ERROR: OutOfMemoryError()Worker 2 terminated.

[drcxcruz]

hi ,

I am doing a random forest tree with data file: https://www.openml.org/d/179

my code worked in many smaller data files. but this the largest data file I tried so far. do you know if ShapML might have a limit in the number of file rows?

@Everywhere function predict_function_mode(model, data)
ŷ = MLJ.predict(model, data)
ŷMode = [convert(Int64, mode(ŷ[i])) for i in 1:length(ŷ)]
data_pred = DataFrame(y_pred = ŷMode)
return data_pred
end # predict_function_mode

    @everywhere pipeRandomForestClassifier = @pipeline RandomForestClassifierPipe(
        selector = FeatureSelector(),
        hot = OneHotEncoder(),
        tree = RandomForestClassifier()) prediction_type = :probabilistic

    cases = [[Symbol(names(X)[j]) for j in 1:i] for i in 1:ncol(X)]   
    r1 = range(pipeRandomForestClassifier, :(selector.features), values = cases)

    tmRandomForestClassifier = TunedModel(
        model = pipeRandomForestClassifier,
        range = r1,
        measures = [cross_entropy, BrierScore()],
        resampling = CV(nfolds = 5)
    )
    mtm = machine(tmRandomForestClassifier, setScientificTypes!(X), categorical(y[:, 1]))
    Base.invokelatest(MLJ.fit!, mtm)

    predictor = predict_function_mode

r = Int(round(nrow(X) / 2))
explain = copy(X[1:r, :])   # Compute Shapley feature-level predictions 
reference = copy(X)         # An optional reference population to compute the baseline prediction.
sample_size = 10            # Number of Monte Carlo samples for Shapley

println("Computing Shapley Effect of Random Forest")
dataShap = ShapML.shap( explain = explain,
                        reference = reference,
                        model = mtm,
                        predict_function = predictor,
                        sample_size = sample_size,
                        parallel = :samples,  # Parallel computation over "sample_size"
                        seed = 20200628
    )

Worker 5 terminated.
ERROR: OutOfMemoryError()Worker 2 terminated.
Stacktrace:

