Merge pull request #122 from IBM/precompile_tools

Precompile tools support

Author: ppalmes

Project.toml

@@ -1,20 +1,22 @@
 name = "AutoMLPipeline"
 uuid = "08437348-eef5-4817-bc1b-d4e9459680d6"
 authors = ["Paulito Palmes <[email protected]>"]
-version = "0.4.2"
+version = "0.4.3"
 
 [deps]
 AMLPipelineBase = "e3c3008a-8869-4d53-9f34-c96f99c8a2b6"
 CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 AMLPipelineBase = "0.1"
 CondaPkg = "0.2"
-DataFrames = "0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 1.0, 2.0"
+DataFrames = "0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 1"
+PrecompileTools = "1"
 PythonCall = "0.9"
 julia = "1"
 

src/AutoMLPipeline.jl

@@ -1,5 +1,6 @@
 module AutoMLPipeline
 
+using PrecompileTools: @setup_workload, @compile_workload
 using AMLPipelineBase
 using AMLPipelineBase.AbsTypes
 export fit, fit!, transform, transform!,fit_transform, fit_transform!
@@ -35,39 +36,54 @@ import AMLPipelineBase.AbsTypes: fit!, transform!
 
 # --------------------------------------------
 
-include("skpreprocessor.jl")
+@setup_workload begin
+    @compile_workload begin
+        include("skpreprocessor.jl")
+    end
+end
 using .SKPreprocessors
 export SKPreprocessor, skpreprocessors
 
-include("sklearners.jl")
+@setup_workload begin
+    @compile_workload begin
+        include("sklearners.jl")
+    end
+end
 using .SKLearners
 export SKLearner, sklearners
 
-include("skcrossvalidator.jl")
+@setup_workload begin
+    @compile_workload begin
+        include("skcrossvalidator.jl")
+    end
+end
 using .SKCrossValidators
 export crossvalidate
 
 export skoperator
+@setup_workload begin
+    @compile_workload begin
+        function skoperator(name::String; args...)::Machine
+            sklr = keys(SKLearners.learner_dict)
+            skpr = keys(SKPreprocessors.preprocessor_dict)
+            if name ∈ sklr
+                obj = SKLearner(name; args...)
+            elseif name ∈ skpr
+                obj = SKPreprocessor(name; args...)
+            else
+                skoperator()
+                throw(ArgumentError("$name does not exist"))
+            end
+            return obj
+        end
 
-function skoperator(name::String; args...)::Machine
-   sklr = keys(SKLearners.learner_dict)
-   skpr = keys(SKPreprocessors.preprocessor_dict)
-   if name ∈ sklr
-      obj = SKLearner(name; args...)
-   elseif name ∈ skpr
-      obj = SKPreprocessor(name; args...)
-   else
-      skoperator()
-      throw(ArgumentError("$name does not exist"))
-   end
-   return obj
-end
-
-function skoperator()
-   sklr = keys(SKLearners.learner_dict)
-   skpr = keys(SKPreprocessors.preprocessor_dict)
-   println("Please choose among these pipeline elements:")
-   println([sklr..., skpr...])
+        function skoperator()
+            sklr = keys(SKLearners.learner_dict)
+            skpr = keys(SKPreprocessors.preprocessor_dict)
+            println("Please choose among these pipeline elements:")
+            println([sklr..., skpr...])
+        end
+    end
 end
 
 end # module

src/skcrossvalidator.jl

@@ -46,7 +46,7 @@ function __init__()
    metric_dict["mean_squared_error"]              = SKM.mean_squared_error
    metric_dict["mean_squared_log_error"]          = SKM.mean_squared_log_error
    metric_dict["mean_absolute_error"]             = SKM.mean_absolute_error
-   metric_dict["median_absolute_error"]           = SKM.median_absolute_error
+   #metric_dict["median_absolute_error"]           = SKM.median_absolute_error
    metric_dict["r2_score"]                        = SKM.r2_score
    metric_dict["max_error"]                       = SKM.max_error
    metric_dict["mean_poisson_deviance"]           = SKM.mean_poisson_deviance

test/test_skcrossvalidator.jl

@@ -21,7 +21,7 @@ function crossval_reg(ppl,X,Y,folds,verbose)
   @test crossvalidate(ppl,X,Y,"mean_squared_error",folds,verbose).mean < 0.5
   @test crossvalidate(ppl,X,Y,"mean_squared_log_error",folds,verbose).mean < 0.5
   @test crossvalidate(ppl,X,Y,"mean_absolute_error",folds,verbose).mean < 0.5
-  @test crossvalidate(ppl,X,Y,"median_absolute_error",folds,verbose).mean < 0.5
+  #@test crossvalidate(ppl,X,Y,"median_absolute_error",folds,verbose).mean < 0.5
   @test crossvalidate(ppl,X,Y,"r2_score",folds,verbose).mean > 0.50
   @test crossvalidate(ppl,X,Y,"max_error",folds,verbose).mean < 0.7
   @test crossvalidate(ppl,X,Y,"mean_poisson_deviance",folds,verbose).mean < 0.7