Initial commit

Author: eliascarv

.gitignore

@@ -1,4 +1,5 @@
 *.jl.*.cov
 *.jl.cov
 *.jl.mem
-/Manifest.toml
+Manifest.toml
+.vscode

Project.toml

@@ -1,7 +1,18 @@
 name = "StatsLearnModels"
 uuid = "c146b59d-1589-421c-8e09-a22e554fd05c"
 authors = ["Elias Carvalho <[email protected]> and contributors"]
-version = "1.0.0-DEV"
+version = "0.1.0"
+
+[deps]
+Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
+
+[weakdeps]
+MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
+
+[extensions]
+StatsLearnModelsMLJModelInterfaceExt = "MLJModelInterface"
 
 [compat]
+MLJModelInterface = "1.9"
+Tables = "1.11"
 julia = "1.9"

README.md

@@ -1,4 +1,4 @@
-# StatsLearnModels
+# [WIP] StatsLearnModels.jl
 
 [![Build Status](https://github.com/JuliaML/StatsLearnModels.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/JuliaML/StatsLearnModels.jl/actions/workflows/CI.yml?query=branch%3Amain)
 [![Coverage](https://codecov.io/gh/JuliaML/StatsLearnModels.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/JuliaML/StatsLearnModels.jl)

ext/StatsLearnModelsMLJModelInterfaceExt.jl

@@ -0,0 +1,29 @@
+module StatsLearnModelsMLJModelInterfaceExt
+
+using Tables
+import StatsLearnModels as SLM
+import MLJModelInterface as MI
+
+isprobabilistic(model::MI.Model) = MI.prediction_type(model) == :probabilistic
+isprobabilistic(model::MI.Probabilistic) = true
+
+function SLM.fit(model::MI.Model, input, output)
+  cols = Tables.columns(output)
+  names = Tables.columnnames(cols)
+  y = Tables.getcolumn(cols, first(names))
+  data = MI.reformat(model, input, y)
+  fitresult, _... = MI.fit(model, 0, data...)
+  SLM.FittedModel(model, fitresult)
+end
+
+function SLM.predict(fmodel::SLM.FittedModel{<:MI.Model}, table)
+  (; model, fitresult) = fmodel
+  data = MI.reformat(model, table)
+  if isprobabilistic(model)
+    MI.predict_mode(model, fitresult, data...)
+  else
+    MI.predict(model, fitresult, data...)
+  end
+end
+
+end

src/StatsLearnModels.jl

@@ -1,5 +1,27 @@
 module StatsLearnModels
 
-# Write your package code here.
+"""
+    StatsLearnModels.fit(model, input, output) -> FittedModel
+
+TODO
+"""
+function fit end
+
+"""
+    StatsLearnModels.predict(model::FittedModel, table)
+
+TODO
+"""
+function predict end
+
+"""
+    StatsLearnModels.FittedModel(model, fitresult)
+
+TODO
+"""
+struct FittedModel{M,F}
+  model::M
+  fitresult::F
+end
 
 end