std::string save_vertex(const graph_type::vertex_type& v) {
std::stringstream sstream;
if (is_user(v)) {
const std::vector<std::pair<double, graphlab::vertex_id_type> >& top_rated
= v.data().top_rated;
const std::vector<std::pair<double, graphlab::vertex_id_type> >& top_pred
= v.data().top_pred;
if (top_rated.size() < 10 || top_pred.size() == 0) {
return "";
}
// save top rated
sstream << v.id() << " ";
sstream << pair2str(top_rated[0]);
for (size_t i = 1; i < top_rated.size(); ++i) {
sstream << "," << (pair2str(top_rated[i]));
}
// save top pred
sstream << " ";
sstream << pair2str(top_pred[0]);
for (size_t i = 1; i < top_pred.size(); ++i) {
sstream << "," << (pair2str(top_pred[i]));
}
sstream << "\n";
return sstream.str();
} else {
return "";
}
}
void edit_mail_mode(USER_DATA *usr, char *argument) {
char arg[INPUT];
while (isspace(*argument))
argument++;
smash_tilde(argument);
usr->timer = 0;
argument = one_argument(argument, arg);
if (arg[0] == '\0') {
func_rnew_mail(usr);
return;
} else if (!str_cmp(arg, "?") || !str_cmp(arg, "h")) {
do_help(usr, "MAIL_DATA-INDEX");
return;
} else if (!str_cmp(arg, "l")) {
func_list_mail(usr);
return;
} else if (!str_cmp(arg, "r")) {
func_reply_mail(usr, argument);
return;
} else if (!str_cmp(arg, "d")) {
func_delete_mail(usr, argument);
return;
} else if (!str_cmp(arg, "q")) {
func_quit_mail(usr);
return;
} else if (!str_cmp(arg, "c")) {
if (argument[0] == '\0') {
syntax("[#Wc#x]ompose <user name>", usr);
return;
}
if (!is_user(argument)) {
send_to_user("No such user.\n\r", usr);
return;
}
if (is_enemy(usr, argument)) {
send_to_user("You can't sent mail to your enemies.\n\r", usr);
return;
}
mail_attach(usr);
if (usr->pCurrentMail->to)
free_string(usr->pCurrentMail->to);
usr->pCurrentMail->to = str_dup(argument);
EDIT_MODE(usr) = EDITOR_MAIL_SUBJECT;
return;
} else if (is_number(arg)) {
func_read_mail(usr, arg);
return;
} else {
send_to_user(
"Unknown mail command, try '?' in order to show help.\n\r", usr);
return;
}
}
void do_mail(USER_DATA *usr, char *argument) {
char arg[INPUT];
one_argument(argument, arg);
if (arg[0] == '\0') {
print_to_user(usr, "\n\rTotal messages: %-3d\n\r\n\r", count_mail(usr));
do_help(usr, "MAIL_DATA-INDEX");
EDIT_MODE(usr) = EDITOR_MAIL;
return;
}
if (!is_user(arg)) {
send_to_user("No such user.\n\r", usr);
return;
}
if (is_enemy(usr, arg)) {
send_to_user("You can't sent mail to your enemies.\n\r", usr);
return;
}
mail_attach(usr);
if (usr->pCurrentMail->to)
free_string(usr->pCurrentMail->to);
usr->pCurrentMail->to = str_dup(arg);
EDIT_MODE(usr) = EDITOR_MAIL_SUBJECT;
}
void collect_function (engine_type::context_type& context,
graph_type::vertex_type& vertex) {
if (is_user(vertex)) {
map_join_pair sum = context.map_reduce<map_join_pair>(COLLECT_TASK, ALL_EDGES);
vertex.data().top_rated = sum.first.get_top_k(10);
vertex.data().top_pred = sum.second.get_top_k(5);
}
}
void finger_mail(USER_DATA *usr, char *name) {
MAIL_DATA *pMail;
MAIL_DATA *fMailFirst;
MAIL_DATA *fMailLast;
USER_DATA *to;
char buf[INPUT];
FILE *fpMail;
int count = 0;
int new_count = 0;
if (!(to = get_user(name))) {
if (!is_user(name)) {
bbs_bug("Finger_mail: No such user %s", name);
send_to_user("ERROR: No such user.\n\r", usr);
return;
}
fMailFirst = NULL;
fMailLast = NULL;
sprintf(buf, "%s%s", MAIL_DIR, capitalize(name));
if (!(fpMail = fopen(buf, "r"))) {
bbs_bug("Finger_mail: Could not open to read %s", buf);
/* send_to_user("ERROR: Could not open mail file.\n\r", usr);
BAXTER */
