Confusion matrix over arbitrary labels (Geron Ch 3, morie.fn hmcfm)

Maps arbitrary labels to 0-based indices and then DELEGATES the counting to morie_geron_confusion_matrix (the grcfm core), exactly as the Python module does. Non-integer labels are ordered by their string form, matching sorted(..., key=str).

Usage

morie_geron_confusion_matrix_labeled(
  y_true,
  y_pred,
  n_classes = NULL,
  labels = NULL
)

Arguments

y_true, y_pred

Equal-length label vectors.

n_classes

Optional class count for integer labels.

labels

Optional explicit level order.

Value

List with matrix, labels, accuracy, precision, recall, f1, support, predicted_totals, macro_f1, n_classes, estimate, n.

Details

This is the metrics core: hmeaf, hmf1 and hmmlb all read their precision/recall/F1 out of this same routine in Python.

Examples

V <- c(1, 2, 3, 4, 5, 6, 7, 8)
morie_geron_confusion_matrix_labeled(V, V)
#> $matrix
#>       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
#>  [1,]    0    0    0    0    0    0    0    0    0
#>  [2,]    0    1    0    0    0    0    0    0    0
#>  [3,]    0    0    1    0    0    0    0    0    0
#>  [4,]    0    0    0    1    0    0    0    0    0
#>  [5,]    0    0    0    0    1    0    0    0    0
#>  [6,]    0    0    0    0    0    1    0    0    0
#>  [7,]    0    0    0    0    0    0    1    0    0
#>  [8,]    0    0    0    0    0    0    0    1    0
#>  [9,]    0    0    0    0    0    0    0    0    1
#> 
#> $labels
#> [1] 0 1 2 3 4 5 6 7 8
#> 
#> $accuracy
#> [1] 1
#> 
#> $precision
#> [1] NA  1  1  1  1  1  1  1  1
#> 
#> $recall
#> [1] NA  1  1  1  1  1  1  1  1
#> 
#> $f1
#> [1] 0 1 1 1 1 1 1 1 1
#> 
#> $support
#> [1] 0 1 1 1 1 1 1 1 1
#> 
#> $predicted_totals
#> [1] 0 1 1 1 1 1 1 1 1
#> 
#> $macro_f1
#> [1] 0.8888889
#> 
#> $n_classes
#> [1] 9
#> 
#> $estimate
#> [1] 1
#> 
#> $n
#> [1] 8
#> 
#> $method
#> [1] "confusion matrix C[i,j] = count(actual i, predicted j); counting delegated to grcfm"
#>