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recordSwap.cpp
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recordSwap.cpp
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/*
* Algorithm for targeted record swapping
* Version: 1.0.1
*/
#include <iostream>
#include <algorithm> // std::count
#include <vector> // std::vector
#include <random>
#include <queue>
#include <array>
#include <map>
#include <unordered_set>
#include <unordered_map>
#include <fstream>
#include "recordSwap.h"
using namespace std;
/*
* Function to reorder data-set given one index vector
*/
std::vector< std::vector<int> > orderData(std::vector< std::vector<int> > &data, int orderIndex){
// initialise ordering vector
std::vector<int> orderVec(data.size());
std::iota(orderVec.begin(),orderVec.end(),0);
// order this vector by order of data[orderIndex]
std::sort(orderVec.begin(),orderVec.end(),
[&](int a, int b) { return data[a][orderIndex] < data[b][orderIndex]; }
);
// reorder data without copying it
for(std::size_t i = 0;i<orderVec.size();i++){
// while orderVec[i] is not yet in place
// every swap places at least one element in it's proper place
while(orderVec[i] != orderVec[orderVec[i]] ){
// swap every "row" of data
for(std::size_t j=0;j<data[0].size();j++){
swap( data[orderVec[i]][j], data[orderVec[orderVec[i]]][j] );
}
// then adjust orderVec[i]
swap( orderVec[i], orderVec[orderVec[i]] );
}
}
return(data);
}
/*
* Function to define levels
* this function returns the hierarchy level over which a unit/household needs to be swapped
* 0 meaning the highest hierarchy level, 1 the second highest hierarchy level, and so on....
*/
std::vector<int> setLevels(std::vector< std::vector<double> > &risk, double risk_threshold) {
// risk: data containing the risk for each hierarchy level and each unit. risk[0] returns the vector of risks for the first unit over all hierarchy levels
// risk_threshold: double defining the risk threshold beyond which a record/household needs to be swapped. This is understood as risk>risk_threshhold.
// initialise parameters
int n=risk.size();
int p=risk[0].size();
std::vector<int> data_level(n);
std::fill(data_level.begin(),data_level.end(),p);
for(int i=0;i<n;i++){
for(int j=0; j<p; j++){
if(risk[i][j]>risk_threshold){ // risk[i][j]>risk_threshold
data_level[i] = j;
break;
}
}
}
return data_level;
}
/*
* Function to set the risk for each individual
* in each hierarchy level
* this is then used as sampling probability
*/
std::vector< std::vector<double> > setRisk(std::vector<std::vector<int> > &data, std::vector<int> &hierarchy, std::vector<int> &risk_variables, int &hid){
// data: data input
// hierarchy: column indices in data corresponding to geo hierarchy of data read left to right (left highest level - right lowest level)
// risk_variables: column indices in data corresponding to risk variables which will be considered for estimating counts in the population
// hid: int correspondig to column index in data which holds the household ID
// initialise parameters
int n = data.size();
int nhier = hierarchy.size();
int nrisk = risk_variables.size();
// needed to temporarily store the risk of an individual in each
// hierarchy
std::vector<double> risk_value(nhier);
int current_ID;
int hsize=0;
// initialise risk data
// prob[0] ~ risk of first record for each hierarchy level
// prob[1] ~ risk of second record for each hierarchy level
// and so on ...
std::vector< std::vector<double> > prob(n,vector<double>(nhier));
//
std::vector<int> loop_index = risk_variables;
loop_index.insert(loop_index.end(),hierarchy.begin(),hierarchy.end());
int loop_n = loop_index.size();
std::vector<int> groups(loop_n);
// initialise counts for groups in every hierarchy
std::map<std::vector<int>,int> group_count;
for(int i=0;i<n;i++){
for(int j=0;j<loop_n;j++){
// ... define group for each hierarchy level
// risk_variable + hierarchy levels
groups[j] = data[i][loop_index[j]];
}
for(int index_hier=0;index_hier<nhier;index_hier++){
std::vector<int> groups_help(&groups[0],&groups[nrisk+index_hier+1]); // +1 needed here!
