New version: MLJBase v0.18.20

[JuliaRegistrator]

• Registering package: MLJBase
• Repository: https://github.com/JuliaAI/MLJBase.jl
• Created by: @ablaom
• Version: v0.18.20
• Commit: a63d6fab5537409c1aafe6dd1f91e37eb156b4ab
• Reviewed by: @ablaom
• Reference: Issue to trigger releases JuliaAI/MLJBase.jl#345 (comment)
• Release notes:

• (enhancement) Integrate new outlier detection types added to MLJModelInterface (Discussion: Outlier Detection API in MLJ JuliaAI/MLJ.jl#780, Outlier Detection Integration JuliaAI/MLJModelInterface.jl#113) and adapt performance evaluation methods to handle them (New version Documenter: 0.22.4 #628)

Relating to measures:

• (enhancement) Measures: Add SphericalScore and LogScore (negative of LogLoss).
• (enhancement) Extend the proper scoring rules SphericalScore, LogScore and BrierScore to handle Continuous and Count data. Supported distributions types from Distributions are: Uniform, Normal, Exponential, Logistic, Chi, Chisq, Beta, Gamma, Cauchy, Poisson, DiscreteUniform, DiscreteNonParameteric (New version DoubleFloats: 0.7.24 #627)
• (enhancement) Add missing and NaN support for all measures, excluding AreaUnderCurve and measures from LossFunctions.jl (which imported library does not support) (New version Interpolations: 0.12.0 #616)
• (enhancement) Add skipinvalid(y) and skipinvalid(yhat, y) methods. The first returns an iterator that skips missing and NaN values - similar to skipmissing and is performant. The second returns the flattened forms of yhat and y with invalid entries removed "commensurately" from both yhat and y, meaning an element of either argument is skipped even if valid, if the corresponding element of the other argument is invalid (New version DoubleFloats: 0.7.24 #627). This method is necessarily less efficient and provided for convenience for pre-processing data for the measures which do not support invalid entries (New version DoubleFloats: 0.7.24 #627, New version Interpolations: 0.12.0 #616)
• (enhancement) Allow most measures for Finite data to be called with "raw" data, that is, data that is not wrapped as CategoricalArray. This includes ConfusionMatrix. A warning is issued to indicate order ambiguity, with the usual suggestion to coerce to OrderedFactor to suppress the warning (New version DoubleFloats: 0.7.24 #627)
• (enhancement) Allow measures to be called on arrays, and not just vectors (New version DoubleFloats: 0.7.24 #627) but see remaining limitations at New version TableReader: 0.4.0 #631.
• (API) Make implementing new measures simpler (New version DoubleFloats: 0.7.24 #627) and less error-prone. See this guide for details.
• (enhancement) Introduce new method MLJBase.call(measure, args...) to call a measure without applying dimension or pool checks.
• (bug fix) Prevent weights passed to measures from Loss functions.jl from being normalized (New version Convex: 0.12.0 #626)

[github-actions]

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