diff options
Diffstat (limited to 'src/python/gudhi/clustering/_tomato.cc')
| -rw-r--r-- | src/python/gudhi/clustering/_tomato.cc | 271 |
1 files changed, 271 insertions, 0 deletions
diff --git a/src/python/gudhi/clustering/_tomato.cc b/src/python/gudhi/clustering/_tomato.cc new file mode 100644 index 00000000..1c896174 --- /dev/null +++ b/src/python/gudhi/clustering/_tomato.cc @@ -0,0 +1,271 @@ +// g++ -O3 -Wall -shared -fPIC `python3 -m pybind11 --includes` XXX.cpp -o tomato`python3-config --extension-suffix` +#include <boost/container/flat_map.hpp> +#include <boost/pending/disjoint_sets.hpp> +#include <boost/property_map/property_map.hpp> +#include <boost/property_map/transform_value_property_map.hpp> +#include <boost/property_map/vector_property_map.hpp> +#include <boost/property_map/function_property_map.hpp> +#include <boost/iterator/counting_iterator.hpp> +#include <boost/range/irange.hpp> +#include <boost/range/adaptor/transformed.hpp> +#include <vector> +#include <unordered_map> +#include <pybind11/pybind11.h> +#include <pybind11/numpy.h> +#include <iostream> + +namespace py=pybind11; + +template<class T, class=std::enable_if_t<std::is_integral<T>::value>> +int getint(int i){return i;} +template<class T, class=decltype(std::declval<T>().template cast<int>())> +int getint(T i){return i.template cast<int>();} + +// Raw clusters are clusters obtained through single-linkage, no merging. + +typedef int Point_index; +typedef int Cluster_index; +struct Merge { Cluster_index first, second; double persist; }; + +template<class Neighbors, class Density, class Order, class ROrder> +auto tomato(Point_index num_points, Neighbors const&neighbors, Density const& density, Order const& order, ROrder const& rorder){ + // point index --> index of raw cluster it belongs to + std::vector<Cluster_index> raw_cluster; raw_cluster.reserve(num_points); + // cluster index --> index of top point in the cluster + Cluster_index n_raw_clusters = 0; // current number of raw clusters seen + // + std::vector<Merge> merges; + struct Data { Cluster_index parent; int rank; Point_index max; }; // information on a cluster + std::vector<Data> ds_base; + // boost::vector_property_map does resize(size+1) for every new element, don't use it + auto ds_data=boost::make_function_property_map<std::size_t>([&ds_base](std::size_t n)->Data&{return ds_base[n];}); + auto ds_parent=boost::make_transform_value_property_map([](auto&p)->Cluster_index&{return p.parent;},ds_data); + auto ds_rank=boost::make_transform_value_property_map([](auto&p)->int&{return p.rank;},ds_data); + boost::disjoint_sets<decltype(ds_rank),decltype(ds_parent)> ds(ds_rank,ds_parent); // on the clusters, not directly the points + std::vector<std::array<double,2>> persistence; // diagram (finite points) + boost::container::flat_map<Cluster_index,Cluster_index> adj_clusters; // first: the merged cluster, second: the raw cluster + // we only care about the raw cluster, we could use a vector to store the second, store first into a set, and only insert in the vector if merged is absent from the set + + for(Point_index i = 0; i < num_points; ++i){ + // auto&& ngb = neighbors[order[i]]; + // TODO: have a specialization where we don't need python types and py::cast + // TODO: move py::cast and getint into Neighbors + py::object ngb = neighbors[py::cast(order[i])]; // auto&& also seems to work + adj_clusters.clear(); + Point_index j = i; // highest neighbor + //for(Point_index k : ngb) + for(auto k_py : ngb) + { + Point_index k = rorder[getint(k_py)]; + if(k >= i || k < 0) // ??? + continue; + if(k < j) + j = k; + Cluster_index rk=raw_cluster[k]; + adj_clusters.emplace(ds.find_set(rk),rk); + // does not insert if ck=ds.find_set(rk) already seen + // which rk we keep from those with the same ck is arbitrary + } + assert((Point_index)raw_cluster.size()==i); + if(i==j){ // local maximum -> new cluster + Cluster_index c = n_raw_clusters++; + ds_base.emplace_back(); // could be integrated in ds_data, but then we would check the size for every access + ds.make_set(c); + ds_base[c].max=i; // max + raw_cluster.push_back(c); + continue; + }else{ // add i to the cluster of j + assert(j<i); + raw_cluster.push_back(raw_cluster[j]); + // FIXME: we are adding point i to the raw cluster of j, but that one might not be in adj_clusters, so we may merge clusters A and B