[1] Worker 4 terminated.Array{Float64,2}
(::Worker 3 terminated.UndefInitializer
, ::Int64, ::Int64) at .\boot.jl:407
[2] matrix(::DataFrame; transpose::Bool) at C:\Users\BCP.julia\packages\Tables\okt7x\src\matrix.jl:73
[3] matrix at C:\Users\BCP.julia\packages\Tables\okt7x\src\matrix.jl:68 [inlined]
[4] #matrix#11 at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\interface\data_utils.jl:9 [inlined]
[5] matrix at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\interface\data_utils.jl:9 [inlined]
[6] matrix(::DataFrame; kw::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}}) at C:\Users\BCP.julia\packages\MLJModelInterface\aA1k2\src\data_utils.jl:27
[7] matrix at C:\Users\BCP.julia\packages\MLJModelInterface\aA1k2\src\data_utils.jl:27 [inlined]
[8] predict(::RandomForestClassifier, ::Tuple{DecisionTree.Ensemble{Float64,UInt32},CategoricalArray{Int64,1,UInt32,Int64,CategoricalValue{Int64,UInt32},Union{}},Array{UInt32,1}}, ::DataFrame) at C:\Users\BCP.julia\packages\MLJModels\i4XcU\src\DecisionTree.jl:188
[9] predict(::NodalMachine{RandomForestClassifier}, ::DataFrame) at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\operations.jl:29
[10] (::Node{NodalMachine{RandomForestClassifier}})(::DataFrame) at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\composition\networks.jl:302
[11] predict(::RandomForestClassifierPipe, ::Node{NodalMachine{RandomForestClassifier}}, ::DataFrame) at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\composition\composites.jl:45
[12] predict(::Machine{RandomForestClassifierPipe}, ::DataFrame) at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\operations.jl:29
[13] predict(::MLJTuning.ProbabilisticTunedModel{Grid,RandomForestClassifierPipe,CPU1{Nothing},CPU1{Nothing}}, ::Machine{RandomForestClassifierPipe}, ::DataFrame) at C:\Users\BCP.julia\packages\MLJTuning\JZ7ZX\src\tuned_models.jl:597
[14] predict(::Machine{MLJTuning.ProbabilisticTunedModel{Grid,RandomForestClassifierPipe,CPU1{Nothing},CPU1{Nothing}}}, ::DataFrame) at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\operations.jl:29
[15] predict_function_mode(::Machine{MLJTuning.ProbabilisticTunedModel{Grid,RandomForestClassifierPipe,CPU1{Nothing},CPU1{Nothing}}}, ::DataFrame) at C:\Users\BCP\github\ICP\GetBlankets.jl:180
[16] _shap_sample(::DataFrame, ::DataFrame, ::Int64, ::Int64, ::Int64, ::Int64, ::Array{String,1}, ::Array{String,1}, ::Array{Symbol,1}, ::Int64, ::Symbol, ::Array{Int64,1}, ::Bool, ::Machine{MLJTuning.ProbabilisticTunedModel{Grid,RandomForestClassifierPipe,CPU1{Nothing},CPU1{Nothing}}}, ::typeof(predict_function_mode), ::Nothing) at C:\Users\BCP.julia\packages\ShapML\sceNA\src\shap_sample.jl:97
[17] shap(; explain::DataFrame, reference::DataFrame, model::Machine{MLJTuning.ProbabilisticTunedModel{Grid,RandomForestClassifierPipe,CPU1{Nothing},CPU1{Nothing}}}, predict_function::Function, target_features::Nothing, sample_size::Int64, parallel::Nothing, seed::Int64, precision::Nothing, chunk::Bool) at C:\Users\BCP.julia\packages\ShapML\sceNA\src\ShapML.jl:117
[18] trainRandomForest(::DataFrame, ::DataFrame) at C:\Users\BCP\github\ICP\GetBlankets.jl:248