send_to_user("Mail: No new messages.\n\r\n\r", usr);
return;
}
while ((pMail = read_mail(fpMail)) != NULL)
LINK(pMail, fMailFirst, fMailLast);
for (pMail = fMailFirst; pMail; pMail = pMail->next) {
if (pMail) {
if (pMail->stamp_time > pMail->read_time)
new_count++;
count++;
}
}
if (new_count > 0)
sprintf(buf, "Mail: %d new message%s.\n\r\n\r", new_count,
new_count > 1 ? "s" : "");
else
sprintf(buf, "Mail: No new messages.\n\r\n\r");
send_to_user(buf, usr);
return;
}
if (unread_mail(to) > 0)
sprintf(buf, "Mail: %d new message%s.\n\r\n\r", unread_mail(to),
unread_mail(to) > 1 ? "s" : "");
else
sprintf(buf, "Mail: No new messages.\n\r\n\r");
send_to_user(buf, usr);
return;
}
void
db_destroy_object(Objid oid)
{
Object *o = dbpriv_find_object(oid);
Verbdef *v, *w;
int i;
db_priv_affected_callable_verb_lookup();
if (!o)
panic("DB_DESTROY_OBJECT: Invalid object!");
if (o->location != NOTHING || o->contents != NOTHING
|| o->parent != NOTHING || o->child != NOTHING)
panic("DB_DESTROY_OBJECT: Not a barren orphan!");
if (is_user(oid)) {
Var t;
t.type = TYPE_OBJ;
t.v.obj = oid;
all_users = setremove(all_users, t);
}
free_str(o->name);
for (i = 0; i < o->propdefs.cur_length; i++) {
/* As an orphan, the only properties on this object are the ones
* defined on it directly, so these two arrays must be the same length.
*/
free_str(o->propdefs.l[i].name);
free_var(o->propval[i].var);
}
if (o->propval)
myfree(o->propval, M_PVAL);
if (o->propdefs.l)
myfree(o->propdefs.l, M_PROPDEF);
for (v = o->verbdefs; v; v = w) {
if (v->program)
free_program(v->program);
free_str(v->name);
w = v->next;
myfree(v, M_VERBDEF);
}
myfree(objects[oid], M_OBJECT);
objects[oid] = 0;
}
/**
* Grab pivot's adjacency list into memory.
*/
int load_edges_into_memory(graphchi_vertex<uint32_t, edge_data> &v) {
assert(is_pivot(v.id()));
assert(is_user(v.id()));
int num_edges = v.num_edges();
dense_adj dadj;
for(int i=0; i<num_edges; i++)
set_new( dadj.edges, v.edge(i)->vertex_id(), v.edge(i)->get_data().up_weight);
//dadj.ratings = zeros(N);
dadj.vid = v.id();
adjs[v.id() - pivot_st] = dadj;
assert(v.id() - pivot_st < adjs.size());
__sync_add_and_fetch(&grabbed_edges, num_edges /*edges_to_larger_id*/);
return num_edges;
}
/**
* Vertex update function.
*/
void update(graphchi_vertex<VertexDataType, edge_data> &v, graphchi_context &gcontext) {
if (debug)
printf("Entered iteration %d with %d\n", gcontext.iteration, is_item(v.id()) ? (v.id() - M + 1): v.id());
/* Even iteration numbers:
* 1) load a subset of users into memory (pivots)
* 2) Find which subset of items is connected to the users
*/
if (gcontext.iteration % 2 == 0) {
if (adjcontainer->is_pivot(v.id()) && is_user(v.id())) {
adjcontainer->load_edges_into_memory(v);
if (debug)
printf("Loading pivot %d intro memory\n", v.id());
}
}
/* odd iteration number:
* 1) For any item connected to a pivot item
* compute itersection
*/
else {
assert(is_item(v.id()));
for (int i=0; i< v.num_edges(); i++) {
if (!adjcontainer->is_pivot(v.edge(i)->vertex_id()))
continue;
if (debug)
printf("comparing user pivot %d to item %d\n", v.edge(i)->vertex_id()+1 , v.id() - M + 1);
adjcontainer->compute_ratings(v, v.edge(i)->vertex_id(), v.edge(i)->get_data().up_weight);
item_pairs_compared++;
if (item_pairs_compared % 1000000 == 0)
Rcpp::Rcout<< std::setw(10) << mytimer.current_time() << ") " << std::setw(10) << item_pairs_compared << " pairs compared " << std::endl;
}
}//end of iteration % 2 == 1
}//end of update function
/**
* Vertex update function.
