rdkit.ML.InfoTheory.rdInfoTheory module

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

class rdkit.ML.InfoTheory.rdInfoTheory.BitCorrMatGenerator((object)arg1)

Bases: instance

A class to generate a pairwise 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()

The resulting correlation matrix is a one dimensional nummeric array containing the lower triangle elements

C++ signature :

void __init__(_object*)

CollectVotes((BitCorrMatGenerator)self, (AtomPairsParameters)bitVect) → None :

For each pair of on bits (bi, bj) in fp increase the correlation count for the pair by 1

ARGUMENTS:

  • fp : a bit vector to collect the fingerprints from

C++ signature :

void CollectVotes(RDInfoTheory::BitCorrMatGenerator*,boost::python::api::object)

GetCorrMatrix((BitCorrMatGenerator)self) → object :

Get the correlation matrix following the collection of votes from a bunch of fingerprints

C++ signature :

_object* GetCorrMatrix(RDInfoTheory::BitCorrMatGenerator*)

SetBitList((BitCorrMatGenerator)self, (AtomPairsParameters)bitList) → None :

Set the list of bits that need to be correllated

This may for example be their top ranking ensemble bits

ARGUMENTS:

  • bitList : an integer list of bit IDs

C++ signature :

void SetBitList(RDInfoTheory::BitCorrMatGenerator*,boost::python::api::object)

rdkit.ML.InfoTheory.rdInfoTheory.ChiSquare((AtomPairsParameters)resArr) → 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 occurrences

    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)

class rdkit.ML.InfoTheory.rdInfoTheory.InfoBitRanker((object)self, (int)nBits, (int)nClasses)

Bases: instance

A class to rank the bits from a series of labelled fingerprints A simple demonstration may help clarify what this class does. Here’s a small set of vectors:

>>> for i,bv in enumerate(bvs): print(bv.ToBitString(),acts[i])
... 
0001 0
0101 0
0010 1
1110 1

Default ranker, using infogain:

>>> ranker = InfoBitRanker(4,2)  
>>> for i,bv in enumerate(bvs): ranker.AccumulateVotes(bv,acts[i])
... 
>>> for bit,gain,n0,n1 in ranker.GetTopN(3): print(int(bit),'%.3f'%gain,int(n0),int(n1))
... 
3 1.000 2 0
2 1.000 0 2
0 0.311 0 1

Using the biased infogain:

>>> ranker = InfoBitRanker(4,2,InfoTheory.InfoType.BIASENTROPY)
>>> ranker.SetBiasList((1,))
>>> for i,bv in enumerate(bvs): ranker.AccumulateVotes(bv,acts[i])
... 
>>> for bit,gain,n0,n1 in ranker.GetTopN(3): print(int(bit),'%.3f'%gain,int(n0),int(n1))
... 
2 1.000 0 2
0 0.311 0 1
1 0.000 1 1

A chi squared ranker is also available:

>>> ranker = InfoBitRanker(4,2,InfoTheory.InfoType.CHISQUARE)
>>> for i,bv in enumerate(bvs): ranker.AccumulateVotes(bv,acts[i])
... 
>>> for bit,gain,n0,n1 in ranker.GetTopN(3): print(int(bit),'%.3f'%gain,int(n0),int(n1))
... 
3 4.000 2 0
2 4.000 0 2
0 1.333 0 1

As is a biased chi squared:

>>> ranker = InfoBitRanker(4,2,InfoTheory.InfoType.BIASCHISQUARE)
>>> ranker.SetBiasList((1,))
>>> for i,bv in enumerate(bvs): ranker.AccumulateVotes(bv,acts[i])
... 
>>> for bit,gain,n0,n1 in ranker.GetTopN(3): print(int(bit),'%.3f'%gain,int(n0),int(n1))
... 
2 4.000 0 2
0 1.333 0 1
1 0.000 1 1
C++ signature :

void __init__(_object*,int,int)

__init__( (object)self, (int)nBits, (int)nClasses, (InfoType)infoType) -> None :

C++ signature :

void __init__(_object*,int,int,RDInfoTheory::InfoBitRanker::InfoType)

