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328 lines
7.9 KiB
C++
328 lines
7.9 KiB
C++
// RogueViz -- SAG embedder: implementation of SAG method evaluation
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// Copyright (C) 2011-24 Zeno Rogue, see 'hyper.cpp' for details
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#include "../rogueviz.h"
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#include "../dhrg/dhrg.h"
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#include "../statistics.cpp"
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namespace rogueviz {
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namespace sag {
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enum eSagMethod { smClosest, smLogistic, smMatch };
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eSagMethod method;
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vector<string> method_names = {"closest", "logistic", "match"};
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int method_count = 3;
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/* parameters for smMatch */
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ld match_a = 1, match_b = 0;
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/* parameters for smLogistic */
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dhrg::logistic lgsag(1, 1), lgsag_pre(1, 1);
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vector<ld> loglik_tab_y, loglik_tab_n;
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dhrg::logistic best;
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bool opt_debug = false;
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bool should_good = false;
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double costat(int vid, int sid) {
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if(vid < 0) return 0;
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double cost = 0;
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switch(method) {
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case smLogistic: {
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auto s = sagdist[sid];
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for(auto j: edges_yes[vid]) if(sagid[j] >= -1)
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cost += loglik_tab_y[s[sagid[j]]];
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for(auto j: edges_no[vid]) if(sagid[j] >= -1)
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cost += loglik_tab_n[s[sagid[j]]];
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return -cost;
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}
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case smMatch: {
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for(auto& e: edge_weights[vid]) {
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auto t2 = e.first;
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if(sagid[t2] != -1) {
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ld cdist = sagdist[sid][sagid[t2]];
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ld expect = match_a / e.second + match_b;
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ld dist = cdist - expect;
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cost += dist * dist;
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}
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}
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return cost;
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}
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case smClosest: {
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for(auto& e: edge_weights[vid]) {
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auto t2 = e.first;
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if(sagid[t2] != -1) cost += sagdist[sid][sagid[t2]] * e.second;
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}
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if(!hubval.empty()) {
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for(auto sid2: neighbors[sid]) {
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int vid2 = sagnode[sid2];
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if(vid2 >= 0 && (hubval[vid] & hubval[vid]) == 0)
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cost += hub_penalty;
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}
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}
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return cost;
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}
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}
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throw hr_exception("unknwon SAG method");
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}
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double cost;
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double best_cost = 1000000000;
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void compute_cost() {
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int DN = isize(sagid);
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cost = 0;
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for(int i=0; i<DN; i++)
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cost += costat(i, sagid[i]);
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cost /= 2;
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}
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void compute_loglik_tab() {
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loglik_tab_y.resize(max_sag_dist);
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loglik_tab_n.resize(max_sag_dist);
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for(int i=0; i<max_sag_dist; i++) {
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loglik_tab_y[i] = lgsag.lyes(i);
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loglik_tab_n[i] = lgsag.lno(i);
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}
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}
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void compute_auto_rt() {
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ld sum0 = 0, sum1 = 0, sum2 = 0;
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for(auto i: sagdist) {
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sum0 ++;
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sum1 += i;
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sum2 += i*i;
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}
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lgsag.R = sum1 / sum0;
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lgsag.T = sqrt((sum2 - sum1*sum1/sum0) / sum0);
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println(hlog, "automatically set R = ", lgsag.R, " and ", lgsag.T, " max_sag_dist = ", max_sag_dist);
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if(method == smLogistic) compute_loglik_tab();
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}
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void optimize_sag_loglik_logistic() {
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vector<int> indist(max_sag_dist, 0);
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const int mul = 1;
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int N = isize(sagid);
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for(int i=0; i<N; i++)
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for(int j=0; j<i; j++) {
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int d = sagdist[sagid[i]][sagid[j]];
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indist[d]++;
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}
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vector<int> pedge(max_sag_dist, 0);
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for(int i=0; i<isize(sagedges); i++) {
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edgeinfo& ei = sagedges[i];
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// if(int(sagdist[sagid[ei.i]][sagid[ei.j]] * mul) == 136) printf("E %d,%d\n", ei.i, ei.j);
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if(ei.i != ei.j)
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if(ei.weight >= sag_edge->visible_from)
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pedge[sagdist[sagid[ei.i]][sagid[ei.j]] * mul]++;
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}
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if(opt_debug) for(int d=0; d<max_sag_dist; d++)
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if(indist[d])
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printf("%2d: %7d/%7d %7.3lf\n",
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d, pedge[d], indist[d], double(pedge[d] * 100. / indist[d]));
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ld loglik = 0;
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for(int d=0; d<max_sag_dist; d++) {
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int p = pedge[d], pq = indist[d];
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int q = pq - p;
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if(p && q) {
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loglik += p * log(p) + q * log(q) - pq * log(pq);
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if(opt_debug) println(hlog, tie(d, p, q), loglik);
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}
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}
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if(opt_debug) println(hlog, "loglikelihood best = ", fts(loglik));
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auto logisticf = [&] (dhrg::logistic& l) {
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ld loglik = 0;
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for(int d=0; d<max_sag_dist; d++) {
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int p = pedge[d], pq = indist[d];
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if(p) loglik += p * l.lyes(d);
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if(pq > p) loglik += (pq-p) * l.lno(d);
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}
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return loglik;
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};
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if(opt_debug) println(hlog, "cost = ", cost, " logisticf = ", logisticf(lgsag), " R= ", lgsag.R, " T= ", lgsag.T);
