rdkit.ML.Data.Quantize module — The RDKit 2025.09.6 documentation

# rdkit.ML.Data.Quantize module

Automatic search for quantization bounds

This uses the expected informational gain to determine where quantization bounds should lie.

Notes:

> - bounds are less than, so if the bounds are [1.,2.], [0.9,1.,1.1,2.,2.2] -> [0,1,1,2,2]

rdkit.ML.Data.Quantize.FindVarMultQuantBounds(vals, nBounds, results, nPossibleRes)

  finds multiple quantization bounds for a single variable

  Arguments

  > - vals: sequence of variable values (assumed to be floats)

  > - nBounds: the number of quantization bounds to find

  > - results: a list of result codes (should be integers)

  > - nPossibleRes: an integer with the number of possible values of the result variable

  Returns

  > - a 2-tuple containing:

  >   1. a list of the quantization bounds (floats)

  >   2. the information gain associated with this quantization

rdkit.ML.Data.Quantize.FindVarQuantBound(vals, results, nPossibleRes)

  Uses FindVarMultQuantBounds, only here for historic reasons

rdkit.ML.Data.Quantize.feq(v1, v2, tol=1e-08)

  floating point equality with a tolerance factor

  Arguments

  > - v1: a float

  > - v2: a float

  > - tol: the tolerance for comparison

  Returns

  > 0 or 1