update TMLE

Project.toml

@@ -34,7 +34,6 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
 
 [compat]
-PackageCompiler = "2.1.16"
 ArgParse = "1.1.4"
 Arrow = "2.5.2"
 CSV = "0.10"
@@ -55,7 +54,8 @@ MLJModels = "0.16"
 MLJXGBoostInterface = "0.3.4"
 MultipleTesting = "0.6.0"
 Optim = "1.7"
-TMLE = "0.15.0"
+PackageCompiler = "2.1.16"
+TMLE = "0.16"
 Tables = "1.10.1"
 YAML = "0.4.9"
 julia = "1.7, 1"

src/utils.jl

@@ -152,27 +152,14 @@ function make_float!(dataset, colnames)
  end
 end
 
-function coerce_types!(dataset, Ψ::ComposedEstimand)
- for arg in Ψ.args
- coerce_types!(dataset, arg)
- end
+function coerce_types!(dataset, colnames)
+ infered_types = autotype(dataset[!, colnames])
+ coerce!(dataset, infered_types)
 end
 
-function coerce_types!(dataset, Ψ)
- # Make Treatments categorical but preserve order
- categorical_variables = Set(keys(Ψ.treatment_values))
- make_categorical!(dataset, categorical_variables, infer_ordered=true)
- # Make Confounders and extra covariates continuous
- continuous_variables = Set(Iterators.flatten(values(Ψ.treatment_confounders)))
- union!(continuous_variables, Ψ.outcome_extra_covariates)
- make_float!(dataset, continuous_variables)
- # Make outcome categorical if binary but do not infer order 
- if TMLE.is_binary(dataset, Ψ.outcome)
- make_categorical!(dataset, Ψ.outcome, infer_ordered=false)
- else
- make_float!(dataset, Ψ.outcome)
- end 
-end
+coerce_types!(dataset, Ψ::TMLE.Estimand) =
+ coerce_types!(dataset, collect(variables(Ψ)))
+
 
 variables(Ψ::TMLE.ComposedEstimand) = union((variables(arg) for arg in Ψ.args)...)
 

test/Project.toml

@@ -11,6 +11,7 @@ LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJLinearModels = "6ee0df7b-362f-4a72-a706-9e79364fb692"
+MLJModels = "d491faf4-2d78-11e9-2867-c94bc002c0b7"
 MLJXGBoostInterface = "54119dfa-1dab-4055-a167-80440f4f7a91"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"

test/runner.jl

@@ -9,6 +9,7 @@ using Serialization
 using YAML
 using JSON
 using MLJBase
+using MLJModels
 
 PKGDIR = pkgdir(TargetedEstimation)
 TESTDIR = joinpath(PKGDIR, "test")
@@ -29,7 +30,8 @@ include(joinpath(TESTDIR, "testutils.jl"))
  @test estimators.OSE isa TMLE.OSE
  @test estimators.TMLE.weighted === true
  @test estimators.TMLE.models.G_default === estimators.OSE.models.G_default
- @test estimators.TMLE.models.G_default isa MLJBase.ProbabilisticStack
+ @test estimators.TMLE.models.G_default.continuous_encoder isa MLJModels.ContinuousEncoder
+ @test estimators.TMLE.models.G_default.probabilistic_stack isa MLJBase.ProbabilisticStack
  # From already constructed estimators
  estimators_new = TargetedEstimation.instantiate_estimators(estimators)
  @test estimators_new === estimators

test/testutils.jl

@@ -114,17 +114,17 @@ function build_dataset(;n=1000, format="csv")
 
  dataset = DataFrame(
  SAMPLE_ID = 1:n,
- T1 = categorical(T₁),
- T2 = categorical(T₂),
+ T1 = T₁,
+ T2 = T₂,
  W1 = W₁, 
  W2 = W₂,
  C1 = C₁,
  )
  # Comma in name
- dataset[!, "CONTINUOUS, OUTCOME"] = categorical(y₁)
+ dataset[!, "CONTINUOUS, OUTCOME"] = y₁
  # Slash in name
- dataset[!, "BINARY/OUTCOME"] = categorical(y₂)
- dataset[!, "EXTREME_BINARY"] = categorical(vcat(0, ones(n-1)))
+ dataset[!, "BINARY/OUTCOME"] = y₂
+ dataset[!, "EXTREME_BINARY"] = vcat(0, ones(n-1))
 
  return dataset
 end

test/utils.jl

@@ -7,6 +7,7 @@ using DataFrames
 using CSV
 using MLJLinearModels
 using CategoricalArrays
+using MLJBase
 
 check_type(treatment_value, ::Type{T}) where T = @test treatment_value isa T
 
@@ -74,21 +75,21 @@ end
  )
 
  dataset = DataFrame(
- Ycont = [1.1, 2.2, missing],
- Ycat = [1., 0., missing],
- T₁ = [1, 0, missing],
- T₂ = [missing, "AC", "CC"],
- W₁ = [1., 0., 0.],
- W₂ = [missing, 0., 0.],
- C = [1, 2, 3]
+ Ycont = [1.1, 2.2, missing, 3.5, 6.6, 0., 4.],
+ Ycat = [1., 0., missing, 1., 0, 0, 0],
+ T₁ = [1, 0, missing, 0, 0, 0, missing],
+ T₂ = [missing, "AC", "CC", "CC", missing, "AA", "AA"],
+ W₁ = [1., 0., 0., 1., 0., 1, 1],
+ W₂ = [missing, 0., 0., 0., 0., 0., 0.],
+ C = [1, 2, 3, 4, 5, 6, 6]
  )
  TargetedEstimation.coerce_types!(dataset, Ψ)
 
- @test dataset.T₁ isa CategoricalArray
- @test dataset.T₂ isa CategoricalArray
- for var in [:W₁, :W₂, :Ycont]
-  @test eltype(dataset[!, var]) <: Union{Missing, Float64}
- end
+ @test scitype(dataset.T₁) == AbstractVector{Union{Missing, OrderedFactor{2}}}
+ @test scitype(dataset.T₂) == AbstractVector{Union{Missing, Multiclass{3}}}
+ @test scitype(dataset.Ycont) == AbstractVector{Union{Missing, MLJBase.Continuous}}
+ @test scitype(dataset.W₁) == AbstractVector{OrderedFactor{2}}
+ @test scitype(dataset.W₂) == AbstractVector{Union{Missing, OrderedFactor{1}}}
 
  Ψ = IATE(
  outcome=:Ycat,
@@ -98,8 +99,8 @@ end
  )
  TargetedEstimation.coerce_types!(dataset, Ψ)
 
- @test dataset.Ycat isa CategoricalArray
- @test eltype(dataset.C) <: Union{Missing, Float64}
+ @test scitype(dataset.Ycat) == AbstractVector{Union{Missing, OrderedFactor{2}}}
+ @test scitype(dataset.C) == AbstractVector{Count}
 end
 
 @testset "Test misc" begin