def compute_output(api, args):
""" Creates one or more models using the `training_set` or uses the ids
of previously created BigML models to make predictions for the `test_set`.
"""
logistic_regression = None
logistic_regressions = None
# no multi-label support at present
# variables from command-line options
resume = args.resume_
logistic_regression_ids = args.logistic_regression_ids_
output = args.predictions
# there's only one logistic regression to be generated at present
args.max_parallel_logistic_regressions = 1
# logistic regressions cannot be published yet.
args.public_logistic_regression = False
# It is compulsory to have a description to publish either datasets or
# logistic regressions
if (not args.description_
and (args.public_logistic_regression or args.public_dataset)):
sys.exit("You should provide a description to publish.")
# When using --new-fields, it is compulsory to specify also a dataset
# id
if args.new_fields and not args.dataset:
sys.exit("To use --new-fields you must also provide a dataset id"
" to generate the new dataset from it.")
path = u.check_dir(output)
session_file = u"%s%s%s" % (path, os.sep, SESSIONS_LOG)
csv_properties = {}
if args.objective_field:
csv_properties.update({'objective_field': args.objective_field})
# If logging is required set the file for logging
log = None
if args.log_file:
u.check_dir(args.log_file)
log = args.log_file
# If --clear_logs the log files are cleared
clear_log_files([log])
# basic pre-model step: creating or retrieving the source related info
source, resume, csv_properties, fields = pms.get_source_info(
api, args, resume, csv_properties, session_file, path, log)
# basic pre-model step: creating or retrieving the dataset related info
dataset_properties = pms.get_dataset_info(api, args, resume, source,
csv_properties, fields,
session_file, path, log)
(_, datasets, test_dataset, resume, csv_properties,
fields) = dataset_properties
if datasets:
# Now we have a dataset, let's check if there's an objective_field
# given by the user and update it in the fields structure
args.objective_id_ = get_objective_id(args, fields)
if args.logistic_file:
# logistic regression is retrieved from the contents of the given local
# JSON file
logistic_regression, csv_properties, fields = u.read_local_resource(
args.logistic_file, csv_properties=csv_properties)
logistic_regressions = [logistic_regression]
logistic_regression_ids = [logistic_regression['resource']]
else:
# logistic regression is retrieved from the remote object
logistic_regressions, logistic_regression_ids, resume = \
plr.logistic_regressions_processing( \
datasets, logistic_regressions, logistic_regression_ids, \
api, args, resume, fields=fields, \
session_file=session_file, path=path, log=log)
if logistic_regressions:
logistic_regression = logistic_regressions[0]
# We update the logistic regression's public state if needed
if logistic_regression:
if isinstance(logistic_regression, basestring):
if not a.has_test(args):
query_string = MINIMUM_MODEL
elif args.export_fields:
query_string = r.ALL_FIELDS_QS
else:
query_string = ''
logistic_regression = u.check_resource(logistic_regression,
api.get_logistic_regression,
query_string=query_string)
logistic_regressions[0] = logistic_regression
if (args.public_logistic_regression
or (args.shared_flag
and r.shared_changed(args.shared, logistic_regression))):
logistic_regression_args = {}
if args.shared_flag and r.shared_changed(args.shared,
logistic_regression):
logistic_regression_args.update(shared=args.shared)
if args.public_logistic_regression:
logistic_regression_args.update( \
r.set_publish_logistic_regression_args(args))
if logistic_regression_args:
logistic_regression = r.update_logistic_regression( \
logistic_regression, logistic_regression_args, args,
api=api, path=path, \
session_file=session_file)
logistic_regressions[0] = logistic_regression
# We get the fields of the logistic_regression if we haven't got
# them yet and need them
if logistic_regression and (args.test_set or args.export_fields):
fields = plr.get_logistic_fields( \
logistic_regression, csv_properties, args)
