fields values
"""
if api is None:
api = bigml.api.BigML()
update_fields = {}
for (column, value) in updated_values.iteritems():
update_fields.update({
fields.field_id(column): value})
message = dated("Updating source. %s\n" %
get_url(source))
log_message(message, log_file=session_file,
console=verbosity)
source = api.update_source(source, {"fields": update_fields})
check_resource_error(source, "Failed to update source: ")
return source
def remote_predict_ensemble(ensemble_id,
test_reader,
prediction_file,
api,
resume=False,
verbosity=True,
output_path=None,
method=PLURALITY_CODE,
tags="",
session_file=None,
log=None,
debug=False,
prediction_info=None):
"""Retrieve predictions remotely and save predictions to file
"""
prediction_args = {"tags": tags, "combiner": method}
test_set_header = test_reader.has_headers()
if output_path is None:
output_path = u.check_dir(prediction_file)
if (not resume or not c.checkpoint(c.are_predictions_created,
prediction_file,
test_reader.number_of_tests(),
debug=debug)):
message = u.dated("Creating remote predictions.")
u.log_message(message, log_file=session_file, console=verbosity)
predictions_file = csv.writer(open(prediction_file, 'w', 0),
lineterminator="\n")
for input_data in test_reader:
input_data_dict = test_reader.dict(input_data)
prediction = api.create_prediction(ensemble_id,
input_data_dict,
by_name=test_set_header,
wait_time=0,
args=prediction_args)
prediction = u.check_resource(prediction, api.get_prediction)
u.check_resource_error(prediction, "Failed to create prediction: ")
u.log_message("%s\n" % prediction['resource'], log_file=log)
prediction_row = prediction_to_row(prediction, prediction_info)
write_prediction(prediction_row, predictions_file, prediction_info,
input_data)
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 update_source_fields(source,
updated_values,
fields,
api=None,
verbosity=True,
session_file=None):
"""Update remote source with new fields values
"""
if api is None:
api = bigml.api.BigML()
update_fields = {}
for (column, value) in updated_values.iteritems():
update_fields.update({fields.field_id(column): value})
message = dated("Updating source. %s\n" % get_url(source))
log_message(message, log_file=session_file, console=verbosity)
source = api.update_source(source, {"fields": update_fields})
check_resource_error(source, "Failed to update source: ")
return source
def publish_dataset(dataset, args, api=None, session_file=None):
"""Publishes dataset and sets its price (if any)
"""
if api is None:
api = bigml.api.BigML()
public_dataset = {"private": False}
if args.dataset_price:
public_dataset.update(price=args.dataset_price)
dataset = update_dataset(dataset,
public_dataset,
args,
api=api,
session_file=session_file)
check_resource_error(dataset, "Failed to update dataset: ")
dataset = check_resource(dataset,
api.get_dataset,
query_string=ALL_FIELDS_QS)
return dataset
def create_evaluations(model_ids, dataset, evaluation_args, args, api=None,
path=None, session_file=None, log=None,
existing_evaluations=0):
"""Create evaluations for a list of models
``model_ids``: list of model ids to create an evaluation of
``dataset``: dataset object or id 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
``seed``: seed for the dataset sampling (when needed)
"""
evaluations = []
if api is None:
api = bigml.api.BigML()
number_of_evaluations = len(model_ids)
message = dated("Creating evaluations.\n")
log_message(message, log_file=session_file,
console=args.verbosity)
for i in range(0, number_of_evaluations):
model = model_ids[i]
if i % args.max_parallel_evaluations == 0 and i > 0:
try:
evaluations[i - 1] = check_resource(
evaluations[i - 1], api.get_evaluation)
except ValueError, exception:
sys.exit("Failed to get a finished evaluation: %s" %
