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ffm.cpp
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ffm.cpp
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#pragma GCC diagnostic ignored "-Wunused-result"
#include <algorithm>
#include <cmath>
#include <iostream>
#include <iomanip>
#include <fstream>
#include <new>
#include <memory>
#include <random>
#include <stdexcept>
#include <string>
#include <cstring>
#include <vector>
#include <pmmintrin.h>
#if defined USEOMP
#include <omp.h>
#endif
#include "ffm.h"
namespace ffm {
namespace {
using namespace std;
ffm_int const kALIGNByte = 16;
ffm_int const kALIGN = kALIGNByte/sizeof(ffm_float);
ffm_int const kCHUNK_SIZE = 10000000;
ffm_int const kMaxLineSize = 100000;
inline ffm_float wTx(
ffm_node *begin,
ffm_node *end,
ffm_float r,
ffm_model &model,
ffm_float kappa=0,
ffm_float alpha=0,
ffm_float beta=0,
ffm_float L1=0,
ffm_float L2=0,
bool do_update=false)
{
ffm_long align0 = (ffm_long)model.k*2;
ffm_long align1 = (ffm_long)model.m*align0;
ffm_long align0_z = (ffm_long)model.k;
ffm_long align1_z = (ffm_long)model.m*align0_z;
__m128 XMMkappa = _mm_set1_ps(kappa);
__m128 XMMalpha = _mm_set1_ps(alpha);
__m128 XMMbeta = _mm_set1_ps(beta);
__m128 XMML1 = _mm_set1_ps(L1);
__m128 XMML2 = _mm_set1_ps(L2);
__m128 XMMt = _mm_setzero_ps(); // all the interaction sum, hypothesis
for(ffm_node *N1 = begin; N1 != end; N1++)
{
ffm_int j1 = N1->j; // column index
ffm_int f1 = N1->f; // field index
ffm_float v1 = N1->v; // value
if(j1 >= model.n || f1 >= model.m)
continue;
for(ffm_node *N2 = N1+1; N2 != end; N2++)
{
ffm_int j2 = N2->j;
ffm_int f2 = N2->f;
ffm_float v2 = N2->v;
if(j2 >= model.n || f2 >= model.m)
continue;
// if two nodes are 1:121:0.3 2:196:0.2
ffm_float *w1 = model.W + j1*align1 + f2*align0; // w121,2
ffm_float *w2 = model.W + j2*align1 + f1*align0; //w196,1
ffm_float *z1 = model.Z + j1*align1_z + f2*align0_z;
ffm_float *z2 = model.Z + j2*align1_z + f1*align0_z;
__m128 XMMv = _mm_set1_ps(v1*v2*r); // 0.3*0.2
if(do_update) // comp grad and update weight
{
__m128 XMMkappav = _mm_mul_ps(XMMkappa, XMMv);
ffm_float kappav;
_mm_store_ss(&kappav, XMMkappav);
ffm_float *wg1 = w1 + model.k;
ffm_float *wg2 = w2 + model.k;
for(ffm_int d = 0; d < model.k; d += 4)
{
// load
