mirror of
https://github.com/rdkit/rdkit.git
synced 2026-06-03 21:44:30 +08:00
first pass, using google style
This commit is contained in:
Greg Landrum
@@ -25,204 +25,211 @@
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namespace python = boost::python;
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namespace RDPickers {
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// REVIEW: the poolSize can be pulled from the numeric array
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RDKit::INT_VECT MaxMinPicks(MaxMinPicker *picker,
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python::object distMat,
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int poolSize,
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int pickSize,
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python::object firstPicks,
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int seed) {
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if(pickSize>=poolSize){
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throw ValueErrorException("pickSize must be less than poolSize");
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}
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if (!PyArray_Check(distMat.ptr())){
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throw ValueErrorException("distance mat argument must be a numpy matrix");
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}
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PyArrayObject *copy;
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copy = (PyArrayObject *)PyArray_ContiguousFromObject(distMat.ptr(),
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PyArray_DOUBLE, 1,1);
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double *dMat = (double *)copy->data;
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RDKit::INT_VECT firstPickVect;
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for(unsigned int i=0;i<python::extract<unsigned int>(firstPicks.attr("__len__")());++i){
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firstPickVect.push_back(python::extract<int>(firstPicks[i]));
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}
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RDKit::INT_VECT res=picker->pick(dMat, poolSize, pickSize,firstPickVect,seed);
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Py_DECREF(copy);
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return res;
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// REVIEW: the poolSize can be pulled from the numeric array
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RDKit::INT_VECT MaxMinPicks(MaxMinPicker *picker, python::object distMat,
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int poolSize, int pickSize,
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python::object firstPicks, int seed) {
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if (pickSize >= poolSize) {
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throw ValueErrorException("pickSize must be less than poolSize");
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}
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class pyobjFunctor {
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public:
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pyobjFunctor(python::object obj,bool useCache) : dp_obj(obj), dp_cache(NULL) {
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if(useCache) dp_cache= new std::map<std::pair<unsigned int,unsigned int>,double>();
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}
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~pyobjFunctor() {
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delete dp_cache;
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}
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double operator()(unsigned int i,unsigned int j) {
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double res;
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std::pair<unsigned int ,unsigned int> idxPair(i,j);
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if(dp_cache && dp_cache->count(idxPair)>0){
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res = (*dp_cache)[idxPair];
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} else {
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res=python::extract<double>(dp_obj(i,j));
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if(dp_cache)
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(*dp_cache)[idxPair]=res;
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}
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return res;
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}
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private:
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python::object dp_obj;
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std::map<std::pair<unsigned int,unsigned int>,double> *dp_cache;
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};
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RDKit::INT_VECT LazyMaxMinPicks(MaxMinPicker *picker,
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python::object distFunc,
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int poolSize,
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int pickSize,
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python::object firstPicks,
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int seed,
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bool useCache) {
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RDKit::INT_VECT firstPickVect;
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for(unsigned int i=0;i<python::extract<unsigned int>(firstPicks.attr("__len__")());++i){
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firstPickVect.push_back(python::extract<int>(firstPicks[i]));
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if (!PyArray_Check(distMat.ptr())) {
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throw ValueErrorException("distance mat argument must be a numpy matrix");
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}
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PyArrayObject *copy;
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copy = (PyArrayObject *)PyArray_ContiguousFromObject(distMat.ptr(),
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PyArray_DOUBLE, 1, 1);
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double *dMat = (double *)copy->data;
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RDKit::INT_VECT firstPickVect;
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for (unsigned int i = 0;
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i < python::extract<unsigned int>(firstPicks.attr("__len__")()); ++i) {
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firstPickVect.push_back(python::extract<int>(firstPicks[i]));
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}
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RDKit::INT_VECT res =
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picker->pick(dMat, poolSize, pickSize, firstPickVect, seed);
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Py_DECREF(copy);
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return res;
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}
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class pyobjFunctor {
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public:
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pyobjFunctor(python::object obj, bool useCache)
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: dp_obj(obj), dp_cache(NULL) {
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if (useCache)
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dp_cache = new std::map<std::pair<unsigned int, unsigned int>, double>();
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}
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~pyobjFunctor() { delete dp_cache; }
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double operator()(unsigned int i, unsigned int j) {
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double res;
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std::pair<unsigned int, unsigned int> idxPair(i, j);
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if (dp_cache && dp_cache->count(idxPair) > 0) {
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res = (*dp_cache)[idxPair];
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} else {
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res = python::extract<double>(dp_obj(i, j));
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if (dp_cache) (*dp_cache)[idxPair] = res;
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}
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RDKit::INT_VECT res;
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pyobjFunctor functor(distFunc,useCache);
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res=picker->lazyPick(functor, poolSize, pickSize,firstPickVect,seed);
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return res;
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}
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// NOTE: TANIMOTO and DICE provably return the same results for the diversity picking
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// this is still here just in case we ever later want to support other methods.
