Mljar Supervised Versions Save

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

v0.11.5

1 year ago

Bug fixes and updates

#595 replace boston example dataset with California housing dataset, replace mse metric with squared_error for tree based algorithms from sklearn
#596 change the import method for dtreeviz package

v0.11.4

1 year ago

Fixes

#590 dynamically set font in a report, thanks @yairVanti!

v0.11.3

1 year ago

Unpin shap version #551

v0.11.2

2 years ago

Enhancements

#523 Add type hints to AutoML class, thank you @DanielR59
#519 save train&validation index to file in train/test split, thanks @filipsPL @MaciekEO

Bug fixes

#496 fix exception in baseline mode, thanks @DanielR59 @moshe-rl
#522 fixed requirements issue, thanks @DanielR59 @MaciekEO
#514 remove warning, thanks @MaciekEO
#511 disable EDA, thanks @MaciekEO

v0.11.0

2 years ago

Bug fixes

#463 change multiprocessing to Parallel with loky
#462 handle large data for tree visualization in regression
#419 remove/hide warnings
#411 loose dependencies for numpy and scipy

0.10.4

2 years ago

Enhancements

#81 add scatter plot predicted vs target in regression
#158 add ROC curve for binary classification
#336 add visualization for Optuna results
#352 add support for Colab
#374 update seaborn
#378 set golden features number
#379 switch off boost_on_errors step in Optuna mode
#380 add custom cross validation strategy
#386 add correlation heatmap
#387 add residual plot
#389 add feature importance heatmap
#390 add custom eval metric
#393 update sklearn

Bug fixes

#308 fix error in kaggle kernel
#353, #355, #366, #368, #376, #382, #383, #384 fixes

Docs

#391 add info about hyperparameters optimization methods

Big thank you for help for: @ecoskian, @xuzhang5788, @xiaobo, @RafaD5, @drorhilman, @strelzoff-erdc, @muxuezi, @tresoldi THANK YOU !!!

0.10.3

3 years ago

Enhancements

#343 set seed in Optuna
#344 set eval_metric directly in all algorithms
#350 add estimated train time in Optuna mode
#342 add optuna_verbose param in AutoML()
#354 add KNN in Optuna
#356 and Neural Network in Optuna
#357, #348 use mljar wrapper for Random Forest and Extra Trees
#358 add extra_tree param in LightGBM
#359 switch off feature engineering in Optuna mode - only highly tuned models are produced
#361 list all eval_metric in error message
#362 add accuracy eval_metric
#340 support for r2

Bug fixes

#347 dont include Optuna tuning time in total_time_limit
#360 missing auc scores for training in CatBoost

0.10.2

3 years ago

Add support to Python 3.9 (#339) Thanks to @rterbush!

0.10.1

3 years ago

Enhancements

#332 We added Optuna framework for hyperparameters tuning. It can be used by setting mode="Optuna" in AutoML. You can read more details at blog post: https://mljar.com/blog/automl-optuna/

0.9.1

3 years ago

Enhancements

#179 add need_retrain() method to detect performance decrease
#226 extract rules from decision tree
#310 add support for MAPE
#312 optimize prediction time
#313 set stacking time threshold depending on best model train time
#320 search for model with prediction time constraint
#322 n_jobs as a parameter
#328 disable stacking for small (nrows < 500) datasets

Bug fixes

#214 move directory after training
#246 raise exception when small time limit and no models are trained
#247 proper display for optimize AUC and R2
#306 add mix_encoding argument in AutoML constructor
#308 fix dependencies error in kaggle notebook
#314 bug fix in hill climbing in Perform mode
#323 fix catboost bug with tree limit
#324 #325 bug for feature importance for small data