Class InfoBitRanker
object --+
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??.instance --+
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InfoBitRanker
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
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AccumulateVotes(...)
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. |
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GetTopN(...)
Returns the top n bits ranked by the information metric... |
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SetBiasList(...)
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. |
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SetMaskBits(...)
Set the mask bits for the calculation... |
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Inherited from unreachable.instance:
__new__
Inherited from object:
__delattr__,
__getattribute__,
__hash__,
__reduce__,
__reduce_ex__,
__repr__,
__setattr__,
__str__
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Inherited from object:
__class__
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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:
AccumulateVotes(RDInfoTheory::InfoBitRanker*, boost::python::api::object, int) -> void*
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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:
GetTopN(RDInfoTheory::InfoBitRanker*, int) -> _object*
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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:
SetBiasList(RDInfoTheory::InfoBitRanker*, boost::python::api::object) -> void*
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Set the mask bits for the calculation
ARGUMENTS:
- maskBits : list of mask bits to use
C++ signature:
SetMaskBits(RDInfoTheory::InfoBitRanker*, boost::python::api::object) -> void*
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C++ signature:
Tester(RDInfoTheory::InfoBitRanker*, boost::python::api::object) -> void*
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Write the bits that have been ranked to a file
C++ signature:
WriteTopBitsToFile(RDInfoTheory::InfoBitRanker {lvalue}, std::string) -> void*
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__init__(...)
(Constructor)
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C++ signature:
__init__(_object*, int nBits, int nClasses) -> void*
C++ signature:
__init__(_object*, int nBits, int nClasses, RDInfoTheory::InfoBitRanker::InfoType infoType) -> void*
- Overrides:
object.__init__
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