NEWS.md

In ModelOriented/forester: Quick and Simple Tools for Training and Testing of Tree-Based Models

forester 1.6.1

• Changed default feature selection method in custom_preprocessing(), and preprocessing_feature_selection() to BORUTA, as it is the most effective one,
• Changed default imputation method for preprocessing() inside train() to knn, as it is the most effective one,
• Fixed an issue for VS plots, where the color was not assigned properly for the models,

forester 1.6.0

• Updated .Rbuildignore, DESCRIPTION, and NAMESPACE.
• Added cran-comments.md file for future CRAN submission.
• Fixed use of the deprecated size parameter in plotting functions, and replaced it with linewidth.
• Fixed issues, which occured while creating plots for outputs with less than 10 models.
• Enhanced the verbose mode, by adding more information about the current stage of the training process, added execution times for different training methods, and improved the overall readability of the messages.
• Introduced parallel training for random search, and bayesian optimization, which significantly speeds up the training process. It works for ranger, and xgboost only, due to the limitations of other packages. It results in an addition of the parallel parameter in train(), random_search(), and train_models_bayesopt() functions.
• Intorduced more verbosity options for bayesian optimization process by introudcing the bayes_info parameter in train(), and train_models_bayesopt() functions. The user can now track the quality of found hyperparameters.
• Introduced the select_models() function, which lets the user shrink the output of train() function to the selected models only. It is useful when the user wants to focus on the best models only, and limit the size of the output, while maintaining the ability to generate reports, and other functionalities.
• Fixed an issue where for different tasks we had incosistent parameters in the output of the train() function, regarding the predictions of all three sets.
• Modified the tests, so they fit with the current package version, and added tests for select_models() function.
• Added check_correlation parameter for both train() and check_Data() functions, which lets the user decide whether to check the correlation between the features or not. In some corner cases, with highly dimensional data, it can be time-consuming, and not necessary.

forester 1.5.0

• Updated .Rbuildignore and .gitignore.
• Updated package DESCRIPTION, and NAMESPACE.
• Removed: choose_best_models.R, forester_palette.R, format_model_details.R
• Updated check_data():
  • Added more checks of input parameters,
  • Modified check_y_balance() sub-function for multiclass classification.
• Updated custom_preprocessing():
  • Added parameter type,
  • Added more checks of input parameters,
  • Added more advanced verbose options.
• Added multiclass classification task, with all extensional functionalities included (report, explainability, plots, etc), which led to the abundance of changes in majority of functions.
• Added more input checks for train_test_balance().
• Updated documentation.
• Added manual test for multiclass classification task.
• Moved old tests into misc/old_tests_03_02_2023 folder.
• Created a new, and more in depth tests for the package from scratch for regression, binary classification, and multiclass classification tasks.

forester 1.4.2

• In the DESCRIPTION updated the description, and RoxygenNote version.
• In README updated guide for catboost installation.
• Moved verbose_cat(), and guess_type(), from their own files to check_data().
• Removed choose_best_models() function, and implemented its logic directly in train().
• Removed the unused create_ranked_list(), and format_models_details() functions.
• Fixed plot_classification() function, associated with the error cannot xtfrm data frames occurred.
• Suppressed meaningless warnings from prepare_data(), and preprocessing_removal() functions.
• Fixed error cannot xtfrm data frames in both reports.
• Added folder misc/manual_tests with supplementary, manual tests of the package, focusing on report generation.
• Removed test-choose_best_model test, and fixed two other tests.