__m128 XMMw1 = _mm_load_ps(w1+d);
__m128 XMMw2 = _mm_load_ps(w2+d);
__m128 XMMwg1 = _mm_load_ps(wg1+d);
__m128 XMMwg2 = _mm_load_ps(wg2+d);
__m128 XMMz1 = _mm_load_ps(z1+d);
__m128 XMMz2 = _mm_load_ps(z2+d);
// calc grad
__m128 XMMg1 = _mm_add_ps(
_mm_mul_ps(XMML2, XMMw1),
_mm_mul_ps(XMMkappav, XMMw2));
//ffm_float g1 = L2 * (*(w1 + d)) + kappav * (*(w2 + d));
__m128 XMMg2 = _mm_add_ps(
_mm_mul_ps(XMML2, XMMw2),
_mm_mul_ps(XMMkappav, XMMw1));
//ffm_float g2 = L2 * (*(w2 + d)) + kappav * (*(w1 + d));
//ffm_float g1 = kappav * (*(w2 + d));
//ffm_float g2 = kappav * (*(w1 + d));
__m128 XMMsigma1 = _mm_div_ps(
_mm_sub_ps(
_mm_sqrt_ps(
_mm_add_ps(XMMwg1,
_mm_mul_ps(XMMg1, XMMg1))),
_mm_sqrt_ps(XMMwg1)), XMMalpha);
__m128 XMMsigma2 = _mm_div_ps(
_mm_sub_ps(
_mm_sqrt_ps(
_mm_add_ps(XMMwg2,
_mm_mul_ps(XMMg2, XMMg2))),
_mm_sqrt_ps(XMMwg2)), XMMalpha);
//ffm_float sigma1 = (sqrt(*(wg1+d) + g1 * g1) - sqrt(*(wg1+d))) / alpha;
//ffm_float sigma2 = (sqrt(*(wg2+d) + g2 * g2) - sqrt(*(wg2+d))) / alpha;
// update z[i]
XMMz1 = _mm_add_ps(XMMz1,
_mm_sub_ps(XMMg1,
_mm_mul_ps(XMMsigma1, XMMw1)));
XMMz2 = _mm_add_ps(XMMz2,
_mm_sub_ps(XMMg2,
_mm_mul_ps(XMMsigma2, XMMw2)));
//*(z1 + d) += g1 - sigma1 * (*w1);
//*(z2 + d) += g2 - sigma2 * (*w2);
_mm_store_ps(z1+d, XMMz1);
_mm_store_ps(z2+d, XMMz2);
// update n[i]
XMMwg1 = _mm_add_ps(XMMwg1,
_mm_mul_ps(XMMg1, XMMg1));
XMMwg2 = _mm_add_ps(XMMwg2,
_mm_mul_ps(XMMg2, XMMg2));
//*(wg1+d) += (g1 * g1);
//*(wg2+d) += (g2 * g2);
_mm_store_ps(wg1+d, XMMwg1);
_mm_store_ps(wg2+d, XMMwg2);
// update w !!SSE may not be any faster TODO:CNevd
for (int i_ = 0; i_ < 4; ++i_) {
int sign = (*(z1 + d + i_)) > 0 ? 1:-1;
if ( sign * (*(z1 + d + i_)) <= L1) {
*(w1 + d + i_) = 0;
}else{
*(w1 + d + i_) = (sign * L1 - (*(z1 + d + i_))) / ((beta + sqrt(*(wg1 + d + i_))) / alpha + L2);
}
sign = (*(z2 + d + i_)) > 0 ? 1:-1;
if ( sign * (*(z2 + d + i_)) <= L1) {
*(w2 + d + i_) = 0;
}else{
*(w2 + d + i_) = (sign * L1 - (*(z2 + d + i_))) / ((beta + sqrt(*(wg2 + d + i_))) / alpha + L2);
}
}
}
}
else
{
for(ffm_int d = 0; d < model.k; d += 4)
{
__m128 XMMw1 = _mm_load_ps(w1+d); // dth element of w121:2 = [v1, v2, vd, ..., vk]
__m128 XMMw2 = _mm_load_ps(w2+d);
XMMt = _mm_add_ps(XMMt,
_mm_mul_ps(_mm_mul_ps(XMMw1, XMMw2), XMMv)); // XMMt = <w121:2,w196,1> * 03 * 0.2 + ...
