In this release, we add two nlp tasks: sequence classification and sequence regression to flaml.AutoML, using transformer-based neural networks. Previously the nlp module was detached from flaml.AutoML with a separate API. We redesigned the API such that the nlp tasks can be accessed from the same API as other tasks, and adding more nlp tasks in future would be easy. Thanks for the hard work @liususan091219 !
We've also continued to make more performance & feature improvements. Examples:
- We added a variation of XGBoost search space which uses limited
max_depth. It includes the default configuration from XGBoost library. The new search space leads to significantly better performance for some regression datasets. - We allow arguments for
flaml.AutoMLto be passed to the constructor. This enables multioutput regression by combining sklearn's MultioutputRegressor and flaml's AutoML. - We made more memory optimization, while allowing users to keep the best model per estimator in memory through the "model_history" option.
What's Changed
- Unify regression and classification for XGBoost by @sonichi in #276
- when max_iter=1, skip search only if retrain_final by @sonichi in #280
- example update by @sonichi in #281
- Merge exp into flaml by @liususan091219 in #210
- add best_loss_per_estimator by @qingyun-wu in #286
- model_history -> save_best_model_per_estimator by @sonichi in #283
- datetime feature engineering by @sonichi in #285
- add warmstart test by @qingyun-wu in #298
- empty search space by @sonichi in #295
- multioutput regression by @sonichi in #292
- add max_depth to xgboost search space by @sonichi in #282
- custom metric function clarification by @sonichi in #300
- checkpoint naming in nonray mode, fix ray mode, delete checkpoints in nonray mode by @liususan091219 in #293
Full Changelog: v0.7.1...v0.8.0