bug fixes to support README examples

Project.toml

@@ -5,7 +5,6 @@ version = "0.4.0"
 
 [deps]
 AMLPipelineBase = "e3c3008a-8869-4d53-9f34-c96f99c8a2b6"
-Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
 CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
@@ -14,7 +13,6 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 AMLPipelineBase = "0.1"
-Conda = "1.0, 1.1, 1.2, 1.3, 1.4, 1.5"
 CondaPkg = "0.2"
 DataFrames = "0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 1.0, 1.1"
 PythonCall = "0.9"

src/skcrossvalidator.jl

@@ -89,10 +89,16 @@ and the following metrics for regression:
 """
 function crossvalidate(pl::Machine,X::DataFrame,Y::Vector,
                        sfunc::String; nfolds=10,verbose::Bool=true)
+
+    YC=Y
+    if !(eltype(YC) <: Real)
+       YC = Y |> Vector{String}
+    end
+
     checkfun(sfunc)
     pfunc = metric_dict[sfunc]
     metric(a,b) = pfunc(a,b) |> (x -> PYC.pyconvert(Float64,x))
-    crossvalidate(pl,X,Y,metric,nfolds,verbose)
+    crossvalidate(pl,X,YC,metric,nfolds,verbose)
 end
 
 function crossvalidate(pl::Machine,X::DataFrame,Y::Vector,sfunc::String,nfolds::Int)

src/sklearners.jl

@@ -158,8 +158,16 @@ function sklearners()
   println("Note: Consult Scikitlearn's online help for more details about the learner's arguments.")
 end
 
-function fit!(skl::SKLearner, xx::DataFrame, y::Vector)::Nothing
+function fit!(skl::SKLearner, xx::DataFrame, yy::Vector)::Nothing
+  # normalize inputs
   x = xx |> Array
+  y = yy
+  skl.model[:predtype] = :numeric
+  if !(eltype(yy) <: Real)
+     y = yy |> Vector{String}
+     skl.model[:predtype] = :alpha
+  end
+
   impl_args  = copy(skl.model[:impl_args])
   learner    = skl.model[:learner]
   py_learner = getproperty(learner_dict[learner],learner)
@@ -171,13 +179,6 @@ function fit!(skl::SKLearner, xx::DataFrame, y::Vector)::Nothing
     end
   end
 
-  if eltype(y) <: Real
-      skl.model[:predtype] = :numeric
-  else
-      skl.model[:predtype] = :alpha
-  end
-
-
   # Train
   modelobj = py_learner(;impl_args...)
   modelobj.fit(x,y)

src/skpreprocessor.jl

@@ -156,8 +156,13 @@ function skpreprocessors()
   println("Note: Please consult Scikitlearn's online help for more details about the preprocessor's arguments.")
 end
 
-function fit!(skp::SKPreprocessor, x::DataFrame, y::Vector=[])::Nothing
+function fit!(skp::SKPreprocessor, x::DataFrame, yc::Vector=[])::Nothing
    features = x |> Array
+   y = yc
+   if !(eltype(yc) <: Real)
+      y = yc |> Vector{String}
+   end
+
    impl_args = copy(skp.model[:impl_args])
    autocomp = skp.model[:autocomponent]
    if autocomp == true