def run(dataset: Dataset, config: TaskConfig):
#TODO: use rpy2 instead? not necessary here though as the call is very simple
log.info(f"\n**** Autoxgboost (R) [{config.framework_version}] ****\n")
is_classification = config.type == 'classification'
here = dir_of(__file__)
meta_results_file = os.path.join(config.output_dir, "meta_results.csv")
run_cmd((
"Rscript --vanilla -e \""
"source('{script}'); "
"run('{train}', '{test}', target.index = {target_index}, '{type}', '{output}', {cores},"
" time.budget = {time_budget}, meta_results_file='{meta_results}')"
"\"").format(script=os.path.join(here, 'exec.R'),
train=dataset.train.path,
test=dataset.test.path,
target_index=dataset.target.index + 1,
type=config.type,
output=config.output_predictions_file,
cores=config.cores,
time_budget=config.max_runtime_seconds,
meta_results=meta_results_file),
_live_output_=True)
log.info("Predictions saved to %s", config.output_predictions_file)
meta_results = read_csv(meta_results_file)
return dict(training_duration=meta_result(meta_results,
'training_duration'),
predict_duration=meta_result(meta_results, 'predict_duration'))
def run(dataset: Dataset, config: TaskConfig):
#TODO: use rpy2 instead? not necessary here though as the call is very simple
log.info("\n**** Random Forest (R) ****\n")
save_metadata(config)
is_classification = config.type == 'classification'
if not is_classification:
raise ValueError('Regression is not supported.')
here = dir_of(__file__)
meta_results_file = os.path.join(config.output_dir, "meta_results.csv")
run_cmd(r"""Rscript --vanilla -e "
source('{script}');
run('{train}', '{test}', '{output}',
cores={cores}, meta_results_file='{meta_results}')
" """.format(script=os.path.join(here, 'exec.R'),
train=dataset.train.path,
test=dataset.test.path,
output=config.output_predictions_file,
meta_results=meta_results_file,
cores=config.cores),
_live_output_=True)
log.info("Predictions saved to %s", config.output_predictions_file)
meta_results = read_csv(meta_results_file)
return dict(training_duration=meta_result(meta_results,
'training_duration'),
predict_duration=meta_result(meta_results, 'predict_duration'))
def run(dataset: Dataset, config: TaskConfig):
#TODO: use rpy2 instead? not necessary here though as the call is very simple
log.info("\n**** Random Forest (R) ****\n")
here = dir_of(__file__)
meta_results_file = os.path.join(config.output_dir, "meta_results.csv")
run_cmd((
"Rscript --vanilla -e \""
"source('{script}'); "
"run('{train}', '{test}', '{output}', cores={cores}, meta_results_file='{meta_results}', task_type='{task_type}')"
"\"").format(script=os.path.join(here, 'exec.R'),
train=dataset.train.path,
test=dataset.test.path,
output=config.output_predictions_file,
meta_results=meta_results_file,
task_type=config.type,
cores=config.cores),
_live_output_=True)
log.info("Predictions saved to %s", config.output_predictions_file)
meta_results = read_csv(meta_results_file)
return dict(training_duration=meta_result(meta_results,
'training_duration'),
predict_duration=meta_result(meta_results, 'predict_duration'))
def load_data(path):
return read_csv(path, as_data_frame=False, header=False)