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typedef enum {
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TANIMOTO=1,
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DICE
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} DistanceMethod;
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private:
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python::object dp_obj;
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std::map<std::pair<unsigned int, unsigned int>, double> *dp_cache;
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};
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template <typename BV>
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class pyBVFunctor {
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public:
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pyBVFunctor(const std::vector<const BV *> &obj,DistanceMethod method,bool useCache) : d_obj(obj), d_method(method), dp_cache(NULL) {
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if(useCache) dp_cache = new std::map<std::pair<unsigned int,unsigned int>,double>();
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}
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~pyBVFunctor() {
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delete dp_cache;
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}
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double operator()(unsigned int i,unsigned int j) {
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double res=0.0;
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std::pair<unsigned int ,unsigned int> idxPair(i,j);
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if(dp_cache && dp_cache->count(idxPair)>0){
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res = (*dp_cache)[idxPair];
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} else {
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switch(d_method){
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RDKit::INT_VECT LazyMaxMinPicks(MaxMinPicker *picker, python::object distFunc,
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int poolSize, int pickSize,
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python::object firstPicks, int seed,
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bool useCache) {
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RDKit::INT_VECT firstPickVect;
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for (unsigned int i = 0;
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i < python::extract<unsigned int>(firstPicks.attr("__len__")()); ++i) {
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firstPickVect.push_back(python::extract<int>(firstPicks[i]));
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}
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RDKit::INT_VECT res;
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pyobjFunctor functor(distFunc, useCache);
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res = picker->lazyPick(functor, poolSize, pickSize, firstPickVect, seed);
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return res;
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}
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// NOTE: TANIMOTO and DICE provably return the same results for the diversity
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// picking
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// this is still here just in case we ever later want to support other
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// methods.
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typedef enum { TANIMOTO = 1, DICE } DistanceMethod;
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template <typename BV>
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class pyBVFunctor {
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public:
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pyBVFunctor(const std::vector<const BV *> &obj, DistanceMethod method,
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bool useCache)
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: d_obj(obj), d_method(method), dp_cache(NULL) {
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if (useCache)
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dp_cache = new std::map<std::pair<unsigned int, unsigned int>, double>();
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}
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~pyBVFunctor() { delete dp_cache; }
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double operator()(unsigned int i, unsigned int j) {
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double res = 0.0;
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std::pair<unsigned int, unsigned int> idxPair(i, j);
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if (dp_cache && dp_cache->count(idxPair) > 0) {
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res = (*dp_cache)[idxPair];
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} else {
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switch (d_method) {
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case TANIMOTO:
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res = 1.-TanimotoSimilarity(*d_obj[i],*d_obj[j]);
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res = 1. - TanimotoSimilarity(*d_obj[i], *d_obj[j]);
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break;
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case DICE:
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res = 1.-DiceSimilarity(*d_obj[i],*d_obj[j]);
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res = 1. - DiceSimilarity(*d_obj[i], *d_obj[j]);
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break;
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default:
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throw_value_error("unsupported similarity value");
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}
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if(dp_cache){
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(*dp_cache)[idxPair]=res;
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}
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}
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return res;
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if (dp_cache) {
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(*dp_cache)[idxPair] = res;
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}
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}
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private:
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const std::vector<const BV *> &d_obj;
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DistanceMethod d_method;
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std::map<std::pair<unsigned int,unsigned int>,double> *dp_cache;
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};
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RDKit::INT_VECT LazyVectorMaxMinPicks(MaxMinPicker *picker,
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python::object objs,
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int poolSize,
