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Merge pull request #1138 from JuliaAI/dev
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For a 0.20.7 release
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ablaom authored Jul 18, 2024
2 parents 5d600f0 + 8026e8a commit f7befce
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6 changes: 3 additions & 3 deletions Project.toml
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@@ -1,7 +1,7 @@
name = "MLJ"
uuid = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
authors = ["Anthony D. Blaom <[email protected]>"]
version = "0.20.6"
version = "0.20.7"

[deps]
CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
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CategoricalArrays = "0.8,0.9, 0.10"
ComputationalResources = "0.3"
Distributions = "0.21,0.22,0.23, 0.24, 0.25"
FeatureSelection = "0.1.1"
FeatureSelection = "0.2"
MLJBalancing = "0.1"
MLJBase = "1"
MLJBase = "1.5"
MLJEnsembles = "0.4"
MLJFlow = "0.5"
MLJIteration = "0.6"
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1 change: 1 addition & 0 deletions docs/ModelDescriptors.toml
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Expand Up @@ -154,6 +154,7 @@ MultitargetNeuralNetworkRegressor_BetaML = ["regression", "neural_networks"]
MultitargetNeuralNetworkRegressor_MLJFlux = ["regression", "iterative_models", "neural_networks"]
MultitargetRidgeRegressor_MultivariateStats = ["regression"]
MultitargetSRRegressor_SymbolicRegression = ["regression"]
NeuralNetworkBinaryClassifier_MLJFlux = ["classification", "neural_networks"]
NeuralNetworkClassifier_BetaML = ["classification", "neural_networks"]
NeuralNetworkClassifier_MLJFlux = ["classification", "iterative_models", "neural_networks"]
NeuralNetworkRegressor_BetaML = ["regression", "neural_networks"]
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3 changes: 2 additions & 1 deletion docs/make.jl
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Expand Up @@ -67,10 +67,11 @@ pages = [
"Generating Synthetic Data" => "generating_synthetic_data.md",
"OpenML Integration" => "openml_integration.md",
],
"Model Basics" => [
"Models" => [
"Model Search" => "model_search.md",
"Loading Model Code" => "loading_model_code.md",
"Transformers and Other Unsupervised models" => "transformers.md",
"Feature Selection" => "feature_selection.md",
"List of Supported Models" => "list_of_supported_models.md",
],
"Meta-algorithms" => [
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13 changes: 13 additions & 0 deletions docs/src/feature_selection.md
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# Feature Selection

For more on feature selection tools, refer to the [FeatureSelection.jl](https://juliaai.github.io/FeatureSelection.jl/dev/) documentation.

- [`FeatureSelector`](@ref)
- [`RecursiveFeatureElimination`](@ref)

## Reference

```@docs
FeatureSelector
RecursiveFeatureElimination
```
11 changes: 5 additions & 6 deletions docs/src/model_search.md
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# [Model Search](@id model_search)

MLJ has a model registry, allowing the user to search models and their
properties, without loading all the packages containing model code. In
turn, this allows one to efficiently find all models solving a given
machine learning task. The task itself is specified with the help of
the `matching` method, and the search executed with the `models`
methods, as detailed below.
In addition to perusing the [Model Browser](@ref), one can programatically search MLJ's
Model Registry, without actually loading all the packages providing model code. This
allows you to efficiently find all models solving a given machine learning task. The task
itself is specified with the help of the `matching` method, and the search executed with
the `models` methods, as detailed below.

For commonly encountered problems with model search, see also
[Preparing Data](@ref).
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36 changes: 24 additions & 12 deletions docs/src/transformers.md
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Expand Up @@ -24,18 +24,16 @@ that learn a probability distribution](@ref) for an example.

## Built-in transformers

```@docs
MLJModels.Standardizer
MLJModels.OneHotEncoder
MLJModels.ContinuousEncoder
MLJModels.FillImputer
MLJModels.UnivariateFillImputer
FeatureSelection.FeatureSelector
MLJModels.UnivariateBoxCoxTransformer
MLJModels.UnivariateDiscretizer
MLJModels.UnivariateTimeTypeToContinuous
```

- [`Standardizer`](@ref)
- [`OneHotEncoder`](@ref)
- [`ContinuousEncoder`](@ref)
- [`FillImputer`](@ref)
- [`UnivariateFillImputer`](@ref)
- [`UnivariateBoxCoxTransformer`](@ref)
- [`InteractionTransformer`](@ref)
- [`UnivariateDiscretizer`](@ref)
- [`UnivariateTimeTypeToContinuous`](@ref)
- [`FeatureSelector`](@ref).

## Static transformers

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```@example predtrans
compare[101:108]
```

## Reference

```@docs
MLJModels.Standardizer
MLJModels.OneHotEncoder
MLJModels.ContinuousEncoder
MLJModels.FillImputer
MLJModels.UnivariateFillImputer
MLJModels.UnivariateBoxCoxTransformer
MLJModels.InteractionTransformer
MLJModels.UnivariateDiscretizer
MLJModels.UnivariateTimeTypeToContinuous
```
6 changes: 6 additions & 0 deletions docs/src/working_with_categorical_data.md
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Expand Up @@ -345,3 +345,9 @@ d_vec = UnivariateFinite(["no", "yes"], yes_probs, augment=true, pool=v)
```

For more options, see [`UnivariateFinite`](@ref).

## Reference

```@docs
UnivariateFinite
```
2 changes: 1 addition & 1 deletion src/MLJ.jl
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Expand Up @@ -146,7 +146,7 @@ export nrows, color_off, color_on,
@pipeline, Stack, Pipeline, TransformedTargetModel,
ResamplingStrategy, Holdout, CV, TimeSeriesCV,
StratifiedCV, evaluate!, Resampler, iterator, PerformanceEvaluation,
default_resource, pretty,
default_resource, default_logger, pretty,
make_blobs, make_moons, make_circles, make_regression,
fit_only!, return!, int, decoder,
default_scitype_check_level,
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6 changes: 5 additions & 1 deletion test/integration.jl
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Expand Up @@ -155,7 +155,11 @@ PATHOLOGIES = filter(MODELS) do model
# too slow to train!
(model.name == "LOCIDetector" && model.package_name == "OutlierDetectionPython") ||
# TO REDUCE TESTING TIME
model.package_name == "MLJScikitLearnInterface"
model.package_name == "MLJScikitLearnInterface" ||
# can be removed after resolution of
# https://github.com/JuliaAI/FeatureSelection.jl/issues/15
# and a Model Registry update
model.name == "RecursiveFeatureElimination"
end

WITHOUT_DATASETS = vcat(WITHOUT_DATASETS, PATHOLOGIES)
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