Fix the implementation and docstring of tversky_loss

src/losses/functions.jl

@@ -511,31 +511,31 @@ end
 
 Return the [Tversky loss](https://arxiv.org/abs/1706.05721).
 Used with imbalanced data to give more weight to false negatives.
-Larger β weigh recall more than precision (by placing more emphasis on false negatives)
+Larger β weigh recall more than precision (by placing more emphasis on false negatives).
 Calculated as:
 
-    1 - sum(|y .* ŷ| + 1) / (sum(y .* ŷ + β*(1 .- y) .* ŷ + (1 - β)*y .* (1 .- ŷ)) + 1)
+    1 - sum(|y .* ŷ| + 1) / (sum(y .* ŷ + (1 - β)*(1 .- y) .* ŷ + β*y .* (1 .- ŷ)) + 1)
 
 # Example
 ```jldoctest
-julia> ŷ = [1, 0, 1, 1, 0];
+julia> y = [0, 1, 0, 1, 1, 1];
 
-julia> y = [1, 0, 0, 1, 0];  # one false negative data point
+julia> ŷ_fp = [1, 1, 1, 1, 1, 1];  # 2 false positive -> 2 wrong predictions
 
-julia> Flux.tversky_loss(ŷ, y)
-0.18918918918918926
+julia> ŷ_fnp = [1, 1, 0, 1, 1, 0];  # 1 false negative, 1 false positive -> 2 wrong predictions
 
-julia> y = [1, 1, 1, 1, 0];  # No false negatives, but a false positive
+julia> Flux.tversky_loss(ŷ_fnp, y)
+0.19999999999999996
 
-julia> Flux.tversky_loss(ŷ, y)  # loss is smaller as more weight given to the false negatives
-0.06976744186046513
+julia> Flux.tversky_loss(ŷ_fp, y)  # should be smaller than tversky_loss(ŷ_fnp, y), as FN is given more weight
+0.1071428571428571
 ```
 """
 function tversky_loss(ŷ, y; β = ofeltype(ŷ, 0.7))
     _check_sizes(ŷ, y)
     #TODO add agg
     num = sum(y .* ŷ) + 1
-    den = sum(y .* ŷ + β * (1 .- y) .* ŷ + (1 - β) * y .* (1 .- ŷ)) + 1
+    den = sum(y .* ŷ + (1 - β) * (1 .- y) .* ŷ + β * y .* (1 .- ŷ)) + 1
     1 - num / den
 end