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int pickSize,
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python::object firstPicks,
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int seed,
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bool useCache
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) {
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std::vector<const ExplicitBitVect *> bvs(poolSize);
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for(int i=0;i<poolSize;++i){
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bvs[i]=python::extract<const ExplicitBitVect *>(objs[i]);
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}
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pyBVFunctor<ExplicitBitVect> functor(bvs,TANIMOTO,useCache);
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RDKit::INT_VECT firstPickVect;
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for(unsigned int i=0;i<python::extract<unsigned int>(firstPicks.attr("__len__")());++i){
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firstPickVect.push_back(python::extract<int>(firstPicks[i]));
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}
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RDKit::INT_VECT res=picker->lazyPick(functor, poolSize, pickSize,firstPickVect,seed);
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return res;
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}
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} // end of namespace RDPickers
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struct MaxMin_wrap {
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static void wrap() {
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python::class_<RDPickers::MaxMinPicker>("MaxMinPicker",
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"A class for diversity picking of items using the MaxMin Algorithm\n")
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private:
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const std::vector<const BV *> &d_obj;
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DistanceMethod d_method;
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std::map<std::pair<unsigned int, unsigned int>, double> *dp_cache;
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};
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RDKit::INT_VECT LazyVectorMaxMinPicks(MaxMinPicker *picker, python::object objs,
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int poolSize, int pickSize,
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python::object firstPicks, int seed,
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bool useCache) {
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std::vector<const ExplicitBitVect *> bvs(poolSize);
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for (int i = 0; i < poolSize; ++i) {
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bvs[i] = python::extract<const ExplicitBitVect *>(objs[i]);
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}
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pyBVFunctor<ExplicitBitVect> functor(bvs, TANIMOTO, useCache);
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RDKit::INT_VECT firstPickVect;
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for (unsigned int i = 0;
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i < python::extract<unsigned int>(firstPicks.attr("__len__")()); ++i) {
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firstPickVect.push_back(python::extract<int>(firstPicks[i]));
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}
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RDKit::INT_VECT res =
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picker->lazyPick(functor, poolSize, pickSize, firstPickVect, seed);
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return res;
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}
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} // end of namespace RDPickers
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struct MaxMin_wrap {
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static void wrap() {
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python::class_<RDPickers::MaxMinPicker>(
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"MaxMinPicker",
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"A class for diversity picking of items using the MaxMin Algorithm\n")
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.def("Pick", RDPickers::MaxMinPicks,
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(python::arg("self"),python::arg("distMat"),python::arg("poolSize"),
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python::arg("pickSize"),python::arg("firstPicks")=python::tuple(),
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python::arg("seed")=-1),
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"Pick a subset of items from a pool of items using the MaxMin Algorithm\n"
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"Ashton, M. et. al., Quant. Struct.-Act. Relat., 21 (2002), 598-604 \n\n"
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(python::arg("self"), python::arg("distMat"),
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python::arg("poolSize"), python::arg("pickSize"),
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python::arg("firstPicks") = python::tuple(),
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python::arg("seed") = -1),
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"Pick a subset of items from a pool of items using the MaxMin "
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"Algorithm\n"
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"Ashton, M. et. al., Quant. Struct.-Act. Relat., 21 (2002), "
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"598-604 \n\n"
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"ARGUMENTS:\n"
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" - distMat: 1D distance matrix (only the lower triangle elements)\n"
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" - distMat: 1D distance matrix (only the lower triangle "
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"elements)\n"
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" - poolSize: number of items in the pool\n"
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" - pickSize: number of items to pick from the pool\n"
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" - firstPicks: (optional) the first items to be picked (seeds the list)\n"
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" - seed: (optional) seed for the random number generator\n"
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)
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" - firstPicks: (optional) the first items to be picked (seeds "
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"the list)\n"
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" - seed: (optional) seed for the random number generator\n")
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.def("LazyPick", RDPickers::LazyMaxMinPicks,
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(python::arg("self"),python::arg("distFunc"),python::arg("poolSize"),
