Autogluon

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)

Changes

General

* Limited sklearn version to <0.23 to resolve import failure in skopt. (460)
* Limited catboost version to <0.24. (460)

Changes

General

* Fixed broken PyPi build failing to import in 0.0.7. (457)

Changes

General

* Updated dependency versions.

Tabular

* Added simplified argument preset options to tabular task.fit(). (453)
* Added options to significantly reduce disk usage by >10x during and after model training. (453)
* Added refit model support which dramatically reduces inference times. (408, 412)
* Added transform_features function to TabularPredictor. (431)
* Added transform_labels function to TabularPredictor. (435)
* Numerous improvements to text handling (440, 451)
* Improved memory stability and inference speed of tabular neural network. (422)
* Added NetworkX directed acyclic graph stack ensemble representation. (385, 403)
* Added linear model support (375)
* Improved leaderboard inference and fit time estimates. (385)
* Added info function to TabularPredictor. (444)
* Added .tsv file detection support. (396)
* Numerous code cleanup and bug fixes. (373, 374, 379, 387, 389, 400, 410, 443, 446, 452)

Image Classification

* Added auto augmentation for images (391)
* Bug fixes. (388, 402, 417, 436, 438)

Object Detection

* Added save() and load() functionality. (405)

Core

* Major refactoring of schedulers and searchers. (445)
* Bug fixes. (384, 386, 427)

Changes

General

* Updated dependency versions.

Tabular

* Added support for calculating feature importance on fitted TabularPredictor.
* Enabled string path input of datasets to TabularPredictor.
* Added option for user to specify model to predict with in TabularPredictor.
* Added support for relative paths to TabularPredictor, enabling users to move the models between directories and machines without introducing loading issues.
* Added support for moving models between machines of different operating systems.
* Numerous major bug fixes and improvements to hyperparameter tuning resulting in significantly more stable functionality.
* Numerous code cleanup.
* Numerous bug fixes.