Merge pull request #558 from JuliaAI/constructor

Base the Model Registry on constructors, rather than model types; and ancillary changes

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJModels"
 uuid = "d491faf4-2d78-11e9-2867-c94bc002c0b7"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "0.16.17"
+version = "0.17.0"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
@@ -37,7 +37,7 @@ Distributions = "0.25"
 InteractiveUtils = "<0.0.1, 1"
 LinearAlgebra = "<0.0.1, 1"
 Markdown = "<0.0.1, 1"
-MLJModelInterface = "1.4"
+MLJModelInterface = "1.10"
 OrderedCollections = "1.1"
 Parameters = "0.12"
 Pkg = "<0.0.1, 1"

src/MLJModels.jl

@@ -59,6 +59,11 @@ nonmissing = nonmissingtype
 
 include("utilities.jl")
 
+# load built-in models:
+include("builtins/Constant.jl")
+include("builtins/Transformers.jl")
+include("builtins/ThresholdPredictors.jl")
+
 Handle = NamedTuple{(:name, :pkg), Tuple{String,String}}
 (::Type{Handle})(name,string) = NamedTuple{(:name, :pkg)}((name, string))
 
@@ -79,11 +84,6 @@ include("loading.jl")
 include("registry/src/Registry.jl")
 using .Registry
 
-# load built-in models:
-include("builtins/Constant.jl")
-include("builtins/Transformers.jl")
-include("builtins/ThresholdPredictors.jl")
-
 # finalize:
 include("init.jl")
 

src/builtins/ThresholdPredictors.jl

@@ -328,14 +328,8 @@ MMI.package_uuid(::Type{<:ThresholdUnion}) = ""
 MMI.is_wrapper(::Type{<:ThresholdUnion}) = true
 MMI.package_url(::Type{<:ThresholdUnion}) =
     "https://github.com/JuliaAI/MLJModels.jl"
-
-for New in THRESHOLD_TYPE_EXS
-    New_str = string(New)
-    quote
-        MMI.load_path(::Type{<:$New{M}}) where M = "MLJModels."*$New_str
-    end |> eval
-end
-
+MMI.load_path(::Type{<:ThresholdUnion}) = "MLJModels.BinaryThresholdPredictor"
+MMI.constructor(::Type{<:ThresholdUnion}) = BinaryThresholdPredictor
 
 for trait in [:supports_weights,
               :supports_class_weights,

src/builtins/Transformers.jl

@@ -183,90 +183,7 @@ function MMI.fitted_params(::FillImputer, fr)
             filler_given_feature=filler_given_feature)
 end
 
-
-# # FOR FEATURE (COLUMN) SELECTION
-
-mutable struct FeatureSelector <: Unsupervised
-    # features to be selected; empty means all
-    features::Union{Vector{Symbol}, Function}
-    ignore::Bool # features to be ignored
-end
-
-# keyword constructor
-function FeatureSelector(
-    ;
-    features::Union{AbstractVector{Symbol}, Function}=Symbol[],
-    ignore::Bool=false
-)
-    transformer = FeatureSelector(features, ignore)
-    message = MMI.clean!(transformer)
-    isempty(message) || throw(ArgumentError(message))
-    return transformer
-end
-
-function MMI.clean!(transformer::FeatureSelector)
-    err = ""
-    if (
-        typeof(transformer.features) <: AbstractVector{Symbol} &&
-        isempty(transformer.features) &&
-        transformer.ignore
-    )
-        err *= "Features to be ignored must be specified in features field."
-    end
-    return err
-end
-
-function MMI.fit(transformer::FeatureSelector, verbosity::Int, X)
-    all_features = Tables.schema(X).names
-
-    if transformer.features isa AbstractVector{Symbol}
-        if isempty(transformer.features)
-           features = collect(all_features)
-        else
-            features = if transformer.ignore
-                !issubset(transformer.features, all_features) && verbosity > -1 &&
-                @warn("Excluding non-existent feature(s).")
-                filter!(all_features |> collect) do ftr
-                   !(ftr in transformer.features)
-                end
-            else
-                issubset(transformer.features, all_features) ||
-                throw(ArgumentError("Attempting to select non-existent feature(s)."))
-                transformer.features |> collect
-            end
-        end
-    else
-        features = if transformer.ignore
-            filter!(all_features |> collect) do ftr
-                !(transformer.features(ftr))
-            end
-        else
-            filter!(all_features |> collect) do ftr
-                transformer.features(ftr)
-            end
-        end
-        isempty(features) && throw(
-            ArgumentError("No feature(s) selected.\n The specified Bool-valued"*
-              " callable with the `ignore` option set to `$(transformer.ignore)` "*
-              "resulted in an empty feature set for selection")
-         )
-    end
-
-    fitresult = features
-    report = NamedTuple()
-    return fitresult, nothing, report
-end
-
-MMI.fitted_params(::FeatureSelector, fitresult) = (features_to_keep=fitresult,)
-
-function MMI.transform(::FeatureSelector, features, X)
-    all(e -> e in Tables.schema(X).names, features) ||
-        throw(ArgumentError("Supplied frame does not admit previously selected features."))
-    return MMI.selectcols(X, features)
-end
-
-
-# # UNIVARIATE DISCRETIZER
+## UNIVARIATE DISCRETIZER
 
 # helper function:
 reftype(::CategoricalArray{<:Any,<:Any,R}) where R = R
@@ -1027,9 +944,14 @@ function MMI.transform(transformer::ContinuousEncoder, fitresult, X)
     features_to_keep, hot_encoder, hot_fitresult = values(fitresult)
 
