Merge pull request #28 from JuliaAI/dev

For a 0.1.5 release

Project.toml

@@ -1,7 +1,7 @@
 name = "MLJBalancing"
 uuid = "45f359ea-796d-4f51-95a5-deb1a414c586"
 authors = ["Essam Wisam <[email protected]>", "Anthony Blaom <[email protected]> and contributors"]
-version = "0.1.4"
+version = "0.1.5"
 
 [deps]
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
@@ -12,9 +12,9 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
-MLJBase = "1"
+MLJBase = "1.4"
 OrderedCollections = "1.6"
-MLJModelInterface = "1.9"
+MLJModelInterface = "1.10"
 MLUtils = "0.4"
 StatsBase = "0.34"
 julia = "1.6"

README.md

@@ -9,7 +9,7 @@ Pkg.add("MLJBalancing")
 
 ## 🚅 Sequential Resampling
 
-This package allows chaining of resampling methods from Imbalance.jl with classification models from MLJ. Simply construct a `BalancedModel` object while specifying the model (classifier) and an arbitrary number of resamplers (also called *balancers* - typically oversamplers and/or under samplers).
+This package allows chaining of resampling methods from `Imbalance.jl` with classification models from `MLJ`. Simply construct a `BalancedModel` object while specifying the model (classifier) and an arbitrary number of resamplers (also called *balancers* - typically oversamplers and/or undersamplers).
 
 ### 📖 Example
 
@@ -29,12 +29,12 @@ logistic_model = LogisticClassifier()
 ```julia
 balanced_model = BalancedModel(model=logistic_model, balancer1=oversampler, balancer2=undersampler)
 ```
-Here training data will be passed to `balancer1` then `balancer2`, whose output is used to train the classifier `model`.  In prediction, the resamplers `balancer1` and `blancer2` are bypassed. 
+Here training data will be passed to `balancer1` then `balancer2`, whose output is used to train the classifier `model`.  When `balanced_model` is used for prediction, the resamplers `balancer1` and `blancer2` are bypassed. 
 
-In general, there can be any number of balancers, and the user can give the balancers arbitrary names. 
+In general, any number of balancers can be passed to the function, and the user can give the balancers arbitrary names while passing them. 
 
-#### At this point, they behave like one single model
-You can fit, predict, cross-validate and fine-tune it like any other MLJ model. Here is an example for fine-tuning
+#### At this point, `balanced_model` behaves like one single model
+You can fit, predict, cross-validate and hyperparamter tune it like any other MLJ model. Here is an example for hyperparameter tuning:
 ```julia
 r1 = range(balanced_model, :(balancer1.k), lower=3, upper=10)
 r2 = range(balanced_model, :(balancer2.min_ratios), lower=0.1, upper=0.9)
@@ -67,7 +67,7 @@ bagging_model = BalancedBaggingClassifier(model=logistic_model, T=10, rng=Random
 ```
 
