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

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default

coverage (#135)

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nlp

apidoc (#116)

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onlineml

coverage (#135)

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scheduler

Add ChaCha (#92)

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searcher

automl fit with starting points (#141)

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tune

automl fit with starting points (#141)

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__init__.py

coverage (#135)

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automl.py

automl fit with starting points (#141)

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config.py

Issue58 (#59)

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data.py

coverage (#135)

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ml.py

constraint (#132)

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model.py

max_leaves (#138)

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training_log.py

Issue58 (#59)

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version.py

automl fit with starting points (#141)

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