Feature request: support validation datasets in AutoML.retrain_from_log()
- Dominant language
- Jupyter Notebook
- Stars
- 4.4k
- Forks
- 565
- Avg merge
- 5d 8m
- Merged PRs (30d)
- 17
Description
Currently `AutoML.fit()` supports custom validation datasets passed using `X_val` and `y_val` but not with `AutoML.retrain_from_log()`. When I train my model with 'Xtrain' and 'y_train' ~~with the validation datasets~~ and use validation datasets for hyperparameter validation and save the logs, I expect to use the logs later to warm start the model. However, it is not possible as `AutoML.retrain_from_log()` doesn't support validation datasets.
Here's the discussion @sonichi and I had about it earlier this week.
### Discussed in https://github.com/microsoft/FLAML/discussions/727
Originally posted by **harshvardhaniimi** September 11, 2022
Hi, I'm trying to retrain a model with LightGBM. Earlier, I trained my model for a period of time and would like the algorithm to pick up where it stopped. `retrain_from_log` allows that, provided I have the log files saved which I do. However, I'm having trouble using the validation dataset with `X_val`. I get the following error.
````
TypeError: fit() got an unexpected keyword argument 'X_val'
````
The code works well when I'm using `automl.fit()`; this error only occurs in `automl.retrain_from_log()`. Any help? Are `X_val` and `Y_val` not valid parameters in `retrain_fromlog()` but only in `fit()`?
Contributor guide
No contributing guide indexed for this repository
Assessment
This issue has not been assessed yet.