Qingyun Wu e24265ee5d automl fit with starting points (#141) ...

* add starting point in fit

* add estimator best config

* add test

* add doc string

* when there are multiple points_to_evaluate in CFO, use the best one to start local search; after that use low cost partial config as the start point; then, remove the points whose performance is worse than the converged, and start local search from the remaining ones ordered by their performance.

Co-authored-by: Qingyun Wu <qingyunwu@Qingyuns-MacBook-Pro-2.local>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>

2021-07-31 13:39:31 -07:00

..

hf

remove learning_rate and weight_decay (#113)

2021-06-19 09:27:51 -07:00

nni

Issue58 (#59)

2021-04-08 09:29:55 -07:00

tune

automl fit with starting points (#141)

2021-07-31 13:39:31 -07:00

__init__.py

v0.1.0

2020-12-04 09:40:27 -08:00

run_electra.py

Issue58 (#59)

2021-04-08 09:29:55 -07:00

test_automl.py

automl fit with starting points (#141)

2021-07-31 13:39:31 -07:00

test_autovw.py

api doc for chacha (#105)

2021-06-11 10:25:45 -07:00

test_notebook_example.py

coverage (#135)

2021-07-20 17:00:44 -07:00

test_python_log.py

coverage (#135)

2021-07-20 17:00:44 -07:00

test_pytorch_cifar10.py

Issue58 (#59)

2021-04-08 09:29:55 -07:00

test_restore.py

Test restore (#103)

2021-06-07 19:49:45 -04:00

test_split.py

coverage (#135)

2021-07-20 17:00:44 -07:00

test_training_log.py

coverage (#135)

2021-07-20 17:00:44 -07:00

test_version.py

Issue58 (#59)

2021-04-08 09:29:55 -07:00

test_xgboost2d_sample_size.py

coverage (#135)

2021-07-20 17:00:44 -07:00

test_xgboost2d.py

coverage (#135)

2021-07-20 17:00:44 -07:00