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