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

This script was prepared for the first forester "hands-on" conducted inside of the MI2 Data Lab researchers and R enthusiasts. The main goal of this event is to check the completeness level of the forester package, find some bugs, check the clarity of the user interface, and obtain ideas for new modules which could increase the value of the package.

To obtain such effects, during this "hands-on," we will briefly introduce the forester package and show the participants how to use its functionalists. After the workshop part, the participants will have time to test the package on their ml datasets prepared beforehand.

The outline of the workshop is presented below:

1. Setup
2. Task 1: Dataset
3. Task 2: Basics of autoML with the forester
4. Task 3: The train mastery
5. Task 4: Advanced preprocessing
6. Task 5: How to understand the output?
7. Task 6: Explain the model
8. Task 7: Report generating
9. Task 8: Your metric
10. Task 9: Free testing
11. Task 10: Discussion and suggestions

For each task, we will define WHY? we are asking you to do this, WHAT? are you supposed to do.

Setup

To run the code, we have to install the forester package from our GitHub repository, load the package, and get to know the primary dataset.

install.packages('DALEX')
install.packages('devtools')
library(devtools)
install_github('ModelOriented/forester')
library(DALEX)
library(forester)
data(lisbon)
View(lisbon)

To better understand our dataset, we will use the forester check_data() function, which was designed to provide basic dataset info for the user and provide him with additional information describing the dataset's quality. As our target column is named 'Price', we will provide it to the check_data() function. Typically this function is run inside of the train(), so we have to assign the output to the variable.

check <- check_data(lisbon, 'Price', verbose = TRUE)

Check data report correctly detected the issues present in the data, such as high correlation of subset of the columns, presence of duplicate and static columns, unequal quantile bins, or suspicious Id columns.

Task 1: Dataset

WHY?

Forester is not picky and accepts datasets in various formats and qualities (e.g., it may contain NA values). You will check how the forester sees the Lisbon dataset.

WHAT?

Based on the check_data() function, write the following information about the dataset.

1. Column number

2. Static columns

3. Dominating values

4. Duplicates

5. NA number

6. Correlated pairs of numerical values number

7. Correlated pairs of categorical values number

8. Optional outliers number

9. Is there any ID column?

Answers:

1. 17 2. Country, District, Municipality 3. Portugal, Lisboa, Lisboa 4. District - Municipality 5. 0 6. 5 7. 1 8. 19 9. yes

Task 2: Basics of AutoML with forester

WHY?

The first task is designed to introduce you to the main function, which is called train(). This function conducts all autoML processes, including data preprocessing, model training, and evaluation.

WHAT?

In this task, you are asked to assign the output of train() to the variable output1. Instead of using default parameters, we want to shorten the computation time and ask you to limit the number of bayesian optimization iterations to 0, hide the output of the function and set the number of random search iterations to 5 In the end, print the ranked list from the output object.

Answers:

output1 <- train(data = lisbon,
                 y = 'Price',
                 bayes_iter = 0,
                 verbose = FALSE,
                 random_evals = 5)

output1$score_test

The output is a pretty complex object later used in other functions. The most important part is, however the ranked_list, which evaluates all models trained. The main metric sorts the output list for each task (classification or regression). Can you guess which metric is the main for the regression task?

Task 3: The train mastery

WHY?

The second task aims to get you to know the train function parameters better. As plenty of the processes hidden behind train() are complex and can also be tuned in some way, these options have to be accessible from train() interface.

WHAT?

This time you are asked to experiment with the other parameters and compare what they do. Try to create the output with ranger, xgboost and decision_tree models only. You can then set bayesian optimization iterations to 5 and random search iterations to 3. What are the differences between these two hyperparameter tuning methods' behavior? Finally, you can turn on the text outputs from the function.

Answers:

output2 <- train(data = lisbon,
                 y = 'Price',
                 bayes_iter = 5,
                 engine = c('ranger', 'xgboost', 'decision_tree'),
                 verbose = TRUE,
                 random_evals = 3)

output2$score_test

Task 4: Advanced preprocessing

WHY?

The check() function suggests hypothetical problems with the dataset, but the train() function also offers turning on the preprocessing option.

WHAT?

