added aqua

Project.toml

@@ -4,9 +4,6 @@ authors = ["Patrick Altmeyer"]
 version = "1.0.0"
 
 [deps]
-CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
-CounterfactualExplanations = "2f13d31b-18db-44c1-bc43-ebaf2cff0be0"
-Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 PackageExtensionCompat = "65ce6f38-6b18-4e1d-a461-8949797d7930"
 
 [weakdeps]
@@ -18,8 +15,16 @@ PyTorchModelExt = "PythonCall"
 RTorchModelExt = "RCall"
 
 [compat]
-CondaPkg = "0.2"
-CounterfactualExplanations = "0.1, 1"
-Flux = "0.12, 0.13, 0.14"
-julia = "1.7, 1.8, 1.9"
+Aqua = "0.8"
 PackageExtensionCompat = "1"
+PythonCall = "0.9.19"
+RCall = "0.14.1"
+Test = "1.7, 1.8, 1.9, 1.10"
+julia = "1.7, 1.8, 1.9, 1.10"
+
+[extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[targets]
+test = ["Aqua", "Test"]

README.md

@@ -5,6 +5,7 @@
 [![Build Status](https://github.com/JuliaTrustworthyAI/TaijaInteroperability.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/JuliaTrustworthyAI/TaijaInteroperability.jl/actions/workflows/CI.yml?query=branch%3Amain)
 [![Coverage](https://codecov.io/gh/JuliaTrustworthyAI/TaijaInteroperability.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/JuliaTrustworthyAI/TaijaInteroperability.jl)
 [![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
+[![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)
 
 A package for enabling interoperability between Python and R machine learning models with Taija.
 

codecov.yml

@@ -2,5 +2,5 @@ coverage:
   status:
     project:
       default:
-        target: 90%    # the required coverage value
+        target: 60%    # the required coverage value
         threshold: 0.5%  # the leniency in hitting the target

test/Project.toml

@@ -1,4 +1,5 @@
 [deps]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
 CounterfactualExplanations = "2f13d31b-18db-44c1-bc43-ebaf2cff0be0"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
@@ -9,3 +10,6 @@ PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 TaijaData = "9d524318-b4e6-4a65-86d2-b2b72d07866c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[compat]
+Aqua = "0.8"

test/aqua.jl

@@ -0,0 +1,8 @@
+using Aqua
+
+@testset "Aqua.jl" begin
+    # Ambiguities needs to be tested seperately until the bug in Aqua package (https://github.com/JuliaTesting/Aqua.jl/issues/77) is fixed
+    Aqua.test_ambiguities([TaijaInteroperability]; recursive=false, broken=false)
+
+    Aqua.test_all(TaijaInteroperability; ambiguities=false)
+end

test/runtests.jl

@@ -14,5 +14,6 @@ include("utils.jl")
 synthetic = _load_synthetic()
 
 @testset "TaijaInteroperability.jl" begin
+    include("aqua.jl")
     include("pytorch.jl")
 end