def split_processing(dataset, api, args, resume,
multi_label_data=None, session_file=None,
path=None, log=None):
"""Splits a dataset into train and test datasets
"""
train_dataset = None
test_dataset = None
sample_rate = 1 - args.test_split
dataset_alternative_args = r.set_dataset_split_args(
"%s - train (%s %%)" % (
args.name, int(sample_rate * 100)),
args.description_, args, sample_rate,
out_of_bag=False,
multi_label_data=multi_label_data)
train_dataset, resume = alternative_dataset_processing(
dataset, "train", dataset_alternative_args, api, args,
resume, session_file=session_file, path=path, log=log)
dataset_alternative_args = r.set_dataset_split_args(
"%s - test (%s %%)" % (
args.name, int(args.test_split * 100)),
args.description_, args,
sample_rate, out_of_bag=True, multi_label_data=multi_label_data)
test_dataset, resume = alternative_dataset_processing(
dataset, "test", dataset_alternative_args, api, args,
resume, session_file=session_file, path=path, log=log)
return train_dataset, test_dataset, resume
def split_processing(dataset, api, args, resume,
multi_label_data=None, session_file=None,
path=None, log=None):
"""Splits a dataset into train and test datasets
"""
train_dataset = None
test_dataset = None
sample_rate = 1 - args.test_split
dataset_alternative_args = r.set_dataset_split_args(
"%s - train (%s %%)" % (
args.name, int(sample_rate * 100)),
args.description_, args, sample_rate,
out_of_bag=False,
multi_label_data=multi_label_data)
train_dataset, resume = alternative_dataset_processing(
dataset, "train", dataset_alternative_args, api, args,
resume, session_file=session_file, path=path, log=log)
dataset_alternative_args = r.set_dataset_split_args(
"%s - test (%s %%)" % (
args.name, int(args.test_split * 100)),
args.description_, args,
sample_rate, out_of_bag=True, multi_label_data=multi_label_data)
test_dataset, resume = alternative_dataset_processing(
dataset, "test", dataset_alternative_args, api, args,
resume, session_file=session_file, path=path, log=log)
return train_dataset, test_dataset, resume
def split_range_processing(dataset, api, args, resume,
multi_label_data=None, session_file=None,
path=None, log=None):
"""Splits a dataset into train and test datasets using ranges
"""
train_dataset = None
test_dataset = None
test_rate = args.test_split
train_rate = 1 - test_rate
split_row = int(dataset["object"]["rows"] * train_rate)
args.range_ = [1, split_row]
args.test_split = 0
dataset_alternative_args = r.set_dataset_split_args(
"%s - train (%s %%)" % (
args.name, int(train_rate * 100)),
args.description_, args,
multi_label_data=multi_label_data)
train_dataset, resume = alternative_dataset_processing(
dataset, "train", dataset_alternative_args, api, args,
resume, session_file=session_file, path=path, log=log)
args.range_ = [split_row + 1, dataset["object"]["rows"]]
dataset_alternative_args = r.set_dataset_split_args(
"%s - test (%s %%)" % (
args.name, int(test_rate * 100)),
args.description_, args, multi_label_data=multi_label_data)
test_dataset, resume = alternative_dataset_processing(
dataset, "test", dataset_alternative_args, api, args,
resume, session_file=session_file, path=path, log=log)
return train_dataset, test_dataset, resume
def split_range_processing(dataset,
api,
args,
resume,
multi_label_data=None,
session_file=None,
path=None,
log=None):
"""Splits a dataset into train and test datasets using ranges
"""
train_dataset = None
test_dataset = None
test_rate = args.test_split
train_rate = 1 - test_rate
split_row = int(dataset["object"]["rows"] * train_rate)
args.range_ = [1, split_row]
args.test_split = 0
dataset_alternative_args = r.set_dataset_split_args(
"%s - train (%s %%)" % (args.name, int(train_rate * 100)),
args.description_,
args,
multi_label_data=multi_label_data)
