Data

Many times in a project we want to perform some sort of clustering on a given set of data. This can be accomplished many different ways. This vignette will showcase how you can take a data set that is prepared, say like the internal iris file and process it with the healthyR.ai function hai_kmeans_automl().

First lets take a look at the data itself.

df_tbl <- iris

glimpse(df_tbl)
#> Rows: 150
#> Columns: 5
#> $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.~
#> $ Sepal.Width  <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.~
#> $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.~
#> $ Petal.Width  <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.~
#> $ Species      <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s~

From here we can see that the data is already prepared and ready to go. There is a factor column that denotes the species or the row data and the columns are already numeric. Now the rest is fairly simple and straight forward. Let’s use the hai_kmeans_automl() function to create the list output that comes from it where we will want to use the Species column as the predictor based upon the features presented.

Use the function

column_names <- names(iris)
target_col <- "Species"
predictor_cols <- setdiff(column_names, target_col)

Now we have our column inputs for the function, so we can go ahead and run it.

h2o.init()

output <- hai_kmeans_automl(
  .data = df_tbl,
  .predictors = predictor_cols,
  .standardize = FALSE
)

h2o.shutdown(prompt = FALSE)

This function gives a lot of output inside of it. From here we will discuss what comes out of the function.

Function Output

Lets take a look at the structure of the output object. It is a list of lists with four main components. They are the following:

• data
• auto_kmeans_obj
• model_id (h2o model id)
• scree_plt (a ggplot2 object)

Lets explor each of these items.

output$data

output$auto_kmeans_obj

output$model_id

print(output$scree_plt)