Merge pull request #49 from alan-turing-institute/dev

Merge dev branch for a 0.5.1 release

Author: ablaom

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJBase"
 uuid = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.5.0"
+version = "0.5.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -27,9 +27,10 @@ julia = "1"
 [extras]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 LossFunctions = "30fc2ffe-d236-52d8-8643-a9d8f7c094a7"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TypedTables = "9d95f2ec-7b3d-5a63-8d20-e2491e220bb9"
 
 [targets]
-test = ["CSV", "DataFrames", "LossFunctions", "Test", "TypedTables"]
+test = ["CSV", "DataFrames", "Distances", "LossFunctions", "Test", "TypedTables"]

src/MLJBase.jl

@@ -20,6 +20,8 @@ export selectrows, selectcols, select, nrows         # data.jl
 export table, levels_seen, matrix, container_type    # data.jl
 export partition, unpack                             # data.jl
 export @set_defaults                                 # utilities.jl
+export @mlj_model                                    # mlj_model_macro.jl
+export metadata_model, metadata_pkg                  # metadata_utilities
 export HANDLE_GIVEN_ID, @more, @constant             # show.jl
 export color_on, color_off                           # show.jl
 export UnivariateFinite, average                     # distributions.jl
@@ -50,6 +52,7 @@ export pdf, mode, median, mean, shuffle!, categorical, shuffle, levels, levels!
 export std
 
 import Base.==
+import Base: @__doc__
 
 using Tables
 using OrderedCollections # already a dependency of StatsBase
@@ -83,7 +86,7 @@ const DEFAULT_SHOW_DEPTH = 0
 include("utilities.jl")
 
 
-## BASE TYPES 
+## BASE TYPES
 
 abstract type MLJType end
 include("equality.jl") # equality for MLJType objects
@@ -116,7 +119,7 @@ abstract type UnsupervisedNetwork <: Unsupervised end
 ## THE MODEL INTERFACE
 
 # every model interface must implement a `fit` method of the form
-# `fit(model, verbosity::Integer, training_args...) -> fitresult, cache, report` 
+# `fit(model, verbosity::Integer, training_args...) -> fitresult, cache, report`
 # or, one the simplified versions
 # `fit(model, training_args...) -> fitresult`
 # `fit(model, X, ys...) -> fitresult`
@@ -169,14 +172,14 @@ clean!(model::Model) = ""
 
 ## TRAITS
 
-""" 
+"""
 
     info(object)
 
 List the traits of an object, such as a model or a performance measure.
 
 """
-info(object) = info(object, Val(ScientificTypes.trait(object))) 
+info(object) = info(object, Val(ScientificTypes.trait(object)))
 
 
 include("model_traits.jl")
@@ -199,6 +202,12 @@ include("datasets.jl") # importing CSV will also load datasets_requires.jl
 include("tasks.jl")
 include("measures.jl")
 
+# mlj model macro to help define models
+include("mlj_model_macro.jl")
+
+# metadata utils
+include("metadata_utilities.jl")
+
 # __init__() function:
 include("init.jl")
 

src/data.jl

@@ -45,12 +45,12 @@ function partition(rows::AbstractVector{Int}, fractions...; shuffle::Bool=false,
 end
 
 """
-    t1, t2, ...., tk = unnpack(table, c1, c2, ... ck; wrap_singles=false)
+    t1, t2, ...., tk = unnpack(table, t1, t2, ... tk; wrap_singles=false)
 
 Split any Tables.jl compatible `table` into smaller tables (or
 vectors) `t1, t2, ..., tk` by making selections *without replacement*
-from the column names defined by the conditionals `c1`, `c2`, ...,
-`ck`. A *conditional* is any object `c` such that `c(name)` is `true`
+from the column names defined by the tests `t1`, `t2`, ...,
+`tk`. A *test* is any object `t` such that `t(name)` is `true`
 or `false` for each column `name::Symbol` of `table`. 
 
 Whenever a returned table contains a single column, it is converted to
@@ -59,7 +59,7 @@ a vector unless `wrap_singles=true`.
 Scientific type conversions can be optionally specified (note
 semicolon):
 
-    unpack(table, c...; wrap_singles=false, col1=>scitype1, col2=>scitype2, ... )
+    unpack(table, t...; wrap_singles=false, col1=>scitype1, col2=>scitype2, ... )
 
