cleanup

Project.toml

@@ -1,7 +1,7 @@
 name = "Lale"
 uuid = "25676c37-aa2f-4f14-ad5b-b63670ababff"
 authors = ["Paulito Palmes <[email protected]>", "Martin Hirzel", "Avraham Shinnar", "Kiran Kate"]
-version = "0.1.4"
+version = "0.1.5"
 
 [deps]
 AMLPipelineBase = "e3c3008a-8869-4d53-9f34-c96f99c8a2b6"
@@ -14,18 +14,17 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 AMLPipelineBase = "0.1"
-DataFrames = "0.17, 0.18, 0.19, 0.20, 0.21, 0.22"
+DataFrames = "0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 1.0"
 Pandas = "1.1, 1.2, 1.3, 1.4"
 PyCall = "1.90, 1.91, 1.92"
 julia = "1"
 
 [extras]
-AutoMLPipeline = "08437348-eef5-4817-bc1b-d4e9459680d6"
 Pandas = "eadc2687-ae89-51f9-a5d9-86b5a6373a9c"
 PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["AutoMLPipeline", "Test", "PyCall", "Random", "Statistics"]
+test = ["Test", "PyCall", "Random", "Statistics"]

test/test_laleoptimizer_ag.jl

@@ -5,15 +5,13 @@ using Test
 using Lale
 using Statistics
 using DataFrames: DataFrame
-using AutoMLPipeline.Utils
 
 const IRIS = getiris()
 const X    = IRIS[:,1:3] |> DataFrame
 const XC   = IRIS[:,1:4] |> DataFrame
 const YC   = IRIS[:,5] |> Vector
 const Y    = IRIS[:,4] |> Vector
 
-
 function pipeline_test()
 
    # lale ops
@@ -82,8 +80,10 @@ function pipeline_test()
 
    plr = @pipeline (catf |> ohe) + (numf |> rb |> pca) |> rfr
    plc = @pipeline (catf |> ohe) + (numf |> rb |> pca) |> rfc
-   @test crossvalidate(plr,X,Y,"mean_absolute_error",3,false).mean < 0.3
-   @test crossvalidate(plc,XC,YC,"accuracy_score",3,false).mean > 0.8
+   perfreg(x,y) = score(:rmse,x,y)
+   perfcl(x,y) = score(:accuracy,x,y)
+   @test crossvalidate(plr,X,Y,perfreg).mean < 0.3
+   @test crossvalidate(plc,XC,YC,perfcl).mean > 80.0
 
 end
 @testset "Lale Pipeline Optimizer" begin

test/test_laleoptimizer_sk.jl

@@ -5,15 +5,13 @@ using Test
 using Lale
 using Statistics
 using DataFrames: DataFrame
-using AutoMLPipeline.Utils
 
 const IRIS = getiris()
 const X    = IRIS[:,1:3] |> DataFrame
 const XC   = IRIS[:,1:4] |> DataFrame
 const YC   = IRIS[:,5] |> Vector
 const Y    = IRIS[:,4] |> Vector
 
-
 function pipeline_test()
 
    # lale ops
@@ -82,8 +80,10 @@ function pipeline_test()
 
    plr = @pipeline (catf |> ohe) + (numf |> rb |> pca) |> rfr
    plc = @pipeline (catf |> ohe) + (numf |> rb |> pca) |> rfc
-   @test crossvalidate(plr,X,Y,"mean_absolute_error",3,false).mean < 0.3
-   @test crossvalidate(plc,XC,YC,"accuracy_score",3,false).mean > 0.8
+   perfreg(x,y) = score(:rmse,x,y)
+   perfcl(x,y) = score(:accuracy,x,y)
+   @test crossvalidate(plr,X,Y,perfreg).mean < 0.3
+   @test crossvalidate(plc,XC,YC,perfcl).mean > 80.0
 
 end
 @testset "Lale Pipeline Optimizer" begin