rdkit.ML.InfoTheory.BitRank module — The RDKit 2016.09.1 documentation

# rdkit.ML.InfoTheory.BitRank module

Functionality for ranking bits using info gains

Definitions used in this module

> - sequence: an object capable of containing other objects which supports __getitem__() and __len__(). Examples of these include lists, tuples, and Numeric arrays.

> - IntVector: an object containing integers which supports __getitem__() and

>     __len__(). Examples include lists, tuples, Numeric Arrays, and BitVects.

NOTE: Neither sequences nor IntVectors need to support item assignment.
  It is perfectly acceptable for them to be read-only, so long as they are random-access.

`rdkit.ML.InfoTheory.BitRank.` `AnalyzeSparseVects`(bitVects, actVals)

  #DOC

  Arguments

  > > - bitVects: a sequence containing SBVs
  > > - actVals: a sequence

  > Returns

  > > a list of floats

  > Notes

  > > - these need to be bit vects and binary activities

`rdkit.ML.InfoTheory.BitRank.` `CalcInfoGains`(bitVects, actVals, nPossibleActs, nPossibleBitVals=2)

  Calculates the information gain for a set of points and activity values

  Arguments

  > > - bitVects: a sequence containing IntVectors
  > > - actVals: a sequence
  > > - nPossibleActs: the (integer) number of possible activity values.
  > > - nPossibleBitVals: (optional) if specified, this integer provides the maximum value attainable by the (increasingly inaccurately named) bits in _bitVects_.

  > Returns

  > > a list of floats

`rdkit.ML.InfoTheory.BitRank.` `FormCounts`(bitVects, actVals, whichBit, nPossibleActs, nPossibleBitVals=2)

  generates the counts matrix for a particular bit

  Arguments

  > - bitVects: a sequence containing IntVectors
  > - actVals: a sequence
  > - whichBit: an integer, the bit number to use.
  > - nPossibleActs: the (integer) number of possible activity values.
  > - nPossibleBitVals: (optional) if specified, this integer provides the maximum value attainable by the (increasingly inaccurately named) bits in _bitVects_.

  Returns

  > a Numeric array with the counts

  Notes

  > This is really intended for internal use.

`rdkit.ML.InfoTheory.BitRank.` `RankBits`(bitVects, actVals, nPossibleBitVals=2, metricFunc=<function CalcInfoGains>)

  Rank a set of bits according to a metric function

  Arguments

  > > - bitVects: a sequence containing IntVectors
  > > - actVals: a sequence
  > > - nPossibleBitVals: (optional) if specified, this integer provides the maximum value attainable by the (increasingly inaccurately named) bits in _bitVects_.
  > > - metricFunc: (optional) the metric function to be used. See _CalcInfoGains()_ for a description of the signature of this function.

  > Returns

  > > A 2-tuple containing:

  > > > - the relative order of the bits (a list of ints)
  > > > - the metric calculated for each bit (a list of floats)

`rdkit.ML.InfoTheory.BitRank.` `SparseRankBits`(bitVects, actVals, metricFunc=<function AnalyzeSparseVects>)

  Rank a set of bits according to a metric function

  Arguments

  > > - bitVects: a sequence containing SBVs
  > > - actVals: a sequence
  > > - metricFunc: (optional) the metric function to be used. See _SparseCalcInfoGains()_ for a description of the signature of this function.

  > Returns

  > > > A 2-tuple containing:

  > > > > - the relative order of the bits (a list of ints)
  > > > > - the metric calculated for each bit (a list of floats)

  > > Notes

  > > > - these need to be bit vects and binary activities