Autogluon

Changes

- Restricted gluoncv install version to <0.9.0 to fix install issues related to namespace collisions (811).

Changes

Tabular

- Complete overhaul of feature generation, major improvements to flexibility, speed, memory usage, and stability Innixma (584, 661).
- Revamped tabular tutorials jwmueller (636).
- Added fastai neural network tabular model (not used by default: requires Torch) gradientsky (627).
- Added LightGBM Extra Trees (LightGBM_XT) model Innixma (681).
- Updated model training priority for multiclass, moved neural networks to train ahead of trees Innixma (676).
- Added .persist_models(), .unpersist_models() methods to TabularPredictor Innixma (640).
- Improved neural network training time jwmueller (598).
- Added example for chunked inference daveharmon (634).
- Improved memory stability on large datasets Innixma (644).
- Reduced maximum memory usage of predictor.leaderboard() Innixma (648).
- Updated LightGBM to v3.x, resulting in ~2x speedup in most cases Innixma (662).
- Updated CatBoost to v0.24.x Innixma (664).
- Updated scikit-learn to <0.24 (from <0.23) Innixma (671).
- Updated pandas version to >=1.0 (from <1.0) Innixma (670).
- Added GPU support for CatBoost Innixma (682).
- Code cleanup Innixma (645, 665, 677, 680, 689).
- Bug Fixes Innixma, gradientsky, jwmueller (643, 666, 678, 688).

Text

- Bug Fixes sxjscience (651, 653).

General

- Upgraded to mxnet 1.7 (from 1.6) sxjscience (650).
- Updated all absolute imports to relative imports Innixma (637).
- Documentation Improvements aaronkl, rdimaio, jwmueller (638, 639, 679).
- Code cleanup tirkarthi (660).
- Bug Fixes Innixma, aaronkl (674, 686).

Changes

Tabular

- Added model distillation jwmueller (547).
- Added FAISS KNN model brc7 (557).
- Refactored Feature Generation (Part 1) Innixma (578).
- Added extra_info argument to predictor.leaderboard Innixma (605).
- Optimized out-of-fold feature memory usage by 50% Innixma (588).
- Added confusion matrix to predictor.evaluate_predictions() output alan-aipe (571).
- Improved output directory generation robustness songqiang (620).
- Improved stability on large datasets by reducing maximum memory usage ratio of RF, XT, and KNN models Innixma (630).

Text

- Added TextPrediction Task sxjscience (556).

General

- Added mxnet 1.7 support sxjscience (546).
- Numerous bug fixes Innixma, jwmueller, sxjscience, zhreshold, yongzhengqi, (559, 568, 577, 590, 592, 597, 600, 604, 621, 625, 629).
- Documentation improvements jwmueller, sxjscience, songqiang, Bharat123rox (554, 561, 585, 609, 628, 631).

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).

Changes

General

* Added bayesopt and bayesopt_hyperband schedulers (501, 507)
* Updated minimum sklearn version from 0.20 to 0.22 (521)

Tabular

* Optimized memory utilization for text features (513)
* Optimized memory utilization for tabular neural network (518)
* Optimized training speed of LightGBM by ~100%-200% on most datasets (511)
* Optimized training speed of CatBoost by ~100% on regression datasets (514)
* Added return_original_features argument to transform_features, plus bug fixes (517)
* Improved tabular neural network training stability on log loss metric (481)
* Numerous fixes and code cleanup (510, 502, 505, 516)

Changes

General

* Removed unnecessary thread workers upon importing autogluon (494, 495)
* Suppressed excessive logging of distributed thread workers (496)
* Capped gluoncv version to 0.x (484)
* Unified scheduler creation (470)

Tabular

* Refactored hyperparameter argument, added options for different models per stack layer (489)
* Optimized CatBoost training time when many features are present (489)
* Enabled automatic type setting to dtypes during inference (463)
* Added feature importance for original features (479)
* Fixed root_mean_squared_error metric (464)
* Fixed pac_score metric (483)
* Various Fixes (465, 472, 474, 489)