str(exception))
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)
log_created_resources("evaluations", path,
bigml.api.get_evaluation_id(evaluation),
open_mode='a')
check_resource_error(evaluation, "Failed to create evaluation: ")
evaluations.append(evaluation)
log_message("%s\n" % evaluation['resource'], log_file=log)
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[:]
model_ids = []
ensemble_args_list = []
if isinstance(ensemble_args, list):
ensemble_args_list = ensemble_args
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)
query_string = ALL_FIELDS_QS
inprogress = []
for i in range(0, number_of_ensembles):
wait_for_available_tasks(inprogress,
args.max_parallel_ensembles,
api.get_ensemble,
"ensemble",
query_string=query_string,
wait_step=args.number_of_models)
if ensemble_args_list:
ensemble_args = ensemble_args_list[i]
ensemble = api.create_ensemble(datasets, ensemble_args)
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,
open_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 publish_dataset(dataset, args, api=None, session_file=None):
"""Publishes dataset and sets its price (if any)
"""
if api is None:
api = bigml.api.BigML()
public_dataset = {"private": False}
if args.dataset_price:
message = dated("Updating dataset. %s\n" %
get_url(dataset))
log_message(message, log_file=session_file,
console=args.verbosity)
public_dataset.update(price=args.dataset_price)
message = dated("Updating dataset. %s\n" %
get_url(dataset))
log_message(message, log_file=session_file,
console=args.verbosity)
dataset = api.update_dataset(dataset, public_dataset)
check_resource_error(dataset, "Failed to update dataset: ")
return dataset
def remote_predict_ensemble(ensemble_id, test_reader, prediction_file, api,
resume=False,
verbosity=True, output_path=None,
method=PLURALITY_CODE, tags="",
session_file=None, log=None, debug=False,
prediction_info=None):
"""Retrieve predictions remotely and save predictions to file
"""
prediction_args = {
"tags": tags,
"combiner": method
}
test_set_header = test_reader.has_headers()
if output_path is None:
output_path = u.check_dir(prediction_file)
if (not resume or
not c.checkpoint(c.are_predictions_created, prediction_file,
test_reader.number_of_tests(), debug=debug)):
message = u.dated("Creating remote predictions.")
u.log_message(message, log_file=session_file,
console=verbosity)
predictions_file = csv.writer(open(prediction_file, 'w', 0),
lineterminator="\n")
for input_data in test_reader:
input_data_dict = test_reader.dict(input_data)
prediction = api.create_prediction(ensemble_id, input_data_dict,
by_name=test_set_header,
wait_time=0,
args=prediction_args)
prediction = u.check_resource(prediction,
api.get_prediction)
u.check_resource_error(prediction,
"Failed to create prediction: ")
u.log_message("%s\n" % prediction['resource'], log_file=log)
prediction_row = prediction_to_row(prediction, prediction_info)
write_prediction(prediction_row, predictions_file,
prediction_info, input_data)
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_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_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 remote_predict_ensemble(ensemble_id, test_reader, prediction_file, api,
args, resume=False, output_path=None,
session_file=None, log=None, exclude=None):
"""Retrieve predictions remotely and save predictions to file
"""
prediction_args = {
"tags": args.tag,
"combiner": args.method
}
test_set_header = test_reader.has_headers()
if output_path is None:
output_path = u.check_dir(prediction_file)
if (not resume or not c.checkpoint(
c.are_predictions_created, prediction_file,
test_reader.number_of_tests(), debug=args.debug)[0]):
message = u.dated("Creating remote predictions.")
