Merge pull request #11 from JuliaAI/dev

For a 0.1.1 release
JuliaAI · Jun 4, 2024 · 5d605c6 · 5d605c6
2 parents cfd3576 + ec0eb11
commit 5d605c6
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diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "FeatureSelection"
 uuid = "33837fe5-dbff-4c9e-8c2f-c5612fe2b8b6"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>", "Samuel Okon <okonsamuel50@gmail.com"]
-version = "0.1.0"
+version = "0.1.1"
 
 [deps]
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
@@ -12,11 +12,11 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 Aqua = "0.8"
 Distributions = "0.25"
 julia = "1.6"
-MLJBase = "1.1"
+MLJBase = "1.4"
 MLJTuning = "0.8"
 MLJDecisionTreeInterface = "0.4"
 MLJScikitLearnInterface = "0.6"
-MLJModelInterface = "1.4"
+MLJModelInterface = "1.10"
 ScientificTypesBase = "3"
 StableRNGs = "1"
 StatisticalMeasures = "0.1"

diff --git a/src/FeatureSelection.jl b/src/FeatureSelection.jl
@@ -10,18 +10,4 @@ const MMI = MLJModelInterface
 include("models/featureselector.jl")
 include("models/rfe.jl")
 
-## Pkg Traits
-MMI.metadata_pkg.(
-    (
-        DeterministicRecursiveFeatureElimination,
-        ProbabilisticRecursiveFeatureElimination, 
-        FeatureSelector
-    ),
-    package_name       = "FeatureSelection",
-    package_uuid       = "33837fe5-dbff-4c9e-8c2f-c5612fe2b8b6",
-    package_url        = "https://github.com/JuliaAI/FeatureSelection.jl",
-    is_pure_julia      = true,
-    package_license    = "MIT"
-)
-
 end # module
diff --git a/src/models/featureselector.jl b/src/models/featureselector.jl
@@ -84,9 +84,20 @@ MMI.metadata_model(
     FeatureSelector,
     input_scitype = Table,
     output_scitype = Table,
-    load_path = "FeatureSelction.FeatureSelector"
+    load_path = "FeatureSelection.FeatureSelector"
 )
 
+## Pkg Traits
+MMI.metadata_pkg(
+    FeatureSelector,
+    package_name = "FeatureSelection",
+    package_uuid = "33837fe5-dbff-4c9e-8c2f-c5612fe2b8b6",
+    package_url = "https://github.com/JuliaAI/FeatureSelection.jl",
+    is_pure_julia = true,
+    package_license = "MIT"
+)
+
+## Docstring
 """
 $(MMI.doc_header(FeatureSelector))
 
