Multilabel thresholded prediction (Geron Ch 3, morie.fn grmlb) — morie_geron_multilabel

Strict > thresholding per label; per-label F1 via morie_geron_f1_score(), plus the micro F1 over pooled counts.

Usage

morie_geron_multilabel_classification(X, Y, thresholds = 0.5)

Arguments

X: (m, K) score matrix.
Y: (m, K) 0/1 targets.
thresholds: Scalar or length-K thresholds.

Value

List with predictions, per_label_f1, per_label_precision, per_label_recall, macro_f1, micro_f1, exact_match_ratio, hamming_loss.

Examples

x <- matrix(c(1, 0, 0.5, 1), 2, byrow = TRUE)
morie_geron_encoder_decoder_seq2seq(function(x) c(sum(x), 1),
    function(yp, cc, t) as.numeric(yp) * 0.5 + cc[1]/t, c(1,
        2, 3), 4)
#> $outputs
#> $outputs[[1]]
#> [1] 6 6
#> 
#> $outputs[[2]]
#> [1] 6 6
#> 
#> $outputs[[3]]
#> [1] 5 5
#> 
#> $outputs[[4]]
#> [1] 4 4
#> 
#> 
#> $context
#> [1] 6 1
#> 
#> $context_dim
#> [1] 2
#> 
#> $n_steps
#> [1] 4
#> 
#> $stopped_early
#> [1] FALSE
#> 
#> $estimate
#> $estimate[[1]]
#> [1] 6 6
#> 
#> $estimate[[2]]
#> [1] 6 6
#> 
#> $estimate[[3]]
#> [1] 5 5
#> 
#> $estimate[[4]]
#> [1] 4 4
#> 
#> 
#> $n
#> [1] 4
#> 
morie_geron_multilabel_classification(X = x, Y = x)
#> $predictions
#>      [,1] [,2]
#> [1,]    1    0
#> [2,]    0    1
#> 
#> $per_label_f1
#> [1] 1 1
#> 
#> $per_label_precision
#> [1] 1 1
#> 
#> $per_label_recall
#> [1] 1 1
#> 
#> $macro_f1
#> [1] 1
#> 
#> $micro_f1
#> [1] 1
#> 
#> $exact_match_ratio
#> [1] 1
#> 
#> $hamming_loss
#> [1] 0
#> 
#> $thresholds
#> [1] 0.5 0.5
#> 
#> $estimate
#> [1] 1
#> 
#> $n
#> [1] 2
#>