u.log_message(message, log_file=session_file,
console=args.verbosity)
with UnicodeWriter(prediction_file) as predictions_file:
for input_data in test_reader:
input_data_dict = test_reader.dict(input_data)
prediction = api.create_prediction(ensemble_id,
input_data_dict,
by_name=test_set_header,
wait_time=0,
args=prediction_args)
prediction = u.check_resource(prediction,
api.get_prediction)
u.check_resource_error(prediction,
"Failed to create prediction: ")
u.log_message("%s\n" % prediction['resource'], log_file=log)
prediction_row = prediction_to_row(prediction,
args.prediction_info)
write_prediction(prediction_row, predictions_file,
args.prediction_info, input_data, exclude)
def create_evaluations(model_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_ids``: list of model 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_ids[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]
wait_for_available_tasks(inprogress, args.max_parallel_evaluations,
api.get_evaluation, "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)
evaluation_id = check_resource_error(evaluation,
"Failed to create evaluation: ")
inprogress.append(evaluation_id)
log_created_resources("evaluations", path, evaluation_id,
open_mode='a')
evaluations.append(evaluation)
log_message("%s\n" % evaluation['resource'], log_file=log)
return evaluations
def publish_model(model, args, api=None, session_file=None):
"""Update model with publish info
"""
if api is None:
api = bigml.api.BigML()
public_model = {}
if args.black_box:
public_model = {"private": False}
if args.white_box:
public_model = {"private": False, "white_box": True}
if args.model_price:
public_model.update(price=args.model_price)
if args.cpp:
public_model.update(credits_per_prediction=args.cpp)
if public_model:
message = dated("Updating model. %s\n" %
get_url(model))
log_message(message, log_file=session_file,
console=args.verbosity)
model = api.update_model(model, public_model)
check_resource_error(model, "Failed to update model %s: " %
model['resource'])
return model
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[:]
model_ids = []
ensemble_args_list = []
if isinstance(ensemble_args, list):
ensemble_args_list = ensemble_args
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)
query_string = ALL_FIELDS_QS
inprogress = []
for i in range(0, number_of_ensembles):
wait_for_available_tasks(inprogress, args.max_parallel_ensembles,
api.get_ensemble, "ensemble",
query_string=query_string,
wait_step=args.number_of_models)
if ensemble_args_list:
ensemble_args = ensemble_args_list[i]
ensemble = api.create_ensemble(datasets, ensemble_args)
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,
open_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_ensembles(dataset, 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[:]
model_ids = []
ensemble_args_list = []
if isinstance(ensemble_args, list):
ensemble_args_list = ensemble_args
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)
query_string = ALL_FIELDS_QS
for i in range(0, number_of_ensembles):
if i % args.max_parallel_ensembles == 0 and i > 0:
try:
ensembles[i - 1] = check_resource(
ensembles[i - 1], api.get_ensemble,
query_string=query_string)
except ValueError, exception:
sys.exit("Failed to get a finished ensemble: %s" %
str(exception))
if ensemble_args_list:
ensemble_args = ensemble_args_list[i]
ensemble = api.create_ensemble(dataset, ensemble_args)
ensemble_id = check_resource_error(ensemble,
"Failed to create ensemble: ")
log_message("%s\n" % ensemble_id, log_file=log)
ensemble_ids.append(ensemble_id)
ensembles.append(ensemble)
log_created_resources("ensembles", path, ensemble_id,
open_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)
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
else ALL_FIELDS_QS)
inprogress = []
for i in range(0, args.number_of_models):
wait_for_available_tasks(inprogress, args.max_parallel_models,
api.get_model, "model",
query_string=query_string)
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 or
(args.test_datasets and args.evaluate)) > 0:
dataset = datasets[i]
model = api.create_model(dataset, model_args)
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)
else:
model = api.create_model(datasets, model_args)
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, open_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)
except ValueError, 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)
api = bigml.api.BigML()
suffix = "" if source_type is None else "%s " % source_type
message = dated("Creating %ssource.\n" % suffix)
log_message(message, log_file=session_file, console=args.verbosity)
source = api.create_source(data_set, source_args,
progress_bar=args.progress_bar)
if path is not None:
try:
suffix = "_" + source_type if source_type else ""
with open("%s/source%s" % (path, suffix), 'w', 0) as source_file:
source_file.write("%s\n" % source['resource'])
source_file.write("%s\n" % source['object']['name'])
except IOError, exc:
sys.exit("%s: Failed to write %s/source" % (str(exc), path))
source_id = check_resource_error(source, "Failed to create source: ")
try:
source = check_resource(source, api.get_source,
query_string=ALL_FIELDS_QS)
except ValueError, exception:
sys.exit("Failed to get a finished source: %s" % str(exception))
message = dated("Source created: %s\n" % get_url(source))
log_message(message, log_file=session_file, console=args.verbosity)
log_message("%s\n" % source_id, log_file=log)
return source
def data_to_source(training_set, test_set,
training_set_header, test_set_header, args):
"""Extracts the flags info to create a source object
def remote_predict(model, test_dataset, batch_prediction_args, args,
api, resume, prediction_file=None, session_file=None,
path=None, log=None):
"""Computes a prediction for each entry in the `test_set`.
