from lightautoml.automl.presets.tabular_presets import TabularAutoML, TabularUtilizedAutoML returns error about lightautoml HOT 7 OPEN

Hello Peter Thank you for your reply. I am having all kinds of problems using LAMA. The same call made with TabularAutoML results the following err msg with TabularUtilizedAutoML. So I'd like to have a clean reinstall to make sure everything is current. The link to the notebook returns a "No results matched your search", could you check that? I am unfortunately running Conda on Windows 11, so I hope the new version and the installation play nice together. Thank you and I look forward to hearing from you. Stefan AttributeError: 'TabularUtilizedAutoML' object has no attribute 'reader'

Hi Peter The new LAMA doesn't play nice with python 3.9.x, so I ran my notebook on Kaggle w/ 3.10, and it was a success. Thank you for your swift response! In the meantime, a question about GPU support. Both LightGBM and Catboost can utilize GPU via CUDA. However, LAMA seems to have its own version of Catboost and LightGBM packaged together, and the fact Github page indicated that GPU support is conditionally available, it suggests to me that I can not run LightGBM on GPU via parameters (same for Catboost) the usual way. Is that the correct understanding? Thank you for your support. S

Hi Peter A few follow up questions. 1. I would like to give LightAutoML_GPU a try, is the GPU version in synch with the regular LAMA? Meaning, it's now based 0.3.8b1? 2. Also, I like to learn more about how to use LAMA to tune hyperparameters. The YouTube video talked about how LAMA tunes hyperparameters, but there is no mention of how to actually do it. There is hardly anything about hyperparameter tuning in the documentation as well. Could you provide more information? 3. In the Kaggle Home Price regression example, as well as the documentation ( https://lightautoml.readthedocs.io/en/latest/_modules/lightautoml/automl/presets/tabular_presets.html#). There are mentions of "lgb_tuned" and "cb_tuned". So I assume that allows users to specify hyperparameter tuning as a part of the training. But how does LAMA actually tune them? Which parameters are tuned, to what range of values? Are the user supplied parameter values considered as the starting value? What are the final resulting hyperparameter values? How does the "optimized" validation compare to non-optimized results, where can I find the comparison? Are they available in the report? Many thanks for your help. Stefan