@@ -164,4 +175,4 @@ julia> transform(fit!(machine(selector, X)), X)
 
 ```
 """
-FeatureSelector
+FeatureSelector
diff --git a/src/models/rfe.jl b/src/models/rfe.jl
@@ -1,7 +1,7 @@
 function warn_double_spec(arg, model)
     return "Using `model=$arg`. Ignoring keyword specification `model=$model`. "
 end
- 
+
 const ERR_SPECIFY_MODEL = ArgumentError(
     "You need to specify model as positional argument or specify `model=...`."
 )
@@ -36,66 +36,67 @@ for (ModelType, ModelSuperType) in  MODELTYPE_GIVEN_SUPERTYPES
     eval(ex)
 end
 
-eval(:(const RFE{M} = Union{$((Expr(:curly, modeltype, :M) for modeltype in MODEL_TYPES)...)}))
+eval(:(const RFE{M} =
+    Union{$((Expr(:curly, modeltype, :M) for modeltype in MODEL_TYPES)...)}))
 
 # Common keyword constructor for both model types
 """
     RecursiveFeatureElimination(model, n_features, step)
 
-This model implements a recursive feature elimination algorithm for feature selection. 
-It recursively removes features, training a base model on the remaining features and 
+This model implements a recursive feature elimination algorithm for feature selection.
+It recursively removes features, training a base model on the remaining features and
 evaluating their importance until the desired number of features is selected.
 
-Construct an instance with default hyper-parameters using the syntax 
-`model = RecursiveFeatureElimination(model=...)`. Provide keyword arguments to override 
-hyper-parameter defaults.  
-        
+Construct an instance with default hyper-parameters using the syntax
+`rfe_model = RecursiveFeatureElimination(model=...)`. Provide keyword arguments to override
+hyper-parameter defaults.
+
 # Training data
-In MLJ or MLJBase, bind an instance `model` to data with
+In MLJ or MLJBase, bind an instance `rfe_model` to data with
 
-    mach = machine(model, X, y)
+    mach = machine(rfe_model, X, y)
 
 OR, if the base model supports weights, as
 
-    mach = machine(model, X, y, w)
+    mach = machine(rfe_model, X, y, w)
 
 Here:
 
 - `X` is any table of input features (eg, a `DataFrame`) whose columns are of the scitype
-  as that required by the base model; check column scitypes with `schema(X)` and column 
+  as that required by the base model; check column scitypes with `schema(X)` and column
   scitypes required by base model with `input_scitype(basemodel)`.
 
-- `y` is the target, which can be any table of responses whose element scitype is 
-    `Continuous` or `Finite` depending on the `target_scitype` required by the base model; 
+- `y` is the target, which can be any table of responses whose element scitype is
+    `Continuous` or `Finite` depending on the `target_scitype` required by the base model;
     check the scitype with `scitype(y)`.
 
-- `w` is the observation weights which can either be `nothing`(default) or an 
-  `AbstractVector` whoose element scitype is `Count` or `Continuous`. This is different 
+- `w` is the observation weights which can either be `nothing`(default) or an
+  `AbstractVector` whoose element scitype is `Count` or `Continuous`. This is different
   from `weights` kernel which is an hyperparameter to the model, see below.
 
 Train the machine using `fit!(mach, rows=...)`.
 
 # Hyper-parameters
-- model: A base model with a `fit` method that provides information on feature 
+- model: A base model with a `fit` method that provides information on feature
   feature importance (i.e `reports_feature_importances(model) == true`)
 
-- n_features::Real = 0: The number of features to select. If `0`, half of the 
-  features are selected. If a positive integer, the parameter is the absolute number 
-  of features to select. If a real number between 0 and 1, it is the fraction of features 
+- n_features::Real = 0: The number of features to select. If `0`, half of the
+  features are selected. If a positive integer, the parameter is the absolute number
+  of features to select. If a real number between 0 and 1, it is the fraction of features
   to select.
 
-- step::Real=1: If the value of step is at least 1, it signifies the quantity of features to 
-  eliminate in each iteration. Conversely, if step falls strictly within the range of 
+- step::Real=1: If the value of step is at least 1, it signifies the quantity of features to
+  eliminate in each iteration. Conversely, if step falls strictly within the range of
   0.0 to 1.0, it denotes the proportion (rounded down) of features to remove during each iteration.
 
 # Operations
 
-- `transform(mach, X)`: transform the input table `X` into a new table containing only 
+- `transform(mach, X)`: transform the input table `X` into a new table containing only
 columns corresponding to features gotten from the RFE algorithm.
 
-- `predict(mach, X)`: transform the input table `X` into a new table same as in 
+- `predict(mach, X)`: transform the input table `X` into a new table same as in
 
-- `transform(mach, X)` above and predict using the fitted base model on the 
+- `transform(mach, X)` above and predict using the fitted base model on the
   transformed table.
 
 # Fitted parameters
@@ -106,11 +107,11 @@ The fields of `fitted_params(mach)` are:
 
 # Report
 The fields of `report(mach)` are:
-- `ranking`: The feature ranking of each features in the training dataset. 
+- `ranking`: The feature ranking of each features in the training dataset.
 
 - `model_report`: report for the fitted base model.
 
-- `features`: names of features seen during the training process. 
+- `features`: names of features seen during the training process.
 
 # Examples
 ```
@@ -131,10 +132,10 @@ selector = RecursiveFeatureElimination(model = rf)
 mach = machine(selector, X, y)
 fit!(mach)
 
