Multinomial classification with tidymodels and #TidyTuesday volcano eruptions

This article is originally published at https://juliasilge.com/blog/

Lately I’ve been publishing screencasts demonstrating how to use the tidymodels framework, from first steps in modeling to how to evaluate complex models. Today’s screencast demonstrates how to implement multiclass or multinomial classification using with this week’s #TidyTuesday dataset on volcanoes. ????

Here is the code I used in the video, for those who prefer reading instead of or in addition to video.

Explore the data

Our modeling goal is to predict the type of volcano from this week’s #TidyTuesday dataset based on other volcano characteristics like latitude, longitude, tectonic setting, etc. There are more than just two types of volcanoes, so this is an example of multiclass or multinomial classification instead of binary classification. Let’s use a random forest model, because this type of model performs well with defaults.

volcano_raw <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-12/volcano.csv")

volcano_raw %>%
  count(primary_volcano_type, sort = TRUE)

## # A tibble: 26 x 2
##    primary_volcano_type     n
##    <chr>                <int>
##  1 Stratovolcano          353
##  2 Stratovolcano(es)      107
##  3 Shield                  85
##  4 Volcanic field          71
##  5 Pyroclastic cone(s)     70
##  6 Caldera                 65
##  7 Complex                 46
##  8 Shield(s)               33
##  9 Submarine               27
## 10 Lava dome(s)            26
## # … with 16 more rows

Well, that’s probably too many types of volcanoes for us to build a model for, especially with just 958 examples. Let’s create a new volcano_type variable and build a model to distinguish between three volcano types:

• stratovolcano
• shield volcano
• everything else (other)

While we use transmute() to create this new variable, let’s also select the variables to use in modeling, like the info about the tectonics around the volcano and the most important rock type.

volcano_df <- volcano_raw %>%
  transmute(
    volcano_type = case_when(
      str_detect(primary_volcano_type, "Stratovolcano") ~ "Stratovolcano",
      str_detect(primary_volcano_type, "Shield") ~ "Shield",
      TRUE ~ "Other"
    ),
    volcano_number, latitude, longitude, elevation,
    tectonic_settings, major_rock_1
  ) %>%
  mutate_if(is.character, factor)

volcano_df %>%
  count(volcano_type, sort = TRUE)

## # A tibble: 3 x 2
##   volcano_type      n
##   <fct>         <int>
## 1 Stratovolcano   461
## 2 Other           379
## 3 Shield          118

This is not a lot of data to be building a random forest model with TBH, but it’s a great dataset for demonstrating how to make a MAP. ????

world <- map_data("world")

ggplot() +
  geom_map(
    data = world, map = world,
    aes(long, lat, map_id = region),
    color = "white", fill = "gray50", size = 0.05, alpha = 0.2
  ) +
  geom_point(
    data = volcano_df,
    aes(longitude, latitude, color = volcano_type),
    alpha = 0.8
  ) +
  theme_void(base_family = "IBMPlexSans") +
  labs(x = NULL, y = NULL, color = NULL)

The biggest thing I know about volcanoes is the Ring of Fire ???? and there it is!

Build a model

Instead of splitting this small-ish dataset into training and testing data, let’s create a set of bootstrap resamples.

library(tidymodels)
volcano_boot <- bootstraps(volcano_df)

volcano_boot

## # Bootstrap sampling 
## # A tibble: 25 x 2
##    splits            id         
##    <list>            <chr>      
##  1 <split [958/350]> Bootstrap01
##  2 <split [958/340]> Bootstrap02
##  3 <split [958/353]> Bootstrap03
##  4 <split [958/354]> Bootstrap04
##  5 <split [958/359]> Bootstrap05
##  6 <split [958/350]> Bootstrap06
##  7 <split [958/356]> Bootstrap07
##  8 <split [958/353]> Bootstrap08
##  9 <split [958/354]> Bootstrap09
## 10 <split [958/360]> Bootstrap10
## # … with 15 more rows

Let’s train our multinomial classification model on these resamples, but keep in mind that the performance estimates are probably pessimistically biased.