}
}
}
}
if(do_update)
return 0;
XMMt = _mm_hadd_ps(XMMt, XMMt);
XMMt = _mm_hadd_ps(XMMt, XMMt);
ffm_float t;
_mm_store_ss(&t, XMMt);
return t;
}
ffm_float* malloc_aligned_float(ffm_long size)
{
void *ptr;
#ifdef _WIN32
ptr = _aligned_malloc(size*sizeof(ffm_float), kALIGNByte);
if(ptr == nullptr)
throw bad_alloc();
#else
int status = posix_memalign(&ptr, kALIGNByte, size*sizeof(ffm_float));
if(status != 0)
throw bad_alloc();
#endif
return (ffm_float*)ptr;
}
ffm_model* init_model(ffm_int n, ffm_int m, ffm_parameter param)
{
ffm_int k_aligned = (ffm_int)ceil((ffm_double)param.k/kALIGN)*kALIGN;
ffm_model *model = new ffm_model;
model->n = n;
model->k = k_aligned;
model->m = m;
model->W = nullptr;
model->Z = nullptr;
model->normalization = param.normalization;
try
{
model->W = malloc_aligned_float((ffm_long)n*m*k_aligned*2);
model->Z = malloc_aligned_float((ffm_long)n*m*k_aligned);
}
catch(bad_alloc const &e)
{
ffm_destroy_model(&model);
throw;
}
ffm_float coef = 1.0f/sqrt(param.k);
ffm_float *w = model->W;
default_random_engine generator;
uniform_real_distribution<ffm_float> distribution(0.0, 1.0);
for(ffm_int j = 0; j < model->n; j++)
{
for(ffm_int f = 0; f < model->m; f++)
{
for(ffm_int d = 0; d < param.k; d++, w++)
*w = coef*distribution(generator);
for(ffm_int d = param.k; d < k_aligned; d++, w++)
*w = 0;
for(ffm_int d = k_aligned; d < 2*k_aligned; d++, w++)
*w = 1;
}
}
memset(model->Z, 0, sizeof(ffm_float)*n*m*k_aligned); // init Z for ftrl
return model;
}
void shrink_model(ffm_model &model, ffm_int k_new)
{
for(ffm_int j = 0; j < model.n; j++)
{
for(ffm_int f = 0; f < model.m; f++)
{
ffm_float *src = model.W + ((ffm_long)j*model.m+f)*model.k*2;
ffm_float *dst = model.W + ((ffm_long)j*model.m+f)*k_new;
copy(src, src+k_new, dst);
}
}
model.k = k_new;
}
vector<ffm_float> normalize(ffm_problem &prob)
{
vector<ffm_float> R(prob.l);
#if defined USEOMP
#pragma omp parallel for schedule(static)
#endif
for(ffm_int i = 0; i < prob.l; i++)
{
ffm_float norm = 0;
for(ffm_long p = prob.P[i]; p < prob.P[i+1]; p++)
norm += prob.X[p].v*prob.X[p].v;
R[i] = 1/norm;
}
return R;
}
shared_ptr<ffm_model> train(
ffm_problem *tr,
vector<ffm_int> &order,
ffm_parameter param,
ffm_problem *va=nullptr)
{
#if defined USEOMP
ffm_int old_nr_threads = omp_get_num_threads();
omp_set_num_threads(param.nr_threads);
#endif
shared_ptr<ffm_model> model =
shared_ptr<ffm_model>(init_model(tr->n, tr->m, param),
[] (ffm_model *ptr) { ffm_destroy_model(&ptr); });
vector<ffm_float> R_tr, R_va;
if(param.normalization)
{
R_tr = normalize(*tr);
if(va != nullptr)
R_va = normalize(*va);
}
else
{
R_tr = vector<ffm_float>(tr->l, 1);
if(va != nullptr)
R_va = vector<ffm_float>(va->l, 1);
}
bool auto_stop = param.auto_stop && va != nullptr && va->l != 0;
ffm_int k_aligned = (ffm_int)ceil((ffm_double)param.k/kALIGN)*kALIGN;
ffm_long w_size = (ffm_long)model->n * model->m * k_aligned * 2;
vector<ffm_float> prev_W;
if(auto_stop)
prev_W.assign(w_size, 0);
ffm_double best_va_loss = numeric_limits<ffm_double>::max();
if(!param.quiet)
{
if(param.auto_stop && (va == nullptr || va->l == 0))