*/
void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {
if (gcontext.iteration == 0) {
if (is_user(vertex.id()) && vertex.num_outedges() > 0) {
vertex_data& user = latent_factors_inmem[vertex.id()];
user.pvec = zeros(D*3);
for(int e=0; e < vertex.num_outedges(); e++) {
rbm_movie mov = latent_factors_inmem[vertex.edge(e)->vertex_id()];
float observation = vertex.edge(e)->get_data();
int r = (int)(observation/rbm_scaling);
assert(r < rbm_bins);
mov.bi[r]++;
}
}
return;
} else if (gcontext.iteration == 1) {
if (vertex.num_inedges() > 0) {
rbm_movie mov = latent_factors_inmem[vertex.id()];
setRand2(mov.w, D*rbm_bins, 0.001);
for(int r = 0; r < rbm_bins; ++r) {
mov.bi[r] /= (double)vertex.num_inedges();
mov.bi[r] = log(1E-9 + mov.bi[r]);
if (mov.bi[r] > 1000) {
assert(false);
Rcpp::Rcerr<<"Numerical overflow" <<std::endl;
}
}
}
return; //done with initialization
}
//go over all user nodes
if (is_user(vertex.id()) && vertex.num_outedges()) {
vertex_data & user = latent_factors_inmem[vertex.id()];
user.pvec = zeros(3*D);
rbm_user usr(user);
vec v1 = zeros(vertex.num_outedges());
//go over all ratings
for(int e=0; e < vertex.num_outedges(); e++) {
float observation = vertex.edge(e)->get_data();
rbm_movie mov = latent_factors_inmem[vertex.edge(e)->vertex_id()];
int r = (int)(observation / rbm_scaling);
assert(r < rbm_bins);
for(int k=0; k < D; k++) {
usr.h[k] += mov.w[D*r + k];
assert(!std::isnan(usr.h[k]));
}
}
for(int k=0; k < D; k++) {
usr.h[k] = sigmoid(usr.h[k]);
if (drand48() < usr.h[k])
usr.h0[k] = 1;
else usr.h0[k] = 0;
}
int i = 0;
double prediction;
for(int e=0; e < vertex.num_outedges(); e++) {
rbm_movie mov = latent_factors_inmem[vertex.edge(e)->vertex_id()];
float observation = vertex.edge(e)->get_data();
predict1(usr, mov, observation, prediction);
int vi = (int)(prediction / rbm_scaling);
v1[i] = vi;
i++;
}
i = 0;
for(int e=0; e < vertex.num_outedges(); e++) {
rbm_movie mov = latent_factors_inmem[vertex.edge(e)->vertex_id()];
int r = (int)v1[i];
for (int k=0; k< D; k++) {
usr.h1[k] += mov.w[r*D+k];
}
i++;
}
for (int k=0; k < D; k++) {
usr.h1[k] = sigmoid(usr.h1[k]);
if (drand48() < usr.h1[k])
usr.h1[k] = 1;
else usr.h1[k] = 0;
}
i = 0;
for(int e=0; e < vertex.num_outedges(); e++) {
rbm_movie mov = latent_factors_inmem[vertex.edge(e)->vertex_id()];
float observation = vertex.edge(e)->get_data();
double prediction;
rbm_predict(user, mov, observation, prediction, NULL);
double pui = prediction / rbm_scaling;
double rui = observation / rbm_scaling;
rmse_vec[omp_get_thread_num()] += (pui - rui) * (pui - rui);
//nn += 1.0;
int vi0 = (int)(rui);
int vi1 = (int)v1[i];
for (int k = 0; k < D; k++) {
mov.w[D*vi0+k] += rbm_alpha * (usr.h0[k] - rbm_beta * mov.w[vi0*D+k]);
assert(!std::isnan(mov.w[D*vi0+k]));
mov.w[D*vi1+k] -= rbm_alpha * (usr.h1[k] + rbm_beta * mov.w[vi1*D+k]);
assert(!std::isnan(mov.w[D*vi1+k]));
}
i++;
}
}
}
/**
* Vertex update function.
