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// $Id$
//
// Copyright (C) 2003-2008 Greg Landrum and Rational Discovery LLC
// @@ All Rights Reserved @@
// This file is part of the RDKit.
// The contents are covered by the terms of the BSD license
// which is included in the file license.txt, found at the root
// of the RDKit source tree.
//
#define NO_IMPORT_ARRAY
#include <boost/python.hpp>
#define PY_ARRAY_UNIQUE_SYMBOL rdinfotheory_array_API
#include "numpy/arrayobject.h"
#include <RDBoost/Wrap.h>
#include <ML/InfoTheory/InfoBitRanker.h>
#include <DataStructs/BitVects.h>
#include <RDBoost/PySequenceHolder.h>
namespace python = boost::python;
namespace RDInfoTheory {
PyObject *getTopNbits(InfoBitRanker *ranker, int num){// int ignoreNoClass=-1) {
double *dres = ranker->getTopN(num);
npy_intp dims[2];
dims[0] = num;
dims[1] = ranker->getNumClasses() + 2;
PyArrayObject *res = (PyArrayObject *)PyArray_SimpleNew(2,dims,NPY_DOUBLE);
memcpy(static_cast<void *>(res->data),
static_cast<void *>(dres), dims[0]*dims[1]*sizeof(double));
return PyArray_Return(res);
}
void AccumulateVotes(InfoBitRanker *ranker, python::object bitVect, int label) {
python::extract<ExplicitBitVect> ebvWorks(bitVect);
python::extract<SparseBitVect> sbvWorks(bitVect);
if (ebvWorks.check()) {
ExplicitBitVect ev = python::extract<ExplicitBitVect>(bitVect);
ranker->accumulateVotes(ev, label);
}
else if (sbvWorks.check()) {
SparseBitVect sv = python::extract<SparseBitVect>(bitVect);
ranker->accumulateVotes(sv, label);
}
else {
throw_value_error("Accumulate Vote can only take a explicitBitVects or SparseBitvects");
}
}
void SetBiasList(InfoBitRanker *ranker, python::object classList) {
RDKit::INT_VECT cList;
PySequenceHolder<int> bList(classList);
cList.reserve(bList.size());
for (unsigned int i = 0; i < bList.size(); i++) {
cList.push_back(bList[i]);
}
ranker->setBiasList(cList);
}
void SetMaskBits(InfoBitRanker *ranker, python::object maskBits) {
RDKit::INT_VECT cList;
PySequenceHolder<int> bList(maskBits);
cList.reserve(bList.size());
for (unsigned int i = 0; i < bList.size(); i++) {
cList.push_back(bList[i]);
}
ranker->setMaskBits(cList);
}
void tester(InfoBitRanker *ranker, python::object bitVect) {
python::extract<SparseBitVect> sbvWorks(bitVect);
if (sbvWorks.check()){
SparseBitVect sv = python::extract<SparseBitVect>(bitVect);
std::cout << "Num of on bits: " << sv.getNumOnBits() << "\n";
}
}
struct ranker_wrap {
static void wrap() {
std::string docString = "A class to rank the bits from a series of labelled fingerprints\n"
"A simple demonstration may help clarify what this class does. \n"
"Here's a small set of vectors:\n"
">>> for i,bv in enumerate(bvs): print bv.ToBitString(),acts[i]\n"
"... \n"
"0001 0\n"
"0101 0\n"
"0010 1\n"
"1110 1\n"
"\n"
"Default ranker, using infogain:\n"
">>> ranker = InfoBitRanker(4,2) \n"
">>> for i,bv in enumerate(bvs): ranker.AccumulateVotes(bv,acts[i])\n"
"... \n"
">>> for bit,gain,n0,n1 in ranker.GetTopN(3): print int(bit),'%.3f'%gain,int(n0),int(n1)\n"
"... \n"
"3 1.000 2 0\n"
"2 1.000 0 2\n"
"0 0.311 0 1\n"