train_dataset, resume = alternative_dataset_processing(
dataset,
"train",
dataset_alternative_args,
api,
args,
resume,
session_file=session_file,
path=path,
log=log)
args.range_ = [split_row + 1, dataset["object"]["rows"]]
dataset_alternative_args = r.set_dataset_split_args(
"%s - test (%s %%)" % (args.name, int(test_rate * 100)),
args.description_,
args,
multi_label_data=multi_label_data)
test_dataset, resume = alternative_dataset_processing(
dataset,
"test",
dataset_alternative_args,
api,
args,
resume,
session_file=session_file,
path=path,
log=log)
return train_dataset, test_dataset, resume
def split_processing(dataset, name, description, api, args, resume,
session_file=None, path=None, log=None):
"""Splits a dataset into train and test datasets
"""
train_dataset = None
test_dataset = None
sample_rate = 1 - args.test_split
# if resuming, try to extract train dataset form log files
if resume:
message = u.dated("Dataset not found. Resuming.\n")
resume, train_dataset = c.checkpoint(
c.is_dataset_created, path, "_train", debug=args.debug,
message=message, log_file=session_file, console=args.verbosity)
if train_dataset is None:
dataset_split_args = r.set_dataset_split_args(
"%s - train (%s %%)" % (name,
int(sample_rate * 100)), description, args,
sample_rate, out_of_bag=False)
train_dataset = r.create_dataset(
dataset, dataset_split_args, args, api, path, session_file,
log, "train")
if train_dataset:
train_dataset = r.get_dataset(train_dataset, api,
args.verbosity, session_file)
# if resuming, try to extract test dataset form log files
if resume:
message = u.dated("Dataset not found. Resuming.\n")
resume, test_dataset = c.checkpoint(
c.is_dataset_created, path, "_test", debug=args.debug,
message=message, log_file=session_file, console=args.verbosity)
if test_dataset is None:
dataset_split_args = r.set_dataset_split_args(
"%s - test (%s %%)" % (name,
int(args.test_split * 100)), description, args,
sample_rate, out_of_bag=True)
test_dataset = r.create_dataset(
dataset, dataset_split_args, args, api, path, session_file,
log, "test")
if test_dataset:
test_dataset = r.get_dataset(test_dataset, api, args.verbosity,
session_file)
return train_dataset, test_dataset, resume
def split_processing(dataset,
name,
description,
api,
args,
resume,
session_file=None,
path=None,
log=None):
"""Splits a dataset into train and test datasets
"""
train_dataset = None
test_dataset = None
sample_rate = 1 - args.test_split
# if resuming, try to extract train dataset form log files
if resume:
message = u.dated("Dataset not found. Resuming.\n")
resume, train_dataset = c.checkpoint(c.is_dataset_created,
path,
"_train",
debug=args.debug,
message=message,
log_file=session_file,
console=args.verbosity)
if train_dataset is None:
dataset_split_args = r.set_dataset_split_args(
"%s - train (%s %%)" % (name, int(sample_rate * 100)),
description,
args,
sample_rate,
out_of_bag=False)
train_dataset = r.create_dataset(dataset, dataset_split_args, args,
api, path, session_file, log, "train")
if train_dataset:
train_dataset = r.get_dataset(train_dataset, api, args.verbosity,
session_file)
# if resuming, try to extract test dataset form log files
if resume:
message = u.dated("Dataset not found. Resuming.\n")
resume, test_dataset = c.checkpoint(c.is_dataset_created,
path,
"_test",
debug=args.debug,
message=message,
log_file=session_file,
console=args.verbosity)
if test_dataset is None:
dataset_split_args = r.set_dataset_split_args(
"%s - test (%s %%)" % (name, int(args.test_split * 100)),
description,
args,
sample_rate,
out_of_bag=True)
test_dataset = r.create_dataset(dataset, dataset_split_args, args, api,
path, session_file, log, "test")
if test_dataset:
test_dataset = r.get_dataset(test_dataset, api, args.verbosity,
session_file)
return train_dataset, test_dataset, resume