cerr << "warning: ignoring auto-stop because there is no validation set" << endl;
cout.width(4);
cout << "iter";
cout.width(13);
cout << "tr_logloss";
if(va != nullptr && va->l != 0)
{
cout.width(13);
cout << "va_logloss";
}
cout << endl;
}
for(ffm_int iter = 1; iter <= param.nr_iters; iter++)
{
ffm_double tr_loss = 0;
if(param.random)
random_shuffle(order.begin(), order.end());
#if defined USEOMP
#pragma omp parallel for schedule(static) reduction(+: tr_loss)
#endif
for(ffm_int ii = 0; ii < (ffm_int)order.size(); ii++) // each instance
{
ffm_int i = order[ii];
ffm_float y = tr->Y[i];
ffm_node *begin = &tr->X[tr->P[i]];
ffm_node *end = &tr->X[tr->P[i+1]];
ffm_float r = R_tr[i];
ffm_float t = wTx(begin, end, r, *model);
ffm_float expnyt = exp(-y*t);
tr_loss += log(1+expnyt);
ffm_float kappa = -y*expnyt/(1+expnyt);
wTx(begin, end, r, *model, kappa, param.alpha, param.beta, param.L1, param.L2, true);
}
if(!param.quiet)
{
tr_loss /= tr->l;
cout.width(4);
cout << iter;
cout.width(13);
cout << fixed << setprecision(5) << tr_loss;
if(va != nullptr && va->l != 0)
{
ffm_double va_loss = 0;
#if defined USEOMP
#pragma omp parallel for schedule(static) reduction(+:va_loss)
#endif
for(ffm_int i = 0; i < va->l; i++)
{
ffm_float y = va->Y[i];
ffm_node *begin = &va->X[va->P[i]];
ffm_node *end = &va->X[va->P[i+1]];
ffm_float r = R_va[i];
ffm_float t = wTx(begin, end, r, *model);
ffm_float expnyt = exp(-y*t);
va_loss += log(1+expnyt);
}
va_loss /= va->l;
cout.width(13);
cout << fixed << setprecision(5) << va_loss;
if(auto_stop)
{
if(va_loss > best_va_loss)
{
memcpy(model->W, prev_W.data(), w_size*sizeof(ffm_float));
cout << endl << "Auto-stop. Use model at " << iter-1 << "th iteration." << endl;
break;
}
else
{
memcpy(prev_W.data(), model->W, w_size*sizeof(ffm_float));
best_va_loss = va_loss;
}
}
}
cout << endl;
}
}
shrink_model(*model, param.k);
#if defined USEOMP
omp_set_num_threads(old_nr_threads);
#endif
return model;
}
// TODO: This function will be merged with train().
shared_ptr<ffm_model> train_on_disk(
string tr_path,
string va_path,
ffm_parameter param)
{
#if defined USEOMP
ffm_int old_nr_threads = omp_get_num_threads();
omp_set_num_threads(param.nr_threads);
#endif
FILE *f_tr = fopen(tr_path.c_str(), "rb");
FILE *f_va = nullptr;
if(!va_path.empty())
f_va = fopen(va_path.c_str(), "rb");
ffm_int m, n, max_l;
ffm_long max_nnz;
fread(&m, sizeof(ffm_int), 1, f_tr);
fread(&n, sizeof(ffm_int), 1, f_tr);
fread(&max_l, sizeof(ffm_int), 1, f_tr);
fread(&max_nnz, sizeof(ffm_long), 1, f_tr);
shared_ptr<ffm_model> model =
shared_ptr<ffm_model>(init_model(n, m, param),
[] (ffm_model *ptr) { ffm_destroy_model(&ptr); });
vector<ffm_float> Y;
Y.reserve(max_l);
vector<ffm_float> R;
R.reserve(max_l);
vector<ffm_long> P;
P.reserve(max_l+1);
vector<ffm_node> X;
X.reserve(max_nnz);
bool auto_stop = param.auto_stop && !va_path.empty();
ffm_int k_aligned = (ffm_int)ceil((ffm_double)param.k/kALIGN)*kALIGN;
ffm_long w_size = (ffm_long)model->n * model->m * k_aligned * 2;
vector<ffm_float> prev_W;
if(auto_stop)
prev_W.assign(w_size, 0);
ffm_double best_va_loss = numeric_limits<ffm_double>::max();
if(!param.quiet)
{