 #### Now it behaves like one single model
-You can fit, predict, cross-validate and fine-tune it like any other probabilistic MLJ model where `X` must be a table input (e.g., a dataframe).
+You can fit, predict, cross-validate and hyperparameter-tune it like any other probabilistic MLJ model where `X` must be a table input (e.g., a dataframe).
 ```julia
 mach = machine(bagging_model, X, y)
 fit!(mach)

src/balanced_bagging.jl

@@ -150,8 +150,9 @@ MMI.metadata_pkg(
 MMI.metadata_model(
     BalancedBaggingClassifier,
     target_scitype = AbstractVector{<:Finite},
-    load_path = "MLJBalancing." * string(BalancedBaggingClassifier),
+    load_path = "MLJBalancing.BalancedBaggingClassifier",
 )
+MMI.constructor(::Type{<:BalancedBaggingClassifier}) = BalancedBaggingClassifier
 
 MMI.iteration_parameter(::Type{<:BalancedBaggingClassifier{<:Any,<:Any,P}}) where P =
     MLJBase.prepend(:model, iteration_parameter(P))

src/balanced_model.jl

@@ -7,8 +7,8 @@ struct BalancedModel <:ProbabilisticNetworkComposite
     model::Probabilistic         # get rid of abstract types
 end
 
-BalancedModel(;model=nothing, balancer=nothing) = BalancedModel(model, balancer) 
-BalancedModel(model; kwargs...) = BalancedModel(; model, kwargs...)    
+BalancedModel(;model=nothing, balancer=nothing) = BalancedModel(model, balancer)
+BalancedModel(model; kwargs...) = BalancedModel(; model, kwargs...)
 
 In the following, we use macros to automate code generation of these for all model
 types
@@ -66,15 +66,15 @@ const ERR_UNSUPPORTED_MODEL(model) = ErrorException(
     "$PRETTY_SUPPORTED_MODEL_TYPES.\n"*
     "Model provided has type `$(typeof(model))`. "
 )
-const ERR_NUM_ARGS_BM = "`BalancedModel` can at most have one non-keyword argument where the model is passed."                
+const ERR_NUM_ARGS_BM = "`BalancedModel` can at most have one non-keyword argument where the model is passed."
 
 
 """
     BalancedModel(; model=nothing, balancer1=balancer_model1, balancer2=balancer_model2, ...)
     BalancedModel(model;  balancer1=balancer_model1, balancer2=balancer_model2, ...)
 
 Given a classification model, and one or more balancer models that all implement the `MLJModelInterface`,
-    `BalancedModel` allows constructing a sequential pipeline that wraps an arbitrary number of balancing models 
+    `BalancedModel` allows constructing a sequential pipeline that wraps an arbitrary number of balancing models
     and a classifier together in a sequential pipeline.
 
 # Operation
@@ -83,7 +83,7 @@ Given a classification model, and one or more balancer models that all implement
 - During prediction, the balancers have no effect.
 
 # Arguments
-- `model::Supervised`: A classification model that implements the `MLJModelInterface`. 
+- `model::Supervised`: A classification model that implements the `MLJModelInterface`.
 - `balancer1::Static=...`: The first balancer model to pass the data to. This keyword argument can have any name.
 - `balancer2::Static=...`: The second balancer model to pass the data to. This keyword argument can have any name.
 - and so on for an arbitrary number of balancers.
@@ -216,14 +216,20 @@ MMI.package_name(::Type{<:UNION_COMPOSITE_TYPES}) = "MLJBalancing"
 MMI.package_license(::Type{<:UNION_COMPOSITE_TYPES}) = "MIT"
 MMI.package_uuid(::Type{<:UNION_COMPOSITE_TYPES}) = "45f359ea-796d-4f51-95a5-deb1a414c586"
 MMI.is_wrapper(::Type{<:UNION_COMPOSITE_TYPES}) = true
-MMI.package_url(::Type{<:UNION_COMPOSITE_TYPES}) ="https://github.com/JuliaAI/MLJBalancing.jl"
+MMI.package_url(::Type{<:UNION_COMPOSITE_TYPES}) =
+    "https://github.com/JuliaAI/MLJBalancing.jl"
+
+# load path should point to constructor:
+MMI.load_path(::Type{<:UNION_COMPOSITE_TYPES}) = "MLJBalancing.BalancedModel"
+MMI.constructor(::Type{<:UNION_COMPOSITE_TYPES}) = BalancedModel
 
 # All the composite types BalancedModelProbabilistic, BalancedModelDeterministic, etc.
 const COMPOSITE_TYPES = values(MODELTYPE_TO_COMPOSITETYPE)
 for composite_type in COMPOSITE_TYPES
     quote
-        MMI.iteration_parameter(::Type{<:$composite_type{balancernames, M}}) where {balancernames, M} =
-            MLJBase.prepend(:model, iteration_parameter(M))
+        MMI.iteration_parameter(
+            ::Type{<:$composite_type{balancernames, M}},
+        ) where {balancernames, M} = MLJBase.prepend(:model, iteration_parameter(M))
     end |> eval
     for trait in [
         :input_scitype,
@@ -241,7 +247,9 @@ for composite_type in COMPOSITE_TYPES
         :is_supervised,
         :prediction_type]
         quote
-            MMI.$trait(::Type{<:$composite_type{balancernames, M}}) where {balancernames, M} = MMI.$trait(M)