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
# If predicting
if logistic_regressions and (a.has_test(args) or \
(test_dataset and args.remote)):
if test_dataset is None:
test_dataset = get_test_dataset(args)
# Remote predictions: predictions are computed as batch predictions
# in bigml.com except when --no-batch flag is set on
if args.remote and not args.no_batch:
# create test source from file
test_name = "%s - test" % args.name
if args.test_source is None:
test_properties = ps.test_source_processing(
api,
args,
resume,
name=test_name,
session_file=session_file,
path=path,
log=log)
(test_source, resume, csv_properties,
test_fields) = test_properties
else:
test_source_id = bigml.api.get_source_id(args.test_source)
test_source = api.check_resource(test_source_id)
if test_dataset is None:
# create test dataset from test source
dataset_args = r.set_basic_dataset_args(args, name=test_name)
test_dataset, resume = pd.alternative_dataset_processing(
test_source,
"test",
dataset_args,
api,
args,
resume,
session_file=session_file,
path=path,
log=log)
else:
test_dataset_id = bigml.api.get_dataset_id(test_dataset)
test_dataset = api.check_resource(test_dataset_id)
csv_properties.update(objective_field=None,
objective_field_present=False)
test_fields = pd.get_fields_structure(test_dataset, csv_properties)
batch_prediction_args = r.set_batch_prediction_args(
args, fields=fields, dataset_fields=test_fields)
remote_lr_prediction(logistic_regression, test_dataset, \
batch_prediction_args, args, \
api, resume, prediction_file=output, \
session_file=session_file, path=path, log=log)
else:
lr_prediction(logistic_regressions,
fields,
args,
session_file=session_file)
# If evaluate flag is on, create remote evaluation and save results in
# json and human-readable format.
if args.evaluate:
# When we resume evaluation and models were already completed, we
# should use the datasets array as test datasets
if args.has_test_datasets_:
test_dataset = get_test_dataset(args)
if args.dataset_off and not args.has_test_datasets_:
args.test_dataset_ids = datasets
if args.test_dataset_ids and args.dataset_off:
# Evaluate the models with the corresponding test datasets.
test_dataset_id = bigml.api.get_dataset_id( \
args.test_dataset_ids[0])
test_dataset = api.check_resource(test_dataset_id)
csv_properties.update(objective_field=None,
objective_field_present=False)
test_fields = pd.get_fields_structure(test_dataset, csv_properties)
resume = evaluate(logistic_regressions,
args.test_dataset_ids,
api,
args,
resume,
fields=fields,
dataset_fields=test_fields,
session_file=session_file,
path=path,
log=log,
objective_field=args.objective_field)
else:
dataset = datasets[0]
if args.test_split > 0 or args.has_test_datasets_:
dataset = test_dataset
dataset = u.check_resource(dataset,
api=api,
query_string=r.ALL_FIELDS_QS)
dataset_fields = pd.get_fields_structure(dataset, None)
resume = evaluate(logistic_regressions, [dataset],
api,
args,
resume,
fields=fields,
dataset_fields=dataset_fields,
session_file=session_file,
path=path,
log=log,
objective_field=args.objective_field)
u.print_generated_files(path,
log_file=session_file,
verbosity=args.verbosity)
if args.reports:
clear_reports(path)
if args.upload:
upload_reports(args.reports, path)
def compute_output(api, args):
""" Creates one or more models using the `training_set` or uses the ids
of previously created BigML models to make predictions for the `test_set`.
"""
logistic_regression = None
logistic_regressions = None
# no multi-label support at present
# variables from command-line options
resume = args.resume_
logistic_regression_ids = args.logistic_regression_ids_
output = args.predictions
# there's only one logistic regression to be generated at present
args.max_parallel_logistic_regressions = 1
# logistic regressions cannot be published yet.
args.public_logistic_regression = False
# It is compulsory to have a description to publish either datasets or
# logistic regressions
if (not args.description_ and (args.public_logistic_regression or
args.public_dataset)):
sys.exit("You should provide a description to publish.")