// ... count number for each group using std::map
group_count[groups_help]++;
}
}
// loop over data again and fill with risks
int i=0;
int h=0;
while(i<n){
current_ID = data[i][hid];
while(i+h<n&¤t_ID==data[i+h][hid]){
for(int j=0;j<loop_n;j++){
// ... define group for each hierarchy level
// risk_variable + hierarchy levels
groups[j] = data[i+h][loop_index[j]];
}
for(int index_hier=0;index_hier<nhier;index_hier++){
// get each grouping ~ risk_variables + hierarchy level 0-nhier
std::vector<int> groups_help(&groups[0],&groups[nrisk+index_hier+1]); //+1 needed here
// select highest risk for hid in each hierarchy level
risk_value[index_hier] = max(risk_value[index_hier],1.0/group_count[groups_help]);
}
hsize++;
h++;
}
// assign highst risk and revers risk to all household members
for(int j=0;j<hsize;j++){
prob[i+j] = risk_value;
}
// reset parameters
i = i+hsize;
hsize=0;
std::fill(risk_value.begin(),risk_value.end(),0.0);
h =0;
}
return prob;
}
/*
* Sampling function
* samples the unordered indices in ID
*/
std::vector<int> randSample(std::unordered_set<int> &ID, int N, std::vector<double> &prob, std::mt19937 &mersenne_engine,
std::vector<int> &IDused, std::unordered_set<int> &mustSwap){
// initialise parameters
std::exponential_distribution<double> exp_dist(1.0); // initialise lambda para for exp distribution
/*
* from R-Package ‘wrswoR’:
* We need the last "size" elements of
* U ^ (1 / prob) ~ log(U) / prob
* ~ -Exp(1) / prob
* ~ prob / Exp(1)
* Here, ~ means "doesn't change order statistics".
*/
// generate random numbers -> prob[i]/exp_dist(mersenne_engine)
// and store them in priority queue
// get index of N largest elements in randVal
// from https://stackoverflow.com/questions/14902876/indices-of-the-k-largest-elements-in-an-unsorted-length-n-array
// use priority_queue
std::priority_queue<std::pair<double, int> > q;
std::vector<int> sampleID(ID.size());
int z = 0;
for(auto s : ID){
if(IDused[s]==0){
if(mustSwap.find(s)!=mustSwap.end()){
sampleID[z]=s;
z++;
}else if(N>0){
q.push(std::pair<double, int>(prob[s]/exp_dist(mersenne_engine), s));
}
}
}
// resize sampling vector
// z values are now in Vector + N are still to come
N = max(0,min<int>(q.size(),N-z));
sampleID.resize(N+z);
// build output vector
if(N>0){
// select index of top elements from priority_queue
for(int i=0;i<N;i++){
sampleID[z+i] = q.top().second; //.top() access top element in queue
q.pop(); // remove top element in queue
}
}
return sampleID;
}
// help function to randomly distribute number of units to draw from
std::map<std::vector<int>,int> distributeRandom(std::map<std::vector<int>,double> &ratioDraws, int &totalDraws,
std::mt19937 &mersenne_engine){
// ratioDraws map containing ratio of units to draw for each map entry
// totalDraws integer containing total number of units to draw
// mersenne_engine random number generator engine
// output
std::map<std::vector<int>,int> numberDraws;
///////////////
// distribute draws at lowest level hierarchy
double draw_excess_help = 0;
double x_excess = 0;
for(auto const&x : ratioDraws){
x_excess = ratioDraws[x.first]*(double)totalDraws;
numberDraws[x.first] = floor(x_excess);
x_excess = x_excess-floor(x_excess);
draw_excess_help = draw_excess_help + x_excess;
}
int draw_excess = std::round(draw_excess_help);
if(draw_excess==0){
// return output of nothing need to be distributed
return numberDraws;
}
// randomly shuffeld index vector
// this is similar to randomly round up and down in each group so on average swaprate will be reached
std::vector<int> add_extra(ratioDraws.size());
std::iota(add_extra.begin(),add_extra.end(),0);
std::shuffle(add_extra.begin(),add_extra.end(),mersenne_engine);
std::sort(add_extra.begin(),add_extra.begin()+draw_excess); // sort first draw_excess elemets in vector
// pick first draw_excess values and add one to them
int z = 0;
int v = 0;
// certain groups will get one more draw
for(auto const&x : numberDraws){
if(add_extra[v]==z){
numberDraws[x.first]++;
v++;
}
