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fix mlj doctest
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@tiemvanderdeure
tiemvanderdeure committed Nov 26, 2024
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Showing 1 changed file with 11 additions and 4 deletions.
15 changes: 11 additions & 4 deletions src/mlj_interface.jl
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Expand Up @@ -46,18 +46,25 @@ MMI.metadata_model(
"""
$(MMI.doc_header(MaxnetBinaryClassifier))
The keywords `link`, and `clamp` are passed to [`Maxnet.predict`](@ref), while all other keywords are passed to [`maxnet`](@ref).
The keywords `link`, and `clamp` are passed to [`predict`](@ref), while all other keywords are passed to [`maxnet`](@ref).
See the documentation of these functions for the meaning of these parameters and their defaults.
# Example
```jldoctest

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doctest failure in ~/work/Maxnet.jl/Maxnet.jl/src/mlj_interface.jl:53-68

```jldoctest
using MLJBase
p_a, env = Maxnet.bradypus()

mach = machine(MaxnetBinaryClassifier(features = "lqp"), env, categorical(p_a), scitype_check_level = 0)
fit!(mach, verbosity = 0)
yhat = MLJBase.predict(mach, env)
yhat[1:5]
# output
5-element UnivariateFiniteVector{Multiclass{2}, Bool, UInt32, Float64}:
 UnivariateFinite{Multiclass{2}}(false=>0.812, true=>0.188)
 UnivariateFinite{Multiclass{2}}(false=>0.811, true=>0.189)
 UnivariateFinite{Multiclass{2}}(false=>0.501, true=>0.499)
 UnivariateFinite{Multiclass{2}}(false=>0.811, true=>0.189)
 UnivariateFinite{Multiclass{2}}(false=>0.752, true=>0.248)
```

Subexpression:

using MLJBase
p_a, env = Maxnet.bradypus()

mach = machine(MaxnetBinaryClassifier(features = "lqp"), env, categorical(p_a), scitype_check_level = 0)
fit!(mach, verbosity = 0)
yhat = MLJBase.predict(mach, env)
yhat[1:5]

Evaluated output:

5-element UnivariateFiniteVector{Multiclass{2}, Bool, UInt32, Float64}:
 UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.812, true=>0.188)
 UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.811, true=>0.189)
 UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.501, true=>0.499)
 UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.811, true=>0.189)
 UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.752, true=>0.248)

Expected output:

5-element UnivariateFiniteVector{Multiclass{2}, Bool, UInt32, Float64}:
 UnivariateFinite{Multiclass{2}}(false=>0.812, true=>0.188)
 UnivariateFinite{Multiclass{2}}(false=>0.811, true=>0.189)
 UnivariateFinite{Multiclass{2}}(false=>0.501, true=>0.499)
 UnivariateFinite{Multiclass{2}}(false=>0.811, true=>0.189)
 UnivariateFinite{Multiclass{2}}(false=>0.752, true=>0.248)


  diff = 
    Warning: Diff output requires color.
    5-element UnivariateFiniteVector{Multiclass{2}, Bool, UInt32, Float64}:
     UnivariateFinite{Multiclass{2}}(false=>0.812, UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.812, true=>0.188)
     UnivariateFinite{Multiclass{2}}(false=>0.811, UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.811, true=>0.189)
     UnivariateFinite{Multiclass{2}}(false=>0.501, UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.501, true=>0.499)
     UnivariateFinite{Multiclass{2}}(false=>0.811, UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.811, true=>0.189)
     UnivariateFinite{Multiclass{2}}(false=>0.752, UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.752, true=>0.248)
using Maxnet, MLJBase
using MLJBase
p_a, env = Maxnet.bradypus()
mach = machine(MaxnetBinaryClassifier(features = "lqp"), env, categorical(p_a))
fit!(mach)
mach = machine(MaxnetBinaryClassifier(features = "lqp"), env, categorical(p_a), scitype_check_level = 0)
fit!(mach, verbosity = 0)
yhat = MLJBase.predict(mach, env)
yhat[1:5]
# output
5-element UnivariateFiniteVector{Multiclass{2}, Bool, UInt32, Float64}:
UnivariateFinite{Multiclass{2}}(false=>0.812, true=>0.188)
UnivariateFinite{Multiclass{2}}(false=>0.811, true=>0.189)
UnivariateFinite{Multiclass{2}}(false=>0.501, true=>0.499)
UnivariateFinite{Multiclass{2}}(false=>0.811, true=>0.189)
UnivariateFinite{Multiclass{2}}(false=>0.752, true=>0.248)
```
"""
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