+            MMI.$trait(
+                ::Type{<:$composite_type{balancernames, M}},
+            ) where {balancernames, M} = MMI.$trait(M)
         end |> eval
     end
 end

test/balanced_bagging.jl

@@ -1,4 +1,3 @@
-
 @testset "group_inds and get_majority_minority_inds_counts" begin
     y = [0, 0, 0, 0, 1, 1, 1, 0]
     @test MLJBalancing.group_inds(y) == Dict(0 => [1, 2, 3, 4, 8], 1 => [5, 6,  7])
@@ -111,7 +110,11 @@ end
     pred_manual = mean([pred1, pred2])
 
     ## using BalancedBagging
-    modelo = BalancedBaggingClassifier(model = model, T = 2, rng = Random.MersenneTwister(42))
+    modelo = BalancedBaggingClassifier(
+        model = model,
+        T = 2,
+        rng = Random.MersenneTwister(42),
+    )
     mach = machine(modelo, X, y)
     fit!(mach)
     pred_auto = MLJBase.predict(mach, Xt)
@@ -123,7 +126,10 @@ end
 
     ## traits
     @test fit_data_scitype(modelo) == fit_data_scitype(model)
-    @test is_wrapper(modelo) 
+    @test is_wrapper(modelo)
+    @test constructor(modelo) == BalancedBaggingClassifier
+    @test package_name(modelo) == "MLJBalancing"
+    @test load_path(modelo) == "MLJBalancing.BalancedBaggingClassifier"
 end
 
 

test/balanced_model.jl

@@ -1,93 +1,95 @@
 @testset "BalancedModel" begin
-	### end-to-end test
-	# Create and split data
-	X, y = generate_imbalanced_data(100, 5; class_probs = [0.2, 0.3, 0.5], rng=Random.MersenneTwister(42))
-	X = DataFrame(X)
-	train_inds, test_inds =
-		partition(eachindex(y), 0.8, shuffle = true, stratify = y, rng = Random.MersenneTwister(42))
-	X_train, X_test = X[train_inds, :], X[test_inds, :]
-	y_train, y_test = y[train_inds], y[test_inds]
-
-	# Load models and balancers
-	DeterministicConstantClassifier = @load DeterministicConstantClassifier pkg=MLJModels
-	LogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels
-
-	# Here are a probabilistic and a deterministic model
-	model_prob = LogisticClassifier()
-	model_det = DeterministicConstantClassifier()
-	# And here are three resamplers from Imbalance. 
-	# The package should actually work with any `Static` transformer of the form  `(X, y) -> (Xout, yout)`
-	# provided that it implements the MLJ interface. Here, the balancer is the transformer
-	balancer1 = Imbalance.MLJ.RandomOversampler(ratios = 1.0, rng = Random.MersenneTwister(42))
-	balancer2 = Imbalance.MLJ.SMOTENC(k = 10, ratios = 1.2, rng = Random.MersenneTwister(42))
-	balancer3 = Imbalance.MLJ.ROSE(ratios = 1.3, rng = Random.MersenneTwister(42))
-
-	### 1. Make a pipeline of the three balancers and a probablistic model
-	## ordinary way
-	mach = machine(balancer1)
-	Xover, yover = MLJBase.transform(mach, X_train, y_train)
-	mach = machine(balancer2)
-	Xover, yover = MLJBase.transform(mach, Xover, yover)
-	mach = machine(balancer3)
-	Xover, yover = MLJBase.transform(mach, Xover, yover)
-
-	mach = machine(model_prob, Xover, yover)
-	fit!(mach)
-	y_pred = MLJBase.predict(mach, X_test)
-
-	# with MLJ balancing 
-	@test_throws  MLJBalancing.ERR_MODEL_UNSPECIFIED begin
-		BalancedModel(b1 = balancer1, b2 = balancer2, b3 = balancer3)
-	end
-    @test_throws(
-        MLJBalancing.ERR_UNSUPPORTED_MODEL(1),
-        BalancedModel(model = 1, b1 = balancer1, b2 = balancer2, b3 = balancer3),
-    )
-	@test_logs (:warn, MLJBalancing.WRN_BALANCER_UNSPECIFIED) begin
-		BalancedModel(model = model_prob)
-	end
-	balanced_model =
-		BalancedModel(model = model_prob, b1 = balancer1, b2 = balancer2, b3 = balancer3)
-	mach = machine(balanced_model, X_train, y_train)
-	fit!(mach)
-	y_pred2 = MLJBase.predict(mach, X_test)
+        ### end-to-end test
+        # Create and split data
+        X, y = generate_imbalanced_data(100, 5; class_probs = [0.2, 0.3, 0.5], rng=Random.MersenneTwister(42))
+        X = DataFrame(X)
+        train_inds, test_inds =
+                partition(eachindex(y), 0.8, shuffle = true, stratify = y, rng = Random.MersenneTwister(42))
+        X_train, X_test = X[train_inds, :], X[test_inds, :]
+        y_train, y_test = y[train_inds], y[test_inds]
+
+        # Load models and balancers
+        DeterministicConstantClassifier = @load DeterministicConstantClassifier pkg=MLJModels
+        LogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels
+
+        # Here are a probabilistic and a deterministic model
+        model_prob = LogisticClassifier()
+        model_det = DeterministicConstantClassifier()
+        # And here are three resamplers from Imbalance.
+        # The package should actually work with any `Static` transformer of the form  `(X, y) -> (Xout, yout)`
