Use CategoricalArray instead of NominalArray

New type merging NominalArray and OrdinalArray in 0.0.5.
JuliaStats · Sep 22, 2016 · 91d789c · 91d789c
1 parent 89225f9
commit 91d789c
Show file tree

Hide file tree

Showing 3 changed files with 17 additions and 17 deletions.
diff --git a/test/contrasts.jl b/test/contrasts.jl
@@ -4,7 +4,7 @@ using Base.Test
 using DataFrames
 
 
-d = DataFrame(x = NominalVector([:a, :b, :c, :a, :a, :b]))
+d = DataFrame(x = CategoricalVector([:a, :b, :c, :a, :a, :b]))
 
 mf = ModelFrame(Formula(nothing, :x), d)
 

diff --git a/test/formula.jl b/test/formula.jl
@@ -136,7 +136,7 @@ module TestFormula
 
     #test_group("expanding a nominal array into a design matrix of indicators for each dummy variable")
 
-    d[:x1p] = NullableNominalArray(d[:x1])
+    d[:x1p] = NullableCategoricalArray(d[:x1])
     mf = ModelFrame(y ~ x1p, d)
     mm = ModelMatrix(mf)
 
@@ -181,24 +181,24 @@ module TestFormula
     ## @test r[:,1] == DataVector(df["x1"])
     ## @test r[:,2] == DataVector(df["x2"])
 
-    ## df["x1"] = NominalArray(x1)
+    ## df["x1"] = CategoricalArray(x1)
     ## r = expand(:x1, df)
     ## @test isa(r, DataFrame)
     ## @test ncol(r) == 3
-    ## @test r == expand(NominalArray(x1), "x1", DataFrame())
+    ## @test r == expand(CategoricalArray(x1), "x1", DataFrame())
 
     ## r = expand(:(x1 + x2), df)
     ## @test isa(r, DataFrame)
     ## @test ncol(r) == 4
-    ## @test r[:,1:3] == expand(NominalArray(x1), "x1", DataFrame())
+    ## @test r[:,1:3] == expand(CategoricalArray(x1), "x1", DataFrame())
     ## @test r[:,4] == DataVector(df["x2"])
 
-    ## df["x2"] = NominalArray(x2)
+    ## df["x2"] = CategoricalArray(x2)
     ## r = expand(:(x1 + x2), df)
     ## @test isa(r, DataFrame)
     ## @test ncol(r) == 6
-    ## @test r[:,1:3] == expand(NominalArray(x1), "x1", DataFrame())
-    ## @test r[:,4:6] == expand(NominalArray(x2), "x2", DataFrame())
+    ## @test r[:,1:3] == expand(CategoricalArray(x1), "x1", DataFrame())
+    ## @test r[:,4:6] == expand(CategoricalArray(x2), "x2", DataFrame())
 
     #test_group("Creating a model matrix using full formulas: y ~ x1 + x2, etc")
 
@@ -215,7 +215,7 @@ module TestFormula
     @test mm.m == [ones(4) x1 x2 x1.*x2]
     @test mm.m == ModelMatrix{sparsetype}(mf).m
 
-    df[:x1] = NominalArray(x1)
+    df[:x1] = CategoricalArray(x1)
     x1e = [[0, 1, 0, 0] [0, 0, 1, 0] [0, 0, 0, 1]]
     f = y ~ x1 * x2
     mf = ModelFrame(f, df)
@@ -234,7 +234,7 @@ module TestFormula
     ## @test mm.m == [ones(4) x1 log(x2)]
 
     ## df = deepcopy(d)
-    ## df["x1"] = NominalArray([5:8])
+    ## df["x1"] = CategoricalArray([5:8])
     ## f = Formula(:(y ~ x1 * (log(x2) + x3)))
     ## mf = ModelFrame(f, df)
     ## mm = ModelMatrix(mf)
@@ -276,7 +276,7 @@ module TestFormula
     ## @test model_response(mf) == y''     # fails: Int64 vs. Float64
 
     df = deepcopy(d)
-    df[:x1] = NullableNominalArray(df[:x1])
+    df[:x1] = NullableCategoricalArray(df[:x1])
 
     f = y ~ x2 + x3 + x3*x2
     mm = ModelMatrix(ModelFrame(f, df))
@@ -333,9 +333,9 @@ module TestFormula
     ## FAILS: behavior is wrong when no lower-order terms (1+x1+x2+x1&x2...)
     ##
     ## df = DataFrame(y=1:27,
-    ##                x1 = NominalArray(vec([x for x in 1:3, y in 4:6, z in 7:9])),
-    ##                x2 = NominalArray(vec([y for x in 1:3, y in 4:6, z in 7:9])),
-    ##                x3 = NominalArray(vec([z for x in 1:3, y in 4:6, z in 7:9])))
+    ##                x1 = CategoricalArray(vec([x for x in 1:3, y in 4:6, z in 7:9])),
+    ##                x2 = CategoricalArray(vec([y for x in 1:3, y in 4:6, z in 7:9])),
+    ##                x3 = CategoricalArray(vec([z for x in 1:3, y in 4:6, z in 7:9])))
     ## f = y ~ x1 & x2 & x3
     ## mf = ModelFrame(f, df)
     ## @test coefnames(mf)[2:end] ==

diff --git a/test/statsmodel.jl b/test/statsmodel.jl
@@ -61,7 +61,7 @@ io = IOBuffer()
 show(io, m)
 
 ## with categorical variables
-d[:x1p] = NullableNominalArray(d[:x1])
+d[:x1p] = NullableCategoricalArray(d[:x1])
 f2 = y ~ x1p
 m2 = fit(DummyMod, f2, d)
 
@@ -73,11 +73,11 @@ m2 = fit(DummyMod, f2, d)
 ## predict w/ new data with _extra_ levels (throws an error)
 d3 = deepcopy(d)
 d3[1, :x1] = 0
-d3[:x1p] = NullableNominalVector(d3[:x1])
+d3[:x1p] = NullableCategoricalVector(d3[:x1])
 @test_throws ArgumentError predict(m2, d3)
 
 ## fit with contrasts specified
-d[:x2p] = NullableNominalVector(d[:x2])
+d[:x2p] = NullableCategoricalVector(d[:x2])
 f3 = y ~ x1p + x2p
 m3 = fit(DummyMod, f3, d)
 fit(DummyMod, f3, d, contrasts = Dict(:x1p => EffectsCoding()))