Tune with 200 estimators in the config seems to hangup forever.
Traceback when the tune script is killed:
2018-03-16 04:19:11,498 DEBUG:revscoring.utilities.tune -- Cross-validated GradientBoosting with n_estimators=100, max_depth=5, max_features="log2", learning_rate=0.1 in
79.197 minutes: pr_auc.macro=0.754
^CProcess ForkPoolWorker-4:
Process ForkPoolWorker-3:
Process ForkPoolWorker-6:
Process ForkPoolWorker-12:
Traceback (most recent call last):
File "/home/codezee/ai/venv/bin/revscoring", line 11, in <module>
Process ForkPoolWorker-5:
Process ForkPoolWorker-2:
Process ForkPoolWorker-13:
Process ForkPoolWorker-7:
load_entry_point('revscoring==2.2.0', 'console_scripts', 'revscoring')()
File "/home/codezee/ai/venv/lib/python3.5/site-packages/revscoring-2.2.0-py3.5.egg/revscoring/revscoring.py", line 51, in main
Traceback (most recent call last):
File "/usr/lib/python3.5/multiprocessing/process.py", line 249, in _bootstrap
self.run()
File "/usr/lib/python3.5/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
Traceback (most recent call last):
File "/usr/lib/python3.5/multiprocessing/pool.py", line 108, in worker
task = get()
File "/usr/lib/python3.5/multiprocessing/queues.py", line 342, in get
with self._rlock:
File "/usr/lib/python3.5/multiprocessing/synchronize.py", line 96, in __enter__
return self._semlock.__enter__()
Traceback (most recent call last):Profiling with individual cv_train calls yielded the following for 200 estimators with 2 folds:
real 143m18.515s user 188m31.496s sys 0m42.880s
The fitness measures were:
pr_auc (micro=0.815, macro=0.783)