fix errors and add tests

Author: ppalmes

AutoTS/CondaPkg.toml

@@ -0,0 +1,5 @@
+[deps]
+pycaret = "3.3.*"
+python = "3.11.*"
+mlflow = ""
+requests = ""

AutoTS/Project.toml

@@ -2,3 +2,45 @@ name = "AutoTS"
 uuid = "9f9003ca-5fd1-4e14-9a26-1226c8bf687b"
 version = "0.1.0"
 authors = ["Paulito Palmes, PhD <[email protected]>"]
+
+[deps]
+AMLPipelineBase = "e3c3008a-8869-4d53-9f34-c96f99c8a2b6"
+ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
+AutoMLPipeline = "08437348-eef5-4817-bc1b-d4e9459680d6"
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
+PDFmerger = "3beb2ed1-af7d-458f-b727-6e9beb3586c0"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
+Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[compat]
+AMLPipelineBase = "0.1.18"
+ArgParse = "1.2.0"
+AutoMLPipeline = "0.4.7"
+CSV = "0.10.15"
+DataFrames = "1.8.1"
+Distributed = "1.11.0"
+PDFmerger = "0.3.3"
+Plots = "1.41.2"
+PythonCall = "0.9.30"
+Random = "1.11.0"
+Reexport = "1.2.2"
+Serialization = "1.11.0"
+Statistics = "1.11.1"
+Test = "1.11.0"
+julia = "1.6"
+
+[extras]
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
+Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[test]
+test = ["DataFrames", "Distributed", "Serialization", "Test"]

AutoTS/src/AutoTS.jl

@@ -2,6 +2,28 @@ module AutoTS
 
 greet() = print("Hello World!")
 
+include("./main.jl")
+
+using Reexport
+@reexport using AutoMLPipeline
+@reexport using AMLPipelineBase
+
+using CSV
+using DataFrames
+using AMLPipelineBase.AbsTypes
+export fit, fit!, transform, transform!, fit_transform, fit_transform!
+import AMLPipelineBase.AbsTypes: fit!, transform!, fit, transform
+using AMLPipelineBase: AbsTypes, Utils
+
+export get_iris
+
+function get_iris()
+  iris = CSV.read(joinpath(Base.@__DIR__, "../../data", "iris.csv"), DataFrame)
+  return iris
+end
+
+# -----------------------
+
 include("carettspredictor.jl")
 using .CaretTSPredictors
 export CaretTSPredictor, carettsdriver

AutoTS/src/main.jl

@@ -1,4 +1,4 @@
-using AutoAD
+using AutoTS
 using ArgParse
 using CSV
 using DataFrames
@@ -44,7 +44,7 @@ function doprediction_only(args::Dict)
   run_id = args[:runid]
   url = args[:url]
   predtype = args[:prediction_type]
-  mlf=AutoMLFlowTSPrediction((Dict(:rund_id=>run_id,:url=>url)))
+  mlf = AutoMLFlowTSPrediction((Dict(:rund_id => run_id, :url => url)))
   Yn = transform!(mlf, X)
   ofile = args[:output_file]
   if ofile != "NONE"
@@ -58,12 +58,12 @@ end
 
 function dotrainandpredict(args::Dict)
   url = args[:url]
-  learner=args[:learner]
+  learner = args[:learner]
   forecast_horizon = args[:forecast_horizon]
   fname = args[:csvfile]
   df = CSV.read(fname, DataFrame)
   X = df[:, 1:1]
-  autots = AutoMLFlowTSPrediction(Dict(:url => url, :impl_args=>Dict(:forecast_horizon=>forecast_horizon,:learner=>learner)))
+  autots = AutoMLFlowTSPrediction(Dict(:url => url, :impl_args => Dict(:forecast_horizon => forecast_horizon, :learner => learner)))
   Yc = fit_transform!(autots, X)
   println("output:", Yc |> x -> first(x, 5))
   return Yc

AutoTS/test/test_caret_tspredictor.jl

@@ -1,7 +1,7 @@
 module TestCaretTSPredictor
 using Distributed
 using Test
-using AutoAD
+using AutoTS
 using DataFrames: DataFrame
 using Serialization