[19] selectRandomForest(::DataFrame, ::DataFrame, ::Int64) at C:\Users\BCP\github\ICP\GetBlankets.jl:155
[20] getBlanketRandomForest(::DataFrame, ::DataFrame, ::String, ::Int64) at C:\Users\BCP\github\ICP\GetBlankets.jl:135
[21] getBlanketRandomForest at C:\Users\BCP\github\ICP\GetBlankets.jl:129 [inlined]
[22] ForestInvariantCausalPrediction(::DataFrame, ::DataFrame, ::DataFrame; α::Float64, selection::String, verbose::Bool) at C:\Users\BCP\github\ICP\InvariantCausalPrediction.jl:305
[23] top-level scope at REPL[10]:1
caused by [exception 1]
ProcessExitedException(2)
Stacktrace:
[1] (::Base.var"#726#728")(::Task) at .\asyncmap.jl:178
[2] foreach(::Base.var"#726#728", ::Array{Any,1}) at .\abstractarray.jl:1919
[3] maptwice(::Function, ::Channel{Any}, ::Array{Any,1}, ::UnitRange{Int64}) at .\asyncmap.jl:178
[4] wrap_n_exec_twice(::Channel{Any}, ::Array{Any,1}, ::Distributed.var"#204#207"{WorkerPool}, ::Function, ::UnitRange{Int64}) at .\asyncmap.jl:154
[5] async_usemap(::Distributed.var"#188#190"{Distributed.var"#188#189#191"{WorkerPool,ShapML.var"#13#15"{DataFrame,Machine{MLJTuning.ProbabilisticTunedModel{Grid,RandomForestClassifierPipe,CPU1{Nothing},CPU1{Nothing}}},typeof(predict_function_mode),Int64,Nothing,Bool,Array{String,1},Array{Symbol,1},Int64,Int64,Int64,Int64,Array{Int64,1}}}}, ::UnitRange{Int64}; ntasks::Function, batch_size::Nothing) at .\asyncmap.jl:103
[6] #asyncmap#710 at .\asyncmap.jl:81 [inlined]
[7] pmap(::Function, ::WorkerPool, ::UnitRange{Int64}; distributed::Bool, batch_size::Int64, on_error::Nothing, retry_delays::Array{Any,1}, retry_check::Nothing) at D:\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.4\Distributed\src\pmap.jl:126
[8] pmap(::Function, ::WorkerPool, ::UnitRange{Int64}) at D:\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.4\Distributed\src\pmap.jl:101
[9] pmap(::Function, ::UnitRange{Int64}; kwargs::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}}) at D:\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.4\Distributed\src\pmap.jl:156
[10] pmap at D:\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.4\Distributed\src\pmap.jl:156 [inlined]
[11] shap(; explain::DataFrame, reference::DataFrame, model::Machine{MLJTuning.ProbabilisticTunedModel{Grid,RandomForestClassifierPipe,CPU1{Nothing},CPU1{Nothing}}}, predict_function::Function, target_features::Nothing, sample_size::Int64, parallel::Symbol, seed::Int64, precision::Nothing, chunk::Bool) at C:\Users\BCP.julia\packages\ShapML\sceNA\src\ShapML.jl:137
[12] trainRandomForest(::DataFrame, ::DataFrame) at C:\Users\BCP\github\ICP\GetBlankets.jl:238
[13] selectRandomForest(::DataFrame, ::DataFrame, ::Int64) at C:\Users\BCP\github\ICP\GetBlankets.jl:155
[14] getBlanketRandomForest(::DataFrame, ::DataFrame, ::String, ::Int64) at C:\Users\BCP\github\ICP\GetBlankets.jl:135
[15] getBlanketRandomForest at C:\Users\BCP\github\ICP\GetBlankets.jl:129 [inlined]
[16] ForestInvariantCausalPrediction(::DataFrame, ::DataFrame, ::DataFrame; α::Float64, selection::String, verbose::Bool) at C:\Users\BCP\github\ICP\InvariantCausalPrediction.jl:305
[17] top-level scope at REPL[10]:1

