Package rdkit :: Package ML :: Package InfoTheory :: Module rdInfoTheory
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Module rdInfoTheory

Module containing bunch of functions for information metrics and a ranker to rank bits

Classes [hide private]
  BitCorrMatGenerator
A class to generate a pariwise correlation matrix between a list of bits The mode of operation for this class is something like this >>> cmg = BitCorrMatGenerator() >>> cmg.SetBitList(blist) >>> for fp in fpList: >>> cmg.CollectVotes(fp) >>> corrMat = cmg.GetCorrMatrix()
  InfoBitRanker
A class to rank the bits from a series of labelled fingerprints A simple demonstration may help clarify what this class does.
  InfoType
Functions [hide private]
 
ChiSquare(...)
ChiSquare( (AtomPairsParameters)arg1) -> float : Calculates the chi squared value for a variable
 
InfoEntropy(results)
InfoEntropy( (AtomPairsParameters)arg1) -> float : calculates the informational entropy of the values in an array
 
InfoGain(...)
InfoGain( (AtomPairsParameters)arg1) -> float : Calculates the information gain for a variable
Variables [hide private]
  BIASCHISQUARE = rdkit.ML.InfoTheory.rdInfoTheory.InfoType.BIAS...
  BIASENTROPY = rdkit.ML.InfoTheory.rdInfoTheory.InfoType.BIASEN...
  CHISQUARE = rdkit.ML.InfoTheory.rdInfoTheory.InfoType.CHISQUARE
  ENTROPY = rdkit.ML.InfoTheory.rdInfoTheory.InfoType.ENTROPY
  __package__ = None
hash(x)
Function Details [hide private]

ChiSquare(...)

 

ChiSquare( (AtomPairsParameters)arg1) -> float :
    Calculates the chi squared value for a variable
    
       ARGUMENTS:
    
         - varMat: a Numeric Array object
           varMat is a Numeric array with the number of possible occurances
             of each result for reach possible value of the given variable.
    
           So, for a variable which adopts 4 possible values and a result which
             has 3 possible values, varMat would be 4x3
    
       RETURNS:
    
         - a Python float object
    

    C++ signature :
        double ChiSquare(boost::python::api::object)

InfoEntropy(results)

 

InfoEntropy( (AtomPairsParameters)arg1) -> float :
    calculates the informational entropy of the values in an array
    
      ARGUMENTS:
        
        - resMat: pointer to a long int array containing the data
        - dim: long int containing the length of the _tPtr_ array.
    
      RETURNS:
    
        a double
    

    C++ signature :
        double InfoEntropy(boost::python::api::object)

InfoGain(...)

 

InfoGain( (AtomPairsParameters)arg1) -> float :
    Calculates the information gain for a variable
    
       ARGUMENTS:
    
         - varMat: a Numeric Array object
           varMat is a Numeric array with the number of possible occurances
             of each result for reach possible value of the given variable.
    
           So, for a variable which adopts 4 possible values and a result which
             has 3 possible values, varMat would be 4x3
    
       RETURNS:
    
         - a Python float object
    
       NOTES
    
         - this is a dropin replacement for _PyInfoGain()_ in entropy.py
    

    C++ signature :
        double InfoGain(boost::python::api::object)


Variables Details [hide private]

BIASCHISQUARE

Value:
rdkit.ML.InfoTheory.rdInfoTheory.InfoType.BIASCHISQUARE

BIASENTROPY

Value:
rdkit.ML.InfoTheory.rdInfoTheory.InfoType.BIASENTROPY