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_dataset(origin_resource,
dataset_args,
args,
api=None,
path=None,
session_file=None,
log=None,
dataset_type=None):
"""Creates remote dataset from source, dataset, cluster 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)
check_fields_struct(dataset_args, "dataset")
# if --json-query or --sql-query are used and no names are set for
# the datasets, we create default naming to A, B, C, etc. for the datasets
# to be used as origin
if ((hasattr(args, 'sql_query') and args.sql_query) or \
(hasattr(args, 'json_query') and args.sql_query)) and \
isinstance(origin_resource, list) and \
((not isinstance(origin_resource[0], dict)) or \
origin_resource[0].get("name") is None):
for index, element in enumerate(origin_resource):
if index < len(DS_NAMES):
if isinstance(element, dict):
if element.get("resource") is not None:
element = {"id": element["resource"]}
element.update({"name": DS_NAMES[index]})
origin_resource[index] = element
elif isinstance(element, basestring):
origin_resource[index] = {
"id": element,
"name": DS_NAMES[index]
}
dataset = api.create_dataset(origin_resource, dataset_args, retries=None)
suffix = "_" + dataset_type if dataset_type else ""
log_created_resources("dataset%s" % suffix,
path,
bigml.api.get_dataset_id(dataset),
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,
raise_on_error=True)
except Exception, exception:
sys.exit("Failed to get a finished dataset: %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
"""
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 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.")
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.")
def compute_output(api, args, training_set, test_set=None, output=None,
objective_field=None,
description=None,
field_attributes=None,
types=None,
dataset_fields=None,
model_fields=None,
name=None, training_set_header=True,
test_set_header=True, model_ids=None,
votes_files=None, resume=False, fields_map=None):
""" 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`.
"""
source = None
dataset = None
model = None
models = None
fields = None
path = u.check_dir(output)
session_file = "%s%s%s" % (path, os.sep, SESSIONS_LOG)
csv_properties = {}
# If logging is required, open 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
if args.clear_logs:
try:
open(log, 'w', 0).close()
except IOError:
pass
if (training_set or (args.evaluate and test_set)):
if resume:
resume, args.source = u.checkpoint(u.is_source_created, path,
bigml.api, debug=args.debug)
if not resume:
message = u.dated("Source not found. Resuming.\n")
u.log_message(message, log_file=session_file,
console=args.verbosity)
# If neither a previous source, dataset or model are provided.
# we create a new one. Also if --evaluate and test data are provided
# we create a new dataset to test with.
data_set = None
if (training_set and not args.source and not args.dataset and
not args.model and not args.models):
data_set = training_set
data_set_header = training_set_header
elif (args.evaluate and test_set and not args.source):
data_set = test_set
data_set_header = test_set_header
if not data_set is None:
source_args = {
"name": name,
"description": description,
"category": args.category,
"tags": args.tag,
"source_parser": {"header": data_set_header}}
message = u.dated("Creating source.\n")
u.log_message(message, log_file=session_file, console=args.verbosity)
source = api.create_source(data_set, source_args,
progress_bar=args.progress_bar)
source = api.check_resource(source, api.get_source)
message = u.dated("Source created: %s\n" % u.get_url(source, api))
u.log_message(message, log_file=session_file, console=args.verbosity)
u.log_message("%s\n" % source['resource'], log_file=log)
fields = Fields(source['object']['fields'],
source['object']['source_parser']['missing_tokens'],
source['object']['source_parser']['locale'])
source_file = open(path + '/source', 'w', 0)
source_file.write("%s\n" % source['resource'])
source_file.write("%s\n" % source['object']['name'])
source_file.flush()
source_file.close()
# If a source is provided, we retrieve it.
elif args.source:
message = u.dated("Retrieving source. %s\n" %
u.get_url(args.source, api))
u.log_message(message, log_file=session_file, console=args.verbosity)
source = api.get_source(args.source)