# When using --new-fields, it is compulsory to specify also a dataset
# id
if args.new_fields and not args.dataset:
sys.exit("To use --new-fields you must also provide a dataset id"
" to generate the new dataset from it.")
path = u.check_dir(output)
session_file = u"%s%s%s" % (path, os.sep, SESSIONS_LOG)
csv_properties = {}
if args.objective_field:
csv_properties.update({'objective_field': args.objective_field})
# If logging is required set the file for logging
log = None
if args.log_file:
u.check_dir(args.log_file)
log = args.log_file
# If --clear_logs the log files are cleared
clear_log_files([log])
# basic pre-model step: creating or retrieving the source related info
source, resume, csv_properties, fields = pms.get_source_info(
api, args, resume, csv_properties, session_file, path, log)
# basic pre-model step: creating or retrieving the dataset related info
dataset_properties = pms.get_dataset_info(
api, args, resume, source,
csv_properties, fields, session_file, path, log)
(_, datasets, test_dataset,
resume, csv_properties, fields) = dataset_properties
if datasets:
# Now we have a dataset, let's check if there's an objective_field
# given by the user and update it in the fields structure
args.objective_id_ = get_objective_id(args, fields)
if args.logistic_file:
# logistic regression is retrieved from the contents of the given local
# JSON file
logistic_regression, csv_properties, fields = u.read_local_resource(
args.logistic_file,
csv_properties=csv_properties)
logistic_regressions = [logistic_regression]
logistic_regression_ids = [logistic_regression['resource']]
else:
# logistic regression is retrieved from the remote object
logistic_regressions, logistic_regression_ids, resume = \
plr.logistic_regressions_processing( \
datasets, logistic_regressions, logistic_regression_ids, \
api, args, resume, fields=fields, \
session_file=session_file, path=path, log=log)
if logistic_regressions:
logistic_regression = logistic_regressions[0]
# We update the logistic regression's public state if needed
if logistic_regression:
if isinstance(logistic_regression, basestring):
if not a.has_test(args):
query_string = MINIMUM_MODEL
elif args.export_fields:
query_string = r.ALL_FIELDS_QS
else:
query_string = ''
logistic_regression = u.check_resource(logistic_regression,
api.get_logistic_regression,
query_string=query_string)
logistic_regressions[0] = logistic_regression
if (args.public_logistic_regression or
(args.shared_flag and r.shared_changed(args.shared,
logistic_regression))):
logistic_regression_args = {}
if args.shared_flag and r.shared_changed(args.shared,
logistic_regression):
logistic_regression_args.update(shared=args.shared)
if args.public_logistic_regression:
logistic_regression_args.update( \
r.set_publish_logistic_regression_args(args))
if logistic_regression_args:
logistic_regression = r.update_logistic_regression( \
logistic_regression, logistic_regression_args, args,
api=api, path=path, \
session_file=session_file)
logistic_regressions[0] = logistic_regression
# We get the fields of the logistic_regression if we haven't got
# them yet and need them
if logistic_regression and (args.test_set or args.export_fields):
fields = plr.get_logistic_fields( \
logistic_regression, csv_properties, args)
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
# If predicting
if logistic_regressions and (a.has_test(args) or \
(test_dataset and args.remote)):
if test_dataset is None:
test_dataset = get_test_dataset(args)
# Remote predictions: predictions are computed as batch predictions
# in bigml.com except when --no-batch flag is set on
if args.remote and not args.no_batch:
# create test source from file
test_name = "%s - test" % args.name
if args.test_source is None:
test_properties = ps.test_source_processing(
api, args, resume, name=test_name,
session_file=session_file, path=path, log=log)
(test_source, resume,
csv_properties, test_fields) = test_properties
else:
test_source_id = bigml.api.get_source_id(args.test_source)
test_source = api.check_resource(test_source_id)
if test_dataset is None:
# create test dataset from test source
dataset_args = r.set_basic_dataset_args(args, name=test_name)
test_dataset, resume = pd.alternative_dataset_processing(
test_source, "test", dataset_args, api, args,
resume, session_file=session_file, path=path, log=log)
else:
test_dataset_id = bigml.api.get_dataset_id(test_dataset)
test_dataset = api.check_resource(test_dataset_id)
csv_properties.update(objective_field=None,
objective_field_present=False)
test_fields = pd.get_fields_structure(test_dataset,
csv_properties)
batch_prediction_args = r.set_batch_prediction_args(
args, fields=fields,
dataset_fields=test_fields)
remote_lr_prediction(logistic_regression, test_dataset, \
batch_prediction_args, args, \
api, resume, prediction_file=output, \
session_file=session_file, path=path, log=log)
else:
lr_prediction(logistic_regressions, fields, args,
session_file=session_file)
