fix predict for Rasters 0.13 (#20)

* use maplayers

* add tests for predicting to rasters

* require rasters 0.13

Project.toml

@@ -42,7 +42,7 @@ Loess = "0.6"
 MLJBase = "1.7.0"
 Makie = "0.20, 0.21"
 PrettyTables = "2"
-Rasters = "0.12, 0.13"
+Rasters = "0.13"
 ScientificTypesBase = "3"
 Shapley = "0.1"
 StatisticalMeasures = "0.1.5"

src/predict.jl

@@ -99,7 +99,7 @@ function _reformat_and_predict_raster(s::Union{<:SDMensemble, SDMgroup, SDMmachi
     rs_preds = rs[predictorkeys(data(s))]
     missing_mask = Rasters.boolmask(rs_preds)
     d = rs_preds[missing_mask]
-    if any(map(x -> Missing <: eltype(x), rs_preds))
+    if any(Rasters.maplayers(x -> Missing <: eltype(x), rs_preds))
         # to get rid of Union{Missing, Float64} etc.
         layertypes = map(x -> Base.nonmissingtype(eltype(x)), (Rasters.layers(rs_preds)))
         nttype = NamedTuple{keys(layertypes), Tuple{values(layertypes)...}}

test/runtests.jl

@@ -2,13 +2,15 @@ using SpeciesDistributionModels, MLJBase, MLJModels, Tables
 import SpeciesDistributionModels as SDM
 using StableRNGs, Distributions, Test
 using Makie
+using Rasters
 
 using MLJGLMInterface: LinearBinaryClassifier
 using EvoTrees: EvoTreeClassifier
 using MLJDecisionTreeInterface: RandomForestClassifier
 
 rng = StableRNG(0)
 #using Random; rng = Random.GLOBAL_RNG
+
 # some mock data
 n = 100
 backgrounddata = (a = rand(rng, n), b = rand(rng, n), c = rand(rng, n))
@@ -48,6 +50,22 @@ presencedata = (a = rand(rng, n), b = rand(rng, n).^2, c = sqrt.(rand(rng, n)))
 
     machine_aucs = SDM.machine_evaluations(evaluation).test.auc
 
+    # explain
+    expl = explain(ensemble; method = ShapleyValues(10; rng))
+    varimp = variable_importance(expl)
+    @test varimp.b > varimp.a
+    @test varimp.c > varimp.a
+
+    # plots
+    interactive_evaluation(ensemble, thresholds = 0:0.001:1)
+    interactive_response_curves(expl)
+    boxplot(evaluation, :auc)
+end
+
+data = sdmdata(presencedata, backgrounddata; resampler = CV(nfolds = 5, shuffle = true))
+ensemble = sdm(data, (; lm = LinearBinaryClassifier()))
+
+@testset "predict" begin
     pr1 = SDM.predict(ensemble, backgrounddata)
     pr2 = SDM.predict(ensemble, backgrounddata; reducer = maximum)
     pr3 = SDM.predict(ensemble, backgrounddata; reducer = x -> sum(x .> 0.5), by_group = true)
@@ -61,16 +79,16 @@ presencedata = (a = rand(rng, n), b = rand(rng, n).^2, c = sqrt.(rand(rng, n)))
     @test_throws ArgumentError SDM.predict(ensemble, backgrounddata[(:a,)])
     @test_throws Exception SDM.predict(ensemble, backgrounddata; by_group = true)
 
-    # explain
-    expl = explain(ensemble; method = ShapleyValues(10; rng))
-    varimp = variable_importance(expl)
-    @test varimp.b > varimp.a
-    @test varimp.c > varimp.a
-
-    # plots
-    interactive_evaluation(ensemble, thresholds = 0:0.001:1)
-    interactive_response_curves(expl)
-    boxplot(evaluation, :auc)
+    ## to a Raster
+    ds = (X(1:100), Y(1:100))
+    rs = RasterStack((a = rand(ds), b = rand(ds), c = rand(ds)), missingval = 0.0)
+    # make the first value missing
+    rs[1] = (a = 0, b = 0, c = 0)
+    raspr = SDM.predict(ensemble, rs)
+    @test all(ismissing, raspr[X=1, Y=1])
+    # all values should be between 0 and 1
+    extr = extrema(skipmissing(raspr))
+    @test extr[1] > 0 && extr[2] < 1
 end
 
 @testset "collinearity" begin