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ablaom merged 1 commit into from
Oct 9, 2026
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Commit Notebook Markdown Changes #93

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10 changes: 5 additions & 5 deletions docs/src/notebooks/MLJTutorial/01_data_representation/notebook.md
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Expand Up @@ -290,7 +290,7 @@ csv_file = Downloads.download(url)
````

````
"/tmp/jl_ruz0kP/horse.csv"
"/tmp/jl_kOThHT/horse.csv"
````

Entering these lines of code downloads the data to a temporary file at the location
Expand Down Expand Up @@ -531,8 +531,8 @@ A = rand(2, 3)

````
2×3 Matrix{Float64}:
0.113155 0.33251 0.397839
0.171581 0.90293 0.436722
0.832781 0.999535 0.163212
0.685279 0.998338 0.966773
````

````@julia
Expand All @@ -550,8 +550,8 @@ Asparse = sparse(A)

````
2×3 SparseArrays.SparseMatrixCSC{Float64, Int64} with 6 stored entries:
0.113155 0.33251 0.397839
0.171581 0.90293 0.436722
0.832781 0.999535 0.163212
0.685279 0.998338 0.966773
````

````@julia
Expand Down
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276 changes: 138 additions & 138 deletions docs/src/notebooks/MLJTutorial/02_models/notebook.md
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16 changes: 8 additions & 8 deletions docs/src/notebooks/MLJTutorial/03_pipelines/notebook.md
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Expand Up @@ -31,8 +31,8 @@ x = rand(100);
````

````
mean(x) = 0.47696749924138343
std(x) = 0.29728842148044776
mean(x) = 0.488231598311445
std(x) = 0.29947481462620856

````

Expand All @@ -46,8 +46,8 @@ xhat = transform(mach, x);

````
[ Info: Training machine(Standardizer(features = Symbol[], …), …).
mean(xhat) = 2.042810365310288e-16
std(xhat) = 1.0
mean(xhat) = -9.769962616701378e-17
std(xhat) = 0.9999999999999999

````

Expand Down Expand Up @@ -501,7 +501,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-190
Tag: DeterministicPipeline-761
Extract:
┌──────────┬───────────┬─────────────┐
│ measure │ operation │ measurement │
Expand Down Expand Up @@ -631,7 +631,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-398
Tag: DeterministicPipeline-289
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
Expand Down Expand Up @@ -672,7 +672,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-611
Tag: DeterministicPipeline-526
Extract:
┌──────────┬───────────┬─────────────┬──────────┐
│ measure │ operation │ measurement │ 1.96*SE │
Expand Down Expand Up @@ -700,7 +700,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: DeterministicPipeline-592
Tag: DeterministicPipeline-221
Extract:
┌──────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
Expand Down
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4 changes: 2 additions & 2 deletions docs/src/notebooks/MLJTutorial/04_tuning/notebook.md
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Expand Up @@ -367,7 +367,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: ProbabilisticPipeline-307
Tag: ProbabilisticPipeline-620
Extract:
┌──────────────────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
Expand All @@ -394,7 +394,7 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: ProbabilisticTunedModel-779
Tag: ProbabilisticTunedModel-280
Extract:
┌──────────────────────┬───────────┬─────────────┬─────────┐
│ measure │ operation │ measurement │ 1.96*SE │
Expand Down
Binary file modified docs/src/notebooks/MLJTutorial/04_tuning/tuning.png
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100 changes: 50 additions & 50 deletions docs/src/notebooks/MLJTutorial/05_composition/notebook.md
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Expand Up @@ -64,11 +64,11 @@ pretty(X)
│ Float64 │ Float64 │ Float64 │
│ Continuous │ Continuous │ Continuous │
├────────────┼────────────┼────────────┤
│ 14.9681 │ -1.47137 │ 12.1949 │
│ -3.26626 │ -0.710617 │ 7.36507 │
│ -4.17429 │ -0.0882215 │ 9.87907 │
│ 6.00034 │ -2.41859 │ 14.9897 │
│ -4.38537 │ -0.187352 │ 7.22233 │
│ -9.85801 │ 0.948133 │ 1.5543 │
│ -9.05017 │ 0.116089 │ 0.3031 │
│ 13.9369 │ -0.530196 │ -2.03829 │
│ -0.617015 │ 2.30274 │ -1.08639 │
│ -8.65166 │ 2.36956 │ 1.34361 │
└────────────┴────────────┴────────────┘

````
Expand All @@ -91,11 +91,11 @@ yhat = predict(mach2, Xstand)

