Merge pull request #180 from JuliaAI/dev

For a 0.7.2 release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJTuning"
 uuid = "03970b2e-30c4-11ea-3135-d1576263f10f"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "0.7.1"
+version = "0.7.2"
 
 [deps]
 ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"

README.md

@@ -4,6 +4,8 @@ Hyperparameter optimization for
 [MLJ](https://github.com/alan-turing-institute/MLJ.jl) machine
 learning models.
 
+See [**Tuning Models · MLJ**](https://alan-turing-institute.github.io/MLJ.jl/dev/tuning_models) for usage examples.
+
 [![Build Status](https://github.com/JuliaAI/MLJTuning.jl/workflows/CI/badge.svg)](https://github.com/JuliaAI/MLJTuning.jl/actions)
 [![codecov.io](http://codecov.io/github/JuliaAI/MLJTuning.jl/coverage.svg?branch=master)](http://codecov.io/github/JuliaAI/MLJTuning.jl?branch=master)
 
@@ -32,7 +34,7 @@ hyperparameter optimization tasks from there.
 MLJTuning is the place for developers to integrate hyperparameter
 optimization algorithms (here called *tuning strategies*) into MLJ,
 either by adding code to [/src/strategies](/src/strategies), or by
-importing MLJTuning into a third-pary package and implementing
+importing MLJTuning into a third-party package and implementing
 MLJTuning's [tuning strategy interface](#how-do-i-implement-a-new-tuning-strategy).
 
 MLJTuning is a component of the [MLJ

src/tuned_models.jl

@@ -19,6 +19,10 @@ const ERR_BOTH_DISALLOWED = ArgumentError(
     "You cannot specify both `model` and `models`. ")
 const ERR_MODEL_TYPE = ArgumentError(
     "Only `Deterministic` and `Probabilistic` model types supported.")
+const ERR_UNINSTANTIATED_MODEL = AssertionError(
+    "Type encountered where model instance expected. (Tuning evaluates "*
+    "models by mutating clones of the provided instance, as specified "*
+    "by `range`.) ")
 const INFO_MODEL_IGNORED =
     "`model` being ignored. Using `model=first(range)`. "
 const ERR_TOO_MANY_ARGUMENTS =
@@ -260,11 +264,11 @@ function TunedModel(args...; model=nothing,
 
     # user can specify model as argument instead of kwarg:
     length(args) < 2 || throw(ERR_TOO_MANY_ARGUMENTS)
-    if length(args) === 1
+    if length(args) == 1
         arg = first(args)
         model === nothing ||
             @warn warn_double_spec(arg, model)
-        model =arg
+        model = arg
     end
 
     # either `models` is specified and `tuning` is set to `Explicit`,
@@ -309,7 +313,10 @@ function TunedModel(args...; model=nothing,
         else
             throw(ERR_MODEL_TYPE)
         end
+    elseif model isa Type
+        throw(ERR_UNINSTANTIATED_MODEL)
     else
+        # Model is probably an instantiated model.
         M = typeof(model)
     end
 

test/runtests.jl

@@ -9,11 +9,7 @@ using StableRNGs
 # Display Number of processes and if necessary number
 # of Threads
 @info "nworkers: $(nworkers())"
-@static if VERSION >= v"1.3.0-DEV.573"
-    @info "nthreads: $(Threads.nthreads())"
-else
-    @info "Running julia $(VERSION). Multithreading tests excluded. "
-end
+@info "nthreads: $(Threads.nthreads())"
 
 include("test_utilities.jl")
 

test/tuned_models.jl

@@ -17,10 +17,10 @@ N = 30
 x1 = rand(N);
 x2 = rand(N);
 x3 = rand(N);
-X = (x1=x1, x2=x2, x3=x3);
+X = (; x1, x2, x3);
 y = 2*x1 .+ 5*x2 .- 3*x3 .+ 0.4*rand(N);
 
-m(K) = KNNRegressor(K=K)
+m(K) = KNNRegressor(; K)
 r = [m(K) for K in 13:-1:2]
 
 # TODO: replace the above with the line below and post an issue on
@@ -66,6 +66,8 @@ r = [m(K) for K in 13:-1:2]
     tm = @test_logs TunedModel(model=first(r), range=r, measure=rms)
     @test tm.tuning isa RandomSearch
     @test input_scitype(tm) == Table(Continuous)
+
+    @test_throws MLJTuning.ERR_UNINSTANTIATED_MODEL TunedModel(; model=KNNRegressor, range=r)
 end
 
 results = [(evaluate(model, X, y,