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Merge pull request #30 from alan-turing-institute/random-search2
Implement RandomSearch
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| Original file line number | Diff line number | Diff line change |
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| ## BOUNDEDNESS TRAIT | ||
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| # For random search and perhaps elsewhere, we need a variation on the | ||
| # built-in boundedness notions: | ||
| abstract type PositiveUnbounded <: Unbounded end | ||
| abstract type Other <: Unbounded end | ||
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| boundedness(::NumericRange{<:Any,<:Bounded}) = Bounded | ||
| boundedness(::NumericRange{<:Any,<:LeftUnbounded}) = Other | ||
| boundedness(::NumericRange{<:Any,<:DoublyUnbounded}) = Other | ||
| function boundedness(r::NumericRange{<:Any,<:RightUnbounded}) | ||
| if r.lower >= 0 | ||
| return PositiveUnbounded | ||
| end | ||
| return Other | ||
| end | ||
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| """ | ||
| MLJTuning.grid([rng, ] prototype, ranges, resolutions) | ||
| Given an iterable `ranges` of `ParamRange` objects, and an iterable | ||
| `resolutions` of the same length, return a vector of models generated | ||
| by cloning and mutating the hyperparameters (fields) of `prototype`, | ||
| according to the Cartesian grid defined by the specifed | ||
| one-dimensional `ranges` (`ParamRange` objects) and specified | ||
| `resolutions`. A resolution of `nothing` for a `NominalRange` | ||
| indicates that all values should be used. | ||
| Specification of an `AbstractRNG` object `rng` implies shuffling of | ||
| the results. Otherwise models are ordered, with the first | ||
| hyperparameter referenced cycling fastest. | ||
| """ | ||
| grid(rng::AbstractRNG, prototype::Model, ranges, resolutions) = | ||
| shuffle(rng, grid(prototype, ranges, resolutions)) | ||
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| function grid(prototype::Model, ranges, resolutions) | ||
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| iterators = broadcast(iterator, ranges, resolutions) | ||
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| A = MLJBase.unwind(iterators...) | ||
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| N = size(A, 1) | ||
| map(1:N) do i | ||
| clone = deepcopy(prototype) | ||
| for k in eachindex(ranges) | ||
| field = ranges[k].field | ||
| recursive_setproperty!(clone, field, A[i,k]) | ||
| end | ||
| clone | ||
| end | ||
| end | ||
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| ## PRE-PROCESSING OF USER-SPECIFIED CARTESIAN RANGE OBJECTS | ||
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| """ | ||
| process_grid_range(user_specified_range, resolution, verbosity) | ||
| Utility to convert a user-specified range (see [`Grid`](@ref)) into a | ||
| pair of tuples `(ranges, resolutions)`. | ||
| For example, if `r1`, `r2` are `NumericRange`s and `s` is a | ||
| NominalRange` with 5 values, then we have: | ||
| julia> MLJTuning.process_grid_range([(r1, 3), r2, s], 42, 1) == | ||
| ((r1, r2, s), (3, 42, 5)) | ||
| true | ||
| If `verbosity` > 0, then a warning is issued if a `Nominal` range is | ||
| paired with a resolution. | ||
| """ | ||
| process_grid_range(user_specified_range, args...) = | ||
| throw(ArgumentError("Unsupported range. ")) | ||
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| process_grid_range(usr::Union{ParamRange,Tuple{ParamRange,Int}}, args...) = | ||
| process_grid_range([usr, ], args...) | ||
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| function process_grid_range(user_specified_range::AbstractVector, | ||
| resolution, verbosity) | ||
| # r unpaired: | ||
| stand(r) = throw(ArgumentError("Unsupported range. ")) | ||
| stand(r::NumericRange) = (r, resolution) | ||
| stand(r::NominalRange) = (r, length(r.values)) | ||
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| # (r, res): | ||
| stand(t::Tuple{NumericRange,Integer}) = t | ||
| function stand(t::Tuple{NominalRange,Integer}) | ||
| verbosity < 0 || | ||
| @warn "Ignoring a resolution specified for a `NominalRange`. " | ||
| return (first(t), length(first(t).values)) | ||
| end | ||
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| ret = zip(stand.(user_specified_range)...) |> collect | ||
| return first(ret), last(ret) | ||
| end | ||
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| """ | ||
| process_random_range(user_specified_range, | ||
| bounded, | ||
| positive_unbounded, | ||
| other) | ||
| Utility to convert a user-specified range (see [`RandomSearch`](@ref)) | ||
| into an n-tuple of `(field, sampler)` pairs. | ||
| """ | ||
| process_random_range(user_specified_range, args...) = | ||
| throw(ArgumentError("Unsupported range #1. ")) | ||
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| const DIST = Distributions.Distribution | ||
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| process_random_range(user_specified_range::Union{ParamRange, Tuple{Any,Any}}, | ||
| args...) = | ||
| process_random_range([user_specified_range, ], args...) | ||
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| function process_random_range(user_specified_range::AbstractVector, | ||
| bounded, | ||
| positive_unbounded, | ||
| other) | ||
| # r not paired: | ||
| stand(r) = throw(ArgumentError("Unsupported range #2. ")) | ||
| stand(r::NumericRange) = stand(r, boundedness(r)) | ||
| stand(r::NumericRange, ::Type{<:Bounded}) = (r.field, sampler(r, bounded)) | ||
| stand(r::NumericRange, ::Type{<:Other}) = (r.field, sampler(r, other)) | ||
| stand(r::NumericRange, ::Type{<:PositiveUnbounded}) = | ||
| (r.field, sampler(r, positive_unbounded)) | ||
| stand(r::NominalRange) = (n = length(r.values); | ||
| (r.field, sampler(r, fill(1/n, n)))) | ||
| # (r, d): | ||
| stand(t::Tuple{ParamRange,Any}) = stand(t...) | ||
| stand(r, d) = throw(ArgumentError("Unsupported range #3. ")) | ||
| stand(r::NominalRange, d::AbstractVector{Float64}) = _stand(r, d) | ||
| stand(r::NumericRange, d:: Union{DIST, Type{<:DIST}}) = _stand(r, d) | ||
| _stand(r, d) = (r.field, sampler(r, d)) | ||
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| # (field, s): | ||
| stand(t::Tuple{Union{Symbol,Expr},Any}) = t | ||
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| return Tuple(stand.(user_specified_range)) | ||
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| # ret = zip(stand.(user_specified_range)...) |> collect | ||
| # return first(ret), last(ret) | ||
| end |
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