````
5-element CategoricalDistributions.UnivariateFiniteVector{ScientificTypesBase.Multiclass{3}, Int64, UInt32, Float64}:
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.993, 2=>0.00372, 3=>0.00319)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.000405, 2=>0.000195, 3=>0.999)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.000309, 2=>0.000249, 3=>0.999)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.00752, 2=>0.992, 3=>0.000947)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.00012, 2=>1.96e-5, 3=>1.0)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>8.09e-5, 2=>8.48e-5, 3=>1.0)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.00351, 2=>0.000732, 3=>0.996)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.995, 2=>0.00474, 3=>0.00011)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.00327, 2=>0.989, 3=>0.00818)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>6.88e-5, 2=>0.00843, 3=>0.991)
````

**Step 1** - Edit your code as follows:
Expand All @@ -119,15 +119,15 @@ yhat = predict(mach2, Xstand)
````

````
Node @440 → LogisticClassifier(…)
Node @623 → LogisticClassifier(…)
args:
1: Node @909 → Standardizer(…)
1: Node @703 → Standardizer(…)
formula:
predict(
machine(LogisticClassifier(lambda = 0.001, …), …),
transform(
machine(Standardizer(features = Symbol[], …), …),
Source @128,
Source @747,
),
)
````
Expand All @@ -150,11 +150,11 @@ Xstand() |> pretty
│ Float64 │ Float64 │ Float64 │
│ Continuous │ Continuous │ Continuous │
├────────────┼────────────┼────────────┤
│ 1.54143 │ -0.508564 │ 0.563239 │
│ -0.597675 │ 0.271238 │ -0.895631 │
│ -0.704197 │ 0.909215 │ -0.136268 │
│ 0.489404 │ -1.47949 │ 1.40741 │
│ -0.728959 │ 0.807603 │ -0.938747 │
│ -0.694096 │ -0.0720163 │ 0.991034 │
│ -0.614108 │ -0.715398 │ 0.185347 │
│ 1.66195 │ -1.21514 │ -1.32235 │
│ 0.220901 │ 0.975443 │ -0.709393 │
│ -0.574649 │ 1.02711 │ 0.855364 │
└────────────┴────────────┴────────────┘

````
Expand All @@ -178,11 +178,11 @@ yhat()

````
5-element CategoricalDistributions.UnivariateFiniteVector{ScientificTypesBase.Multiclass{3}, Int64, UInt32, Float64}:
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.993, 2=>0.00372, 3=>0.00319)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.000405, 2=>0.000195, 3=>0.999)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.000309, 2=>0.000249, 3=>0.999)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.00752, 2=>0.992, 3=>0.000947)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.00012, 2=>1.96e-5, 3=>1.0)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>8.09e-5, 2=>8.48e-5, 3=>1.0)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.00351, 2=>0.000732, 3=>0.996)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.995, 2=>0.00474, 3=>0.00011)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.00327, 2=>0.989, 3=>0.00818)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>6.88e-5, 2=>0.00843, 3=>0.991)
````

The node `yhat` is the "descendant" (in an associated DAG we have
Expand All @@ -194,7 +194,7 @@ origins(yhat)

````
1-element Vector{MLJBase.Source}:
Source @128 ⏎ `ScientificTypesBase.Table{AbstractVector{ScientificTypesBase.Continuous}}`
Source @747 ⏎ `ScientificTypesBase.Table{AbstractVector{ScientificTypesBase.Continuous}}`
````

The data at the source node is replaced by `Xnew` to obtain a
Expand All @@ -207,8 +207,8 @@ yhat(Xnew)

````
2-element CategoricalDistributions.UnivariateFiniteVector{ScientificTypesBase.Multiclass{3}, Int64, UInt32, Float64}:
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>1.13e-8, 2=>1.0, 3=>6.09e-8)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>2.89e-16, 2=>1.0, 3=>4.36e-13)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>0.0461, 2=>0.954, 3=>1.47e-10)
UnivariateFinite{ScientificTypesBase.Multiclass{3}}(1=>1.0, 2=>8.32e-5, 3=>1.38e-9)
````

**Step 2** - Export the learning network as a new stand-alone model type
Expand Down Expand Up @@ -238,15 +238,15 @@ yhat = predict(mach2, Xstand)
````

````
Node @530 → :classifier
Node @708 → :classifier
args:
1: Node @804 → :standardizer
1: Node @363 → :standardizer
formula:
predict(
machine(:classifier, …),
transform(
machine(:standardizer, …),
Source @128,
Source @747,
),
)
````
Expand Down Expand Up @@ -380,7 +380,7 @@ y = source(y)
````

