def make_mode_partial_cv(data, seed, configuration, num_run, metric, fold, folds):
global evaluator
evaluator = CVEvaluator(data, configuration, cv_folds=folds, seed=seed, num_run=num_run, **_get_base_dict())
evaluator.partial_fit(fold)
signal.signal(15, empty_signal_handler)
scores, _, _, _ = evaluator.predict()
duration = time.time() - evaluator.starttime
score = scores[metric]
additional_run_info = ";".join(["%s: %s" % (m_, value) for m_, value in scores.items()])
additional_run_info += ";" + "duration: " + str(duration)
print(metric, score, additional_run_info)
print(
"Result for ParamILS: %s, %f, 1, %f, %d, %s"
% ("SAT", abs(duration), score, evaluator.seed, additional_run_info)
)
def make_mode_partial_cv(data, seed, configuration, num_run, metric, fold,
folds):
global evaluator
evaluator = CVEvaluator(data,
configuration,
cv_folds=folds,
seed=seed,
num_run=num_run,
**_get_base_dict())
evaluator.partial_fit(fold)
signal.signal(15, empty_signal_handler)
scores, _, _, _ = evaluator.predict()
duration = time.time() - evaluator.starttime
score = scores[metric]
additional_run_info = ';'.join(
['%s: %s' % (m_, value) for m_, value in scores.items()])
additional_run_info += ';' + 'duration: ' + str(duration)
print(metric, score, additional_run_info)
print('Result for ParamILS: %s, %f, 1, %f, %d, %s' %
('SAT', abs(duration), score, evaluator.seed, additional_run_info))