"\n"
"Using the biased infogain:\n"
">>> ranker = InfoBitRanker(4,2,InfoTheory.InfoType.BIASENTROPY)\n"
">>> ranker.SetBiasList((1,))\n"
">>> for i,bv in enumerate(bvs): ranker.AccumulateVotes(bv,acts[i])\n"
"... \n"
">>> for bit,gain,n0,n1 in ranker.GetTopN(3): print int(bit),'%.3f'%gain,int(n0),int(n1)\n"
"... \n"
"2 1.000 0 2\n"
"0 0.311 0 1\n"
"1 0.000 1 1\n"
"\n"
"A chi squared ranker is also available:\n"
">>> ranker = InfoBitRanker(4,2,InfoTheory.InfoType.CHISQUARE)\n"
">>> for i,bv in enumerate(bvs): ranker.AccumulateVotes(bv,acts[i])\n"
"... \n"
">>> for bit,gain,n0,n1 in ranker.GetTopN(3): print int(bit),'%.3f'%gain,int(n0),int(n1)\n"
"... \n"
"3 4.000 2 0\n"
"2 4.000 0 2\n"
"0 1.333 0 1\n"
"\n"
"As is a biased chi squared:\n"
">>> ranker = InfoBitRanker(4,2,InfoTheory.InfoType.BIASCHISQUARE)\n"
">>> ranker.SetBiasList((1,))\n"
">>> for i,bv in enumerate(bvs): ranker.AccumulateVotes(bv,acts[i])\n"
"... \n"
">>> for bit,gain,n0,n1 in ranker.GetTopN(3): print int(bit),'%.3f'%gain,int(n0),int(n1)\n"
"... \n"
"2 4.000 0 2\n"
"0 1.333 0 1\n"
"1 0.000 1 1\n";
python::class_<InfoBitRanker>("InfoBitRanker",
docString.c_str(),
python::init<int, int>(python::args("nBits", "nClasses")))
.def(python::init<int, int, InfoBitRanker::InfoType>
(python::args("nBits", "nClasses", "infoType")))
.def("AccumulateVotes", AccumulateVotes,
"Accumulate the votes for all the bits turned on in a bit vector\n\n"
"ARGUMENTS:\n\n"
" - bv : bit vector either ExplicitBitVect or SparseBitVect operator\n"
" - label : the class label for the bit vector. It is assumed that 0 <= class < nClasses \n")
.def ("SetBiasList", SetBiasList,
"Set the classes to which the entropy calculation should be biased\n\n"
"This list contains a set of class ids used when in the BIASENTROPY mode of ranking bits. \n"
"In this mode, a bit must be correlated higher with one of the biased classes than all the \n"
"other classes. For example, in a two class problem with actives and inactives, the fraction of \n"
"actives that hit the bit has to be greater than the fraction of inactives that hit the bit\n\n"
"ARGUMENTS: \n\n"
" - classList : list of class ids that we want a bias towards\n")
.def ("SetMaskBits", SetMaskBits,
"Set the mask bits for the calculation\n\n"
"ARGUMENTS: \n\n"
" - maskBits : list of mask bits to use\n")
.def("GetTopN", getTopNbits,
"Returns the top n bits ranked by the information metric\n"
"This is actually the function where most of the work of ranking is happening\n\n"
"ARGUMENTS:\n\n"
" - num : the number of top ranked bits that are required\n")
.def("WriteTopBitsToFile", &InfoBitRanker::writeTopBitsToFile,
"Write the bits that have been ranked to a file")
.def("Tester", tester)
;
python::enum_<InfoBitRanker::InfoType>("InfoType")
.value("ENTROPY", InfoBitRanker::ENTROPY)
.value("BIASENTROPY", InfoBitRanker::BIASENTROPY)
.value("CHISQUARE", InfoBitRanker::CHISQUARE)
.value("BIASCHISQUARE", InfoBitRanker::BIASCHISQUARE)
;
};
};
}
void wrap_ranker() {
RDInfoTheory::ranker_wrap::wrap();
}
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