````
Source @555 ⏎ `AbstractVector{ScientificTypesBase.Continuous}`
Source @704 ⏎ `AbstractVector{ScientificTypesBase.Continuous}`
````

**First layer and target transformation:**
Expand All @@ -396,13 +396,13 @@ z = MLJ.transform(mach2, y)
````

````
Node @022 → UnivariateBoxCoxTransformer(…)
Node @116 → UnivariateBoxCoxTransformer(…)
args:
1: Source @555
1: Source @704
formula:
transform(
machine(UnivariateBoxCoxTransformer(n = 171, …), …),
Source @555,
Source @704,
)
````

Expand All @@ -419,18 +419,18 @@ zhat = 0.5*predict(mach3, W) + 0.5*predict(mach4, W)
````

````
Node @135
Node @464
args:
1: Node @083
2: Node @797
1: Node @919
2: Node @316
formula:
+(
var"#*##0#*##1"(
predict(
machine(RidgeRegressor(lambda = 0.1, …), …),
transform(
machine(Standardizer(features = Symbol[], …), …),
Source @100,
Source @991,
),
),
),
Expand All @@ -439,7 +439,7 @@ Node @135
machine(RandomForestRegressor(max_depth = -1, …), …),
transform(
machine(Standardizer(features = Symbol[], …), …),
Source @100,
Source @991,
),
),
),
Expand All @@ -453,9 +453,9 @@ yhat = inverse_transform(mach2, zhat)
````

````
Node @539 → UnivariateBoxCoxTransformer(…)
Node @061 → UnivariateBoxCoxTransformer(…)
args:
1: Node @135
1: Node @464
formula:
inverse_transform(
machine(UnivariateBoxCoxTransformer(n = 171, …), …),
Expand All @@ -465,7 +465,7 @@ Node @539 → UnivariateBoxCoxTransformer(…)
machine(RidgeRegressor(lambda = 0.1, …), …),
transform(
machine(Standardizer(features = Symbol[], …), …),
Source @100,
Source @991,
),
),
),
Expand All @@ -474,7 +474,7 @@ Node @539 → UnivariateBoxCoxTransformer(…)
machine(RandomForestRegressor(max_depth = -1, …), …),
transform(
machine(Standardizer(features = Symbol[], …), …),
Source @100,
Source @991,
),
),
),
Expand All @@ -491,9 +491,9 @@ yhat(rows=1:3)

````
3-element Vector{Float64}:
4.1049434111127105
4.674958202525163
4.112097582032496
1.5198312953553146
0.8824006167264453
1.57930874294795
````

Now for the new model type:
Expand Down Expand Up @@ -547,20 +547,20 @@ PerformanceEvaluation object with these fields:
per_fold, per_observation,
fitted_params_per_fold, report_per_fold,
train_test_rows, resampling, repeats
Tag: CompositeModel-983
Tag: CompositeModel-665
Extract:
┌───┬────────────────────────┬───────────┬─────────────┬─────────┐
│ │ measure │ operation │ measurement │ 1.96*SE │
├───┼────────────────────────┼───────────┼─────────────┼─────────┤
│ A │ RootMeanSquaredError() │ predict │ 3.87 │ 0.46 │
│ B │ LPLoss( │ predict │ 2.454 │ 0.04 │
│ A │ RootMeanSquaredError() │ predict │ 3.95 │ 0.68 │
│ B │ LPLoss( │ predict │ 2.49 │ 0.26 │
│ │ p = 1) │ │ │ │
└───┴────────────────────────┴───────────┴─────────────┴─────────┘
┌───┬──────────────────────────────────────┐
│ │ per_fold │
├───┼──────────────────────────────────────┤
│ A │ [4.45, 4.28, 3.41, 3.3, 4.21, 3.37] │
│ B │ [2.45, 2.46, 2.46, 2.38, 2.53, 2.45] │
│ A │ [3.09, 5.37, 3.61, 3.81, 3.63, 3.82] │
│ B │ [2.17, 2.98, 2.62, 2.24, 2.35, 2.57] │
└───┴──────────────────────────────────────┘
Apply `describe` to this result for a named tuple summary.
````
Expand Down
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