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if(should_good && abs(cost + logisticf(lgsag)) > 0.1) throw hr_exception("computation error");
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dhrg::fast_loglik_cont(lgsag, logisticf, nullptr, 1, 1e-5);
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if(opt_debug) println(hlog, "loglikelihood logistic = ", logisticf(lgsag), " R= ", lgsag.R, " T= ", lgsag.T);
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if(method == smLogistic) {
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compute_loglik_tab();
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compute_cost();
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if(opt_debug) println(hlog, "cost = ", cost);
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}
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}
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void optimize_sag_loglik_match() {
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if(state &~ SS_WEIGHTED) return;
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stats::leastsquare_solver<2> lsqs;
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for(auto& ei: sagedges) {
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ld y = sagdist[sagid[ei.i]][sagid[ei.j]];
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ld x = 1. / ei.weight;
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lsqs.add_data({{x, 1}}, y);
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}
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array<ld, 2> solution = lsqs.solve();
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match_a = solution[0];
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match_b = solution[1];
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println(hlog, "got a = ", match_a, " b = ", match_b);
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if(method == smMatch)
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prepare_graph();
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}
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void optimize_sag_loglik_auto() {
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if(method == smLogistic) optimize_sag_loglik_logistic();
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if(method == smMatch) optimize_sag_loglik_match();
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}
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pair<ld, ld> compute_mAP() {
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int DN = isize(sagid);
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ld meanrank = 0;
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int tgood = 0;
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ld maprank = 0;
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for(int i=0; i<DN; i++) {
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vector<int> alldist(max_sag_dist, 0);
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for(int j=0; j<DN; j++) if(i != j) alldist[sagdist[sagid[i]][sagid[j]]]++;
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vector<int> edgedist(max_sag_dist, 0);
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for(auto j: edges_yes[i]) edgedist[sagdist[sagid[i]][sagid[j]]]++;
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int pgood = 0;
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ld bad = 0;
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ld ap = 0;
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ld pall = 0;
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for(int j=0; j<max_sag_dist; j++) {
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ld good = edgedist[j];
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ld all = alldist[j];
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ld err = all - good;
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bad += err / 2.;
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meanrank += bad * good;
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bad += err / 2.;
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for(int k=0; k<good; k++) {
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pgood++, pall++;
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pall += err/2 / good;
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ap += pgood / pall;
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pall += err/2 / good;
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}
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if(!good) pall += err;
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}
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tgood += pgood;
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if(pgood) maprank += ap / pgood;
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}
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return make_pair(maprank / DN, meanrank / tgood);
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}
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ld kendall;
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void compute_kendall() {
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compute_cost(); println(hlog, "cost = ", cost);
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vector<vector<ld> > weights;
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int DN = isize(sagid);
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weights.resize(DN);
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for(int i=0; i<DN; i++) weights[i].resize(DN, 0);
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for(auto& e: sagedges) weights[e.i][e.j] += e.weight2, weights[e.j][e.i] += e.weight2;
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vector<pair<int, ld>> kdata;
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for(int i=0; i<DN; i++) for(int j=0; j<i; j++) kdata.emplace_back(sagdist[sagid[i]][sagid[j]], -weights[i][j]);
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kendall = stats::kendall(kdata);
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}
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void prepare_method() {
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if(method == smLogistic) compute_loglik_tab();
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optimize_sag_loglik_auto();
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if(method == smClosest) compute_cost();
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}
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bool known_pairs = false;
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int function_read_args() {
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#if CAP_COMMANDLINE
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using namespace arg;
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if(0) ;
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else if(argis("-sagrt")) {
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shift(); sag::lgsag.R = argf();
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shift(); sag::lgsag.T = argf();
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if(method == smLogistic) compute_loglik_tab();
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}
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else if(argis("-sagmatch-ab")) {
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shift(); sag::match_a = argf();
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shift(); sag::match_b = argf();
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if(method == smMatch) prepare_graph();
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}
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else if(argis("-sagrt-auto")) {
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compute_auto_rt();
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}
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else if(argis("-sag-kendall")) {
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compute_kendall();
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println(hlog, "kendall = ", kendall);
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}
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else if(argis("-sag-method-closest")) {
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method = smClosest; prepare_method();
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}
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else if(argis("-sag-method-logistic")) {
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method = smLogistic; prepare_method();
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}
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else if(argis("-sag-method-match")) {
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method = smMatch; prepare_method();
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}
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else if(argis("-sag-logistic-recalc")) {
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method = smLogistic; prepare_method();
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}
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else if(argis("-sagloglik-l")) {
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sag::optimize_sag_loglik_logistic();
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}
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else if(argis("-sagloglik-m")) {
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sag::optimize_sag_loglik_match();
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}
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else if(argis("-sagloglik-a")) {
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sag::optimize_sag_loglik_auto();
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}
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else return 1;
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#endif
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return 0;
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}
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int ahfun = addHook(hooks_args, 100, function_read_args);
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}
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}
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