Add a new method to benchmarks: DoubleEnsemble #286
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Derek-Wds
approved these changes
Feb 24, 2021
you-n-g
reviewed
Mar 1, 2021
| @@ -232,6 +232,7 @@ Here is a list of models built on `Qlib`. | |||
| - [SFM based on pytorch (Liheng Zhang, et al. 2017)](qlib/contrib/model/pytorch_sfm.py) | |||
| - [TFT based on tensorflow (Bryan Lim, et al. 2019)](examples/benchmarks/TFT/tft.py) | |||
| - [TabNet based on pytorch (Sercan O. Arik, et al. 2019)](qlib/contrib/model/pytorch_tabnet.py) | |||
| - [DoubleEnsemble based on LightGBM (Chuheng Zhang, et al. 2020)](qlib/contrib/model/double_ensemble.py) | |||
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Could you update your model results here ?
https://github.com/microsoft/qlib/tree/main/examples/benchmarks
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The results are up to date now.
| **kwargs | ||
| ): | ||
| self.base = base # "gbm" or "mlp", specifically, we use lgbm for "gbm" | ||
| self.k = k |
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Please give more details about the parameters.
Give it a full name at least
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OK. The new names are "base_model" and "num_models".
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@meng-ustc |
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Description
Motivation and Context
How Has This Been Tested?
pytest qlib/tests/test_all_pipeline.pyunder upper directory ofqlib.Screenshots of Test Results (if appropriate):
Types of changes