through a point of C. It is strange, but I don't know if it can really cause problems. We could just not set j at all and use arbitrarily the first element of adj_clusters. + } + // possibly merge clusters + // we could sort, in case there are several merges, but that doesn't seem so useful + Cluster_index rj = raw_cluster[j]; + Cluster_index cj = ds.find_set(rj); + Cluster_index orig_cj = cj; + for(auto ckk : adj_clusters){ + Cluster_index rk = ckk.second; + Cluster_index ck = ckk.first; + if(ck==orig_cj) + continue; + assert(ck==ds.find_set(rk)); + Point_index j = ds_base[cj].max; + Point_index k = ds_base[ck].max; + Point_index young = std::max(j,k); + Point_index old = std::min(j,k); + auto d_young = density[order[young]]; + auto d_i = density[order[i]]; + assert(d_young >= d_i); + // Always merge (the non-hierarchical algorithm would only conditionally merge here + persistence.push_back({d_young, d_i}); + assert(ds.find_set(rj) != ds.find_set(rk)); + ds.link(cj,ck); + cj = ds.find_set(cj); + ds_base[cj].max=old; // just one parent, no need for find_set + // record the raw clusters, we don't know what will have already been merged. + merges.push_back({rj, rk, d_young-d_i}); + } + } + { + boost::counting_iterator<int> b(0), e(ds_base.size()); + ds.compress_sets(b, e); + // Now we stop using find_sets and look at the parent directly + // rank is reused to rename clusters contiguously 0, 1, etc + } + // Maximum for each connected component + std::vector<double> max_cc; + //for(Point_index i = 0; i < num_points; ++i){ + // if(ds_base[ds_base[raw_cluster[i]].parent].max == i) + // max_cc.push_back(density[order[i]]); + //} + for(Cluster_index i = 0; i < n_raw_clusters; ++i){ + if(ds_base[i].parent == i) + max_cc.push_back(density[order[ds_base[i].max]]); + } + assert((Cluster_index)(merges.size()+max_cc.size())==n_raw_clusters); + + // TODO: create a "noise" cluster, merging all those not prominent enough? + //int nb_clusters=0; + //for(int i=0;i<(int)ds_base.size();++i){ + // if(ds_parent[i]!=i) continue; + // ds_data[i].rank=nb_clusters++; + //} + + // Replay the merges, in increasing order of prominence, to build the hierarchy + std::sort(merges.begin(), merges.end(), [](Merge const&a, Merge const&b){return a.persist < b.persist;}); + std::vector<std::array<Cluster_index,2>> children; children.reserve(merges.size()); + { + struct Dat { Cluster_index parent; int rank; Cluster_index name; }; + std::vector<Dat> ds_bas(2*n_raw_clusters-1); + Cluster_index i; + auto ds_dat=boost::make_function_property_map<std::size_t>([&ds_bas](std::size_t n)->Dat&{return ds_bas[n];}); + auto ds_par=boost::make_transform_value_property_map([](auto&p)->Cluster_index&{return p.parent;},ds_dat); + auto ds_ran=boost::make_transform_value_property_map([](auto&p)->int&{return p.rank;},ds_dat); + boost::disjoint_sets<decltype(ds_ran),decltype(ds_par)> ds(ds_ran,ds_par); + for(i=0;i<n_raw_clusters;++i) { ds.make_set(i); ds_bas[i].name=i; } + for(Merge const& m : merges){ + Cluster_index j = ds.find_set(m.first); + Cluster_index k = ds.find_set(m.second); + assert(j!=k); + children.push_back({ds_bas[j].name,ds_bas[k].name}); + ds.make_set(i); + ds.link(i,j); + ds.link(i,k); + ds_bas[ds.find_set(i)].name=i; + ++i; + } + } + + std::vector<Cluster_index> raw_cluster_ordered(num_points); + for(int i=0; i<num_points; ++i) raw_cluster_ordered[i]=raw_cluster[rorder[i]]; + // return raw_cluster, children, persistence + // TODO avoid copies: https://github.com/pybind/pybind11/issues/1042 + return py::make_tuple( + py::array(raw_cluster_ordered.size(), raw_cluster_ordered.data()), + py::array(children.size(), children.data()), + py::array(persistence.size(), persistence.data()), + py::array(max_cc.size(), max_cc.data())); +} + +auto merge(py::array_t<Cluster_index, py::array::c_style> children, Cluster_index n_leaves, Cluster_index n_final){ + // Should this really be an error? + if(n_final > n_leaves) + throw std::runtime_error("The number of clusters required is larger than the number of mini-clusters"); + py::buffer_info cbuf = children.request(); + if((cbuf.ndim!=2 || cbuf.shape[1]!=2) && (cbuf.ndim!=1 || cbuf.shape[0]!