initiated mlj interface port

Author: PyDataBlog

Project.toml

@@ -4,6 +4,7 @@ authors = ["Bernard Brenyah", "Andrey Oskin"]
 version = "0.1.0"
 
 [deps]
+MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
@@ -12,8 +13,9 @@ julia = "1.3"
 
 [extras]
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 
 [targets]
-test = ["Test", "Random", "Suppressor"]
+test = ["Test", "Random", "Suppressor", "MLJBase"]

src/mlj_interface.jl

@@ -0,0 +1,13 @@
+# TODO 1: a using MLJModelInterface or import MLJModelInterface statement
+using MLJModelInterface
+using ParallelKMeans
+
+# TODO 2: MLJ-compatible model types and constructors,
+
+# TODO 3: implementation of fit, predict/transform and optionally fitted_params for your models,
+
+# TODO 4: metadata for your package and for each of your models
+
+
+
+