-# view the feature importances 
+# view the feature importances
 feature_importances(mach)
 
-# predict using the base model 
+# predict using the base model
 Xnew = MLJ.table(rand(rng, 50, 10));
 predict(mach, Xnew)
 
@@ -160,7 +161,7 @@ function RecursiveFeatureElimination(
     #TODO: Check that the specifed model implements the predict method.
     # probably add a trait to check this
     MMI.reports_feature_importances(model) || throw(ERR_FEATURE_IMPORTANCE_SUPPORT)
-    if model isa Deterministic    
+    if model isa Deterministic
         selector = DeterministicRecursiveFeatureElimination{typeof(model)}(
             model, Float64(n_features), Float64(step)
         )
@@ -170,7 +171,7 @@ function RecursiveFeatureElimination(
         )
     else
         throw(ERR_MODEL_TYPE)
-    end 
+    end
     message = MMI.clean!(selector)
     isempty(message) || @warn(message)
     return selector
@@ -204,21 +205,21 @@ function MMI.fit(selector::RFE, verbosity::Int, X, y, args...)
     n_features_select = selector.n_features
     ## zero indicates that half of the features be selected.
     if n_features_select == 0
-        n_features_select = div(nfeatures, 2) 
+        n_features_select = div(nfeatures, 2)
     elseif 0 < n_features_select < 1
         n_features_select = round(Int, n_features_select * nfeatures)
     else
         n_features_select = round(Int, n_features_select)
     end
 
     step = selector.step
-    
+
     if 0 < step < 1
         step = round(Int, max(1, step * n_features_select))
     else
-        step = round(Int, step) 
+        step = round(Int, step)
     end
-    
+
     support = trues(nfeatures)
     ranking = ones(Int, nfeatures) # every feature has equal rank initially
     mask = trues(nfeatures) # for boolean indexing of ranking vector in while loop below
@@ -230,7 +231,7 @@ function MMI.fit(selector::RFE, verbosity::Int, X, y, args...)
         # Rank the remaining features
         model = selector.model
         verbosity > 0 && @info("Fitting estimator with $(n_features_left) features.")
-    
+
         data = MMI.reformat(model, MMI.selectcols(X, features_left), args...)
 
         fitresult, _, report = MMI.fit(model, verbosity - 1, data...)
@@ -263,14 +264,14 @@ function MMI.fit(selector::RFE, verbosity::Int, X, y, args...)
     data = MMI.reformat(selector.model, MMI.selectcols(X, features_left), args...)
     verbosity > 0 && @info ("Fitting estimator with $(n_features_left) features.")
     model_fitresult, _, model_report = MMI.fit(selector.model, verbosity - 1, data...)
-    
+
     fitresult = (
         support = support,
         model_fitresult = model_fitresult,
         features_left = features_left,
         features = features
     )
-    report = ( 
+    report = (
         ranking = ranking,
         model_report = model_report
     )
@@ -294,7 +295,7 @@ end
 
 function MMI.transform(::RFE, fitresult, X)
     sch = Tables.schema(Tables.columns(X))
-    if (length(fitresult.features) == length(sch.names) && 
+    if (length(fitresult.features) == length(sch.names) &&
         !all(e -> e in sch.names, fitresult.features))
             throw(
                 ERR_FEATURES_SEEN
@@ -312,7 +313,7 @@ function MMI.save(model::RFE, fitresult)
     atomic_fitresult = fitresult.model_fitresult
     features_left = fitresult.features_left
     features = fitresult.features
-    
+
     atom = model.model
     return (
         support = copy(support),
@@ -337,14 +338,12 @@ function MMI.restore(model::RFE, serializable_fitresult)
     )
 end
 
