[Feature Request] weighted KNN imputation

[bvenn]

FSharp.Stats already supports the KNN imputation via FSharp.Stats.ML.Impute.kNearestImpute. The current implementation takes the k nearest neighbors and computes the average of these at the index of interest. This average replaces the missing value of the incomplete data point. I suggest to make the following changes/additions:

• rename the module to Imputation to be consistent within the library
• add the possibility to define how a missing value is encoded (e.g., 0.0 or nan)
• add an optional converter function that processes the distance measure. When using Pearson's correlation coefficient you determine the similarity rather the distance and therefore you have to take the reciprocal.
• add a weighted version in which the averaging can be weighted according to the distance of the nearest neighbors
• add proper documentation

Keywords

• Local Least Squares

[github-actions]

🤖 This is an automated response from Repo Assist.

I've opened a draft PR that implements the distance-weighted KNN imputation variant: [Repo Assist] feat: add Imputation.kNearestWeightedImpute for distance-weighted KNN (#aw_pr318).

What the PR adds

Imputation.kNearestWeightedImpute accepts two extra parameters compared to the existing kNearestImpute:

Parameter	Role
distanceMetric : DistanceMetrics.Distance<float[]>	Pluggable distance function; use DistanceMetrics.Array.euclideanNaNSquared to skip NaN positions
distanceToWeight : float → float	Converts the raw distance to a non-negative weight; for Euclidean use fun d → 1.0 / (d + eps), for a correlation similarity just pass id (or its reciprocal)

let imputer =
    Imputation.kNearestWeightedImpute
        FSharp.Stats.DistanceMetrics.Array.euclideanNaNSquared
        (fun d -> 1.0 / (d + System.Double.Epsilon))
        k
let imputed = Imputation.imputeBy imputer System.Double.IsNaN rawData

What's not yet covered from the issue

• Renaming Impute → Imputation is already in progress (the old module is [<Obsolete>]).
• Missing-value encoding parameterisation (passing a custom isMissing) already works via the imputeBy driver.
• XML documentation stubs — happy to add in a follow-up if helpful.

Tests: 7 new tests, 1200/1200 pass.

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