Predictions are computed remotely using the batch predictions call.
"""
if args.ensemble is not None and not args.dataset_off:
model_or_ensemble = args.ensemble
elif args.dataset_off:
if hasattr(args, "ensemble_ids_") and args.ensemble_ids_:
models = args.ensemble_ids_
else:
models = args.model_ids_
test_datasets = args.test_dataset_ids
else:
model_or_ensemble = bigml.api.get_model_id(model)
# if resuming, try to extract dataset form log files
if resume:
message = u.dated("Batch prediction not found. Resuming.\n")
resume, batch_prediction = c.checkpoint(
c.is_batch_prediction_created, path, debug=args.debug,
message=message, log_file=session_file, console=args.verbosity)
if not resume:
if not args.dataset_off:
batch_prediction = create_batch_prediction(
model_or_ensemble, test_dataset, batch_prediction_args,
args, api, session_file=session_file, path=path, log=log)
else:
batch_predictions = []
for index, test_dataset_n in enumerate(test_datasets):
batch_predictions.append(create_batch_prediction( \
models[index], test_dataset_n, batch_prediction_args,
args, api, session_file=session_file, path=path, log=log))
if not args.no_csv and not args.dataset_off:
file_name = api.download_batch_prediction(batch_prediction,
prediction_file)
if file_name is None:
sys.exit("Failed downloading CSV.")
if args.to_dataset and not args.dataset_off:
batch_prediction = bigml.api.check_resource(batch_prediction, api=api)
new_dataset = bigml.api.get_dataset_id(
batch_prediction['object']['output_dataset_resource'])
if new_dataset is not None:
message = u.dated("Batch prediction dataset created: %s\n"
% u.get_url(new_dataset))
u.log_message(message, log_file=session_file,
console=args.verbosity)
u.log_created_resources("batch_prediction_dataset",
path, new_dataset, mode='a')
elif args.to_dataset and args.dataset_off:
predictions_datasets = []
for batch_prediction in batch_predictions:
batch_prediction = bigml.api.check_resource(batch_prediction,
api=api)
new_dataset = bigml.api.get_dataset_id(
batch_prediction['object']['output_dataset_resource'])
if new_dataset is not None:
predictions_datasets.append(new_dataset)
message = u.dated("Batch prediction dataset created: %s\n"
% u.get_url(new_dataset))
u.log_message(message, log_file=session_file,
console=args.verbosity)
u.log_created_resources("batch_prediction_dataset",
path, new_dataset, mode='a')
multi_dataset = api.create_dataset(predictions_datasets)
log_created_resources("dataset_pred", path,
bigml.api.get_dataset_id(multi_dataset),
mode='a')
dataset_id = check_resource_error(multi_dataset,
"Failed to create dataset: ")
try:
multi_dataset = api.check_resource(multi_dataset)
except ValueError, exception:
sys.exit("Failed to get a finished dataset: %s" % str(exception))
message = dated("Predictions dataset created: %s\n" %
get_url(multi_dataset))
log_message(message, log_file=session_file, console=args.verbosity)
log_message("%s\n" % dataset_id, log_file=log)
if not args.no_csv:
file_name = api.download_dataset(dataset_id, prediction_file)
if file_name is None:
sys.exit("Failed downloading CSV.")
