Merge pull request #17 from alan-turing-institute/dev

For a 0.1.4 release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJTuning"
 uuid = "03970b2e-30c4-11ea-3135-d1576263f10f"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.1.3"
+version = "0.1.4"
 
 [deps]
 ComputationalResources = "ed09eef8-17a6-5b46-8889-db040fac31e3"
@@ -12,7 +12,7 @@ RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 
 [compat]
 ComputationalResources = "^0.3"
-MLJBase = "^0.11.1"
+MLJBase = "^0.11"
 RecipesBase = "^0.8"
 julia = "^1"
 

README.md

@@ -69,6 +69,8 @@ This repository contains:
   - [ ] Latin hypercubes
 
   - [ ] random search
+
+  - [ ] bandit
 
   - [ ] simulated annealing
 

src/learning_curves.jl

@@ -20,10 +20,10 @@ has the following keys: `:parameter_name`, `:parameter_scale`,
 `:parameter_values`, `:measurements`.
 
 To generate multiple curves for a `model` with a random number
-generator (RNG) as a hyperparameter, specify the name of the (possibly
-nested) RNG field, and a vector `rngs` of RNG's, one for each
-curve. Alternatively, set `rngs` to the number of curves desired, in
-which case RNG's are automatically generated. The individual curve
+generator (RNG) as a hyperparameter, specify the name, `rng_name`, of
+the (possibly nested) RNG field, and a vector `rngs` of RNG's, one for
+each curve. Alternatively, set `rngs` to the number of curves desired,
+in which case RNG's are automatically generated. The individual curve
 computations can be distributed across multiple processes using
 `acceleration=CPUProcesses()`. See the second example below for a
 demonstration.
@@ -46,7 +46,7 @@ plot(curve.parameter_values,
 If using a `Holdout()` `resampling` strategy (with no shuffling) and
 if the specified hyperparameter is the number of iterations in some
 iterative model (and that model has an appropriately overloaded
-`MLJBase.update` method) then training is not restarted from scratch
+`MLJModelInterface.update` method) then training is not restarted from scratch
 for each increment of the parameter, ie the model is trained
 progressively.
 

src/strategies/grid.jl

@@ -25,10 +25,10 @@ Example 2:
 
     [(range(model, :hyper1, lower=1, upper=10), 15),
       range(model, :hyper2, lower=2, upper=4),
-      range(model, :hyper3, values=[:ball, :tree]]
+      range(model, :hyper3, values=[:ball, :tree])]
 
 Note: All the `field` values of the `ParamRange` objects (`:hyper1`,
-`:hyper2`, `:hyper3` in the precedng example) must refer to field
+`:hyper2`, `:hyper3` in the preceding example) must refer to field
 names a of single model (the `model` specified during `TunedModel`
 construction).
 
@@ -46,7 +46,7 @@ resolutions apply.
 In all cases the models generated are shuffled using `rng`, unless
 `shuffle=false`.
 
-See also [TunedModel](@ref), [range](@ref).
+See also [`TunedModel`](@ref), [`range`](@ref).
 
 """
 mutable struct Grid <: TuningStrategy

src/tuned_models.jl

@@ -66,7 +66,7 @@ Calling `fit!(mach)` on a machine `mach=machine(tuned_model, X, y)` or
 
 - Fit an internal machine, based on the optimal model
   `fitted_params(mach).best_model`, wrapping the optimal `model`
-  object in *all* the provided data `X, y` (or in `task`). Calling
+  object in *all* the provided data `X`, `y`(, `w`). Calling
   `predict(mach, Xnew)` then returns predictions on `Xnew` of this
   internal machine. The final train can be supressed by setting
   `train_best=false`.
@@ -90,9 +90,9 @@ every measure specified will be computed and reported in
 generated report.
 
 Specify `repeats > 1` for repeated resampling per model evaluation. See
-[`evaluate!](@ref) options for details.
+[`evaluate!`](@ref) options for details.
 
-*Important.* If a custom measure `measure` is used, and the measure is
+*Important.* If a custom `measure` is used, and the measure is
 a score, rather than a loss, be sure to check that
 `MLJ.orientation(measure) == :score` to ensure maximization of the
 measure, rather than minimization. Override an incorrect value with