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* Added spark support for parallel training. * Added tests and fixed a bug * Added more tests and updated docs * Updated setup.py and docs * Added customize_learner and tests * Update spark tests and setup.py * Update docs and verbose * Update logging, fix issue in cloud notebook * Update github workflow for spark tests * Update github workflow * Remove hack of handling _choice_ * Allow for failures * Fix tests, update docs * Update setup.py * Update Dockerfile for Spark * Update tests, remove some warnings * Add test for notebooks, update utils * Add performance test for Spark * Fix lru_cache maxsize * Fix test failures on some platforms * Fix coverage report failure * resovle PR comments * resovle PR comments 2nd round * resovle PR comments 3rd round * fix lint and rename test class * resovle PR comments 4th round * refactor customize_learner to broadcast_code
20 lines
561 B
Python
20 lines
561 B
Python
from flaml.automl.model import LGBMEstimator
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from flaml import tune
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class MyLargeLGBM(LGBMEstimator):
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@classmethod
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def search_space(cls, **params):
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return {
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"n_estimators": {
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"domain": tune.lograndint(lower=4, upper=32768),
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"init_value": 32768,
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"low_cost_init_value": 4,
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},
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"num_leaves": {
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"domain": tune.lograndint(lower=4, upper=32768),
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"init_value": 32768,
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"low_cost_init_value": 4,
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},
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}
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