forester 1.4.1

• In the DESCRIPTION:
  • Added in Imports the patchwork package, and moved arules from Imports to Suggests,
  • Updated the authors of the package.
• In report() function:
  • Added separate generation of binary classification and regression reports,
  • Enhanced the quality of plots in the report,
  • Fixed the bugs leading to lower quality of the report,
  • Added detailed description of how to interpret the visualizations for the beginners.
• Created plot_classifcation file, which overrides plot() function for the binary classification object returned by train(). The function lets us create plots for binary classification tasks, such as metrics comparison line plot, ROC curve, confusion matrix, and train vs test plot.
• Created plot_regression file, which overrides plot() function for the regression object returned by train(). The function lets us create plots for regression tasks, such as residuals box plot, observed vs prediction plot, and train vs test plot.
• Removed report.Rmd file, and created two separate files for different tasks called report_binary.Rmd, and report_regression.Rmd.
• In train() function: added different classes for the outcomes, depending on the task type.
• In plot_metrics(), changed color palette to colors_discrete_forester.
• In check_data(), added a small fix for the report.

forester 1.4.0

• In the DESCRIPTION: Added in Suggests SurvMetrics, randomForestSRC, and survival packages.
• In train() function:
  • Added parameters time, and status describing the survival analysis task,
  • Added parameters time, and status to the functions output,
  • Modified inner methods, to work with survival analysis task.
• In check data() function:
  • Added parameters time, and status describing the survival analysis task,
  • Modified the methods: basic_info(), check_missing(), check_cor(), and check_y_balance(), so they also work for the survival analysis.
• In preprocessing() function, added the binarization of survival task target (status).
• In prepare_data() function, added a method for the survival analysis task.
• In train_models() function, added a method for training a survival analysis model from randomForestSRC package.
• In random_search() function, added a method for tuning a survival analysis model.
• In train_models_bayesopt() function, added a method for tuning a survival analysis model.
• In predict_models_all(), predict_models() , and predict_new functions, added a method for predicting a survival analysis model.
• In score_models(), added a method for evaluation of survival analysis models with Brier Score, and Concordance Index (CIN).
• In guess_type(), added method of detecting the survival analysis task.
• Modified tests of the package, to cover the changes made in the package.

forester 1.3.0

• In the DESCRIPTION: Added in Imports the VIM package, and in the Suggests sivs, parallel, rmcfs, and varrank packages.
• In check_data() function:
  • removed 'no', and added 'index' to id_names used for id-like columns detection,
  • Fixed minor printing and code formatting issues.
• Added a custom_preprocessing() function, which is more advanced and customizable approach for the preprocessing pipeline. It executes other new functions implementing three major pillars of preprocessing. The functions are preprocessing_removal(), preprocessing_imputation(), preprocessing_feature_selection():
  • preprocessing_removal() - This function includes 6 modules for the removal of unwanted features / observations. We can remove duplicate columns, the ID-like columns, static columns (with specified staticity threshold), sparse columns (with specified sparsity threshold), and highly correlated ones (with specified high correlation threshold). Additionally we can remove the observations that are too sparse (sparsity threshold), and have missing target value. One can turn on and off each module by setting proper logical values.
  • preprocessing_imputation() - Imputes missing values according to one of four prepared methods:
    • median-other - The numeric features are imputed with median value, whereas the categorical ones with the 'other' string,
    • median-frequency - The numeric features are imputed with median value, whereas the categorical ones with the most frequent value,
    • knn - All features are imputed with KNN algorithm,
    • mice - All features are imputed with MICE algorithm.
  • preprocessing_feature_selection() - Conducts a feature selection process with one out of four proposed methods:
    • VI - The variable importance method based on random forest,
    • MCFS - The Monte Carlo Feature Selection,
    • MI - The Varrank method based on mutual information scores,
    • BORUTA - The BORUTA algorithm - short time.
• Added tests for the custom_preprocessing(), preprocessing_removal(), preprocessing_imputation(), and preprocessing_feature_selection() functions.
• In the train() function:
  • Removed advanced_preprocessing parameter, as custom_preprocessing() is more advanced version of it,
  • Added custom_preprocessing parameter, which takes the output of a custom_preprocessing() function,
  • Added deleted_rows value to the output,
  • Changed output parameter from columns to deleted_columns.
• In the preprocessing() function:
  • Removed advanced parameter, as custom_preprocessing() is more advanced version of it,
  • Changed output parameter from columns to rm_columns.
  • Removed the example from this function.
• In the save() function:
  • Renamed to save_forest(),
  • Merged parameters name and path into file as it is standard approach
  • Added an example of loading saved data to documentation.
• In the prepare_data() function added encoding for the ctree model so it can use columns with more than 30 levels.
• In the random_search() function added parameter verbose. When set to TRUE the function provides information about the training.
• Removed plenty of examples from functions that are not meant for the user.
• Fixed plenty of formatting issues in various functions.

