Extract elements of a tidyclust model object — extract-tidyclust • tidyclust

These functions extract various elements from a clustering object. If they do not exist yet, an error is thrown.

extract_fit_engine() returns the engine specific fit embedded within a tidyclust model fit. For example, when using k_means() with the "lm" engine, this returns the underlying kmeans object.
extract_parameter_set_dials() returns a set of dials parameter objects.

Usage

# S3 method for cluster_fit
extract_fit_engine(x, ...)

# S3 method for cluster_spec
extract_parameter_set_dials(x, ...)

Arguments

x: A cluster_fit object or a cluster_spec object.
...: Not currently used.

Value

The extracted value from the tidyclust object, x, as described in the description section.

Details

Extracting the underlying engine fit can be helpful for describing the model (via print(), summary(), plot(), etc.) or for variable importance/explainers.

However, users should not invoke the predict() method on an extracted model. There may be preprocessing operations that tidyclust has executed on the data prior to giving it to the model. Bypassing these can lead to errors or silently generating incorrect predictions.

Good:

   tidyclust_fit %>% predict(new_data)

Bad:

   tidyclust_fit %>% extract_fit_engine() %>% predict(new_data)

Examples

kmeans_spec <- k_means(num_clusters = 2)
kmeans_fit <- fit(kmeans_spec, ~., data = mtcars)

extract_fit_engine(kmeans_fit)
#> K-means clustering with 2 clusters of sizes 18, 14
#> 
#> Cluster means:
#>        mpg      cyl     disp        hp     drat       wt     qsec
#> 2 23.97222 4.777778 135.5389  98.05556 3.882222 2.609056 18.68611
#> 1 15.10000 8.000000 353.1000 209.21429 3.229286 3.999214 16.77214
#>          vs        am     gear     carb
#> 2 0.7777778 0.6111111 4.000000 2.277778
#> 1 0.0000000 0.1428571 3.285714 3.500000
#> 
#> Clustering vector:
#>           Mazda RX4       Mazda RX4 Wag          Datsun 710 
#>                   1                   1                   1 
#>      Hornet 4 Drive   Hornet Sportabout             Valiant 
#>                   1                   2                   1 
#>          Duster 360           Merc 240D            Merc 230 
#>                   2                   1                   1 
#>            Merc 280           Merc 280C          Merc 450SE 
#>                   1                   1                   2 
#>          Merc 450SL         Merc 450SLC  Cadillac Fleetwood 
#>                   2                   2                   2 
#> Lincoln Continental   Chrysler Imperial            Fiat 128 
#>                   2                   2                   1 
#>         Honda Civic      Toyota Corolla       Toyota Corona 
#>                   1                   1                   1 
#>    Dodge Challenger         AMC Javelin          Camaro Z28 
#>                   2                   2                   2 
#>    Pontiac Firebird           Fiat X1-9       Porsche 914-2 
#>                   2                   1                   1 
#>        Lotus Europa      Ford Pantera L        Ferrari Dino 
#>                   1                   2                   1 
#>       Maserati Bora          Volvo 142E 
#>                   2                   1 
#> 
#> Within cluster sum of squares by cluster:
#> [1] 58920.54 93643.90
#>  (between_SS / total_SS =  75.5 %)
#> 
#> Available components:
#> 
#> [1] "cluster"      "centers"      "totss"        "withinss"    
#> [5] "tot.withinss" "betweenss"    "size"         "iter"        
#> [9] "ifault"