if(v>(draw_excess-1)){
break; // if all draw_excess have been distributed break procedure
}
z++;
}
// return output
return numberDraws;
}
std::map<std::vector<int>,int> distributeDraws2(std::map<std::vector<int>,std::unordered_set<int> > &group_hier,
std::vector<std::vector<double>> &risk,
int &nhid, double &swaprate,
std::uniform_int_distribution<std::mt19937::result_type> &runif01,
std::mt19937 &mersenne_engine){
// group_hier map which contains all household indices per hierarchy level (only all hierarchy levels are used atm)
// nhid int containing number of households in total
// swaprate double containing the swaprate
// runif01 & mersenne_engine for sampling procedures
///////////////
// define total number of swaps according to swaprate
// swaprate/2 ensures that in the end this percentage of households is swapped
// so 1 swap is counted double since with each swap 2 households are swapped
int totalDraws = 0;
if(runif01(mersenne_engine)==0){
totalDraws = ceil(nhid*(swaprate/2));
}else{
totalDraws = floor(nhid*(swaprate/2));
}
///////////////
// define number of units to swap at lowest level hierarchy
// loop through all hierarchies
// and estimate distribution ratio only at lowest level hierarchy
std::map<std::vector<int>,double > ratioRisk; // get ratio of numbers to draw in lowest level hierarchy
std::map<std::vector<int>, std::vector<double> > sumRisk; // sum of Risk in each hierarchy level
int nhier = risk[0].size(); // number of hierarchies
// calcualte sum of risk in each hierarchy level
for(auto const&x : group_hier){
std::vector<double> sumRisk_help(nhier); // help vector to fill sumRisk
for(int h=nhier;--h >=0;){
//std::cout<<"h= "<<h<<"\n";
for (const auto& indexI: x.second){
sumRisk_help[h] += risk[indexI][h];
}
}
sumRisk[x.first] = sumRisk_help;
// get draw ratio ~ percentage of units to draw in each lowest level hierarchy
ratioRisk[x.first] = sumRisk[x.first].back();
}
//normalize ratioRisk
double sum_ratioRisk = 0.0;
for(auto const&x : ratioRisk){
sum_ratioRisk += x.second;
}
for(auto const&x : ratioRisk){
ratioRisk[x.first] = x.second/sum_ratioRisk;
}
// calculate number of draws on lowest level hierarchy
std::map<std::vector<int>,int> numberDraws = distributeRandom(ratioRisk, totalDraws,mersenne_engine);
// loop over hierarchy and distribute each entry in numberDraws
// over the hierarchies vertically
double helpSum = 0.0;
std::vector<int> hl;
for(auto const&x : group_hier){
// sum over risks vertically
helpSum = 0.0;
for (auto& r : sumRisk[x.first]){
helpSum += r;
}
std::map<std::vector<int>,double > ratioHelp;
hl = x.first;
hl.push_back(1);
for(int h=0;h<nhier;h++){
hl.back() = h+1;
ratioHelp[hl] = sumRisk[x.first][h]/helpSum;
}
std::map<std::vector<int>,int> helpDist = distributeRandom(ratioHelp, numberDraws[x.first],mersenne_engine);
for(auto const&x : helpDist){
std::vector<int> help_hier(x.first.begin(),x.first.begin() + x.first.back());
if(nhier!=x.first.back()){
// for higher hierarchies
// add to existing values
numberDraws[help_hier] += x.second;
}else{
// for lowest hierarchy overwrite value
numberDraws[help_hier] = x.second;
}
}
}
return numberDraws;
}
/*
* Function to distribute n draws over a given number of groups
* the distribution is always proportional to group size
*/
std::map<std::vector<int>,std::pair<int,int>> distributeDraws(std::map<std::vector<int>,std::unordered_set<int> > &group_hier,
int &nhid, double &swaprate,
std::uniform_int_distribution<std::mt19937::result_type> &runif01,
std::mt19937 &mersenne_engine){
// group_hier map which contains all household indices per hierarchy level (only all hierarchy levels are used atm)
// nhid int containing number of households in total
// swaprate double containing the swaprate
// runif01 & mersenne_engine for sampling procedures
// swaprate/2 ensures that in the end this percentage of households is swapped
// so 1 swap is counted double since with each swap 2 households are swapped
int total_swaps = 0;
if(runif01(mersenne_engine)==0){