[drcxcruz]

hi,

the non parallel version is also running out of memory. Perhaps, your code needs to call GC.gc( ) at certain places. thanks

Computing Shapley Effect of Random Forest
ERROR: OutOfMemoryError()
Stacktrace:
[1] copy(::Array{Float64,1}) at .\array.jl:352
[2] DataFrame(::Array{AbstractArray{T,1} where T,1}, ::DataFrames.Index; copycols::Bool) at C:\Users\BCP.julia\packages\DataFrames\S3ZFo\src\dataframe\dataframe.jl:126
[3] fromcolumns(::NamedTuple{(:age, :sex, :capital_gain, :capital_loss, :hours_per_week, Symbol("workclass__ ?"), Symbol("workclass__ Federal-gov"), Symbol("workclass__ Local-gov"), Symbol("workclass__ Never-worked"), Symbol("workclass__ Private"), Symbol("workclass__ Self-emp-inc"), Symbol("workclass__ Self-emp-not-inc"), Symbol("workclass__ State-gov"), Symbol("marital_status__ Divorced"), Symbol("marital_status__ Married-AF-spouse"), Symbol("marital_status__ Married-civ-spouse"), Symbol("marital_status__ Married-spouse-absent"), Symbol("marital_status__ Never-married"), Symbol("marital_status__ Separated"), Symbol("occupation__ ?"), Symbol("occupation__ Adm-clerical"), Symbol("occupation__ Armed-Forces"), Symbol("occupation__ Craft-repair"), Symbol("occupation__ Exec-managerial"), Symbol("occupation__ Farming-fishing"), Symbol("occupation__ Handlers-cleaners"), Symbol("occupation__ Machine-op-inspct"), Symbol("occupation__ Other-service"), Symbol("occupation__ Priv-house-serv"), Symbol("occupation__ Prof-specialty"), Symbol("occupation__ Protective-serv"), Symbol("occupation__ Sales"), Symbol("occupation__ Tech-support"), Symbol("relationship__ Husband"), Symbol("relationship__ Not-in-family"), Symbol("relationship__ Other-relative"), Symbol("relationship__ Own-child"), Symbol("relationship__ Unmarried"), Symbol("race__ Amer-Indian-Eskimo"), Symbol("race__ Asian-Pac-Islander"), Symbol("race__ Black"), Symbol("race__ Other"), Symbol("native_country__ ?"), Symbol("native_country__ Cambodia"), Symbol("native_country__ Canada"), Symbol("native_country__ China"), Symbol("native_country__ Columbia"), Symbol("native_country__ Cuba"), Symbol("native_country__ Dominican-Republic"), Symbol("native_country__ Ecuador"), Symbol("native_country__ El-Salvador"), Symbol("native_country__ England"), Symbol("native_country__ France"), Symbol("native_country__ Germany"), Symbol("native_country__ Greece"), Symbol("native_country__ Guatemala"), Symbol("native_country__ Haiti"), Symbol("native_country__ Holand-Netherlands"), Symbol("native_country__ Honduras"), Symbol("native_country__ Hong"), Symbol("native_country__ Hungary"), Symbol("native_country__ India"), Symbol("native_country__ Iran"), Symbol("native_country__ Ireland"), Symbol("native_country__ Italy"), Symbol("native_country__ Jamaica"), Symbol("native_country__ Japan"), Symbol("native_country__ Laos"), Symbol("native_country__ Mexico"), Symbol("native_country__ Nicaragua"), Symbol("native_country__ Outlying-US(Guam-USVI-etc)"), Symbol("native_country__ Peru"), Symbol("native_country__ Philippines"), Symbol("native_country__ Poland"), Symbol("native_country__ Portugal"), Symbol("native_country__ Puerto-Rico"), Symbol("native_country__ Scotland"), Symbol("native_country__ South"), Symbol("native_country__ Taiwan"), Symbol("native_country__ Thailand"), Symbol("native_country__ Trinadad&Tobago"), Symbol("native_country__ United-States")),NTuple{82,Array{Float64,1}}}, ::Array{Symbol,1}; copycols::Bool) at C:\Users\BCP.julia\packages\DataFrames\S3ZFo\src\other\tables.jl:25
[4] DataFrame(::NamedTuple{(:age, :sex, :capital_gain, :capital_loss, :hours_per_week, Symbol("workclass__ ?"), Symbol("workclass__ Federal-gov"), Symbol("workclass__ Local-gov"), Symbol("workclass__ Never-worked"), Symbol("workclass__ Private"), Symbol("workclass__ Self-emp-inc"), Symbol("workclass__ Self-emp-not-inc"), Symbol("workclass__ State-gov"), Symbol("marital_status__ Divorced"), Symbol("marital_status__ Married-AF-spouse"), Symbol("marital_status__ Married-civ-spouse"), Symbol("marital_status__ Married-spouse-absent"), Symbol("marital_status__ Never-married"), Symbol("marital_status__ Separated"), Symbol("occupation__ ?"), Symbol("occupation__ Adm-clerical"), Symbol("occupation__ Armed-Forces"), Symbol("occupation__ Craft-repair"), Symbol("occupation__ Exec-managerial"), Symbol("occupation__ Farming-fishing"), Symbol("occupation__ Handlers-cleaners"), Symbol("occupation__ Machine-op-inspct"), Symbol("occupation__ Other-service"), Symbol("occupation__ Priv-house-serv"), Symbol("occupation__ Prof-specialty"), Symbol("occupation__ Protective-serv"), Symbol("occupation__ Sales"), Symbol("occupation__ Tech-support"), Symbol("relationship__ Husband"), Symbol("relationship__ Not-in-family"), Symbol("relationship__ Other-relative"), Symbol("relationship__ Own-child"), Symbol("relationship__ Unmarried"), Symbol("race__ Amer-Indian-Eskimo"), Symbol("race__ Asian-Pac-Islander"), Symbol("race__ Black"), Symbol("race__ Other"), Symbol("native_country__ ?"), Symbol("native_country__ Cambodia"), Symbol("native_country__ Canada"), Symbol("native_country__ China"), Symbol("native_country__ Columbia"), Symbol("native_country__ Cuba"), Symbol("native_country__ Dominican-Republic"), Symbol("native_country__ Ecuador"), Symbol("native_country__ El-Salvador"), Symbol("native_country__ England"), Symbol("native_country__ France"), Symbol("native_country__ Germany"), Symbol("native_country__ Greece"), Symbol("native_country__ Guatemala"), Symbol("native_country__ Haiti"), Symbol("native_country__ Holand-Netherlands"), Symbol("native_country__ Honduras"), Symbol("native_country__ Hong"), Symbol("native_country__ Hungary"), Symbol("native_country__ India"), Symbol("native_country__ Iran"), Symbol("native_country__ Ireland"), Symbol("native_country__ Italy"), Symbol("native_country__ Jamaica"), Symbol("native_country__ Japan"), Symbol("native_country__ Laos"), Symbol("native_country__ Mexico"), Symbol("native_country__ Nicaragua"), Symbol("native_country__ Outlying-US(Guam-USVI-etc)"), Symbol("native_country__ Peru"), Symbol("native_country__ Philippines"), Symbol("native_country__ Poland"), Symbol("native_country__ Portugal"), Symbol("native_country__ Puerto-Rico"), Symbol("native_country__ Scotland"), Symbol("native_country__ South"), Symbol("native_country__ Taiwan"), Symbol("native_country__ Thailand"), Symbol("native_country__ Trinadad&Tobago"), Symbol("native_country__ United-States")),NTuple{82,Array{Float64,1}}}; copycols::Bool) at C:\Users\BCP.julia\packages\DataFrames\S3ZFo\src\other\tables.jl:42
[5] DataFrame at C:\Users\BCP.julia\packages\DataFrames\S3ZFo\src\other\tables.jl:31 [inlined]
[6] #table#15 at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\interface\data_utils.jl:64 [inlined]
[7] table(::NamedTuple{(:age, :sex, :capital_gain, :capital_loss, :hours_per_week, Symbol("workclass__ ?"), Symbol("workclass__ Federal-gov"), Symbol("workclass__ Local-gov"), Symbol("workclass__ Never-worked"), Symbol("workclass__ Private"), Symbol("workclass__ Self-emp-inc"), Symbol("workclass__ Self-emp-not-inc"), Symbol("workclass__ State-gov"), Symbol("marital_status__ Divorced"), Symbol("marital_status__ Married-AF-spouse"), Symbol("marital_status__ Married-civ-spouse"), Symbol("marital_status__ Married-spouse-absent"), Symbol("marital_status__ Never-married"), Symbol("marital_status__ Separated"), Symbol("occupation__ ?"), Symbol("occupation__ Adm-clerical"), Symbol("occupation__ Armed-Forces"), Symbol("occupation__ Craft-repair"), Symbol("occupation__ Exec-managerial"), Symbol("occupation__ Farming-fishing"), Symbol("occupation__ Handlers-cleaners"), Symbol("occupation__ Machine-op-inspct"), Symbol("occupation__ Other-service"), Symbol("occupation__ Priv-house-serv"), Symbol("occupation__ Prof-specialty"), Symbol("occupation__ Protective-serv"), Symbol("occupation__ Sales"), Symbol("occupation__ Tech-support"), Symbol("relationship__ Husband"), Symbol("relationship__ Not-in-family"), Symbol("relationship__ Other-relative"), Symbol("relationship__ Own-child"), Symbol("relationship__ Unmarried"), Symbol("race__ Amer-Indian-Eskimo"), Symbol("race__ Asian-Pac-Islander"), Symbol("race__ Black"), Symbol("race__ Other"), Symbol("native_country__ ?"), Symbol("native_country__ Cambodia"), Symbol("native_country__ Canada"), Symbol("native_country__ China"), Symbol("native_country__ Columbia"), Symbol("native_country__ Cuba"), Symbol("native_country__ Dominican-Republic"), Symbol("native_country__ Ecuador"), Symbol("native_country__ El-Salvador"), Symbol("native_country__ England"), Symbol("native_country__ France"), Symbol("native_country__ Germany"), Symbol("native_country__ Greece"), Symbol("native_country__ Guatemala"), Symbol("native_country__ Haiti"), Symbol("native_country__ Holand-Netherlands"), Symbol("native_country__ Honduras"), Symbol("native_country__ Hong"), Symbol("native_country__ Hungary"), Symbol("native_country__ India"), Symbol("native_country__ Iran"), Symbol("native_country__ Ireland"), Symbol("native_country__ Italy"), Symbol("native_country__ Jamaica"), Symbol("native_country__ Japan"), Symbol("native_country__ Laos"), Symbol("native_country__ Mexico"), Symbol("native_country__ Nicaragua"), Symbol("native_country__ Outlying-US(Guam-USVI-etc)"), Symbol("native_country__ Peru"), Symbol("native_country__ Philippines"), Symbol("native_country__ Poland"), Symbol("native_country__ Portugal"), Symbol("native_country__ Puerto-Rico"), Symbol("native_country__ Scotland"), Symbol("native_country__ South"), Symbol("native_country__ Taiwan"), Symbol("native_country__ Thailand"), Symbol("native_country__ Trinadad&Tobago"), Symbol("native_country__ United-States")),NTuple{82,Array{Float64,1}}}; kw::Base.Iterators.Pairs{Symbol,DataFrame,Tuple{Symbol},NamedTuple{(:prototype,),Tuple{DataFrame}}}) at C:\Users\BCP.julia\packages\MLJModelInterface\aA1k2\src\data_utils.jl:210
[8] transform(::OneHotEncoder, ::MLJModels.OneHotEncoderResult, ::DataFrame) at C:\Users\BCP.julia\packages\MLJModels\i4XcU\src\builtins\Transformers.jl:831
[9] transform(::NodalMachine{OneHotEncoder}, ::DataFrame) at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\operations.jl:29
[10] (::Node{NodalMachine{OneHotEncoder}})(::DataFrame) at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\composition\networks.jl:302
[11] #57 at .\none:0 [inlined]
[12] iterate at .\generator.jl:47 [inlined]
[13] collect(::Base.Generator{Tuple{Node{NodalMachine{OneHotEncoder}}},MLJBase.var"#57#58"{DataFrame}}) at .\array.jl:665
[14] (::Node{NodalMachine{RandomForestClassifier}})(::DataFrame) at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\composition\networks.jl:302
[15] predict(::RandomForestClassifierPipe, ::Node{NodalMachine{RandomForestClassifier}}, ::DataFrame) at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\composition\composites.jl:45
[16] predict(::Machine{RandomForestClassifierPipe}, ::DataFrame) at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\operations.jl:29
[17] predict(::MLJTuning.ProbabilisticTunedModel{Grid,RandomForestClassifierPipe,CPU1{Nothing},CPU1{Nothing}}, ::Machine{RandomForestClassifierPipe}, ::DataFrame) at C:\Users\BCP.julia\packages\MLJTuning\JZ7ZX\src\tuned_models.jl:597
[18] predict(::Machine{MLJTuning.ProbabilisticTunedModel{Grid,RandomForestClassifierPipe,CPU1{Nothing},CPU1{Nothing}}}, ::DataFrame) at C:\Users\BCP.julia\packages\MLJBase\O5b6j\src\operations.jl:29
[19] predict_function_mode(::Machine{MLJTuning.ProbabilisticTunedModel{Grid,RandomForestClassifierPipe,CPU1{Nothing},CPU1{Nothing}}}, ::DataFrame) at .\REPL[12]:2
[20] _shap_sample(::DataFrame, ::DataFrame, ::Int64, ::Int64, ::Int64, ::Int64, ::Array{String,1}, ::Array{String,1}, ::Array{Symbol,1}, ::Int64, ::Symbol, ::Array{Int64,1}, ::Bool, ::Machine{MLJTuning.ProbabilisticTunedModel{Grid,RandomForestClassifierPipe,CPU1{Nothing},CPU1{Nothing}}}, ::typeof(predict_function_mode), ::Nothing) at C:\Users\BCP.julia\packages\ShapML\sceNA\src\shap_sample.jl:97
[21] shap(; explain::DataFrame, reference::DataFrame, model::Machine{MLJTuning.ProbabilisticTunedModel{Grid,RandomForestClassifierPipe,CPU1{Nothing},CPU1{Nothing}}}, predict_function::Function, target_features::Nothing, sample_size::Int64, parallel::Nothing, seed::Int64, precision::Nothing, chunk::Bool) at C:\Users\BCP.julia\packages\ShapML\sceNA\src\ShapML.jl:117
[22] trainRandomForest(::DataFrame, ::DataFrame) at .\REPL[13]:53
[23] selectRandomForest(::DataFrame, ::DataFrame, ::Int64) at C:\Users\BCP\github\ICP\GetBlankets.jl:157
[24] getBlanketRandomForest(::DataFrame, ::DataFrame, ::String, ::Int64) at C:\Users\BCP\github\ICP\GetBlankets.jl:137
[25] getBlanketRandomForest at C:\Users\BCP\github\ICP\GetBlankets.jl:131 [inlined]
[26] ForestInvariantCausalPrediction(::DataFrame, ::DataFrame, ::DataFrame; α::Float64, selection::String, verbose::Bool) at C:\Users\BCP\github\ICP\InvariantCausalPrediction.jl:305
[27] top-level scope at REPL[14]:1

julia>

[drcxcruz]

Hi,

do we must hot encode categorical variables? or does ShapML handle categorical variables?

thanks