Knowledge_check.Rmd

---
title: "Knowledge Check"
author: "Patryk Słowakiewicz, Ada Grudzień, Hubert Ruczyński"
date: "2022-11-13"
output:
  html_document: default
  pdf_document: default
toc: yes
toc_float: yes
number_sections: yes
vignette: >
  %\VignetteIndexEntry{tutorial} 
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown} 
---

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.

```{r Setup, eval = FALSE, echo = TRUE}
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.

```{r data, eval=FALSE, echo=TRUE}
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?

<details>
<summary>Answers:</summary>
1. 17
2. Country, District, Municipality
3. Portugal, Lisboa, Lisboa
4. District - Municipality
5. 0
6. 5
7. 1
8. 19
9. yes
</details>

# 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.

<details>
<summary>Answers:</summary>
```{r Train, eval = FALSE, echo = TRUE}
output1 <- train(data = lisbon,
                 y = 'Price',
                 bayes_iter = 0,
                 verbose = FALSE,
                 random_evals = 5)

output1$score_test
```
</details>

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.

<details>
<summary>Answers:</summary>
```{r train 2, eval = FALSE, echo = TRUE}
output2 <- train(data = lisbon,
                 y = 'Price',
                 bayes_iter = 5,
                 engine = c('ranger', 'xgboost', 'decision_tree'),
                 verbose = TRUE,
                 random_evals = 3)

output2$score_test
```
</details>

# 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.

<details>
<summary>Answers:</summary>
```{r train 3, eval = FALSE, echo = TRUE}
output3 <- train(data = lisbon,
                 y = 'Price',
                 bayes_iter = 0,
                 verbose = FALSE,
                 random_evals = 5,
                 advanced_preprocessing = TRUE)
```
</details>

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

```{r ranked_list, eval = FALSE, echo = TRUE}
output3$score_test[1, ]
```

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

```{r ranked_lists, eval=FALSE, echo=TRUE}
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)

<details>
<summary>Answers:</summary>
```{r save, eval=FALSE, echo=TRUE}
save(train = output2,
     name = 'hands_on_save',
     return_name = TRUE)


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

# 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).

<details>
<summary>Answers:</summary>
```{r explain, eval=FALSE, echo=TRUE}
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)
```
</details>

# 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.

<details>
<summary>Answers:</summary>
```{r report, eval=FALSE, echo=TRUE}
report(train_output = output2,
       output_file  = 'hands_on_report')
```
</details>

# 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.

<details>
<summary>Answers:</summary>
Example:

```{r metrics, eval=FALSE, echo=TRUE}
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
```
</details>

# 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
  
&nbsp;