=0)) + throw std::runtime_error("internal error: children have to be (n,2) or empty"); + const int n_merges=cbuf.shape[0]; + Cluster_index*d=(Cluster_index*)cbuf.ptr; + // Should this really be an error? + //std::cerr << "n_merges: " << n_merges << ", n_final: " << n_final << ", n_leaves: " << n_leaves << '\n'; + if(n_merges + n_final < n_leaves) + throw std::runtime_error(std::string("The number of clusters required ")+std::to_string(n_final)+" is smaller than the number of connected components "+std::to_string(n_leaves-n_merges)); + + struct Dat { Cluster_index parent; int rank; int name; }; + std::vector<Dat> ds_bas(2*n_leaves-1); + auto ds_dat=boost::make_function_property_map<std::size_t>([&ds_bas](std::size_t n)->Dat&{return ds_bas[n];}); + auto ds_par=boost::make_transform_value_property_map([](auto&p)->Cluster_index&{return p.parent;},ds_dat); + auto ds_ran=boost::make_transform_value_property_map([](auto&p)->int&{return p.rank;},ds_dat); + boost::disjoint_sets<decltype(ds_ran),decltype(ds_par)> ds(ds_ran,ds_par); + Cluster_index i; + for(i=0;i<n_leaves;++i) { ds.make_set(i); ds_bas[i].name=-1; } + for(Cluster_index m=0; m < n_leaves-n_final; ++m){ + Cluster_index j = ds.find_set(d[2*m]); + Cluster_index k = ds.find_set(d[2*m+1]); + assert(j!=k); + ds.make_set(i); + ds.link(i,j); + ds.link(i,k); + ++i; + } + Cluster_index next_cluster_name = 0; + std::vector<Cluster_index> ret; ret.reserve(n_leaves); + for(Cluster_index j=0;j<n_leaves;++j){ + Cluster_index k = ds.find_set(j); + if(ds_bas[k].name == -1) ds_bas[k].name = next_cluster_name++; + ret.push_back(ds_bas[k].name); + } + return py::array(ret.size(), ret.data()); +} + +// Do a special version when ngb is a numpy array, where we can cast to int[k][n] ? +auto plouf(py::handle ngb, py::array_t<double, py::array::c_style | py::array::forcecast> density){ + // used to be py::iterable ngb, but that's inconvenient if it doesn't come pre-sorted + // use py::handle and check if [] (aka __getitem__) works? But then we need to build an object to pass it to [] + // (I _think_ handle is ok and we don't need object here) + py::buffer_info wbuf = density.request(); + if(wbuf.ndim!=1) + throw std::runtime_error("density must be 1D"); + const int n=wbuf.shape[0]; + double*d=(double*)wbuf.ptr; + // Vector { 0, 1, ..., n-1 } + std::vector<Point_index> order(boost::counting_iterator<Point_index>(0), boost::counting_iterator<Point_index>(n)); + // Permutation of the indices to get points in decreasing order of density + std::sort(std::begin(order), std::end(order), [=](Point_index i, Point_index j){ return d[i] > d[j]; }); + // Inverse permutation + std::vector<Point_index> rorder(n); + for(Point_index i : boost::irange(0, n)) rorder[order[i]] = i; + // Used as: + // order[i] is the index of the point with i-th largest density + // rorder[i] is the rank of the i-th point in order of decreasing density + // TODO: put a wrapper on ngb and d so we don't need to pass (r)order (there is still the issue of reordering the output) + return tomato(n, ngb, d, order, rorder); +} +#if 0 +struct Vec { + int const* base; + int k; + int const* begin()const{return base;} + int const* end()const{return base+k;} + Vec operator*()const{return *this;} +}; +auto plaf(py::array_t<int, py::array::c_style | py::array::forcecast> ngb, py::array_t<double, py::array::c_style | py::array::forcecast> density, double threshold){ + py::buffer_info nbuf = ngb.request(); + if(nbuf.ndim!=2) + throw std::runtime_error("neighbors must be 2D"); + const int n=nbuf.shape[0]; + const int k=nbuf.shape[1]; + int*nptr=(int*)nbuf.ptr; + auto neighbors=boost::adaptors::transform(boost::irange(0,n),[=](int i){return Vec{nptr+i*k,k};}); + py::buffer_info wbuf = density.request(); + if(wbuf.ndim!=1) + throw std::runtime_error("density must be 1D"); + if(n!=wbuf.shape[0]) + throw std::runtime_error("incompatible sizes"); + double*d=(double*)wbuf.ptr; + return tomato(n,neighbors,d); +} +#endif +PYBIND11_MODULE(_tomato, m) { + m.doc() = "Internals of tomato clustering"; + m.def("doit", &plouf, "does the clustering"); + //m.def("doit2", &plaf, "does the clustering faster"); + m.def("merge", &merge, "merge clusters"); +} + +// https://github.com/pybind/pybind11/issues/1042 pour convertir vector en numpy array +// +// py::isinstance<py::array_t<std::int32_t>> (ou py::isinstance<py::array> et tester dtype) et flags&c_style +// ou overload (en virant forcecast?) +// aussi le faire au cas où on n'aurait pas un tableau, mais où chaque liste de voisins serait un tableau ? |