+        # provided that it implements the MLJ interface. Here, the balancer is the transformer
+        balancer1 = Imbalance.MLJ.RandomOversampler(ratios = 1.0, rng = Random.MersenneTwister(42))
+        balancer2 = Imbalance.MLJ.SMOTENC(k = 10, ratios = 1.2, rng = Random.MersenneTwister(42))
+        balancer3 = Imbalance.MLJ.ROSE(ratios = 1.3, rng = Random.MersenneTwister(42))
+
+        ### 1. Make a pipeline of the three balancers and a probablistic model
+        ## ordinary way
+        mach = machine(balancer1)
+        Xover, yover = MLJBase.transform(mach, X_train, y_train)
+        mach = machine(balancer2)
+        Xover, yover = MLJBase.transform(mach, Xover, yover)
+        mach = machine(balancer3)
+        Xover, yover = MLJBase.transform(mach, Xover, yover)
+
+        mach = machine(model_prob, Xover, yover)
+        fit!(mach)
+        y_pred = MLJBase.predict(mach, X_test)
+
+        # with MLJ balancing
+        @test_throws  MLJBalancing.ERR_MODEL_UNSPECIFIED begin
+                BalancedModel(b1 = balancer1, b2 = balancer2, b3 = balancer3)
+        end
+        @test_throws(
+            MLJBalancing.ERR_UNSUPPORTED_MODEL(1),
+            BalancedModel(model = 1, b1 = balancer1, b2 = balancer2, b3 = balancer3),
+        )
+        @test_logs (:warn, MLJBalancing.WRN_BALANCER_UNSPECIFIED) begin
+                BalancedModel(model = model_prob)
+        end
+        balanced_model =
+            BalancedModel(model = model_prob, b1 = balancer1, b2 = balancer2, b3 = balancer3)
+        @test constructor(balanced_model) == BalancedModel
+
+        mach = machine(balanced_model, X_train, y_train)
+        fit!(mach)
+        y_pred2 = MLJBase.predict(mach, X_test)
 
         @test y_pred ≈ y_pred2
 
         # traits:
         @test fit_data_scitype(balanced_model) ==  fit_data_scitype(model_prob)
         @test is_wrapper(balanced_model)
-    
-
-	### 2. Make a pipeline of the three balancers and a deterministic model
-	## ordinary way
-	mach = machine(balancer1)
-	Xover, yover = MLJBase.transform(mach, X_train, y_train)
-	mach = machine(balancer2)
-	Xover, yover = MLJBase.transform(mach, Xover, yover)
-	mach = machine(balancer3)
-	Xover, yover = MLJBase.transform(mach, Xover, yover)
-
-	mach = machine(model_det, Xover, yover)
-	fit!(mach)
-	y_pred = MLJBase.predict(mach, X_test)
-
-	# with MLJ balancing
-	balanced_model =
-		BalancedModel(model = model_det, b1 = balancer1, b2 = balancer2, b3 = balancer3)
-	mach = machine(balanced_model, X_train, y_train)
-	fit!(mach)
-	y_pred2 = MLJBase.predict(mach, X_test)
-
-	@test y_pred == y_pred2
-
-	### check that setpropertyname and getpropertyname work
-	Base.getproperty(balanced_model, :b1) == balancer1
-	Base.setproperty!(balanced_model, :b1, balancer2)
-	Base.getproperty(balanced_model, :b1) == balancer2
+
+
+        ### 2. Make a pipeline of the three balancers and a deterministic model
+        ## ordinary way
+        mach = machine(balancer1)
+        Xover, yover = MLJBase.transform(mach, X_train, y_train)
+        mach = machine(balancer2)
+        Xover, yover = MLJBase.transform(mach, Xover, yover)
+        mach = machine(balancer3)
+        Xover, yover = MLJBase.transform(mach, Xover, yover)
+
+        mach = machine(model_det, Xover, yover)
+        fit!(mach)
+        y_pred = MLJBase.predict(mach, X_test)
+
+        # with MLJ balancing
+        balanced_model =
+                BalancedModel(model = model_det, b1 = balancer1, b2 = balancer2, b3 = balancer3)
+        mach = machine(balanced_model, X_train, y_train)
+        fit!(mach)
+        y_pred2 = MLJBase.predict(mach, X_test)
+
+        @test y_pred == y_pred2
+
+        ### check that setpropertyname and getpropertyname work
+        Base.getproperty(balanced_model, :b1) == balancer1
+        Base.setproperty!(balanced_model, :b1, balancer2)
+        Base.getproperty(balanced_model, :b1) == balancer2
     @test_throws(
         MLJBalancing.ERR_NO_PROP,
-	Base.setproperty!(balanced_model, :name11, balancer2),
+        Base.setproperty!(balanced_model, :name11, balancer2),
     )
 end
 
@@ -96,7 +98,7 @@ end
     ## setup parameters
     R = Random.MersenneTwister(42)
     LogisticClassifier = @load LogisticClassifier pkg = MLJLinearModels verbosity = 0
-	balancer1 = Imbalance.MLJ.RandomOversampler(ratios = 1.0, rng = Random.MersenneTwister(42))
+        balancer1 = Imbalance.MLJ.RandomOversampler(ratios = 1.0, rng = Random.MersenneTwister(42))
     model = LogisticClassifier()
     BalancedModel(model=model, balancer1=balancer1) == BalancedModel(model; balancer1=balancer1)