total_swaps = ceil(nhid*(swaprate/2));
}else{
total_swaps = floor(nhid*(swaprate/2));
}
// cout << "total swaps:" << total_swaps<<endl;
// distribute them among smallest hierarchy level
// draw_group[key].first correponds to number of households
// draw_group[key].second to number of swaps
std::map<std::vector<int>,std::pair<int,int>> draw_group;
double draw_excess_help = 0;
double x_excess = 0;
for(auto const&x : group_hier){
draw_group[x.first].first = x.second.size(); // this is needed later on
x_excess = (double)x.second.size()/(double)nhid*(double)total_swaps;
draw_group[x.first].second = floor(x_excess);
x_excess = x_excess-floor(x_excess);
draw_excess_help = draw_excess_help + x_excess;
}
int draw_excess = std::round(draw_excess_help);
// randomly shuffeld index vector
// this is similar to randomly round up and down in each group so on average swaprate will be reached
std::vector<int> add_extra(group_hier.size());
std::iota(add_extra.begin(),add_extra.end(),0);
std::shuffle(add_extra.begin(),add_extra.end(),mersenne_engine);
std::sort(add_extra.begin(),add_extra.begin()+draw_excess); // sort first draw_excess elemets in vector
// pick first draw_excess values and add one to them
int z = 0;
int v = 0;
int count_swaps=0;
// certain groups will get one more draw
for(auto const&x : draw_group){
if(add_extra[v]==z){
draw_group[x.first].second++;
v++;
}
count_swaps = count_swaps+draw_group[x.first].second;
if(v>(draw_excess-1)){
break; // if all draw_excess have been distributed break procedure
}
z++;
}
return draw_group;
}
/*
* Function to sample from donor set
* this is done differently than the inital sampling to make the procedure more efficient
*/
std::vector<int> sampleDonor(std::vector< std::vector<int> > &data, std::vector<std::vector<int>> &similar,
std::vector<int> &IDswap, std::unordered_set<int> &IDswap_pool,
std::map<double,int> &IDdonor_pool, std::vector<int> &IDused, int &hid){
// data: data input data.size() ~ number of records - data.[0].size ~ number of varaibles per record
// similar: column indices in data corresponding to variables (household/personal) which should be considered when swapping,
// e.g. swapping onlys household with same houshoeld size
// IDswap: vector containing household IDs to be swapped
// IDswap_pool: unordered set containing sampling pool from which IDswap was drawn
// IDdono_pool: map containing every possible donor ID (ordered by sampling probability in ascending order)
// IDused: integer vector which takes on 1 if ID was sampled
// define parameter
std::vector<int> IDdonor(IDswap.size(),-1); // output initialize with -1
// if value stays -1 then no donor was found for corresponding value in IDswap
bool similar_true=true;
int index_donor = 0;
// select donor based on similarity constrains
// iterate over both unordered sets
// iterate over IDdonor_pool in reverse order since it is sorted in ascending order by risk
for(std::size_t i=0; i<IDswap.size();i++){
// find donor for index_samp
// iterate over similarity profiles
for(std::size_t profile=0;profile<similar.size();profile++){
// iterate over complete donor set in reverse order
for( auto it = IDdonor_pool.end();it!=IDdonor_pool.begin(); ){
// it->second access the value
// it->first access the key
it--; // decrement iterator first since you loop from the back
index_donor = it->second;
// if was not used and it is not in the same hierarchy ~ IDswap_pool.find(s.second)==IDswap_pool.end()
// it is a possible donor
if(IDused[index_donor]==0 && IDswap_pool.find(index_donor)==IDswap_pool.end()){
// IDswap[i] is similar to index_donor
// by using similarity indices of the profile
similar_true=true;
for(std::size_t sim=0;sim<similar[profile].size();sim++){
if(data[IDswap[i]][similar[profile][sim]]!=data[index_donor][similar[profile][sim]]){
// similarity variables do not match
// set similar_true=false and break loop
similar_true=false;
break;
}
}
if(similar_true){
IDdonor[i] = index_donor;
IDused[it->second] = 1;