Let’s preprocess our data next, using a recipe. Since there are significantly fewer shield volcanoes compared to the other groups, let’s use SMOTE upsampling (via the themis package) to balance the classes.

library(themis)

volcano_rec <- recipe(volcano_type ~ ., data = volcano_df) %>%
  update_role(volcano_number, new_role = "Id") %>%
  step_other(tectonic_settings) %>%
  step_other(major_rock_1) %>%
  step_dummy(tectonic_settings, major_rock_1) %>%
  step_zv(all_predictors()) %>%
  step_normalize(all_predictors()) %>%
  step_smote(volcano_type)

Let’s walk through the steps in this recipe.

• First, we must tell the recipe() what our model is going to be (using a formula here) and what data we are using.
• Next, we update the role for volcano number, since this is a variable we want to keep around for convenience as an identifier for rows but is not a predictor or outcome.
• There are a lot of different tectonic setting and rocks in this dataset, so let’s collapse some of the less frequently occurring levels into an "Other" category, for each predictor.
• Next, we can create indicator variables and remove variables with zero variance.
• Before oversampling, we center and scale (i.e. normalize) all the predictors.
• Finally, we implement SMOTE oversampling so that the volcano types are balanced!

volcano_prep <- prep(volcano_rec)
juice(volcano_prep)

## # A tibble: 1,383 x 14
##    volcano_number latitude longitude elevation volcano_type tectonic_settin…
##             <dbl>    <dbl>     <dbl>     <dbl> <fct>                   <dbl>
##  1         213004   0.746      0.101   -0.131  Other                  -0.289
##  2         284141   0.172      1.11    -1.39   Other                  -0.289
##  3         282080   0.526      0.975   -0.535  Other                  -0.289
##  4         285070   0.899      1.10    -0.263  Other                  -0.289
##  5         320020   1.44      -1.45     0.250  Other                  -0.289
##  6         221060  -0.0377     0.155   -0.920  Other                  -0.289
##  7         273088   0.0739     0.888    0.330  Other                  -0.289
##  8         266020  -0.451      0.918   -0.0514 Other                  -0.289
##  9         233011  -0.873      0.233   -0.280  Other                  -0.289
## 10         257040  -0.989      1.32    -0.380  Other                  -0.289
## # … with 1,373 more rows, and 8 more variables:
## #   tectonic_settings_Rift.zone...Oceanic.crust....15.km. <dbl>,
## #   tectonic_settings_Subduction.zone...Continental.crust...25.km. <dbl>,
## #   tectonic_settings_Subduction.zone...Oceanic.crust....15.km. <dbl>,
## #   tectonic_settings_other <dbl>, major_rock_1_Basalt...Picro.Basalt <dbl>,
## #   major_rock_1_Dacite <dbl>,
## #   major_rock_1_Trachybasalt...Tephrite.Basanite <dbl>,
## #   major_rock_1_other <dbl>

Before using prep() these steps have been defined but not actually run or implemented. The prep() function is where everything gets evaluated. You can use juice() to get the preprocessed data back out and check on your results.

Now it’s time to specify our model. I am using a workflow() in this example for convenience; these are objects that can help you manage modeling pipelines more easily, with pieces that fit together like Lego blocks. This workflow() contains both the recipe and the model, a random forest classifier. The ranger implementation for random forest can handle multinomial classification without any special handling.

rf_spec <- rand_forest(trees = 1000) %>%
  set_mode("classification") %>%
  set_engine("ranger")

volcano_wf <- workflow() %>%
  add_recipe(volcano_rec) %>%
  add_model(rf_spec)

volcano_wf

## ══ Workflow ════════════════════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: rand_forest()
## 
## ── Preprocessor ────────────────────────────────────────────────────────────────────
## 6 Recipe Steps
## 
## ● step_other()
## ● step_other()
## ● step_dummy()
## ● step_zv()
## ● step_normalize()
## ● step_smote()
## 
## ── Model ───────────────────────────────────────────────────────────────────────────
## Random Forest Model Specification (classification)
## 
## Main Arguments:
##   trees = 1000
## 
## Computational engine: ranger

Now we can fit our workflow to our resamples.

volcano_res <- fit_resamples(
  volcano_wf,
  resamples = volcano_boot,
  control = control_resamples(save_pred = TRUE)
)

Explore results

One of the biggest differences when working with multiclass problems is that your performance metrics are different. The yardstick package provides implementations for many multiclass metrics.

volcano_res %>%
  collect_metrics()

## # A tibble: 2 x 5
##   .metric  .estimator  mean     n std_err
##   <chr>    <chr>      <dbl> <int>   <dbl>
## 1 accuracy multiclass 0.661    25 0.00297
## 2 roc_auc  hand_till  0.796    25 0.00304

We can create a confusion matrix to see how the different classes did.

volcano_res %>%
  collect_predictions() %>%
  conf_mat(volcano_type, .pred_class)

##                Truth
## Prediction      Other Shield Stratovolcano
##   Other          2049    344           801
##   Shield          223    585           204
##   Stratovolcano  1251    179          3215

Even after using SMOTE oversampling, the stratovolcanoes are easiest to identify.

