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KBinsDiscretizer

Cuts each feature into bins, at sklearn.preprocessing.KBinsDiscretizer parity.

public sealed class KBinsDiscretizer

Properties — FeatureCount and SampleCount are the shape it was fitted on. BinEdges is each feature's edges, ascending, the reference's bin_edges_. BinCounts is how many bins each feature ended with, the reference's n_bins_, which can be fewer than asked for. OutputFeatureCount is how wide a transformed row is: one column per feature under BinEncoding.Ordinal, one per bin under OneHot.

Example — ten ages into four bins of equal count.

using Lodestar.Preprocessing;

double[] ages = [19.0, 22.0, 25.0, 31.0, 38.0, 44.0, 52.0, 61.0, 67.0, 74.0];

var options = new KBinsDiscretizerOptions { BinCount = 4, Encoding = BinEncoding.Ordinal };

KBinsDiscretizer bins = KBinsDiscretizer.Fit(ages, featureCount: 1, options);

string edges = string.Join(",", bins.BinEdges[0]);        // => 19,25,41,61,74
string codes = string.Join(",", bins.Transform(ages));    // => 0,0,1,1,1,2,2,3,3,3

Remarks — this is the transformer for a model that reads order but not distance. A tree, a naive Bayes over categories, or a human reading a report all want "the third quartile of age", not 61.4; the discretizer is what turns one into the other, and InverseTransform is what reads it back — as the bin's centre, since the value itself is gone.

It is not ML.NET's NormalizeBinning. That one also cuts a feature into bins, but emits a position in [0, 1] rather than the bin: the binning is a rescaling there and a categorisation here, and only the second answers "which group is this row in".

A feature can come back with fewer bins than asked for. Two edges within 1e-8 of each other are one edge — the reference's own threshold — which happens on a column that is nearly constant, or whose quantiles repeat because a value dominates it. BinCounts is where that shows, and it is the number to read before indexing into a one-hot row. The gaps compared are the successive ones of the edges as they were fitted, not the distance to the last edge kept: on a run of narrow gaps the two readings give different bins, and the first is the reference's (#1128).

One bin is the floor, which is a deliberate divergence. Where the removal leaves a single edge the reference reports n_bins_ = 0 and then breaks on itself — its inverse_transform raises IndexError on such a fit, and so does its fit under one-hot encoding. This keeps one bin spanning the feature's range, which transforms, inverts and encodes. Decision 0007 admits a divergence exactly where reproducing the reference would reproduce a defect; the Python equivalence table carries the measurement.

Applies to — net10.0, netstandard2.0.

See also — KBinsDiscretizerOptions, BinStrategy, QuantileTransformer, the feature transforming index, the Python equivalence table.

Members

Member	What it does
KBinsDiscretizer.Fit	Fits bin edges on a row-major matrix.
KBinsDiscretizer.InverseTransform	Reads each bin back as its centre.
KBinsDiscretizer.Transform	Reads each value as the bin it falls in.