// if index_donor was used
// remove it from IDdonor_pool and do not increment it
IDdonor_pool.erase(it);
goto next_index_samp;
}
}
}
}
// must have ; after the goto label since goto label is not a statement
next_index_samp:
;
}
return IDdonor;
}
/*
* Function to perform record swapping
*/
std::vector< std::vector<int> > recordSwap(std::vector< std::vector<int> > data, int hid,
std::vector<int> hierarchy,
std::vector< std::vector<int> > similar,
double swaprate,
std::vector< std::vector<double> > risk, double risk_threshold,
int k_anonymity, std::vector<int> risk_variables,
std::vector<int> carry_along,
int &count_swapped_records,
int &count_swapped_hid,
std::string log_file_name,
int seed = 123456){
// data: data input data.size() ~ number of records - data.[0].size ~ number of varaibles per record
// hid: int correspondig to column index in data which holds the household ID
// hierarchy: column indices in data corresponding to geo hierarchy of data read left to right (left highest level - right lowest level)
// similar: column indices in data corresponding to variables (household/personal) which should be considered when swapping,
// e.g. swapping onlys household with same houshoeld size
// swaprate: double defining the ratio of households to be swapped
// risk: double vector of vectors containing the risk for each individual in each record - risk_record[0] risk for first record an each hierarchy level
// risk_threshold: double cutoff for defining highrisk households. if risk>risk_threshold then household is high risk is will definitely be swapped
// k_anonymity: int defining a threshold, each group with counts lower than the threshold will automatically be swapped.
// risk_variables: column indices in data corresponding to risk variables which will be considered for estimating counts in the population
// carry_along: swap additional variables in addition to hierarchy variable. These variables do not interfere with the procedure of
// finding a record to swap with. This parameter is only used at the end of the procedure when swapping the hierarchies.
// count_swapped_records, count_swapped_hid: count number of households and records swapped.
// seed: integer seed for random number generator.
// log_file_name: name of file to save HIDs of non-swapped households.
// initialise parameters
int n = data.size(); // number of obesrvations
int nhier = hierarchy.size(); // number of hierarchy levels
std::unordered_set<int> IDnotUsed;
// needed for running random number generator and
// set random seed according to input parameter
std::mt19937 mersenne_engine;
mersenne_engine.seed(seed);
// initialise random number generator for exponential distribution
std::exponential_distribution<double> exp_dist(1.0);
// initialize random number generator for uniform distribution
std::uniform_int_distribution<std::mt19937::result_type> runif01(0,1);
////////////////////////////////////////////////////
// order data by hid
// not needed at the moment -> order data outside function
// orderData(data,hid);
////////////////////////////////////////////////////
////////////////////////////////////////////////////
// define risk data if not supplied by user
// using risk_variables and 1/counts
std::vector< std::vector<double> > prob(n,std::vector<double>(nhier));
if(risk.size()==0){
prob = setRisk(data, hierarchy, risk_variables, hid);
}else{
prob = risk;
}
////////////////////////////////////////////////////
////////////////////////////////////////////////////
// define minimum swap level for each household
if(risk_threshold==0){
if(k_anonymity==0){
risk_threshold = 2.0;
}else{
risk_threshold = 1.0/(double)k_anonymity;
}
}
std::vector<int> levels = setLevels(prob,risk_threshold);
////////////////////////////////////////////////////
////////////////////////////////////////////////////
// get household size for each household ID
// initialise map for household size
std::unordered_map<int,int> map_hsize;
// loop over data and ...