We computed accuracy and AUC during fit_resamples(), but we can always go back and compute other metrics we are interested in if we saved the predictions. We can even group_by() resample, if we like.

volcano_res %>%
  collect_predictions() %>%
  group_by(id) %>%
  ppv(volcano_type, .pred_class)

## # A tibble: 25 x 4
##    id          .metric .estimator .estimate
##    <chr>       <chr>   <chr>          <dbl>
##  1 Bootstrap01 ppv     macro          0.643
##  2 Bootstrap02 ppv     macro          0.659
##  3 Bootstrap03 ppv     macro          0.656
##  4 Bootstrap04 ppv     macro          0.639
##  5 Bootstrap05 ppv     macro          0.580
##  6 Bootstrap06 ppv     macro          0.651
##  7 Bootstrap07 ppv     macro          0.680
##  8 Bootstrap08 ppv     macro          0.617
##  9 Bootstrap09 ppv     macro          0.636
## 10 Bootstrap10 ppv     macro          0.651
## # … with 15 more rows

What can we learn about variable importance, using the vip package?

library(vip)

rf_spec %>%
  set_engine("ranger", importance = "permutation") %>%
  fit(
    volcano_type ~ .,
    data = juice(volcano_prep) %>%
      select(-volcano_number) %>%
      janitor::clean_names()
  ) %>%
  vip(geom = "point")

The spatial information is really important for the model, followed by the presence of basalt. Let’s explore the spatial information a bit further, and make a map showing how right or wrong our modeling is across the world. Let’s join the predictions back to the original data.

volcano_pred <- volcano_res %>%
  collect_predictions() %>%
  mutate(correct = volcano_type == .pred_class) %>%
  left_join(volcano_df %>%
    mutate(.row = row_number()))

volcano_pred

## # A tibble: 8,851 x 14
##    id    .pred_Other .pred_Shield .pred_Stratovol…  .row .pred_class
##    <chr>       <dbl>        <dbl>            <dbl> <int> <fct>      
##  1 Boot…       0.474       0.149             0.377     1 Other      
##  2 Boot…       0.190       0.0771            0.733     3 Stratovolc…
##  3 Boot…       0.162       0.106             0.732     6 Stratovolc…
##  4 Boot…       0.233       0.0510            0.716     8 Stratovolc…
##  5 Boot…       0.206       0.0781            0.716    10 Stratovolc…
##  6 Boot…       0.351       0.0969            0.552    16 Stratovolc…
##  7 Boot…       0.428       0.0776            0.494    20 Stratovolc…
##  8 Boot…       0.148       0.0118            0.841    21 Stratovolc…
##  9 Boot…       0.258       0.389             0.352    26 Shield     
## 10 Boot…       0.433       0.457             0.110    29 Shield     
## # … with 8,841 more rows, and 8 more variables: volcano_type <fct>,
## #   correct <lgl>, volcano_number <dbl>, latitude <dbl>, longitude <dbl>,
## #   elevation <dbl>, tectonic_settings <fct>, major_rock_1 <fct>

Then, let’s make a map using stat_summary_hex(). Within each hexagon, let’s take the mean of correct to find what percentage of volcanoes were classified correctly, across all our bootstrap resamples.

ggplot() +
  geom_map(
    data = world, map = world,
    aes(long, lat, map_id = region),
    color = "white", fill = "gray90", size = 0.05, alpha = 0.5
  ) +
  stat_summary_hex(
    data = volcano_pred,
    aes(longitude, latitude, z = as.integer(correct)),
    fun = "mean",
    alpha = 0.7, bins = 50
  ) +
  scale_fill_gradient(high = "cyan3", labels = scales::percent) +
  theme_void(base_family = "IBMPlexSans") +
  labs(x = NULL, y = NULL, fill = "Percent classified\ncorrectly")

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This article is originally published at https://juliasilge.com/blog/
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