forester 1.2.1

• In the train() function:
  • Added the split_seed parameter which enables the user to set the seed for the train-test split method,
  • Added the train_inds, test_inds, and valid_inds vectors to the train output, which enable the user to recover the information which observation went to the train, test, and validation sets.
• In the train_models_bayesopt() function:
  • Removed unwanted seeds before starting the Bayesian optimization.

forester 1.2.0

• In the check_data() function:
  • Fixed typos.
• Updated the documentation or examples in:
  • choose_best_model(),
  • create_ranked_list(),
  • format_models_details(),
  • draw_scatter_plot(),
  • predict_models(),
  • predict_models_all(),
  • save(),
  • train_models(),
  • train_test_balance().
• In the explain() function:
  • Updated the documentation and the examples,
  • Fixed the handling of the factors.
• In the draw_radar_plot() function:
  • Updated the documentation,
  • Deleted unnecessary prints.
• In the prepare_data() function:
  • Updated the documentation,
  • Fixed the process of creating an 'other' label during the preparation of train / test splits.
• In the preprocessing() function:
  • Updated the documentation,
  • Removed unused parameter type,
  • Added a factorization of the target for binary classification task.
• In the random_search() function:
  • Updated the documentation,
  • Fixed the method for the ranger model,
  • Unified the grid with the Bayesian Optimization grid.
• In the score_models() function:
  • Updated the documentation,
  • Added RMSE as a main metric for the regression task,
  • Added accuracy as a main metric for the binary classification task,
  • Fixed the scoring method for the regression.
• In the train() function:
  • Updated the documentation,
  • Enabled providing the tibble as a data parameter (with warning),
  • Added a warning if the user tries to use incorrect task type,
  • Added score_train and predictions train to the output of the function.
• In the train_models_bayesopt() function:
  • Updated the documentation,
  • Fixed the Bayesian Optimization. Before hand it didn't work at all for models other than ranger,
  • Changed tuned hyperparameters and their values for all models, which increased the performance of the method.
• Updated the majority of tests so they match the current version.
• Added alternative catboost installation in the README.

forester 1.1.4

• In the report() function:
  • Removed engine and tuning columns from the ranked list table,
  • Explained how the model name is created, so the columns engine and tuning are not needed,
  • Fixed the radar plot issues,
• In the draw_radar_plot() function:
  • Fixed radar plots, so the models are named correctly (by model names, not engines),
  • Fixed issue where the plot for binary classification had an empty observation name,
  • Removed unnecessary and invalid Metric names (the ones that are not used and engine, name, tuning).
• In the check_data() function:
  • Added a vector of possible outliers to the output,
  • The outliers indexes are printed if there are less than 50 of them.
• In the train() function:
  • Added a vector of possible outliers to the output.