*/
void update(graphchi_vertex<VertexDataType, EdgeDataType> &v, graphchi_context &gcontext) {
if (debug)
printf("Entered iteration %d with %d\n", gcontext.iteration, v.id());
/* even iteration numbers:
* 1) load a subset of items into memory (pivots)
* 2) Find which subset of items needs to compared to the users
*/
if (gcontext.iteration % 2 == 0) {
if (adjcontainer->is_pivot(v.id()) && is_item(v.id())){
adjcontainer->load_edges_into_memory(v);
if (debug)
printf("Loading pivot %dintro memory\n", v.id());
}
else if (is_user(v.id())){
//in the zero iteration, if using AA/RA/PROB distance metric, initialize array
//with node degrees
if (gcontext.iteration == 0 && (distance_metric == AA || distance_metric == RA || distance_metric == PROB)){
latent_factors_inmem[v.id()].degree = v.num_edges();
}
//check if this user is connected to any pivot item
bool has_pivot = false;
int pivot = -1;
for(int i=0; i<v.num_edges(); i++) {
graphchi_edge<uint32_t> * e = v.edge(i);
//assert(is_item(e->vertexid));
if (adjcontainer->is_pivot(e->vertexid)) {
has_pivot = true;
pivot = e->vertexid;
break;
}
}
if (debug)
printf("user %d is linked to pivot %d\n", v.id(), pivot);
if (!has_pivot) //this user is not connected to any of the pivot item nodes and thus
//it is not relevant at this point
return;
//this user is connected to a pivot items, thus all connected items should be compared
for(int i=0; i<v.num_edges(); i++) {
graphchi_edge<uint32_t> * e = v.edge(i);
//assert(v.id() != e->vertexid);
relevant_items[e->vertexid - M] = true;
}
}//is_user
} //iteration % 2 = 1
/* odd iteration number:
* 1) For any item connected to a pivot item
* compute itersection
*/
else {
if (!relevant_items[v.id() - M]){
if (debug)
logstream(LOG_DEBUG)<<"Skipping item: " << v.id() << " since not relevant" << std::endl;
return;
}
std::vector<index_val> heap;
for (vid_t i=adjcontainer->pivot_st; i< adjcontainer->pivot_en; i++){
//if JACCARD which is symmetric, compare only to pivots which are smaller than this item id
if ((distance_metric != ASYM_COSINE && i >= v.id()) || (!relevant_items[i-M]))
continue;
else if (distance_metric == ASYM_COSINE && i == v.id())
continue;
double dist = adjcontainer->calc_distance(v, i, distance_metric);
item_pairs_compared++;
if (item_pairs_compared % 10000000 == 0)
logstream(LOG_INFO)<< std::setw(10) << mytimer.current_time() << ") " << std::setw(10) << item_pairs_compared << " pairs compared " << std::setw(10) <<sum(written_pairs) << " written. " << std::endl;
if (debug)
printf("comparing %d to pivot %d distance is %g\n", i - M + 1, v.id() - M + 1, dist);
if (dist != 0){
heap.push_back(index_val(i, dist));
}
else zero_dist++;
}
sort(heap.begin(), heap.end(), &Greater);
int thread_num = omp_get_thread_num();
if (heap.size() < K)
not_enough++;
for (uint i=0; i< std::min(heap.size(), (size_t)K); i++){
int rc = fprintf(out_files[thread_num], "%u %u %.12lg\n", v.id()-M+1, heap[i].index-M+1, (double)heap[i].val);//write item similarity to file
written_pairs[omp_get_thread_num()]++;
if (rc <= 0){
perror("Failed to write output");
logstream(LOG_FATAL)<<"Failed to write output to: file: " << training << omp_get_thread_num() << ".out" << std::endl;
}
}
}//end of iteration % 2 == 1
}//end of update function
/**
* calc distance between two items.
