def run(dataset, config):
log.info("\n**** GAMA %s ****", __version__)
log.info("sklearn == %s", sklearn.__version__)
log.info("category_encoders == %s", category_encoders.__version__)
is_classification = (config.type == 'classification')
# Mapping of benchmark metrics to GAMA metrics
metrics_mapping = dict(
acc='accuracy',
auc='roc_auc',
f1='f1',
logloss='neg_log_loss',
mae='neg_mean_absolute_error',
mse='neg_mean_squared_error',
msle='neg_mean_squared_log_error',
r2='r2',
rmse='neg_root_mean_squared_error',
)
scoring_metric = metrics_mapping[config.metric] if config.metric in metrics_mapping else None
if scoring_metric is None:
raise ValueError("Performance metric {} not supported.".format(config.metric))
training_params = {k: v for k, v in config.framework_params.items() if not k.startswith('_')}
n_jobs = config.framework_params.get('_n_jobs', config.cores) # useful to disable multicore, regardless of the dataset config
*_, did, fold = dataset.train_path.split('/')
fold = fold.split('.')[0].split('_')[-1]
log_file = os.path.join(config.output_dir, "logs", '{}_{}.log'.format(did, fold))
utils.touch(log_file)
log.info('Running GAMA with a maximum time of %ss on %s cores, optimizing %s.',
config.max_runtime_seconds, n_jobs, scoring_metric)
estimator = GamaClassifier if is_classification else GamaRegressor
gama_automl = estimator(n_jobs=n_jobs,
max_total_time=config.max_runtime_seconds,
scoring=scoring_metric,
random_state=config.seed,
keep_analysis_log=log_file,
**training_params)
with utils.Timer() as training:
gama_automl.fit_arff(dataset.train_path, dataset.target, encoding='utf-8')
log.info('Predicting on the test set.')
predictions = gama_automl.predict_arff(dataset.test_path, dataset.target, encoding='utf-8')
if is_classification is not None:
probabilities = gama_automl.predict_proba_arff(dataset.test_path, dataset.target, encoding='utf-8')
else:
probabilities = None
return result(
output_file=config.output_predictions_file,
predictions=predictions,
probabilities=probabilities,
target_is_encoded=False,
models_count=len(gama_automl._final_pop),
training_duration=training.duration
)
def write_csv(df, path):
utils.touch(path)
df.to_csv(path, header=True, index=False)