Merge pull request #201 from JuliaAI/explicit-better-checks

Add prediction type check for Explicit strategy

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJTuning"
 uuid = "03970b2e-30c4-11ea-3135-d1576263f10f"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.8.0"
+version = "0.8.1"
 
 [deps]
 ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"

src/strategies/explicit.jl

@@ -1,15 +1,22 @@
+const WARN_INCONSISTENT_PREDICTION_TYPE =
+    "Not all models to be evaluated have the same prediction type, and this may "*
+    "cause problems for some measures. For example, a probabilistic metric "*
+    "like `log_loss` cannot be applied to a model making point (deterministic) "*
+    "predictions. Inspect the prediction type with "*
+        "`prediction_type(model)`. "
+
 mutable struct Explicit <: TuningStrategy end
 
 struct ExplicitState{R, N}
     range::R # a model-generating iterator
-    next::N # to hold output of `iterate(range)`
+    next::N  # to hold output of `iterate(range)`
+    prediction_type::Symbol
+    user_warned::Bool
 end
 
-ExplictState(r::R, n::N) where {R,N} = ExplicitState{R, Union{Nothing, N}}(r, n)
-
 function MLJTuning.setup(tuning::Explicit, model, range, n, verbosity)
     next = iterate(range)
-    return ExplicitState(range, next)
+    return ExplicitState(range, next, MLJBase.prediction_type(model), false)
 end
 
 # models! returns as many models as possible but no more than `n_remaining`:
@@ -20,11 +27,21 @@ function MLJTuning.models(tuning::Explicit,
                           n_remaining,
                           verbosity)
 
-    range, next  = state.range, state.next
+    range, next, prediction_type, user_warned =
+        state.range, state.next, state.prediction_type, state.user_warned
+
+    function check(m)
+        if !user_warned && verbosity > -1 && MLJBase.prediction_type(m) != prediction_type
+            @warn WARN_INCONSISTENT_PREDICTION_TYPE
+            user_warned = true
+        end
+    end
 
     next === nothing && return nothing, state
 
     m, s = next
+    check(m)
+
     models = Any[m, ] # types not known until run-time
 
     next = iterate(range, s)
@@ -33,12 +50,13 @@ function MLJTuning.models(tuning::Explicit,
     while i < n_remaining
         next === nothing && break
         m, s = next
+        check(m)
         push!(models, m)
         i += 1
         next = iterate(range, s)
     end
 
-    new_state = ExplicitState(range, next)
+    new_state = ExplicitState(range, next, prediction_type, user_warned)
 
     return models, new_state
 

src/tuned_models.jl

@@ -431,9 +431,11 @@ function event!(metamodel,
                state)
     model = _first(metamodel)
     metadata = _last(metamodel)
+    force = typeof(resampling_machine.model.model) !=
+        typeof(model)
     resampling_machine.model.model = model
     verb = (verbosity >= 2 ? verbosity - 3 : verbosity - 1)
-    fit!(resampling_machine, verbosity=verb)
+    fit!(resampling_machine; verbosity=verb, force)
     E = evaluate(resampling_machine)
     entry0 = (model       = model,
               measure     = E.measure,

test/strategies/explicit.jl

@@ -1,6 +1,7 @@
 good = KNNClassifier(K=2)
 bad = KNNClassifier(K=10)
 ugly = ConstantClassifier()
+evil = DeterministicConstantClassifier()
 
 r = [good, bad, ugly]
 
@@ -44,4 +45,42 @@ X, y = make_blobs(rng=rng)
     @test_throws ArgumentError TunedModel(; models=[dcc, dcc])
 end
 
+r = [good, bad, evil, ugly]
+
+@testset "inconsistent prediction types" begin
+    # case where different predictions types is actually okay (but still
+    # a warning is issued):
+    tmodel = TunedModel(
+       models=r,
+        resampling = Holdout(),
+        measure=accuracy,
+    )
+    @test_logs(
+    (:warn, MLJTuning.WARN_INCONSISTENT_PREDICTION_TYPE),
+    MLJBase.fit(tmodel, 0, X, y),
+    );
+
+    # verbosity = -1 suppresses the warning:
+    @test_logs(
+    MLJBase.fit(tmodel, -1, X, y),
+    );
+
+    # case where there really is a problem with different prediction types:
+    tmodel = TunedModel(
+       models=r,
+        resampling = Holdout(),
+        measure=log_loss,
+    )
+    @test_logs(
+        (:warn, MLJTuning.WARN_INCONSISTENT_PREDICTION_TYPE),
+        (:error,),
+        (:info,),
+        (:info,),
+        @test_throws(
+            ArgumentError, # indicates the problem is with incompatible measure
+            MLJBase.fit(tmodel, 0, X, y),
+        )
+    )
+end
+
 true

test/tuned_models.jl

@@ -79,7 +79,7 @@ results = [(evaluate(model, X, y,
     tm = TunedModel(
         models=r,
         resampling=CV(nfolds=2),
-        measures=cross_entropy
+        measures=cross_entropy,
     )
     @test_logs((:error, r"Problem"),
                (:info, r""),