Preprocessing powertransformer

Development build. This page describes main, not a released package. The latest published Lodestar.Preprocessing is 0.2.0 — read its documentation.

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PowerTransformer

Raises each feature to the power that makes it most nearly normal, at sklearn.preprocessing.PowerTransformer parity.

public sealed class PowerTransformer

Properties — FeatureCount and SampleCount are the shape it was fitted on. Lambdas is each feature's fitted exponent — the reference's lambdas_.

Example — a right-skewed income column.

using Lodestar.Preprocessing;

double[] income = [22.0, 25.0, 28.0, 31.0, 35.0, 42.0, 55.0, 78.0, 120.0, 260.0];

PowerTransformer transformer = PowerTransformer.Fit(income, featureCount: 1);

double lambda = Math.Round(transformer.Lambdas[0], 4);   // => -0.8072

double[] symmetric = transformer.Transform(income);

double smallest = Math.Round(symmetric[0], 4);   // => -1.4543
double largest = Math.Round(symmetric[9], 4);    // => 1.7177

Remarks — this is the gentle counterpart to QuantileTransformer. Both answer "make this column look normal"; this one does it with a single exponent fitted by maximum likelihood, so the order and the relative spacing survive — two values close together stay close together, and a new value outside the fitted range is transformed by the same formula rather than clamped. The cost is that one exponent may not be enough: a bimodal column has no power that makes it normal, and the transformer will still return the best one.

Conformance here is 1e-5, not the 1e-9 the rest of this repository meets. The exponent is fitted by maximising a log-likelihood whose curvature at the optimum is about 176 against a value around 443; a double carries that value to roughly 3e-11, so the data itself pins the exponent only to about 6e-7, and moving it that far moves the transformed values by 9.1e-7 relative. The reference's own optimiser stops at 1.48e-8 on the argument, well inside what the objective can distinguish. The Python equivalence table carries the arithmetic; it is the one family in this package held to a stated wider tolerance.

Applies to — net10.0, netstandard2.0.

See also — PowerTransformerOptions, PowerMethod, QuantileTransformer, the feature transforming index, the Python equivalence table.

Members

Member	What it does
PowerTransformer.Fit	Fits one exponent per feature by maximum likelihood.
PowerTransformer.InverseTransform	Undoes Transform.
PowerTransformer.Transform	Raises each feature to its fitted power.