# If we already have source, we check that is finished and extract the
# fields, and update them if needed.
if source:
if source['object']['status']['code'] != bigml.api.FINISHED:
message = u.dated("Retrieving source. %s\n" %
u.get_url(source, api))
u.log_message(message, log_file=session_file,
console=args.verbosity)
source = api.check_resource(source, api.get_source)
csv_properties = {'missing_tokens':
source['object']['source_parser']['missing_tokens'],
'data_locale':
source['object']['source_parser']['locale']}
fields = Fields(source['object']['fields'], **csv_properties)
update_fields = {}
if field_attributes:
for (column, value) in field_attributes.iteritems():
update_fields.update({
fields.field_id(column): value})
message = u.dated("Updating source. %s\n" %
u.get_url(source, api))
u.log_message(message, log_file=session_file,
console=args.verbosity)
source = api.update_source(source, {"fields": update_fields})
update_fields = {}
if types:
for (column, value) in types.iteritems():
update_fields.update({
fields.field_id(column): {'optype': value}})
message = u.dated("Updating source. %s\n" %
u.get_url(source, api))
u.log_message(message, log_file=session_file,
console=args.verbosity)
source = api.update_source(source, {"fields": update_fields})
if (training_set or args.source or (args.evaluate and test_set)):
if resume:
resume, args.dataset = u.checkpoint(u.is_dataset_created, path,
bigml.api,
debug=args.debug)
if not resume:
message = u.dated("Dataset not found. Resuming.\n")
u.log_message(message, log_file=session_file,
console=args.verbosity)
# If we have a source but not dataset or model has been provided, we
# create a new dataset if the no_dataset option isn't set up. Also
# if evaluate is set and test_set has been provided.
if ((source and not args.dataset and not args.model and not model_ids and
not args.no_dataset) or
(args.evaluate and args.test_set and not args.dataset)):
dataset_args = {
"name": name,
"description": description,
"category": args.category,
"tags": args.tag
}
if args.json_filter:
dataset_args.update(json_filter=args.json_filter)
elif args.lisp_filter:
dataset_args.update(lisp_filter=args.lisp_filter)
input_fields = []
if dataset_fields:
for name in dataset_fields:
input_fields.append(fields.field_id(name))
dataset_args.update(input_fields=input_fields)
message = u.dated("Creating dataset.\n")
u.log_message(message, log_file=session_file, console=args.verbosity)
dataset = api.create_dataset(source, dataset_args)
dataset = api.check_resource(dataset, api.get_dataset)
message = u.dated("Dataset created: %s\n" % u.get_url(dataset, api))
u.log_message(message, log_file=session_file, console=args.verbosity)
u.log_message("%s\n" % dataset['resource'], log_file=log)
dataset_file = open(path + '/dataset', 'w', 0)
dataset_file.write("%s\n" % dataset['resource'])
dataset_file.flush()
dataset_file.close()
# If a dataset is provided, let's retrieve it.
elif args.dataset:
message = u.dated("Retrieving dataset. %s\n" %
u.get_url(args.dataset, api))
u.log_message(message, log_file=session_file, console=args.verbosity)
dataset = api.get_dataset(args.dataset)
# If we already have a dataset, we check the status and get the fields if
# we hadn't them yet.
if dataset:
if dataset['object']['status']['code'] != bigml.api.FINISHED:
message = u.dated("Retrieving dataset. %s\n" %
u.get_url(dataset, api))
u.log_message(message, log_file=session_file,
console=args.verbosity)
dataset = api.check_resource(dataset, api.get_dataset)
if not csv_properties:
csv_properties = {'data_locale':
dataset['object']['locale']}
if args.public_dataset:
if not description:
raise Exception("You should provide a description to publish.")
public_dataset = {"private": False}
if args.dataset_price:
message = u.dated("Updating dataset. %s\n" %
u.get_url(dataset, api))
u.log_message(message, log_file=session_file,
console=args.verbosity)
public_dataset.update(price=args.dataset_price)
message = u.dated("Updating dataset. %s\n" %
u.get_url(dataset, api))
u.log_message(message, log_file=session_file,
console=args.verbosity)
dataset = api.update_dataset(dataset, public_dataset)
fields = Fields(dataset['object']['fields'], **csv_properties)