for(int i=0;i<n;i++){
// ... get household size map
map_hsize[data[i][hid]]++;
}
////////////////////////////////////////////////////
////////////////////////////////////////////////////
// apply swapping algorithm
// go from highest to lowest level
// swapp at each higher level the number of households that have to be swapped at that level according to "k_anonymity" (see setLevels())
// at lowest level swap remaining number of households (according to swap) if not enough households have been swapped
// every household can only be swapped once
std::map<std::vector<int>,std::unordered_set<int> > group_hier; //
std::map<std::vector<int>,int> countSwaps; // count swaps already done in each hierarchy
std::unordered_map<int,std::unordered_set<int> > group_levels; // map containing all IDs which must be swapped at a certain level (~key of map)
std::vector<int> hier_help(nhier); // help vector to get hierarchy groups
std::unordered_map<int,int> swappedIndex; // map for indices that have already been used -> unorderd map constant time access
std::vector<int> IDused(n,0); // 0-1 vector if 1 this index was already swapped and cant be swapped again
std::unordered_set<int> IDdonor_all; // set for all IDs for quick lookup
std::map<int,std::map<double,int>> samp_order_donor;
int z=0; // counter used for while() ect...
int nhid = 0;
/////////////////////////////
// create map containing subgroups according to hierarchy
// and IDs of each subgroup
// use hhsize for this to speed things up
while(z<n){
// ... define hierarchy group
for(int j=0;j<nhier;j++){
hier_help[j] = data[z][hierarchy[j]];
}
// supply new household index to each group
// use only indices to speed up construction of output data
group_hier[hier_help].insert(z);
// create map for levels
if(levels[z]<nhier){
group_levels[levels[z]].insert(z);
}
// create set of IDs for quick lookup
IDdonor_all.insert(z);
// create map for random numbers (ordered)
// makes sampling in each iteration obsolete
// look up in these maps instead
for(int j=0;j<nhier;j++){
samp_order_donor[j][prob[z][j]/exp_dist(mersenne_engine)] = z;
}
// count number of households
nhid++;
// skip all other household member, only need first one
z += map_hsize[data[z][hid]];
}
/////////////////////////////
/////////////////////////////
// get number of households to be swapped at the lowest level hierarchy
// this is only used at lowest hierarchy level
// draw_group[].first -> number of households in lowest level hierarchy
// draw_group[].second -> number of swaps in lowest level hierarchy
std::map<std::vector<int>,std::pair<int,int>> draw_group = distributeDraws(group_hier, nhid, swaprate,
runif01, mersenne_engine);
//std::map<std::vector<int>,int> draw_group = distributeDraws2(group_hier, risk, nhid, swaprate,
// runif01, mersenne_engine);
/////////////////////////////
/////////
// this is needed only for the lowest hierarchy
// will be changed for the final version
std::vector<double> prob_help(n);
for(int i=0;i<n;i++){
prob_help[i] = prob[i][nhier-1];
}
/////////////////////////////
// Procedure for swapping starts here:
// loop over hierarchies
// start at highest hierarchy
for(int h=0;h<nhier;h++){
// int h=3;
// values of map element that must be swapped at current stage
// if no elements need to be swapped than skip this step
std::unordered_set<int> mustSwap;
if(group_levels.find(h)!=group_levels.end()){
mustSwap = group_levels[h];
}
std::map<std::vector<int>,unordered_set<int> > group_hier_help;
hier_help.resize(h+1);
/////////////////
// get combined map for hierarchy h
for(auto const&x : group_hier){
// get higher hierarchy
std::copy(x.first.begin(),x.first.begin()+h+1,hier_help.begin());
// discard every index that has already been used
// more efficient to do this at this step then later on in the code
for(auto s : x.second){
if(IDused[s]==0 && IDnotUsed.find(s)==IDnotUsed.end()){
group_hier_help[hier_help].insert(s);
}else if(swappedIndex.find(s) != swappedIndex.end()){
// count how many IDs were already swapped inside this hierarchy
// used IDswap not used in IDdonor