# If evaluate flag is on, create remote evaluation and save results in
# json and human-readable format.
if args.evaluate:
# When we resume evaluation and models were already completed, we
# should use the datasets array as test datasets
if args.has_test_datasets_:
test_dataset = get_test_dataset(args)
if args.dataset_off and not args.has_test_datasets_:
args.test_dataset_ids = datasets
if args.test_dataset_ids and args.dataset_off:
# Evaluate the models with the corresponding test datasets.
resume = evaluate(logistic_regressions, args.test_dataset_ids, api,
args, resume,
fields=fields, dataset_fields=dataset_fields,
session_file=session_file, path=path,
log=log, labels=labels, all_labels=all_labels,
objective_field=args.objective_field)
u.print_generated_files(path, log_file=session_file,
verbosity=args.verbosity)
if args.reports:
clear_reports(path)
if args.upload:
upload_reports(args.reports, path)
def compute_output(api, args):
""" Creates one or more models using the `training_set` or uses the ids
of previously created BigML models to make predictions for the `test_set`.
"""
time_series = None
time_series_set = None
# variables from command-line options
resume = args.resume_
time_series_ids = args.time_series_ids_
output = args.predictions
# there's only one time_series to be generated at present
args.max_parallel_time_series = 1
args.max_parallel_evaluations = 1
# time_series cannot be published yet.
args.public_time_series = False
# no cross-validations
args.dataset_off = False
args.cross_validation_rate = 0
args.number_of_evaluations = 1
# It is compulsory to have a description to publish either datasets or
# time_series
if (not args.description_ and (args.public_time_series or
args.public_dataset)):
sys.exit("You should provide a description to publish.")
# When using --new-fields, it is compulsory to specify also a dataset
# id
if args.new_fields and not args.dataset:
sys.exit("To use --new-fields you must also provide a dataset id"
" to generate the new dataset from it.")
path = u.check_dir(output)
session_file = u"%s%s%s" % (path, os.sep, SESSIONS_LOG)
csv_properties = {}
if args.objective_field:
csv_properties.update({'objective_field': args.objective_field})
# If logging is required set the file for logging
log = None
if args.log_file:
u.check_dir(args.log_file)
log = args.log_file
# If --clear_logs the log files are cleared
clear_log_files([log])
# basic pre-model step: creating or retrieving the source related info
source, resume, csv_properties, fields = pms.get_source_info(
api, args, resume, csv_properties, session_file, path, log)
# basic pre-model step: creating or retrieving the dataset related info
dataset_properties = pms.get_dataset_info(
api, args, resume, source,
csv_properties, fields, session_file, path, log)
(_, datasets, test_dataset,
resume, csv_properties, fields) = dataset_properties
if datasets:
# Now we have a dataset, let's check if there's an objective_field
# given by the user and update it in the fields structure
args.objective_id_ = get_objective_id(args, fields)
# if the time series is going to be evaluated, and we don't have
# test data, we need to divide the rows using ranges, so we'll need
# max rows
args.max_rows = datasets[0]["object"]["rows"]
if args.time_series_file:
# time-series is retrieved from the contents of the given local
# JSON file
time_series, csv_properties, fields = u.read_local_resource(
args.time_series_file,
csv_properties=csv_properties)
time_series_set = [time_series]
time_series_ids = [time_series['resource']]
else:
# time-series is retrieved from the remote object
time_series_set, time_series_ids, resume = \
pts.time_series_processing( \
datasets, time_series_set, time_series_ids, \
api, args, resume, fields=fields, \
session_file=session_file, path=path, log=log)
if time_series_set:
time_series = time_series_set[0]
# We update the time-series' public state if needed
if time_series:
if isinstance(time_series, basestring):
query_string = r.ALL_FIELDS_QS
time_series = u.check_resource(time_series,
api.get_time_series,
query_string=query_string)
time_series_set[0] = time_series
if (args.public_time_series or
(args.shared_flag and r.shared_changed(args.shared,
time_series))):
time_series_args = {}
if args.shared_flag and r.shared_changed(args.shared,
time_series):
time_series_args.update(shared=args.shared)
if args.public_time_series:
time_series_args.update( \
r.set_publish_time_series_args(args))
if time_series_args:
time_series = r.time_series( \
time_series, time_series_args, args,
api=api, path=path, \
session_file=session_file)
time_series_set[0] = time_series
"""
# We get the fields of the time-series if we haven't got
# them yet and need them
if time_series and (args.test_set or args.export_fields):
fields = pts.get_time_series_fields( \
time_series, csv_properties, args)
"""
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
# If forecasting
if time_series_set and a.has_ts_test(args):
if args.remote:
forecast_args = r.set_forecast_args(
args, fields=fields)
remote_forecast(time_series, forecast_args, args, \
api, resume, prediction_file=output, \
session_file=session_file, path=path, log=log)
else:
forecast(time_series, args,
session_file=session_file)
