MLJ Interface WIP 1

Author: PyDataBlog

src/mlj_interface.jl

@@ -2,12 +2,71 @@
 using MLJModelInterface
 using ParallelKMeans
 
+
+####
+#### MODEL DEFINITION
+####
 # TODO 2: MLJ-compatible model types and constructors,
+@mlj_model mutable struct ParaKMeans <: MLJModelInterface.Unsupervised
+    # Hyperparameters of the model
+    algo::AbstractKMeansAlg     = Lloyd::(_ in (Lloyd, Hamerly, Elkan))
+    k_init::String              = "k-means++"::(_ in ("k-means++", String))
+    k::Int                      = 3::(_ > 0)
+    tol::Float                  = 1e-6::(_ < 1)
+    max_iters::Int              = 300::(_ > 0)
+end
+
+
+# TODO 3: implementation of fit, predict, and fitted_params of the model
+####
+#### FIT FUNCTION
+####
+
+function MLJModelInterface.fit(m::ParaKMeans, verbosity::Int, X, y, w=nothing)
+    # body ...
+    return (fitresult, cache, report)
+end
+
+
+function MLJModelInterface.fitted_params(model::ParaKMeans, fitresult)
+    # extract what's relevant from `fitresult`
+    # ...
+    # then return as a NamedTuple
+    return (learned_param1 = ..., learned_param2 = ...)
+end
+
+
+####
+#### PREDICT FUNCTION
+####
+function MLJModelInterface.predict(m::ParaKMeans, fitresult, Xnew)
+    # ...
+end
 
-# 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
+####
+#### METADATA
+####
 
+# TODO 4: metadata for the package and for each of your models
+const PARAKMEANS_MODELS = Union{ParaKMeans}
 
+metadata_pkg.(PARAKMEANS_MODELS,
+    name = "ParallelKMeans",
+    uuid = "42b8e9d4-006b-409a-8472-7f34b3fb58af", # see your Project.toml
+    url  = "https://github.com/PyDataBlog/ParallelKMeans.jl",  # URL to your package repo
+    julia = true,          # is it written entirely in Julia?
+    license = "MIT",       # your package license
+    is_wrapper = false,    # does it wrap around some other package?
+)
 
 
+# Metadata for ParaKMeans model
+metadata_model(ParaKMeans,
+    input   = MLJModelInterface.Table(MLJModelInterface.Continuous),  # what input data is supported?           # for a supervised model, what target?
+    output  = MLJModelInterface.Table(MLJModelInterface.Count),  # for an unsupervised, what output?
+    weights = false,                                             # does the model support sample weights?
+    descr   = "Parallel & lightning fast implementation of all variants of the KMeans clustering algorithm in native Julia.",
+	path	= "ParallelKMeans.src.mlj_interface.ParaKMeans"
+	#path    = "YourPackage.SubModuleContainingModelStructDefinition.YourModel1"
+    )