# If we have a dataset but not a model, we create the model if the no_model
# flag hasn't been set up.
if (dataset and not args.model and not model_ids and not args.no_model):
model_args = {
"name": name,
"description": description,
"category": args.category,
"tags": args.tag
}
if objective_field is not None:
model_args.update({"objective_field":
fields.field_id(objective_field)})
# If evaluate flag is on, we choose a deterministic sampling with 80%
# of the data to create the model
if args.evaluate:
if args.sample_rate == 1:
args.sample_rate = EVALUATE_SAMPLE_RATE
seed = SEED
model_args.update(seed=seed)
input_fields = []
if model_fields:
for name in model_fields:
input_fields.append(fields.field_id(name))
model_args.update(input_fields=input_fields)
if args.pruning and args.pruning != 'smart':
model_args.update(stat_pruning=(args.pruning == 'statistical'))
model_args.update(sample_rate=args.sample_rate,
replacement=args.replacement,
randomize=args.randomize)
model_ids = []
models = []
if resume:
resume, model_ids = u.checkpoint(u.are_models_created, path,
args.number_of_models,
bigml.api, debug=args.debug)
if not resume:
message = u.dated("Found %s models out of %s. Resuming.\n" %
(len(model_ids),
args.number_of_models))
u.log_message(message, log_file=session_file,
console=args.verbosity)
models = model_ids
args.number_of_models -= len(model_ids)
model_file = open(path + '/models', 'w', 0)
for model_id in model_ids:
model_file.write("%s\n" % model_id)
last_model = None
if args.number_of_models > 0:
message = u.dated("Creating %s.\n" %
u.plural("model", args.number_of_models))
u.log_message(message, log_file=session_file,
console=args.verbosity)
for i in range(1, args.number_of_models + 1):
if i > args.max_parallel_models:
api.check_resource(last_model, api.get_model)
model = api.create_model(dataset, model_args)
u.log_message("%s\n" % model['resource'], log_file=log)
last_model = model
model_ids.append(model['resource'])
models.append(model)
model_file.write("%s\n" % model['resource'])
model_file.flush()
if args.number_of_models < 2 and args.verbosity:
if model['object']['status']['code'] != bigml.api.FINISHED:
model = api.check_resource(model, api.get_model)
models[0] = model
message = u.dated("Model created: %s.\n" %
u.get_url(model, api))
u.log_message(message, log_file=session_file,
console=args.verbosity)
model_file.close()
# If a model is provided, we retrieve it.
elif args.model:
message = u.dated("Retrieving model. %s\n" %
u.get_url(args.model, api))
u.log_message(message, log_file=session_file, console=args.verbosity)
model = api.get_model(args.model)
elif args.models or args.model_tag:
models = model_ids[:]
if model_ids and test_set and not args.evaluate:
model_id = ""
if len(model_ids) == 1:
model_id = model_ids[0]
message = u.dated("Retrieving %s. %s\n" %
(u.plural("model", len(model_ids)),
u.get_url(model_id, api)))
u.log_message(message, log_file=session_file, console=args.verbosity)
if len(model_ids) < args.max_batch_models:
models = []
for model in model_ids:
model = api.check_resource(model, api.get_model)
models.append(model)
model = models[0]
else:
model = api.check_resource(model_ids[0], api.get_model)
models[0] = model
# We check that the model is finished and get the fields if haven't got
# them yet.
if model and not args.evaluate and (test_set or args.black_box
or args.white_box):
if model['object']['status']['code'] != bigml.api.FINISHED:
message = u.dated("Retrieving model. %s\n" %
u.get_url(model, api))
u.log_message(message, log_file=session_file,
console=args.verbosity)
model = api.check_resource(model, api.get_model)
if args.black_box:
if not description:
raise Exception("You should provide a description to publish.")
model = api.update_model(model, {"private": False})
if args.white_box:
if not description:
raise Exception("You should provide a description to publish.")