if(param.auto_stop && va_path.empty())
cerr << "warning: ignoring auto-stop because there is no validation set" << endl;
cout.width(4);
cout << "iter";
cout.width(13);
cout << "tr_logloss";
if(!va_path.empty())
{
cout.width(13);
cout << "va_logloss";
}
cout << endl;
}
for(ffm_int iter = 1; iter <= param.nr_iters; iter++)
{
ffm_double tr_loss = 0;
fseek(f_tr, 3*sizeof(ffm_int)+sizeof(ffm_long), SEEK_SET);
ffm_int tr_l = 0;
while(true)
{
ffm_int l;
fread(&l, sizeof(ffm_int), 1, f_tr);
tr_l += l;
if(l == 0)
break;
Y.resize(l);
fread(Y.data(), sizeof(ffm_float), l, f_tr);
R.resize(l);
fread(R.data(), sizeof(ffm_float), l, f_tr);
P.resize(l+1);
fread(P.data(), sizeof(ffm_long), l+1, f_tr);
X.resize(P[l]);
fread(X.data(), sizeof(ffm_node), P[l], f_tr);
#if defined USEOMP
#pragma omp parallel for schedule(static) reduction(+: tr_loss)
#endif
for(ffm_int i = 0; i < l; i++)
{
ffm_float y = Y[i];
ffm_node *begin = &X[P[i]];
ffm_node *end = &X[P[i+1]];
ffm_float r = param.normalization? R[i] : 1;
ffm_float t = wTx(begin, end, r, *model);
ffm_float expnyt = exp(-y*t);
tr_loss += log(1+expnyt);
ffm_float kappa = -y*expnyt/(1+expnyt);
wTx(begin, end, r, *model, kappa, param.alpha, param.beta, param.L1, param.L2, true);
}
}
if(!param.quiet)
{
tr_loss /= tr_l;
cout.width(4);
cout << iter;
cout.width(13);
cout << fixed << setprecision(5) << tr_loss;
if(f_va != nullptr)
{
fseek(f_va, 3*sizeof(ffm_int)+sizeof(ffm_long), SEEK_SET);
ffm_int va_l = 0;
ffm_double va_loss = 0;
while(true)
{
ffm_int l;
fread(&l, sizeof(ffm_int), 1, f_va);
va_l += l;
if(l == 0)
break;
vector<ffm_float> Y(l);
fread(Y.data(), sizeof(ffm_float), l, f_va);
vector<ffm_float> R(l);
fread(R.data(), sizeof(ffm_float), l, f_va);
vector<ffm_long> P(l+1);
fread(P.data(), sizeof(ffm_long), l+1, f_va);
vector<ffm_node> X(P[l]);
fread(X.data(), sizeof(ffm_node), P[l], f_va);
#if defined USEOMP
#pragma omp parallel for schedule(static) reduction(+: va_loss)
#endif
for(ffm_int i = 0; i < l; i++)
{
ffm_float y = Y[i];
ffm_node *begin = &X[P[i]];
ffm_node *end = &X[P[i+1]];
ffm_float r = param.normalization? R[i] : 1;
ffm_float t = wTx(begin, end, r, *model);
ffm_float expnyt = exp(-y*t);
va_loss += log(1+expnyt);
}
}
va_loss /= va_l;
cout.width(13);
cout << fixed << setprecision(5) << va_loss;
if(auto_stop)
{
if(va_loss > best_va_loss)
{
memcpy(model->W, prev_W.data(), w_size*sizeof(ffm_float));
cout << endl << "Auto-stop. Use model at " << iter-1 << "th iteration." << endl;
break;
}
else
{
memcpy(prev_W.data(), model->W, w_size*sizeof(ffm_float));
best_va_loss = va_loss;
}
}
}
cout << endl;
}
}
shrink_model(*model, param.k);
fclose(f_tr);
if(!va_path.empty())
fclose(f_va);
#if defined USEOMP
omp_set_num_threads(old_nr_threads);
#endif
return model;
}
} // unnamed namespace
ffm_problem* ffm_read_problem(char const *path)
{
if(strlen(path) == 0)
return nullptr;
FILE *f = fopen(path, "r");
if(f == nullptr)
return nullptr;
ffm_problem *prob = new ffm_problem;
prob->l = 0;
prob->n = 0;
prob->m = 0;
prob->X = nullptr;
prob->P = nullptr;
prob->Y = nullptr;
char line[kMaxLineSize];
ffm_long nnz = 0;
for(; fgets(line, kMaxLineSize, f) != nullptr; prob->l++)