countSwaps[hier_help]++;
}
}
}
/////////////////
/////////////////
// int sampSize=0;
// int countUsed=0;
int countRest=0;
/////////////////
// loop over levels of hierarchy
for(auto &x : group_hier_help){
// std::vector<int> xfirst = group_hier_help.begin()->first;
// std::vector<int> xsecond = group_hier_help.begin()->second;
// get values that need to be swapped at this hierarchy level and which are
// in this hierarchy stage
std::vector<int> IDswap(x.second.size());
if(h<(nhier-1)){
// in all but the last hierarchy level do the follwing:
if(mustSwap.size()>0){
// loop over values in x.second
z=0;
for(auto s : x.second){
// if there are any households that must be swapped due to variable k_anonymity/risk_threshold
if(IDused[s]==0 && mustSwap.find(s)!=mustSwap.end()){
IDswap[z]=s;
z++;
}
}
IDswap.resize(z);
}else{
IDswap.resize(0); // nothing needs to be swapped
}
}else{
// in the last hierarchy level do the following:
// if at lowest level get number of households that need to be swapped
// according to swap and check if this number was already reached
// by previous swappings
// not enough households have been swapped
// when checking at lowest level
// Number of IDs that need to be swapped - already swapped IDs - IDs that have to be swapped at lowest level:
countRest = draw_group[x.first].second - countSwaps[x.first];
countRest = std::max(0,countRest);
std::unordered_set<int> IDswap_draw = x.second;
// apply sampling here -> should still be quick because IDswap_draw will not be extremely large
// in randSample households that must be swapped are automatically choosen
std::vector<int> IDswap_help = randSample(IDswap_draw,countRest,prob_help,mersenne_engine,IDused,mustSwap);
IDswap.resize(IDswap_help.size());
IDswap = IDswap_help;
}
// if any IDs need to be swapped:
if(IDswap.size()>0){
// get donor set
// if IDdonor is -1 at a position ==> no donor for IDswap at same position
std::vector<int> IDdonor = sampleDonor(data, similar, IDswap, x.second,
samp_order_donor[h], IDused, hid);
// set Index to used
for(std::size_t i=0;i<IDdonor.size();i++){
if(IDdonor[i]>-1){
IDused[IDdonor[i]]=1;
IDused[IDswap[i]]=1;
// store results from sampling in swappedIndex
swappedIndex[IDswap[i]] = IDdonor[i];
}else{
IDnotUsed.insert(IDswap[i]);
}
}
}
/////////////////
}
}
////////////////////////////////////////////////////
// Create output using swappedIndex
carry_along.insert( carry_along.end(), hierarchy.begin(), hierarchy.end() );
int nvalues = carry_along.size();
int swap_value,swap_value_with;
int hsize=0;
int hsizewith=0;
for(auto const&x : swappedIndex){
hsize = map_hsize[data[x.first][hid]];
hsizewith = map_hsize[data[x.second][hid]];
count_swapped_records = count_swapped_records + hsize + hsizewith; // count how many records are swapped
// erase elements if they have been used during the procedure
// donor was not found on highest hierarchy
// but donor was found on lowest...this might actually be a bug...
// IDnotUsed.erase(x.first);
// IDnotUsed.erase(x.second);
// loop over variables to swapp
for(int j=0;j<nvalues;j++){
swap_value = data[x.first][carry_along[j]];
swap_value_with = data[x.second][carry_along[j]];
for(int h=0;h<max(hsize,hsizewith);h++){
// swap variable value for every household member in x.first
if(h<hsize){
data[x.first+h][carry_along[j]] = swap_value_with;
}
// swap variable value for every household member in x.second
if(h<hsizewith){
data[x.second+h][carry_along[j]] = swap_value;
}
}
}
}
// save number of swaped hids
count_swapped_hid = swappedIndex.size()*2;
if(IDnotUsed.size()==0){
cout<<"Recordswapping was successful!"<<endl;
}else{
cout<<"Donor household was not found in "<<IDnotUsed.size()<<" cases.\nSee log.txt for a detailed list"<<endl;
FILE* pFile = fopen(log_file_name.c_str(), "w");
fprintf(pFile, "%lu household IDs for which a suitable donor for swapping was not found\n -------------------------------------------\n",IDnotUsed.size());
for(auto const&x : IDnotUsed){
fprintf(pFile, " %u\n",data[x][hid]);
}
fclose(pFile);
}
return data;
}