AccumulateVotes((InfoBitRanker)self, (AtomPairsParameters)bitVect, (int)label) → None :

Accumulate the votes for all the bits turned on in a bit vector

ARGUMENTS:

  • bv : bit vector either ExplicitBitVect or SparseBitVect operator

  • label : the class label for the bit vector. It is assumed that 0 <= class < nClasses

C++ signature :

void AccumulateVotes(RDInfoTheory::InfoBitRanker*,boost::python::api::object,int)

GetTopN((InfoBitRanker)self, (int)num) → object :

Returns the top n bits ranked by the information metric This is actually the function where most of the work of ranking is happening

ARGUMENTS:

  • num : the number of top ranked bits that are required

C++ signature :

_object* GetTopN(RDInfoTheory::InfoBitRanker*,int)

SetBiasList((InfoBitRanker)self, (AtomPairsParameters)classList) → None :

Set the classes to which the entropy calculation should be biased

This list contains a set of class ids used when in the BIASENTROPY mode of ranking bits. In this mode, a bit must be correlated higher with one of the biased classes than all the other classes. For example, in a two class problem with actives and inactives, the fraction of actives that hit the bit has to be greater than the fraction of inactives that hit the bit

ARGUMENTS:

  • classList : list of class ids that we want a bias towards

C++ signature :

void SetBiasList(RDInfoTheory::InfoBitRanker*,boost::python::api::object)

SetMaskBits((InfoBitRanker)self, (AtomPairsParameters)maskBits) → None :

Set the mask bits for the calculation

ARGUMENTS:

  • maskBits : list of mask bits to use

C++ signature :

void SetMaskBits(RDInfoTheory::InfoBitRanker*,boost::python::api::object)

Tester((InfoBitRanker)self, (AtomPairsParameters)bitVect) → None :
C++ signature :

void Tester(RDInfoTheory::InfoBitRanker*,boost::python::api::object)

WriteTopBitsToFile((InfoBitRanker)self, (str)fileName) → None :

Write the bits that have been ranked to a file

C++ signature :

void WriteTopBitsToFile(RDInfoTheory::InfoBitRanker {lvalue},std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >)

rdkit.ML.InfoTheory.rdInfoTheory.InfoEntropy((AtomPairsParameters)resArr) → 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)

rdkit.ML.InfoTheory.rdInfoTheory.InfoGain((AtomPairsParameters)resArr) → float :

Calculates the information gain for a variable

ARGUMENTS:

  • varMat: a Numeric Array object varMat is a Numeric array with the number of possible occurrences

    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)

class rdkit.ML.InfoTheory.rdInfoTheory.InfoType

Bases: enum

BIASCHISQUARE = rdkit.ML.InfoTheory.rdInfoTheory.InfoType.BIASCHISQUARE
BIASENTROPY = rdkit.ML.InfoTheory.rdInfoTheory.InfoType.BIASENTROPY
CHISQUARE = rdkit.ML.InfoTheory.rdInfoTheory.InfoType.CHISQUARE
ENTROPY = rdkit.ML.InfoTheory.rdInfoTheory.InfoType.ENTROPY
names = {'BIASCHISQUARE': rdkit.ML.InfoTheory.rdInfoTheory.InfoType.BIASCHISQUARE, 'BIASENTROPY': rdkit.ML.InfoTheory.rdInfoTheory.InfoType.BIASENTROPY, 'CHISQUARE': rdkit.ML.InfoTheory.rdInfoTheory.InfoType.CHISQUARE, 'ENTROPY': rdkit.ML.InfoTheory.rdInfoTheory.InfoType.ENTROPY}
values = {1: rdkit.ML.InfoTheory.rdInfoTheory.InfoType.ENTROPY, 2: rdkit.ML.InfoTheory.rdInfoTheory.InfoType.BIASENTROPY, 3: rdkit.ML.InfoTheory.rdInfoTheory.InfoType.CHISQUARE, 4: rdkit.ML.InfoTheory.rdInfoTheory.InfoType.BIASCHISQUARE}