{
strtok(line, " \t");
for(; ; nnz++)
{
char *ptr = strtok(nullptr," \t");
if(ptr == nullptr || *ptr == '\n')
break;
}
}
rewind(f);
prob->X = new ffm_node[nnz];
prob->P = new ffm_long[prob->l+1];
prob->Y = new ffm_float[prob->l];
ffm_long p = 0;
prob->P[0] = 0;
for(ffm_int i = 0; fgets(line, kMaxLineSize, f) != nullptr; i++)
{
char *y_char = strtok(line, " \t");
ffm_float y = (atoi(y_char)>0)? 1.0f : -1.0f;
prob->Y[i] = y;
for(; ; p++)
{
char *field_char = strtok(nullptr,":");
char *idx_char = strtok(nullptr,":");
char *value_char = strtok(nullptr," \t");
if(field_char == nullptr || *field_char == '\n')
break;
ffm_int field = atoi(field_char);
ffm_int idx = atoi(idx_char);
ffm_float value = atof(value_char);
prob->m = max(prob->m, field+1);
prob->n = max(prob->n, idx+1);
prob->X[p].f = field;
prob->X[p].j = idx;
prob->X[p].v = value;
}
prob->P[i+1] = p;
}
fclose(f);
return prob;
}
int ffm_read_problem_to_disk(char const *txt_path, char const *bin_path)
{
FILE *f_txt = fopen(txt_path, "r");
if(f_txt == nullptr)
return 1;
FILE *f_bin = fopen(bin_path, "wb");
if(f_bin == nullptr)
return 1;
vector<char> line(kMaxLineSize);
ffm_int m = 0;
ffm_int n = 0;
ffm_int max_l = 0;
ffm_long max_nnz = 0;
ffm_long p = 0;
vector<ffm_float> Y;
vector<ffm_float> R;
vector<ffm_long> P(1, 0);
vector<ffm_node> X;
auto write_chunk = [&] ()
{
ffm_int l = Y.size();
ffm_long nnz = P[l];
max_l = max(max_l, l);
max_nnz = max(max_nnz, nnz);
fwrite(&l, sizeof(ffm_int), 1, f_bin);
fwrite(Y.data(), sizeof(ffm_float), l, f_bin);
fwrite(R.data(), sizeof(ffm_float), l, f_bin);
fwrite(P.data(), sizeof(ffm_long), l+1, f_bin);
fwrite(X.data(), sizeof(ffm_node), nnz, f_bin);
Y.clear();
R.clear();
P.assign(1, 0);
X.clear();
p = 0;
};
fwrite(&m, sizeof(ffm_int), 1, f_bin);
fwrite(&n, sizeof(ffm_int), 1, f_bin);
fwrite(&max_l, sizeof(ffm_int), 1, f_bin);
fwrite(&max_nnz, sizeof(ffm_long), 1, f_bin);
while(fgets(line.data(), kMaxLineSize, f_txt))
{
char *y_char = strtok(line.data(), " \t");
ffm_float y = (atoi(y_char)>0)? 1.0f : -1.0f;
ffm_float scale = 0;
for(; ; p++)
{
char *field_char = strtok(nullptr,":");
char *idx_char = strtok(nullptr,":");
char *value_char = strtok(nullptr," \t");
if(field_char == nullptr || *field_char == '\n')
break;
ffm_node N;
N.f = atoi(field_char);
N.j = atoi(idx_char);
N.v = atof(value_char);
X.push_back(N);
m = max(m, N.f+1);
n = max(n, N.j+1);
scale += N.v*N.v;
}
scale = 1/scale;
Y.push_back(y);
R.push_back(scale);
P.push_back(p);
if(X.size() > (size_t)kCHUNK_SIZE)
write_chunk();
}
write_chunk();
write_chunk();
rewind(f_bin);
fwrite(&m, sizeof(ffm_int), 1, f_bin);
fwrite(&n, sizeof(ffm_int), 1, f_bin);
fwrite(&max_l, sizeof(ffm_int), 1, f_bin);
fwrite(&max_nnz, sizeof(ffm_long), 1, f_bin);
fclose(f_bin);
fclose(f_txt);
return 0;
}
void ffm_destroy_problem(ffm_problem **prob)
{
if(prob == nullptr || *prob == nullptr)
return;
delete[] (*prob)->X;
delete[] (*prob)->P;
delete[] (*prob)->Y;
delete *prob;
*prob = nullptr;
}
ffm_int ffm_save_model(ffm_model *model, char const *path)
{
ofstream f_out(path);
if(!f_out.is_open())
return 1;
