v0.0.12

Changes

General

• Removed gluonnlp from dependencies, gluonnlp can now be installed as an optional dependency to enable the text module (#512).
• Documentation improvements (#503, #529, #549).

Tabular

• Added custom model support (#551).
• Added support for specifying tuning_data argument in TabularPrediction.fit() with test data without the label column to improve data preprocessing and final predictive accuracy on the test data (#551).
• Fixed major defect added in 0.0.11 which caused the Tabular neural network model to crash during training when categorical features with many possible values were present (#542).
• Disabled usage of text ngram features in KNN models to dramatically improve inference speed on NLP problems (#531).
• Added fit_weighted_ensemble() function to TabularPredictor class. Now the user can train additional weighted ensembles post-fit using any subset of the existing trained models (#550).
• Added AG_args_fit argument to enable advanced model training control such as per-model time limit and memory usage (#531).
• Added excluded_model_types argument to TabularPrediction.fit() to enable simplified removal of model types without editing the hyperparameters argument (#543).
• Added version check when loading a predictor, will log a warning if the predictor was trained on a different version of AutoGluon (#536).
• Improved support for GPU on CatBoost (#527).
• Moved CatBoost to lazy import to enable running Tabular without installing CatBoost (#534).
• Added support for training models with no features, in order to get a best guess prediction based only on the average label value (#537).
• Major refactor of internal feature_types_metadata object and AutoFeatureGenerator (#548).
• Major refactor of internal variable names (#551).

Core

• Minor scheduler cleanup (#523, #540).