* Let a be all the users rated item 1
* Let b be all the users rated item 2
* Let intersection (a,b) be the number of users rated both items
* Let size(a) be the number of users rated item 1
* Let size(b) be the number of users rated item 2
*
* Only for prob similarity:
* Let M be the total number of users
* Let N be the total number of iterms
* Let L be the total number of training ratings
*
* 0) Using Jackard index:
* Dist_12 = intersection(a,b) / (size(a) + size(b) - size(intersection(a,b))
*
* 1) Using AA index:
* Dist_12 = sum_user k in intersection(a,b) [ 1 / log(degree(k)) ]
*
* 2) Using RA index:
* Dist_12 = sum_user k in intersection(a,b) [ 1 / degree(k) ]
*
* 3) Using Asym Cosine:
* Dist_12 = intersection(a,b) / size(a)^alpha * size(b)^(1-alpha)
*
* 4) Using prob similarity:
* Dist_12 = intersection(a,b) / [ sum(user k in b) p(k,1) ]
* where p(k,1) = 1 / [ 1 + (L / (MN-L)) ((N - degree(k))/degree(K)) * ((M - degree(1)) / degree(1)) ]
*
*/
double calc_distance(graphchi_vertex<uint32_t, uint32_t> &v, vid_t pivot, int distance_metric) {
//assert(is_pivot(pivot));
//assert(is_item(pivot) && is_item(v.id()));
dense_adj &pivot_edges = adjs[pivot - pivot_st];
int num_edges = v.num_edges();
//if there are not enough neighboring user nodes to those two items there is no need
//to actually count the intersection
if (num_edges < min_allowed_intersection || pivot_edges.count < min_allowed_intersection)
return 0;
std::vector<vid_t> edges;
edges.resize(num_edges);
for(int i=0; i < num_edges; i++) {
vid_t other_vertex = v.edge(i)->vertexid;
edges[i] = other_vertex;
}
sort(edges.begin(), edges.end());
std::set<vid_t> intersection;
std::set_intersection(
pivot_edges.adjlist, pivot_edges.adjlist + pivot_edges.count,
edges.begin(), edges.end(),
std::inserter(intersection, intersection.begin()));
double intersection_size = (double)intersection.size();
//not enough user nodes rated both items, so the pairs of items are not compared.
if (intersection_size < (double)min_allowed_intersection)
return 0;
if (distance_metric == JACCARD){
uint set_a_size = v.num_edges(); //number of users connected to current item
uint set_b_size = acount(pivot); //number of users connected to current pivot
return intersection_size / (double)(set_a_size + set_b_size - intersection_size); //compute the distance
}
else if (distance_metric == AA){
double dist = 0;
for (std::set<vid_t>::iterator i= intersection.begin() ; i != intersection.end(); i++){
vid_t user = *i;
assert(latent_factors_inmem.size() == M && is_user(user));
assert(latent_factors_inmem[user].degree > 0);
dist += 1.0 / log(latent_factors_inmem[user].degree);
}
return dist;
}
else if (distance_metric == RA){
double dist = 0;
for (std::set<vid_t>::iterator i= intersection.begin() ; i != intersection.end(); i++){
vid_t user = *i;
assert(latent_factors_inmem.size() == M && is_user(user));
assert(latent_factors_inmem[user].degree > 0);
dist += 1.0 / latent_factors_inmem[user].degree;
}
return dist;
}
/* 3) Using Asym Cosine:
* Dist_12 = intersection(a,b) / size(a)^alpha * size(b)^(1-alpha)
*/
else if (distance_metric == ASYM_COSINE){
uint set_a_size = v.num_edges(); //number of users connected to current item
uint set_b_size = acount(pivot); //number of users connected to current pivot
return intersection_size / (pow(set_a_size,asym_cosine_alpha) * pow(set_b_size,1-asym_cosine_alpha));
}
/* 4) Using prob similarity:
* Dist_12 = intersection(a,b) / [ sum(user k in b) p(k,1) ]
* where p(k,1) = 1 / [ 1 + (L / (MN-L)) ((N - degree(k))/degree(K)) * ((M - degree(1)) / degree(1)) ]
*/
else if (distance_metric == PROB){
double sum = 0;
for(int i=0; i<pivot_edges.count; i++) {
int node_k = pivot_edges.adjlist[i];
int degree_k = latent_factors_inmem[node_k].degree;
assert(degree_k > 0);
