Preprocessing quantilemethod

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QuantileMethod

Which percentile convention a quantile-strategy fit reads.

public enum QuantileMethod

Values — Linear interpolates between the two neighbouring order statistics; numpy's 'linear'. AveragedInvertedCdf reads the inverted empirical CDF, averaged where it jumps; numpy's 'averaged_inverted_cdf', and the default KBinsDiscretizerOptions takes.

Example — six values cut into three.

using Lodestar.Preprocessing;

double[] six = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];

var ordinal = new KBinsDiscretizerOptions { BinCount = 3, Encoding = BinEncoding.Ordinal };

KBinsDiscretizer averaged = KBinsDiscretizer.Fit(six, 1, ordinal);

double firstEdge = averaged.BinEdges[0][1];    // => 2.5
double secondEdge = averaged.BinEdges[0][2];   // => 4.5

Remarks — these are two definitions of the same word, not two approximations of one answer. The third of six values is genuinely ambiguous: AveragedInvertedCdf answers 2.5, the midpoint of the two order statistics that straddle the level, and Linear answers 2.667, the point two thirds of the way from the second to the third. Neither is more accurate; they differ in what "the 33rd percentile" is taken to mean.

The arithmetic order matters, and is not the obvious one. The position is n × (percent / 100) and not n × percent / 100: for nine values at a third, the first gives 3.0000000000000004 and reads one order statistic, the second gives exactly 3.0 and averages two — a whole different answer from a rounding difference. This follows numpy, which the reference calls.

RobustScaler reads a third convention, numpy's 'linear' under a different name, because its own reference calls numpy.percentile with the default.

Applies to — net10.0, netstandard2.0.

See also — KBinsDiscretizerOptions, BinStrategy.