 ### Example
 
@@ -82,7 +82,7 @@ julia> Z
 ```
  
 """
-function unpack(X, conditionals...; wrap_singles=false, pairs...)
+function unpack(X, tests...; wrap_singles=false, pairs...)
 
     if isempty(pairs)
         Xfixed = X
@@ -94,7 +94,7 @@ function unpack(X, conditionals...; wrap_singles=false, pairs...)
     names_left = schema(Xfixed).names |> collect
     history = ""
     counter = 1
-    for c in conditionals
+    for c in tests
         names = filter(c, names_left)
         filter!(!in(names), names_left)
         history *= "selection $counter: $names\n remaining: $names_left\n"

src/distributions.jl

@@ -2,9 +2,11 @@
 
 const Dist = Distributions
 
-
 ## EQUALITY OF DISTRIBUTIONS (minor type piracy)
 
+# TODO: We should get rid of this. I think it is used only in
+# MLJModels/test. 
+
 function ==(d1::D, d2::D) where D<:Dist.Sampleable
     ret = true
     for fld in fieldnames(D)
@@ -108,8 +110,10 @@ end
 
 function UnivariateFinite(classes::AbstractVector{L},
                           p::AbstractVector{<:Real}) where L
-    L <: CategoricalElement || error("classes must have CategoricalValue or "*
-                                     "CategoricalString type.")
+    L <: CategoricalElement ||
+        error("`classes` must have type `AbstractVector{T}` where "*
+              "`T <: Union{CategoricalValue,CategoricalString}. "*
+              "Perhaps you have `T=Any`? ")
     Dist.@check_args(UnivariateFinite, length(classes)==length(p))
     prob_given_class = LittleDict([classes[i]=>p[i] for i in eachindex(p)])
     return  UnivariateFinite(prob_given_class)
@@ -138,6 +142,28 @@ function Base.show(stream::IO, d::UnivariateFinite)
     print(stream, str)
 end
 
+"""
+    isapprox(d1::UnivariateFinite, d2::UnivariateFinite; kwargs...)
+
+Returns `true` if and only if `Set(classes(d1) == Set(classes(d2))`
+and the corresponding probabilities are approximately equal. The
+key-word arguments `kwargs` are passed through to each call of
+`isapprox` on probabiliity pairs. Returns `false` otherwise.
+
+"""
+function Base.isapprox(d1::UnivariateFinite, d2::UnivariateFinite; kwargs...)
+
+    classes1 = classes(d1)
+    classes2 = classes(d2)
+
+    for c in classes1
+        c in classes2 || return false
+        isapprox(pdf(d1, c), pdf(d2, c); kwargs...) ||
+            return false # pdf defined below
+    end
+    return true
+end
+
 function average(dvec::AbstractVector{UnivariateFinite{L,U,T}};
                  weights=nothing) where {L,U,T}
 

src/metadata_utilities.jl

@@ -0,0 +1,57 @@
+"""
+docstring_ext
+
+Helper function to generate the docstring for a package.
+"""
+function docstring_ext(T; descr::String="")
+    package_name = MLJBase.package_name(T)
+    package_url  = MLJBase.package_url(T)
+    model_name   = MLJBase.name(T)
+    # the message to return
+    message      = "$descr"
+    message     *= "\n→ based on [$package_name]($package_url)"
+    message     *= "\n→ do `@load $model_name` to use the model"
+    message     *= "\n→ do `?$model_name` for documentation."
+end
+
+"""
+metadata_pkg
+
+Helper function to write the metadata for a package.
+"""
+function metadata_pkg(T; name::String="unknown", uuid::String="unknown", url::String="unknown",
+                         julia::Union{Missing,Bool}=missing, license::String="unknown",
+                         is_wrapper::Bool=false)
+    ex = quote
+        package_name(::Type{<:$T})    = $name
+        package_uuid(::Type{<:$T})    = $uuid
+        package_url(::Type{<:$T})     = $url
+        is_pure_julia(::Type{<:$T})   = $julia
+        package_license(::Type{<:$T}) = $license
+        is_wrapper(::Type{<:$T})      = $is_wrapper
+    end
+    eval(ex)
+end
+
+"""
+metadata_model
+
+Helper function to write the metadata for a single model of a package (complements
+[`metadata_ext`](@ref)).
+"""
+function metadata_model(T; input=Unknown, target=Unknown,
+                         output=Unknown, weights::Bool=false,
+                         descr::String="", path::String="")
+    if isempty(path)
+        path = "MLJModels.$(package_name(T))_.$(name(T))"
+    end
+    ex = quote
+        input_scitype(::Type{<:$T})    = $input
+        output_scitype(::Type{<:$T})   = $output
+        target_scitype(::Type{<:$T})   = $target
+        supports_weights(::Type{<:$T}) = $weights
+        docstring(::Type{<:$T})        = docstring_ext($T, descr=$descr)
+        load_path(::Type{<:$T})        = $path
+    end
+    eval(ex)
+end