# If evaluate flag is on, create remote evaluation and save results in
# json and human-readable format.
if args.evaluate:
# When we resume evaluation and models were already completed, we
# should use the datasets array as test datasets
if args.has_test_datasets_:
test_dataset = get_test_dataset(args)
if args.dataset_off and not args.has_test_datasets_:
args.test_dataset_ids = datasets
if args.test_dataset_ids and args.dataset_off:
# Evaluate the models with the corresponding test datasets.
test_dataset_id = bigml.api.get_dataset_id( \
args.test_dataset_ids[0])
test_dataset = api.check_resource(test_dataset_id)
csv_properties.update(objective_field=None,
objective_field_present=False)
test_fields = pd.get_fields_structure(test_dataset,
csv_properties)
resume = evaluate(time_series_set, args.test_dataset_ids, api,
args, resume,
fields=fields, dataset_fields=test_fields,
session_file=session_file, path=path,
log=log,
objective_field=args.objective_field)
else:
dataset = datasets[0]
if args.test_split > 0 or args.has_test_datasets_:
dataset = test_dataset
else:
args.range_ = [int(args.max_rows * r.EVALUATE_SAMPLE_RATE),
args.max_rows]
dataset = u.check_resource(dataset, api=api,
query_string=r.ALL_FIELDS_QS)
dataset_fields = pd.get_fields_structure(dataset, None)
resume = evaluate(time_series_set, [dataset], api,
args, resume,
fields=fields, dataset_fields=dataset_fields,
session_file=session_file, path=path,
log=log,
objective_field=args.objective_field)
u.print_generated_files(path, log_file=session_file,
verbosity=args.verbosity)
if args.reports:
clear_reports(path)
if args.upload:
upload_reports(args.reports, path)
def compute_output(api, args):
""" Creates one or more models using the `training_set` or uses the ids
of previously created BigML models to make predictions for the `test_set`.
"""
time_series = None
time_series_set = None
# variables from command-line options
resume = args.resume_
time_series_ids = args.time_series_ids_
output = args.predictions
# there's only one time_series to be generated at present
args.max_parallel_time_series = 1
args.max_parallel_evaluations = 1
# time_series cannot be published yet.
args.public_time_series = False
# no cross-validations
args.dataset_off = False
args.cross_validation_rate = 0
args.number_of_evaluations = 1
# It is compulsory to have a description to publish either datasets or
# time_series
if (not args.description_
and (args.public_time_series or args.public_dataset)):
sys.exit("You should provide a description to publish.")
# When using --new-fields, it is compulsory to specify also a dataset
# id
if args.new_fields and not args.dataset:
sys.exit("To use --new-fields you must also provide a dataset id"
" to generate the new dataset from it.")
