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https://github.com/tidymodels/themis

Extra recipes steps for dealing with unbalanced data
https://github.com/tidymodels/themis

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Extra recipes steps for dealing with unbalanced data

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```



# themis 



[![R-CMD-check](https://github.com/tidymodels/themis/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/tidymodels/themis/actions/workflows/R-CMD-check.yaml)

[![Codecov test coverage](https://codecov.io/gh/tidymodels/themis/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidymodels/themis?branch=main)

[![CRAN status](https://www.r-pkg.org/badges/version/themis)](https://CRAN.R-project.org/package=themis)

[![Downloads](http://cranlogs.r-pkg.org/badges/themis)](https://CRAN.R-project.org/package=themis)

[![Lifecycle: maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)



**themis** contains extra steps for the

[`recipes`](https://CRAN.R-project.org/package=recipes) package for

dealing with unbalanced data. The name **themis** is that of the [ancient Greek god](https://thishollowearth.wordpress.com/2012/07/02/god-of-the-week-themis/) who is typically  depicted with a balance.



## Installation



You can install the released version of themis from [CRAN](https://CRAN.R-project.org) with:



``` r

install.packages("themis")

```



Install the development version from GitHub with:



``` r

# install.packages("pak")

pak::pak("tidymodels/themis")

```



## Example



Following is a example of using the [SMOTE](https://jair.org/index.php/jair/article/view/10302/24590) algorithm to deal with unbalanced data



```{r example, message=FALSE}

library(recipes)

library(modeldata)

library(themis)



data("credit_data", package = "modeldata")



credit_data0 <- credit_data %>%

  filter(!is.na(Job))



count(credit_data0, Job)



ds_rec <- recipe(Job ~ Time + Age + Expenses, data = credit_data0) %>%

  step_impute_mean(all_predictors()) %>%

  step_smote(Job, over_ratio = 0.25) %>%

  prep()



ds_rec %>%

  bake(new_data = NULL) %>%

  count(Job)

```



## Methods



Below is some unbalanced data. Used for examples latter.



```{r}

#| fig-alt: "Bar chart with 5 columns. class on the x-axis and count on the y-axis. Class a has height 10, b has 20, c has 30, d has 40, and e has 50."

example_data <- data.frame(class = letters[rep(1:5, 1:5 * 10)],

                           x = rnorm(150))



library(ggplot2)



example_data %>%

  ggplot(aes(class)) +

  geom_bar()

```



### Upsample / Over-sampling



The following methods all share the tuning parameter `over_ratio`, which is the ratio of the minority-to-majority frequencies.



| name | function | Multi-class |

|---|---|---|

| Random minority over-sampling with replacement | `step_upsample()` | :heavy_check_mark: |

| Synthetic Minority Over-sampling Technique | `step_smote()` | :heavy_check_mark: |

| Borderline SMOTE-1 | `step_bsmote(method = 1)` | :heavy_check_mark: |

| Borderline SMOTE-2 | `step_bsmote(method = 2)` | :heavy_check_mark: |

| Adaptive synthetic sampling approach for imbalanced learning | `step_adasyn()` | :heavy_check_mark: |

| Generation of synthetic data by Randomly Over Sampling Examples| `step_rose()` | |



By setting `over_ratio = 1` you bring the number of samples of all minority classes equal to 100% of the majority class.



```{r}

#| fig-alt: "Bar chart with 5 columns. class on the x-axis and count on the y-axis. class a, b, c, d, and e all have a height of 50."

recipe(~., example_data) %>%

  step_upsample(class, over_ratio = 1) %>%

  prep() %>%

  bake(new_data = NULL) %>%

  ggplot(aes(class)) +

  geom_bar()

```



and by setting `over_ratio = 0.5` we upsample any minority class with less samples then 50% of the majority up to have 50% of the majority.



```{r}

#| fig-alt: "Bar chart with 5 columns. class on the x-axis and count on the y-axis. Class a has height 25, b has 25, c has 30, d has 40, and e has 50."

recipe(~., example_data) %>%

  step_upsample(class, over_ratio = 0.5) %>%

  prep() %>%

  bake(new_data = NULL) %>%

  ggplot(aes(class)) +

  geom_bar()

```



### Downsample / Under-sampling



Most of the the following methods all share the tuning parameter `under_ratio`, which is the ratio of the majority-to-minority frequencies.



| name | function | Multi-class | under_ratio |

|---|---|---|---|

| Random majority under-sampling with replacement | `step_downsample()` | :heavy_check_mark: | :heavy_check_mark: | 

| NearMiss-1 | `step_nearmiss()` | :heavy_check_mark: |:heavy_check_mark: | 

| Extraction of majority-minority Tomek links | `step_tomek()` | | |



By setting `under_ratio = 1` you bring the number of samples of all majority classes equal to 100% of the minority class.



```{r}

#| fig-alt: "Bar chart with 5 columns. class on the x-axis and count on the y-axis. Class a, b, c, d, and e all have a height of 10."

recipe(~., example_data) %>%

  step_downsample(class, under_ratio = 1) %>%

  prep() %>%

  bake(new_data = NULL) %>%

  ggplot(aes(class)) +

  geom_bar()

```



and by setting `under_ratio = 2` we downsample any majority class with more then 200% samples of the minority class down to have to 200% samples of the minority.



```{r}

#| fig-alt: "Bar chart with 5 columns. class on the x-axis and count on the y-axis. Class a has height 10, b, c, d, and e have ha height of 20."

recipe(~., example_data) %>%

  step_downsample(class, under_ratio = 2) %>%

  prep() %>%

  bake(new_data = NULL) %>%

  ggplot(aes(class)) +

  geom_bar()

```



## Contributing



This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.



- For questions and discussions about tidymodels packages, modeling, and machine learning, [join us on RStudio Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).



- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/themis/issues).



- Either way, learn how to create and share a [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html) (a minimal, reproducible example), to clearly communicate about your code.



- Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).