     # dump unseen or untransformable features:
-    selector = FeatureSelector(features=features_to_keep)
-    selector_fitresult, _, _ = MMI.fit(selector, 0, X)
-    X0 = transform(selector, selector_fitresult, X)
+    if !issubset(features_to_keep, MMI.schema(X).names)
+        throw(
+            ArgumentError(
+                "Supplied frame does not admit previously selected features."
+            )
+        )
+    end
+    X0 = MMI.selectcols(X, features_to_keep)
 
     # one-hot encode:
     X1 = transform(hot_encoder, hot_fitresult, X0)
@@ -1080,11 +1002,18 @@ end
 # # METADATA FOR ALL BUILT-IN TRANSFORMERS
 
 metadata_pkg.(
-    (FeatureSelector, UnivariateStandardizer,
-     UnivariateDiscretizer, Standardizer,
-     UnivariateBoxCoxTransformer, UnivariateFillImputer,
-     OneHotEncoder, FillImputer, ContinuousEncoder,
-     UnivariateTimeTypeToContinuous, InteractionTransformer),
+    ( 
+        UnivariateStandardizer,
+        UnivariateDiscretizer,
+        Standardizer,
+        UnivariateBoxCoxTransformer,
+        UnivariateFillImputer,
+        OneHotEncoder,
+        FillImputer,
+        ContinuousEncoder,
+        UnivariateTimeTypeToContinuous,
+        InteractionTransformer
+    ),
     package_name       = "MLJModels",
     package_uuid       = "d491faf4-2d78-11e9-2867-c94bc002c0b7",
     package_url        = "https://github.com/JuliaAI/MLJModels.jl",
@@ -1106,11 +1035,6 @@ metadata_model(FillImputer,
     output_scitype = Table,
     load_path    = "MLJModels.FillImputer")
 
-metadata_model(FeatureSelector,
-    input_scitype   = Table,
-    output_scitype = Table,
-    load_path    = "MLJModels.FeatureSelector")
-
 metadata_model(UnivariateDiscretizer,
     input_scitype   = AbstractVector{<:Continuous},
     output_scitype = AbstractVector{<:OrderedFactor},
@@ -1371,86 +1295,6 @@ See also [`UnivariateFillImputer`](@ref).
 """
 FillImputer
 
-"""
-$(MLJModelInterface.doc_header(FeatureSelector))
-
-Use this model to select features (columns) of a table, usually as
-part of a model `Pipeline`.
-
-
-# Training data
-
-In MLJ or MLJBase, bind an instance `model` to data with
-
-    mach = machine(model, X)
-
-where
-
-- `X`: any table of input features, where "table" is in the sense of Tables.jl
-
-Train the machine using `fit!(mach, rows=...)`.
-
-
-# Hyper-parameters
-
-- `features`: one of the following, with the behavior indicated:
-
-  - `[]` (empty, the default): filter out all features (columns) which
-    were not encountered in training
-
-  - non-empty vector of feature names (symbols): keep only the
-    specified features (`ignore=false`) or keep only unspecified
-    features (`ignore=true`)
-
-  - function or other callable: keep a feature if the callable returns
-    `true` on its name. For example, specifying
-    `FeatureSelector(features = name -> name in [:x1, :x3], ignore =
-    true)` has the same effect as `FeatureSelector(features = [:x1,
-    :x3], ignore = true)`, namely to select all features, with the
-    exception of `:x1` and `:x3`.
-
-- `ignore`: whether to ignore or keep specified `features`, as
-  explained above
-
-
-# Operations
-
-- `transform(mach, Xnew)`: select features from the table `Xnew` as
-  specified by the model, taking features seen during training into
-  account, if relevant
-
-
-# Fitted parameters
-
-The fields of `fitted_params(mach)` are:
-
-- `features_to_keep`: the features that will be selected
-
-
-# Example
-
-```
-using MLJ
-
-X = (ordinal1 = [1, 2, 3],
-     ordinal2 = coerce(["x", "y", "x"], OrderedFactor),
-     ordinal3 = [10.0, 20.0, 30.0],
-     ordinal4 = [-20.0, -30.0, -40.0],
-     nominal = coerce(["Your father", "he", "is"], Multiclass));
-
-selector = FeatureSelector(features=[:ordinal3, ], ignore=true);
-
-julia> transform(fit!(machine(selector, X)), X)
-(ordinal1 = [1, 2, 3],
- ordinal2 = CategoricalValue{Symbol,UInt32}["x", "y", "x"],
- ordinal4 = [-20.0, -30.0, -40.0],
- nominal = CategoricalValue{String,UInt32}["Your father", "he", "is"],)
-
-```
-"""
-FeatureSelector
-
-
 """
 $(MLJModelInterface.doc_header(Standardizer))
 

src/metadata.jl

@@ -26,7 +26,14 @@ function decode_dic(s::String)
         if  s[1] == ':'
             return Symbol(s[2:end])
         elseif s[1] == '`' && s[2] != '`' # to exclude strings starting with ```
-            return eval(Meta.parse(s[2:end-1]))
+            ex = Meta.parse(s[2:end-1])
+            # We need a `try` here because `constructor` trait generally returns a
+            # function not in the namespace, because pkg defining it has not been loaded.
+            return try
+                eval(ex)
+            catch
+                ex
+            end
         else
             return s
         end
@@ -150,4 +157,3 @@ function model_traits_in_registry(info_given_handle)
     first_entry = info_given_handle[Handle("ConstantRegressor")]
     return keys(first_entry) |> collect
 end
-