celery

The goal of celery is to provide a tidy, unified interface to clustering models. The packages is closely modeled after the parsnip package.

Installation

You can install the development version of celery from GitHub with:

# install.packages("devtools")
devtools::install_github("EmilHvitfeldt/celery")

Example

The first thing you do is to create a cluster specification. For this example we are creating a K-means model, using the stats engine.

library(celery)

kmeans_spec <- k_means(k = 3) %>%
  set_engine_celery("stats") 

kmeans_spec
#> K Means Cluster Specification (partition)
#> 
#> Main Arguments:
#>   k = 3
#> 
#> Computational engine: stats

This specification can then be fit using data.

kmeans_spec_fit <- kmeans_spec %>%
  fit(~., data = mtcars)
kmeans_spec_fit
#> celery cluster object
#> 
#> K-means clustering with 3 clusters of sizes 7, 11, 14
#> 
#> Cluster means:
#>        mpg cyl     disp        hp     drat       wt     qsec        vs
#> 1 19.74286   6 183.3143 122.28571 3.585714 3.117143 17.97714 0.5714286
#> 2 26.66364   4 105.1364  82.63636 4.070909 2.285727 19.13727 0.9090909
#> 3 15.10000   8 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000
#>          am     gear     carb
#> 1 0.4285714 3.857143 3.428571
#> 2 0.7272727 4.090909 1.545455
#> 3 0.1428571 3.285714 3.500000
#> 
#> Clustering vector:
#>           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive 
#>                   1                   1                   2                   1 
#>   Hornet Sportabout             Valiant          Duster 360           Merc 240D 
#>                   3                   1                   3                   2 
#>            Merc 230            Merc 280           Merc 280C          Merc 450SE 
#>                   2                   1                   1                   3 
#>          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
#>                   3                   3                   3                   3 
#>   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
#>                   3                   2                   2                   2 
#>       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
#>                   2                   3                   3                   3 
#>    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa 
#>                   3                   2                   2                   2 
#>      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
#>                   3                   1                   3                   2 
#> 
#> Within cluster sum of squares by cluster:
#> [1] 13954.34 11848.37 93643.90
#>  (between_SS / total_SS =  80.8 %)
#> 
#> Available components:
#> 
#> [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
#> [6] "betweenss"    "size"         "iter"         "ifault"

Once you have a fitted celery object, you can do a number of things. predict() returns the cluster a new observation belongs to

predict(kmeans_spec_fit, mtcars[1:4, ])
#> # A tibble: 4 × 1
#>   .pred_cluster
#>   <fct>        
#> 1 1            
#> 2 1            
#> 3 2            
#> 4 1

extract_cluster_assignment() returns the cluster assignments of the training observations

extract_cluster_assignment(kmeans_spec_fit)
#> # A tibble: 32 × 1
#>    .cluster
#>    <fct>   
#>  1 1       
#>  2 1       
#>  3 2       
#>  4 1       
#>  5 3       
#>  6 1       
#>  7 3       
#>  8 2       
#>  9 2       
#> 10 1       
#> # … with 22 more rows

and extract_clusters() returns the locations of the clusters

extract_clusters(kmeans_spec_fit)
#> # A tibble: 3 × 12
#>   .cluster   mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>   <fct>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1         19.7     6  183. 122.   3.59  3.12  18.0 0.571 0.429  3.86  3.43
#> 2 2         26.7     4  105.  82.6  4.07  2.29  19.1 0.909 0.727  4.09  1.55
#> 3 3         15.1     8  353. 209.   3.23  4.00  16.8 0     0.143  3.29  3.5