fix for fitted_params in multinomial case with no intercept

Author: tlienart

.gitignore

@@ -1,3 +1,4 @@
 *DS_Store
 Manifest.toml
 docs/build
+sandbox.jl

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJLinearModels"
 uuid = "6ee0df7b-362f-4a72-a706-9e79364fb692"
 authors = ["Thibaut Lienart <[email protected]>"]
-version = "0.5.4"
+version = "0.5.5"
 
 [deps]
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"

src/mlj/interface.jl

@@ -88,7 +88,7 @@ function MMI.fitted_params(m::Union{CLF_MODELS...}, (θ, features, classes, c))
         if m.fit_intercept
             return _fitted_params(W, features, W[end, :])
         end
-        return _fitted_params(W[1:end-1, :], features, nothing)
+        return _fitted_params(W, features, nothing)
     end
     # single class (necessarily c==0)
     m.fit_intercept && return _fitted_params(θ[1:end-1], features, θ[end])

test/interface/fitpredict.jl

@@ -49,9 +49,14 @@ end
     Xt = MLJBase.table(X)
     yc = MLJBase.categorical(y1)
 
-    mc = MultinomialClassifier(lambda=λ, gamma=γ)
+    mc = MultinomialClassifier(lambda=λ, gamma=γ, fit_intercept=false)
     fr, = MLJBase.fit(mc, 1, Xt, yc)
 
+    mach = MLJBase.machine(mc, Xt, yc)
+    MLJBase.fit!(mach)
+    fp = MLJBase.fitted_params(mach)
+    @test length(fp.coefs) == 5
+
     ŷ = MLJBase.predict(mc, fr, Xt)
     ŷ = MLJBase.mode.(ŷ)