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python::arg("pickSize"),python::arg("firstPicks")=python::tuple(),
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python::arg("seed")=-1,python::arg("useCache")=true),
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"Pick a subset of items from a pool of items using the MaxMin Algorithm\n"
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"Ashton, M. et. al., Quant. Struct.-Act. Relat., 21 (2002), 598-604 \n"
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(python::arg("self"), python::arg("distFunc"),
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python::arg("poolSize"), python::arg("pickSize"),
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python::arg("firstPicks") = python::tuple(),
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python::arg("seed") = -1, python::arg("useCache") = true),
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"Pick a subset of items from a pool of items using the MaxMin "
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"Algorithm\n"
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"Ashton, M. et. al., Quant. Struct.-Act. Relat., 21 (2002), "
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"598-604 \n"
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"ARGUMENTS:\n\n"
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" - distFunc: a function that should take two indices and return the\n"
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" - distFunc: a function that should take two indices and return "
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"the\n"
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" distance between those two points.\n"
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" NOTE: the implementation caches distance values, so the\n"
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" client code does not need to do so; indeed, it should not.\n"
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" NOTE: the implementation caches distance values, "
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"so the\n"
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" client code does not need to do so; indeed, it "
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"should not.\n"
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" - poolSize: number of items in the pool\n"
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" - pickSize: number of items to pick from the pool\n"
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" - firstPicks: (optional) the first items to be picked (seeds the list)\n"
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" - firstPicks: (optional) the first items to be picked (seeds "
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"the list)\n"
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" - seed: (optional) seed for the random number generator\n"
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" - useCache: (optional) toggles use of a cache for the distance calculation\n"
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" This trades memory usage for speed.\n"
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)
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" - useCache: (optional) toggles use of a cache for the distance "
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"calculation\n"
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" This trades memory usage for speed.\n")
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.def("LazyBitVectorPick", RDPickers::LazyVectorMaxMinPicks,
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(python::arg("self"),python::arg("objects"),python::arg("poolSize"),
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python::arg("pickSize"),python::arg("firstPicks")=python::tuple(),
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python::arg("seed")=-1,
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python::arg("useCache")=true),
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"Pick a subset of items from a pool of bit vectors using the MaxMin Algorithm\n"
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"Ashton, M. et. al., Quant. Struct.-Act. Relat., 21 (2002), 598-604 \n"
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(python::arg("self"), python::arg("objects"),
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python::arg("poolSize"), python::arg("pickSize"),
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python::arg("firstPicks") = python::tuple(),
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python::arg("seed") = -1, python::arg("useCache") = true),
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"Pick a subset of items from a pool of bit vectors using the "
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"MaxMin Algorithm\n"
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"Ashton, M. et. al., Quant. Struct.-Act. Relat., 21 (2002), "
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"598-604 \n"
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"ARGUMENTS:\n\n"
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" - vectors: a sequence of the bit vectors that should be picked from.\n"
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" - vectors: a sequence of the bit vectors that should be picked "
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"from.\n"
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" - poolSize: number of items in the pool\n"
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" - pickSize: number of items to pick from the pool\n"
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" - firstPicks: (optional) the first items to be picked (seeds the list)\n"
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" - firstPicks: (optional) the first items to be picked (seeds "
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"the list)\n"
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" - seed: (optional) seed for the random number generator\n"
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" - useCache: (optional) toggles use of a cache for the distance calculation\n"
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" - useCache: (optional) toggles use of a cache for the distance "
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"calculation\n"
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" This trades memory usage for speed.\n"
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)
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;
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};
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);
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};
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};
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void wrap_maxminpick() {
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MaxMin_wrap::wrap();
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}
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void wrap_maxminpick() { MaxMin_wrap::wrap(); }
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