dataset_fields = pd.get_fields_structure(dataset, None)
models_or_ensembles = ensemble_ids if ensemble_ids != [] else models
resume = evaluate(
models_or_ensembles,
[dataset],
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,
)
# If cross_validation_rate is > 0, create remote evaluations and save
# results in json and human-readable format. Then average the results to
# issue a cross_validation measure set.
if args.cross_validation_rate > 0:
args.sample_rate = 1 - args.cross_validation_rate
cross_validate(models, dataset, fields, api, args, resume, session_file=session_file, path=path, log=log)
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)
else models)
resume = evaluate(models_or_ensembles, [dataset], output, api,
args, resume, name=name, description=description,
fields=fields, dataset_fields=dataset_fields,
fields_map=fields_map,
session_file=session_file, path=path,
log=log, labels=labels, all_labels=all_labels,
objective_field=objective_field)
# If cross_validation_rate is > 0, create remote evaluations and save
# results in json and human-readable format. Then average the results to
# issue a cross_validation measure set.
if args.cross_validation_rate > 0:
args.sample_rate = 1 - args.cross_validation_rate
cross_validate(models, dataset, fields, api, args, resume,
name=name, description=description,
fields_map=fields_map, session_file=session_file,
path=path, log=log)
# Workaround to restore windows console cp850 encoding to print the tree
if sys.platform == "win32" and sys.stdout.isatty():
import locale
data_locale = locale.getlocale()
if not data_locale[0] is None:
locale.setlocale(locale.LC_ALL, (data_locale[0], "850"))
message = (u"\nGenerated files:\n\n" +
unicode(u.print_tree(path, u" "), "utf-8") + u"\n")
else:
message = (u"\nGenerated files:\n\n" +
u.print_tree(path, u" ") + u"\n")
u.log_message(message, log_file=session_file, console=args.verbosity)
if args.reports:
# results in json and human-readable format. Then average the results to
# issue a cross_validation measure set.
if args.cross_validation_rate > 0:
args.sample_rate = 1 - args.cross_validation_rate
if args.number_of_evaluations > 0:
number_of_evaluations = args.number_of_evaluations
else:
number_of_evaluations = int(MONTECARLO_FACTOR *
args.cross_validation_rate)
cross_validate(models,
dataset,
number_of_evaluations,
name,
description,
fields,
fields_map,
api,
args,
resume,
session_file=session_file,
path=path,
log=log)
# Workaround to restore windows console cp850 encoding to print the tree
if sys.platform == "win32" and sys.stdout.isatty():
import locale
data_locale = locale.getlocale()
if not data_locale[0] is None:
locale.setlocale(locale.LC_ALL, (data_locale[0], "850"))
message = (u"\nGenerated files:\n\n" +
unicode(u.print_tree(path, " "), "utf-8") + u"\n")
log=log,
labels=labels,
all_labels=all_labels,
objective_field=objective_field)
# If cross_validation_rate is > 0, create remote evaluations and save
# results in json and human-readable format. Then average the results to
# issue a cross_validation measure set.
if args.cross_validation_rate > 0:
args.sample_rate = 1 - args.cross_validation_rate
cross_validate(models,
dataset,
fields,
api,
args,
resume,
name=name,
description=description,
fields_map=fields_map,
session_file=session_file,
path=path,
log=log)
# Workaround to restore windows console cp850 encoding to print the tree
if sys.platform == "win32" and sys.stdout.isatty():
import locale
data_locale = locale.getlocale()
if not data_locale[0] is None:
locale.setlocale(locale.LC_ALL, (data_locale[0], "850"))
message = (u"\nGenerated files:\n\n" +
unicode(u.print_tree(path, " "), "utf-8") + u"\n")
else:
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)
models_or_ensembles = (ensemble_ids if ensemble_ids != []
else models)
resume = evaluate(models_or_ensembles, [dataset], 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)
# If cross_validation_rate is > 0, create remote evaluations and save
# results in json and human-readable format. Then average the results to
# issue a cross_validation measure set.
if args.cross_validation_rate > 0:
args.sample_rate = 1 - args.cross_validation_rate
cross_validate(models, dataset, fields, api, args, resume,
session_file=session_file,
path=path, log=log)
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)
resume = evaluate(model, dataset, name, description, fields,
fields_map, output, api, args, resume,
session_file=session_file, path=path, log=log)
# If cross_validation_rate is > 0, create remote evaluations and save
# results in json and human-readable format. Then average the results to
# issue a cross_validation measure set.
if args.cross_validation_rate > 0:
args.sample_rate = 1 - args.cross_validation_rate
if args.number_of_evaluations > 0:
number_of_evaluations = args.number_of_evaluations
else:
number_of_evaluations = int(MONTECARLO_FACTOR *
args.cross_validation_rate)
cross_validate(models, dataset, number_of_evaluations, name,
description, fields, fields_map, api, args, resume,
session_file=session_file, path=path, log=log)
# Workaround to restore windows console cp850 encoding to print the tree
if sys.platform == "win32" and sys.stdout.isatty():
import locale
data_locale = locale.getlocale()
if not data_locale[0] is None:
locale.setlocale(locale.LC_ALL, (data_locale[0], "850"))
message = (u"\nGenerated files:\n\n" +
unicode(u.print_tree(path, " "), "utf-8") + u"\n")
else:
message = "\nGenerated files:\n\n" + u.print_tree(path, " ") + "\n"
u.log_message(message, log_file=session_file, console=args.verbosity)
