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#include "khash.h"
#include <pthread.h>
#define computeGradientBoostLoss(g, h) (-(g)*(g)/(h))
// we use khash to make iteration faster than lua tables
KHASH_SET_INIT_INT64(long)
// defines the data we need for running an instance of thet and its constructor/destructor
typedef struct {
khash_t(long)* exampleMap;
THLongTensor *exampleIdsWithFeature_cache;
long minLeafSize;
} GBRunData;
// allocates data that cannot be shared between threads
static void gb_local_create_run_data(GBRunData *run_data) {
run_data->exampleMap = kh_init(long);
run_data->exampleIdsWithFeature_cache = THLongTensor_new();
}
static void gb_create_run_data(GBRunData *run_data, int minLeafSize) {
gb_local_create_run_data(run_data);
run_data->minLeafSize = minLeafSize;
}
static void gb_destroy_run_data(GBRunData *run_data) {
THLongTensor_free(run_data->exampleIdsWithFeature_cache);
kh_destroy(long, run_data->exampleMap);
}
// initializes the data required by the optimizer for the given feature.
static THLongTensor *gb_internal_prepare(lua_State *L, THLongTensor *exampleIds,
THLongTensor *exampleIdsWithFeature_cache, int input_index, long feature_id,
khash_t(long)* exampleMap) {
long *exampleIds_data = THLongTensor_data(exampleIds);
long exampleIds_size = THLongTensor_size(exampleIds, 0);
int ret = 0;
// if the the input is a table, then we have a sparse dataset
if (lua_istable(L, input_index)) {
if (exampleIds_size == 0) {
return NULL;
}
else {
// loops over the examples' ids that this node has to evaluate and, if they have the feature
// we're looking for, marks them as present and stores them in the order provided by the
// dataset
THLongTensor_resize1d(exampleIdsWithFeature_cache, exampleIds_size);
kh_clear(long, exampleMap);
kh_resize(long, exampleMap, exampleIds_size*8);
long *exampleIdsWithFeature_data = THLongTensor_data(exampleIdsWithFeature_cache);
long j = 0;
// for each sample to be evaluated
for (long i = 0; i < exampleIds_size; i++) {
// gets the representation for the example
lua_pushinteger(L, exampleIds_data[i]);
lua_gettable(L, input_index);
// builds the index, which happens only once per thread for efficiency
lua_pushstring(L, "buildIndex");
lua_gettable(L, -2);
lua_pushvalue(L, -2);
lua_call(L, 1, 0);
// tries to get the feature for this sample
lua_pushinteger(L, feature_id);
lua_gettable(L, -2);
// if present, then...
if (!lua_isnil(L, -1)) {
// saves the example
exampleIdsWithFeature_data[j] = exampleIds_data[i];
j++;
// marks it as present in the hash table
kh_put(long, exampleMap, exampleIds_data[i], &ret);
}
lua_pop(L, 2);
}
// resizes to fit only the samples that have the feature
THLongTensor_resize1d(exampleIdsWithFeature_cache, j);
kh_resize(long, exampleMap, j*8);
return exampleIdsWithFeature_cache;
}
}
else {
// if the input isn't a table, then it's dense and we cannot have exampleIds missing, so it
// depends on feature_id
// since exampleIds is fixed between calls and this is going to store the same values to the
// same position, we can cache it between calls
if (kh_size(exampleMap) == 0) {
kh_resize(long, exampleMap, exampleIds_size*8);
for (long i = 0; i < exampleIds_size; i++) {
kh_put(long, exampleMap, exampleIds_data[i], &ret);
}
}
// notice that we just return the given tensor of ids instead of copying it. the rest of the
// code handles this transparently
return exampleIds;
}
}
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