1. Perform automatic preprocessing with the appropriate parameter data. To reduce the time of counting functions, the number of optimization iterations Bayesian to 0 and random search to 5. Compare the input with the data after preprocessing. Check which columns have been removed and compare if they match this with the information from check_data.

Answers:

output3 <- train(data = lisbon,
                 y = 'Price',
                 bayes_iter = 0,
                 verbose = FALSE,
                 random_evals = 5,
                 advanced_preprocessing = TRUE)

1. Which model achieves the best results? Is it a model with default parameters, after Bayesian optimization or a random search?

output3$score_test[1, ]

1. Compare the performance of the best model trained on the data with preprocessing output3 and without output1. Have the results improved?

output3$score_test[1, ]
output1$score_test[1, ]

Task 5: How to understand the output?

WHY?

As the forester team, our goal is to make the training process quick and simple and understandable, even for newcomers. Our extensional tools are one of the points that differentiate us from other autoML frameworks. In this chapter, we will use these extra functions to show the full capabilities of the forester.

WHAT?

This time we will ask you to use the output created in the previous task and explore four functions provided by the forester:

1. The first task is to save and load the output via save() function
2. The Second one is creating a subset from the original lisbon data frame and running the predict_new() method on this. The method enables the user to make predictions on the data unseen by models before. Why is this function needed?
3. The third one is associated with the well-known package in MI2 Lab - DALEX. You are asked to create an explainer via explain() method and use some DALEX visualization on the outcome. (Task 6)
4. The last task is to create and read a report() outcome. (Task 7)

Answers:

save(train = output2,
     name = 'hands_on_save',
     return_name = TRUE)


new_lisbon <- lisbon[50:100, ]
predict_new(train_out = output2,
            data = new_lisbon)

Task 6: Explain the model

WHY?

Explaining the model helps you understand it better. The forester does not include its functions to explain the model but uses a ready-made DALEX package. The simple explain() function creates an object adapted to the DALEX functions.

WHAT?

Create a DALEX object from the best model from output2. Then create feature importance, use the DALEX::model_parts() function and introduce it in the chart (plot() function).

Answers:

exp_list <- forester::explain(models = output2$best_models[[1]],
               test_data = output2$test_data,
               y = output2$y)
exp <- exp_list$xgboost_bayes
p1 <- DALEX::model_parts(exp)
plot(p1)

Task 7: Report generating

WHY?

For convenient storage and comparison of the training results of the models, the forester offers a report-generating function - containing the most important information about the data and training. Thanks to this, it is possible to store the results of work in independent files and come back to them at any time.

WHAT?

Generate a report based on output2. Then based on the report, find out which model is the best, assess the dispersion of the training set, and test. Compare the feature importance chart to that created in Task 6.

Answers:

report(train_output = output2,
       output_file  = 'hands_on_report')

Task 8: Your own metric

WHY?

Forester offers some of the most commonly used metrics for model evaluation, however, it does not preclude using a different, proprietary metric. Applying self-made allows a fuller use of the forester's potential for the individual needs of the user.

WHAT?

Use the train() function with the default parameters but with your metric added.
It's a good idea to turn off Bayes Optimization and decrease Random Search iterations to build models faster.

Answers:

Example:

max_error <- function(predictions, observed) {
  return(max(abs(predictions - observed)))
}
output4 <- train(data = lisbon,
                 y = 'Price',
                 bayes_iter = 0,
                 random_evals = 3,
                 metric_function = max_error,
                 metric_function_name = 'Max Error',
                 metric_function_decreasing = FALSE)

output4$score_test

Task 9: Free testing

WHY?

As the "hands-on" goal is getting valuable feedback from this workshop, we want you to test the forester on your own datasets prepared beforehand. This way, we can test these features in the new environment, which might generate some bugs and issues.

WHAT?

During the last task, we ask you to 'play' with the package, delve into its documentation and not only do everything we did before on the new data, but also, try to do the things we didn't discuss during the workshop.

Task 10: Discussion and suggestions

WHY?

We want to develop and improve the forester package, so your feedback is very valuable to us.

WHAT?

As a forester team, we ask you to join the discussion about features that should be improved and what you would change in the forester or add. If you find any bug, please post it on our GitHub Issues page. https://github.com/ModelOriented/forester/issues

 

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