public_model = {"private": False, "white_box": True}
if args.model_price:
message = u.dated("Updating model. %s\n" %
u.get_url(model, api))
u.log_message(message, log_file=session_file,
console=args.verbosity)
public_model.update(price=args.model_price)
if args.cpp:
message = u.dated("Updating model. %s\n" %
u.get_url(model, api))
u.log_message(message, log_file=session_file,
console=args.verbosity)
public_model.update(credits_per_prediction=args.cpp)
model = api.update_model(model, public_model)
if not csv_properties:
csv_properties = {'data_locale':
model['object']['locale']}
csv_properties.update(verbose=True)
if args.user_locale:
csv_properties.update(data_locale=args.user_locale)
fields = Fields(model['object']['model']['fields'], **csv_properties)
if model and not models:
models = [model]
if models and test_set and not args.evaluate:
objective_field = models[0]['object']['objective_fields']
if isinstance(objective_field, list):
objective_field = objective_field[0]
predict(test_set, test_set_header, models, fields, output,
objective_field, args.remote, api, log,
args.max_batch_models, args.method, resume, args.tag,
args.verbosity, session_file, args.debug)
# When combine_votes flag is used, retrieve the predictions files saved
# in the comma separated list of directories and combine them
if votes_files:
model_id = re.sub(r'.*(model_[a-f0-9]{24})__predictions\.csv$',
r'\1', votes_files[0]).replace("_", "/")
model = api.check_resource(model_id, api.get_model)
local_model = Model(model)
message = u.dated("Combining votes.\n")
u.log_message(message, log_file=session_file,
console=args.verbosity)
u.combine_votes(votes_files, local_model.to_prediction,
output, args.method)
# If evaluate flag is on, create remote evaluation and save results in
# json and human-readable format.
if args.evaluate:
if resume:
resume, evaluation = u.checkpoint(u.is_evaluation_created, path,
bigml.api,
debug=args.debug)
if not resume:
message = u.dated("Evaluation not found. Resuming.\n")
u.log_message(message, log_file=session_file,
console=args.verbosity)
if not resume:
evaluation_file = open(path + '/evaluation', 'w', 0)
evaluation_args = {
"name": name,
"description": description,
"tags": args.tag
}
if not fields_map is None:
update_map = {}
for (dataset_column, model_column) in fields_map.iteritems():
update_map.update({
fields.field_id(dataset_column):
fields.field_id(model_column)})
evaluation_args.update({"fields_map": update_map})
if not ((args.dataset or args.test_set)
and (args.model or args.models or args.model_tag)):
evaluation_args.update(out_of_bag=True, seed=SEED,
sample_rate=args.sample_rate)
message = u.dated("Creating evaluation.\n")
u.log_message(message, log_file=session_file,
console=args.verbosity)
evaluation = api.create_evaluation(model, dataset, evaluation_args)
u.log_message("%s\n" % evaluation['resource'], log_file=log)
evaluation_file.write("%s\n" % evaluation['resource'])
evaluation_file.flush()
evaluation_file.close()
message = u.dated("Retrieving evaluation. %s\n" %
u.get_url(evaluation, api))
u.log_message(message, log_file=session_file, console=args.verbosity)
evaluation = api.check_resource(evaluation, api.get_evaluation)
evaluation_json = open(output + '.json', 'w', 0)
evaluation_json.write(json.dumps(evaluation['object']['result']))
evaluation_json.flush()
evaluation_json.close()
evaluation_txt = open(output + '.txt', 'w', 0)
api.pprint(evaluation['object']['result'],
evaluation_txt)
evaluation_txt.flush()
evaluation_txt.close()
# 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)
from bigml.api import BigML
api_user= os.getenv('BIGML_USERNAME')
api_key= os.getenv('BIGML_KEY')
api_project= os.getenv('BIGML_PROJECT')
api = BigML (project = api_project)
# LOAD DATA SET into BIG ML
source_train = api.create_source('train.csv')
source_test = api.create_source('test.csv')
# DATASET CREATION
dataset_train = api.create_dataset(source_train)
datatest_test = api.create_dataset(source_test)
# TRAIN / VAL SPLIT CREATION
trainset = api.create_dataset(dataset_train, {"name": "Train 80% ", "sample_rate": 0.8})
valset = api.create_dataset(dataset_train, {"name": "Validation 20% ", "sample_rate": 0.8, "out_of_bag": True})
# MODEL ENSEMBLE
ensemble_args = {"objective_field": "SeriousDlqin2yrs"}
ensemble = api.create_ensemble(trainset, ensemble_args)
# EVALUATION ENSEMBLE
evaluation = api.create_evaluation(ensemble, valset)