path = u.check_dir(output)
session_file = u"%s%s%s" % (path, os.sep, SESSIONS_LOG)
csv_properties = {}
if args.objective_field:
csv_properties.update({'objective_field': args.objective_field})
# If logging is required set the file for logging
log = None
if args.log_file:
u.check_dir(args.log_file)
log = args.log_file
# If --clear_logs the log files are cleared
clear_log_files([log])
# basic pre-model step: creating or retrieving the source related info
source, resume, csv_properties, fields = pms.get_source_info(
api, args, resume, csv_properties, session_file, path, log)
# basic pre-model step: creating or retrieving the dataset related info
dataset_properties = pms.get_dataset_info(api, args, resume, source,
csv_properties, fields,
session_file, path, log)
(_, datasets, test_dataset, resume, csv_properties,
fields) = dataset_properties
if datasets:
# Now we have a dataset, let's check if there's an objective_field
# given by the user and update it in the fields structure
args.objective_id_ = get_objective_id(args, fields)
# if the time series is going to be evaluated, and we don't have
# test data, we need to divide the rows using ranges, so we'll need
# max rows
args.max_rows = datasets[0]["object"]["rows"]
if args.time_series_file:
# time-series is retrieved from the contents of the given local
# JSON file
time_series, csv_properties, fields = u.read_local_resource(
args.time_series_file, csv_properties=csv_properties)
time_series_set = [time_series]
time_series_ids = [time_series['resource']]
else:
# time-series is retrieved from the remote object
time_series_set, time_series_ids, resume = \
pts.time_series_processing( \
datasets, time_series_set, time_series_ids, \
api, args, resume, fields=fields, \
session_file=session_file, path=path, log=log)
if time_series_set:
time_series = time_series_set[0]
# We update the time-series' public state if needed
if time_series:
if isinstance(time_series, basestring):
query_string = r.ALL_FIELDS_QS
time_series = u.check_resource(time_series,
api.get_time_series,
query_string=query_string)
time_series_set[0] = time_series
if (args.public_time_series or
(args.shared_flag and r.shared_changed(args.shared, time_series))):
time_series_args = {}
if args.shared_flag and r.shared_changed(args.shared, time_series):
time_series_args.update(shared=args.shared)
if args.public_time_series:
time_series_args.update( \
r.set_publish_time_series_args(args))
if time_series_args:
time_series = r.time_series( \
time_series, time_series_args, args,
api=api, path=path, \
session_file=session_file)
time_series_set[0] = time_series
"""
# We get the fields of the time-series if we haven't got
# them yet and need them
if time_series and (args.test_set or args.export_fields):
fields = pts.get_time_series_fields( \
time_series, csv_properties, args)
"""
if fields and args.export_fields:
fields.summary_csv(os.path.join(path, args.export_fields))
# If forecasting
if time_series_set and a.has_ts_test(args):
if args.remote:
forecast_args = r.set_forecast_args(args, fields=fields)
remote_forecast(time_series, forecast_args, args, \
api, resume, prediction_file=output, \
session_file=session_file, path=path, log=log)
else:
forecast(time_series, args, session_file=session_file)
# If evaluate flag is on, create remote evaluation and save results in
# json and human-readable format.
if args.evaluate:
# When we resume evaluation and models were already completed, we
# should use the datasets array as test datasets
if args.has_test_datasets_:
test_dataset = get_test_dataset(args)
if args.dataset_off and not args.has_test_datasets_:
args.test_dataset_ids = datasets
if args.test_dataset_ids and args.dataset_off:
# Evaluate the models with the corresponding test datasets.
test_dataset_id = bigml.api.get_dataset_id( \
args.test_dataset_ids[0])
test_dataset = api.check_resource(test_dataset_id)
csv_properties.update(objective_field=None,
objective_field_present=False)
test_fields = pd.get_fields_structure(test_dataset, csv_properties)
resume = evaluate(time_series_set,
args.test_dataset_ids,
api,
args,
resume,
fields=fields,
dataset_fields=test_fields,
session_file=session_file,
path=path,
log=log,
objective_field=args.objective_field)
else:
dataset = datasets[0]
if args.test_split > 0 or args.has_test_datasets_:
dataset = test_dataset
else:
args.range_ = [
int(args.max_rows * r.EVALUATE_SAMPLE_RATE), args.max_rows
]
dataset = u.check_resource(dataset,
api=api,
query_string=r.ALL_FIELDS_QS)
dataset_fields = pd.get_fields_structure(dataset, None)
resume = evaluate(time_series_set, [dataset],
api,
args,
resume,
fields=fields,
dataset_fields=dataset_fields,
session_file=session_file,
path=path,
log=log,
objective_field=args.objective_field)
u.print_generated_files(path,
log_file=session_file,
verbosity=args.verbosity)
if args.reports:
clear_reports(path)
if args.upload:
upload_reports(args.reports, path)