forester 1.1.3

• In the train() function:
  • Added objects to the output of the function:
    • Vector: valid_observed.
    • Vectors: train_observed_labels, test_observed_labels, valid_observed_labels, predictions_all_labels predictions_best_best as labels for binary classification task.
  • Removed catboost from default engines.
  • Added check if catboost is installed.
  • Removed parameters: loss, validation, tuning, keep, because they weren't used.
  • Added train_test_split parameter that determines proportions of train, test and validation subsets.
  • Conditioned the message about columns deleted during the advanced preprocessing.
• In the check_data() function:
  • Fixed an issue of too much unique values for the Crammer V's correlation. If the number of combinations exceeds 2\^29, the correlation is not calculated and the user is informed about it (it is caused by R limitations).
  • Fixed an issue with omitting strong correlations that are negative.
  • Added ticks and crosses that indicate whether something is of the high or low quality.
  • Added the information which class is the dominating one in the unbalanced classification data set.
• In the preprocessing() function:
  • Added a factorization of the target at the beginning of the binarize_target() sub function.
  • Resolved issues with data frames where columns are not saved as factors.
• The report() function:
  • If tinytex is not installed raises an error.
  • If ggradar is not installed, there will be no error, no plot, and a note about installing the ggradar package.
• Removed engine parameter from predict_models_all().
• Removed misspelled function predcit_models_all().
• The train_models() function no longer returns NULL objects if engine is not selected.
• The train_models_bayesopt() function no longer returns NULL objects if engine is not selected.
• For the format_models_details() function fixed a bug with the method not working for a classification task for an xgboost model.
• In the DESCRIPTION catboost, ggradar and tinytex dependencies moved from Suggests to Imports and added crayon to Import.
• Added installation guides for catboost, ggradar and tinytex in the README.md, which solves installation issues on macOS.

forester 1.1.2

• Cleared typos and inconsistent notation in the train() function.
• Added info that the Bayesian Optimization takes a lot of time.
• Fixed 'The best model details' section in the report to be seen.
• Added info about deleted columns during preprocessing().
• Added more details of the train() function output.
• Added parameter 'seed' to keep the data division comparable.
• Cleared structure of the predict_new() function.
• Kept numeric values of model$raw_data$lightgbm_data as numeric, and not string values.
• Used different parameters for the xgboost model to remove the warnings.
• Changed [1,2] target values to [0,1] for the regression task.
• Added info about the type of correlation used between columns.

forester 1.1.1

• In the save() the file name has a right month.
• The train() function returns table with metrics on validation subset.
• The score() function returns tables with additional columns: engine and tuning.
• Added a new vignette 'Knowledge Check' with extended use cases.
• Setting bayes_iter = 0 causes that Bayestian_opt() is not ran anymore.
• Changed the parameter name from random_iter to random_evals.
• Setting verbose = FALSE disables the check_data entirely.

forester 1.1.0

• Changes in a train() function by adding parameters to:
  • Choose metrics.
  • Choose metric to sort model.
  • Add self-created metric function. # forester 1.0.0
• General code quality and readability improvements as well as improving the quality of the documentation.
• check_data() added detection of id columns and reformatted the outputs.
• create_ranked_list() able to work with missing values.
• Improved data sets documentation.
• explain() function redesigned to work on single and multiple models.
• plot_metrics() improved all plots and changed them into ggplot visualizations, added a feature importance plot.
• Renamed predcit_models_all() into predict_models_all() and enabled prediction on non-fixed, larger amount of models.
• Added predict_new() function for new observations.
• Expanded preprocessing() function by optional, advanced preprocessing consisting of deleting correlated values, deleting id columns, selecting only the most important features via the BORUTA algorithm.
• Redesigned a file generated by the 'report()` function, by enhancing the quality of the plots.
• Added save() function which saves the output of the train() function.
• Redesigned a score_models() function, so that the user can add their own metric function for scoring models.
• Redesigned a train() function by:
  • Adding a verbose parameter for silent runs.
  • Adding parameters for limiting the iterations of random search and Bayesian optimization.
  • Turning on the advanced preprocessing.
  • Increased the output information by adding engine, predictions of all models, predictions of the best models and raw_training data set (unprocessed by prepare_data()).
• Added a verbose_cat() function for optional messages.

ModelOriented/forester documentation built on June 6, 2024, 7:29 a.m.