Merge pull request #20 from alan-turing-institute/dev

For 0.2.0 release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJTuning"
 uuid = "03970b2e-30c4-11ea-3135-d1576263f10f"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.1.4"
+version = "0.2.0"
 
 [deps]
 ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"
@@ -12,7 +12,7 @@ RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 
 [compat]
 ComputationalResources = "^0.3"
-MLJBase = "^0.11"
+MLJBase = "^0.12"
 RecipesBase = "^0.8"
 julia = "^1"
 

README.md

@@ -285,8 +285,8 @@ tuning strategy should like to handle, and the tuning strategy
 functions to be implemented are dispatched on these types. Here are
 the range objects supported by `Grid`:
 
-  - one-dimensional `NumericRange` or `NominalRange` objects (these
-  types are provided by MLJBase)
+  - one-dimensional `NumericRange` or `NominalRange` objects (of
+    abstract type `ParamRange`) provided by MLJBase.
 
   - a tuple `(p, r)` where `p` is one of the above range objects, and
 	`r` a resolution to override the default `resolution` of the
@@ -296,15 +296,19 @@ the range objects supported by `Grid`:
 	where `r1` and `r2` are `NumericRange` objects and `r3` a
 	`NominalRange` object.
 
+Both `NumericRange` and `NominalRange` are constructed with the
+`MLJBase` extension to the `range` function. Use the `iterator` and
+`sampler` methods to convert ranges into one-dimensional grids or for
+random sampling, respectively. See the docstrings for details.
+
 Recall that `NominalRange` has a `values` field, while `NumericRange`
 has the fields `upper`, `lower`, `scale`, `unit` and `origin`. The
 `unit` field specifies a preferred length scale, while `origin` a
 preferred "central value". These default to `(upper - lower)/2` and
 `(upper + lower)/2`, respectively, in the bounded case (neither `upper
 = Inf` nor `lower = -Inf`). The fields `origin` and `unit` are used in
-generating grids for unbounded ranges (and could be used in other
-strategies - for fitting two-parameter probability distributions, for
-example).
+generating grids or fitting probability distributions to unbounded
+ranges.
 
 A `ParamRange` object is always associated with the name of a
 hyperparameter (a field of the prototype in the context of tuning)

src/ranges.jl

@@ -1,6 +1,3 @@
-# TODO: move this next line to MLJBase:
-MLJBase.iterator(r::NominalRange, ::Nothing) = iterator(r)
-
 """
     MLJTuning.grid([rng, ] prototype, ranges, resolutions)