def run(dataset, config):
log.info("\n**** Random Forest (sklearn %s) ****\n", sklearn.__version__)
is_classification = config.type == 'classification'
# Impute any missing data (can test using -t 146606)
X_train, X_test = impute(dataset.train.X_enc, dataset.test.X_enc)
y_train, y_test = dataset.train.y, dataset.test.y
log.info(
"Running RandomForest with a maximum time of {}s on {} cores.".format(
config.max_runtime_seconds, config.cores))
log.warning(
"We completely ignore the requirement to stay within the time limit.")
log.warning(
"We completely ignore the advice to optimize towards metric: {}.".
format(config.metric))
estimator = RandomForestClassifier if is_classification else RandomForestRegressor
rfc = estimator(n_jobs=config.cores, **config.framework_params)
rfc.fit(X_train, y_train)
predictions = rfc.predict(X_test)
probabilities = rfc.predict_proba(X_test) if is_classification else None
return ns(output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test,
target_is_encoded=False)
def run(dataset: Dataset, config: TaskConfig):
with TmpDir() as tmpdir:
ds = ns(
train=ns(
X_enc=os.path.join(tmpdir, 'train.X_enc'),
y=os.path.join(tmpdir, 'train.y')
),
test=ns(
X_enc=os.path.join(tmpdir, 'test.X_enc'),
y=os.path.join(tmpdir, 'test.y')
)
)
write_csv(dataset.train.X_enc, ds.train.X_enc),
write_csv(dataset.train.y.reshape(-1, 1), ds.train.y),
write_csv(dataset.test.X_enc, ds.test.X_enc),
write_csv(dataset.test.y.reshape(-1, 1), ds.test.y),
dataset.release()
config.result_token = str(uuid.uuid1())
config.result_dir = tmpdir
params = json_dumps(dict(dataset=ds, config=config), style='compact')
output, err = run_cmd('{python} {here}/exec_proc.py'.format(python=PYTHON, here=dir_of(__file__)), _input_str_=params)
out = io.StringIO(output)
res = ns()
for line in out:
li = line.rstrip()
if li == config.result_token:
res = json_loads(out.readline(), as_namespace=True)
break
def load_data(path):
return read_csv(path, as_data_frame=False, header=False)
log.debug("Result from subprocess:\n%s", res)
save_predictions_to_file(dataset=dataset,
output_file=res.output_file,
probabilities=load_data(res.probabilities) if res.probabilities is not None else None,
predictions=load_data(res.predictions).squeeze(),
truth=load_data(res.truth).squeeze(),
target_is_encoded=res.target_is_encoded)
config_user = config_load(
os.path.join(args.userdir if args.userdir is not None else config.user_dir,
"config.yaml"))
# config listing properties set by command line
config_args = ns.parse(
{'results.save': args.keep_scores},
input_dir=args.indir,
output_dir=args.outdir,
user_dir=args.userdir,
run_mode=args.mode,
script=os.path.basename(__file__),
sid=sid,
) + ns.parse(extras)
if args.mode != 'local':
config_args + ns.parse({'monitoring.frequency_seconds': 0})
config_args = ns({k: v for k, v in config_args if v is not None})
log.debug("Config args: %s.", config_args)
# merging all configuration files
automl.resources.from_configs(config, config_user, config_args)
try:
if args.mode == 'local':
bench = automl.Benchmark(args.framework,
args.benchmark,
parallel_jobs=args.parallel)
elif args.mode == 'docker':
bench = automl.DockerBenchmark(args.framework,
args.benchmark,
parallel_jobs=args.parallel)
elif args.mode == 'aws':
bench = automl.AWSBenchmark(args.framework,
return ns(output_file=config.output_predictions_file,
probabilities=probabilities,
predictions=predictions,
truth=y_test,
target_is_encoded=False)
if __name__ == '__main__':
params = json_loads(sys.stdin.read(), as_namespace=True)
def load_data(path):
return read_csv(path, as_data_frame=False, header=False)
ds = ns(train=ns(X_enc=load_data(params.dataset.train.X_enc),
y=load_data(params.dataset.train.y).squeeze()),
test=ns(
X_enc=load_data(params.dataset.test.X_enc),
y=load_data(params.dataset.test.y).squeeze(),
))
config = params.config
config.framework_params = ns.dict(config.framework_params)
result = run(ds, config)
res = copy.copy(result)
res.predictions = os.path.join(config.result_dir, 'predictions')
res.truth = os.path.join(config.result_dir, 'truth')
write_csv(result.predictions.reshape(-1, 1), res.predictions)
write_csv(result.truth.reshape(-1, 1), res.truth)
if result.probabilities is not None:
res.probabilities = os.path.join(config.result_dir, 'probabilities')
write_csv(result.probabilities, res.probabilities)