-## Traits definitions
-function MMI.load_path(::Type{<:DeterministicRecursiveFeatureElimination})
-    return "FeatureSelection.DeterministicRecursiveFeatureElimination"
-end
+## Trait definitions
 
-function MMI.load_path(::Type{<:ProbabilisticRecursiveFeatureElimination})
-    return "FeatureSelection.ProbabilisticRecursiveFeatureElimination"
-end
+# load path points to constructor not type:
+MMI.load_path(::Type{<:RFE}) = "FeatureSelection.RecursiveFeatureElimination"
+MMI.constructor(::Type{<:RFE}) = RecursiveFeatureElimination
+MMI.package_name(::Type{<:RFE}) = "FeatureSelection"
 
 for trait in [
     :supports_weights,
@@ -387,4 +386,17 @@ end
 ## TRAINING LOSSES SUPPORT
 function MMI.training_losses(model::RFE, rfe_report)
     return MMI.training_losses(model.model, rfe_report.model_report)
-end
+end
+
+## Pkg Traits
+MMI.metadata_pkg.(
+    (
+        DeterministicRecursiveFeatureElimination,
+        ProbabilisticRecursiveFeatureElimination,
+    ),
+    package_name       = "FeatureSelection",
+    package_uuid       = "33837fe5-dbff-4c9e-8c2f-c5612fe2b8b6",
+    package_url        = "https://github.com/JuliaAI/FeatureSelection.jl",
+    is_pure_julia      = true,
+    package_license    = "MIT"
+)
diff --git a/test/models/rfe.jl b/test/models/rfe.jl
@@ -17,12 +17,15 @@ const DTM = DummyTestModels
     @test_throws FeatureSelection.ERR_SPECIFY_MODEL RecursiveFeatureElimination()
     reg = DTM.DeterministicConstantRegressor()
     @test_throws(
-        FeatureSelection.ERR_FEATURE_IMPORTANCE_SUPPORT, 
+        FeatureSelection.ERR_FEATURE_IMPORTANCE_SUPPORT,
         RecursiveFeatureElimination(model = DTM.DeterministicConstantRegressor())
     )
     rf = MLJDecisionTreeInterface.RandomForestRegressor(rng = rng)
     selector = RecursiveFeatureElimination(model = rf)
     @test selector isa FeatureSelection.DeterministicRecursiveFeatureElimination
+    @test MLJBase.constructor(selector) == RecursiveFeatureElimination
+    @test MLJBase.package_name(selector) == "FeatureSelection"
+    @test MLJBase.load_path(selector) == "FeatureSelection.RecursiveFeatureElimination"
 
     # Fit
     selector_mach = machine(selector, Xt, y)
@@ -34,7 +37,7 @@ const DTM = DummyTestModels
         selector_mach.model.model, selector_mach.fitresult.model_fitresult
     )
     @test feature_importances(selector_mach) == [
-        :x1 => 6.0, :x2 => 5.0, :x3 => 4.0, :x4 => 3.0, :x5 => 2.0, 
+        :x1 => 6.0, :x2 => 5.0, :x3 => 4.0, :x4 => 3.0, :x5 => 2.0,
         :x6 => 1.0, :x7 => 1.0, :x8 => 1.0, :x9 => 1.0, :x10 => 1.0
     ]
     rpt = report(selector_mach)
@@ -94,7 +97,7 @@ end
         measure = rms,
         tuning = Grid(rng=rng),
         resampling = StratifiedCV(nfolds = 5),
-        range = range(rfecv, :n_features, values = 1:10)                                                                                                           
+        range = range(rfecv, :n_features, values = 1:10)
     )
     self_tuning_rfe_mach = machine(tuning_rfe_model, Xs, ys)
     fit!(self_tuning_rfe_mach)
@@ -127,4 +130,4 @@ end
     mach2 = MLJBase.machine(io)
     close(io)
     @test MLJBase.predict(mach2, (; x1=rand(2), x2 = rand(2))) ==  yhat
-end
+end