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microsoft / SynapseML

Simple and Distributed Machine Learning
http://aka.ms/spark
MIT License
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Examples for HyperParameter Tuning in Learning to Rank tasks with Python API #934

Open victor-sobreira opened 3 years ago

victor-sobreira commented 3 years ago

I'm trying to run MMLSpark with LightGBM algorithms and applying hyper parameters tuning, but it is not so easy to adapt it based on the actual examples and with the Python API.

I would like to see some examples where the learning to rank algorithms are successfully applied and the parameters are also tuned with TuneHyperparameters class.

Additionally: 1) the different options of input datasets would be presented, e.g. libsvm, Parquet, etc; 2) integration with other optimization libraries would be a plus, e.g. HyperOpt, RAPIDS, Optuna, ...

welcome[bot] commented 3 years ago

👋 Thanks for opening your first issue here! If you're reporting a 🐞 bug, please make sure you include steps to reproduce it.

victor-sobreira commented 3 years ago

@imatiach-msft Dear Ilya, I have noticed you are very active in the foruns and have helped with many issues about mmlspark. Would you give some direction about this issue, please? Is there any work in progress (or planned) for this? Or would it be better to try out other options?