f_out << "n " << model->n << "\n";
f_out << "m " << model->m << "\n";
f_out << "k " << model->k << "\n";
f_out << "normalization " << model->normalization << "\n";
ffm_float *ptr = model->W;
for(ffm_int j = 0; j < model->n; j++)
{
for(ffm_int f = 0; f < model->m; f++)
{
f_out << "w" << j << "," << f << " ";
for(ffm_int d = 0; d < model->k; d++, ptr++)
f_out << *ptr << " ";
f_out << "\n";
}
}
return 0;
}
ffm_model* ffm_load_model(char const *path)
{
ifstream f_in(path);
if(!f_in.is_open())
return nullptr;
string dummy;
ffm_model *model = new ffm_model;
model->W = nullptr;
f_in >> dummy >> model->n >> dummy >> model->m >> dummy >> model->k
>> dummy >> model->normalization;
try
{
model->W = malloc_aligned_float((ffm_long)model->m*model->n*model->k);
}
catch(bad_alloc const &e)
{
ffm_destroy_model(&model);
return nullptr;
}
ffm_float *ptr = model->W;
for(ffm_int j = 0; j < model->n; j++)
{
for(ffm_int f = 0; f < model->m; f++)
{
f_in >> dummy;
for(ffm_int d = 0; d < model->k; d++, ptr++)
f_in >> *ptr;
}
}
return model;
}
void ffm_destroy_model(ffm_model **model)
{
if(model == nullptr || *model == nullptr)
return;
#ifdef _WIN32
_aligned_free((*model)->W);
_aligned_free((*model)->Z);
#else
free((*model)->W);
free((*model)->Z);
#endif
delete *model;
*model = nullptr;
}
ffm_parameter ffm_get_default_param()
{
ffm_parameter param;
param.alpha = 0.3;
param.beta = 1.0;
param.L1 = 0.;
param.L2 = 0.;
param.nr_iters = 15;
param.k = 4;
param.nr_threads = 1;
param.quiet = false;
param.normalization = true;
param.random = true;
param.auto_stop = false;
return param;
}
ffm_model* ffm_train_with_validation(ffm_problem *tr, ffm_problem *va, ffm_parameter param)
{
vector<ffm_int> order(tr->l);
for(ffm_int i = 0; i < tr->l; i++)
order[i] = i;
shared_ptr<ffm_model> model = train(tr, order, param, va);
ffm_model *model_ret = new ffm_model;
model_ret->n = model->n;
model_ret->m = model->m;
model_ret->k = model->k;
model_ret->normalization = model->normalization;
model_ret->W = model->W;
model_ret->Z = model->Z;
model->W = nullptr;
model->Z = nullptr;
return model_ret;
}
ffm_model* ffm_train(ffm_problem *prob, ffm_parameter param)
{
return ffm_train_with_validation(prob, nullptr, param);
}
ffm_model* ffm_train_with_validation_on_disk(
char const *tr_path,
char const *va_path,
ffm_parameter param)
{
shared_ptr<ffm_model> model = train_on_disk(tr_path, va_path, param);
ffm_model *model_ret = new ffm_model;
model_ret->n = model->n;
model_ret->m = model->m;
model_ret->k = model->k;
model_ret->normalization = model->normalization;
model_ret->W = model->W;
model_ret->Z = model->Z;
model->W = nullptr;
model->Z = nullptr;
return model_ret;
}
ffm_model* ffm_train_on_disk(char const *prob_path, ffm_parameter param)
{
return ffm_train_with_validation_on_disk(prob_path, "", param);
}
ffm_float ffm_predict(ffm_node *begin, ffm_node *end, ffm_model *model)
{
ffm_float r = 1;
if(model->normalization)
{
r = 0;
for(ffm_node *N = begin; N != end; N++)
r += N->v*N->v;
r = 1/r;
}
ffm_long align0 = (ffm_long)model->k;
ffm_long align1 = (ffm_long)model->m*align0;
ffm_float t = 0;
for(ffm_node *N1 = begin; N1 != end; N1++)