double p_k_1 = 1.0 / ( 1.0 + prob_sim_normalization_constant * ((N - degree_k)/(double)degree_k) * ((M - num_edges) / (double)num_edges));
assert(p_k_1 > 0 && p_k_1 <= 1.0);
sum += p_k_1;
}
return intersection_size / sum;
}
else {
assert(false);
}
return -1; //just to avoid warning
}
/*
* Vertex update function - computes the least square step
*/
void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {
//go over all user nodes
if (is_user(vertex.id())){
vertex_data & user = latent_factors_inmem[vertex.id()];
time_svdpp_usr usr(user);
unsigned int userRatings = vertex.num_outedges();
double rRuNum = 1/sqrt(userRatings+10);
int dim = D;
double sumY = 0.0;
//go over all ratings
for(int e=0; e < vertex.num_outedges(); e++) {
uint pos = vertex.edge(e)->vertex_id();
assert(pos >= M && pos < M+N);
vertex_data & data = latent_factors_inmem[pos];
time_svdpp_movie movie(data);
Map<vec> y(movie.y, D);
sumY += sum((const vec&)y); //y
}
for( int k=0; k<dim; ++k) {
usr.ptemp[k] = usr.pu[k] + rRuNum * sumY; // pTemp = pu + rRuNum*sumY
}
vec sum = zeros(dim);
for(int e=0; e < vertex.num_edges(); e++) {
//edge_data & edge = scope.edge_data(oedgeid);
//float rui = edge.weight;
float rui = vertex.edge(e)->get_data().weight;
uint t = (uint)(vertex.edge(e)->get_data().time - 1); // we assume time bins start from 1
assert(t < M+N+K);
vertex_data & data = latent_factors_inmem[vertex.edge(e)->vertex_id()];
time_svdpp_movie mov(data);
time_svdpp_time time(latent_factors_inmem[t]);
double pui = 0;
time_svdpp_predict(usr, mov, time, rui, pui);
double eui = rui - pui;
*usr.bu += tsp.lrate*(eui - tsp.beta* *usr.bu);
*mov.bi += tsp.lrate * (eui - tsp.beta* *mov.bi);
for (int k = 0; k < dim; k++) {
double oldValue = mov.q[k];
double userValue = usr.ptemp[k] + usr.pu[k] * time.pt[k];
sum[k] += eui * mov.q[k];
mov.q[k] += tsp.lrate * (eui * userValue - tsp.gamma*mov.q[k]);
usr.ptemp[k] += tsp.lrate * ( eui * oldValue - tsp.gamma * usr.ptemp[k]);
usr.p[k] += tsp.lrate * ( eui * oldValue - tsp.gamma*usr.p[k] );
usr.pu[k] += tsp.lrate * (eui * oldValue * time.pt[k] - tsp.gamma * usr.pu[k]);
time.pt[k] += tsp.lrate * (eui * oldValue * usr.pu[k] - tsp.gamma * time.pt[k]);
double xOldValue = usr.x[k];
double zOldValue = time.z[k];
usr.x[k] += tsp.lrate * (eui * zOldValue - tsp.gamma * xOldValue);
time.z[k] += tsp.lrate * (eui * xOldValue - tsp.gamma * zOldValue);
}
rmse_vec[omp_get_thread_num()] += eui*eui;
}
for(int e=0; e < vertex.num_edges(); e++) {
time_svdpp_movie mov = latent_factors_inmem[vertex.edge(e)->vertex_id()];
for(int k=0;k<dim;k++){
mov.y[k] += tsp.lrate * (rRuNum * sum[k]- tsp.gamma*mov.y[k]);
}
}
}
};
/**
* calc distance between two items.
* Let a be all the users rated item 1
* Let b be all the users rated item 2
*
* 1) Using Jackard index:
* Dist_ab = intersection(a,b) / (size(a) + size(b) - size(intersection(a,b))
*
* 2) Using AA index:
* Dist_ab = sum_user k in intersection(a,b) [ 1 / log(degree(k)) ]
*
* 3) Using RA index:
* Dist_ab = sum_user k in intersection(a,b) [ 1 / degree(k) ]
*
* 4) Using Asym Cosine:
* Dist_ab = intersection(a,b) / size(a)^alpha * size(b)^(1-alpha)
*/
double calc_distance(graphchi_vertex<uint32_t, uint32_t> &v, vid_t pivot, int distance_metric) {
//assert(is_pivot(pivot));
//assert(is_item(pivot) && is_item(v.id()));
dense_adj &pivot_edges = adjs[pivot - pivot_st];
int num_edges = v.num_edges();
//if there are not enough neighboring user nodes to those two items there is no need
//to actually count the intersection
if (num_edges < min_allowed_intersection || pivot_edges.count < min_allowed_intersection)
return 0;
std::vector<vid_t> edges;
edges.resize(num_edges);
for(int i=0; i < num_edges; i++) {
vid_t other_vertex = v.edge(i)->vertexid;
edges[i] = other_vertex;
}
sort(edges.begin(), edges.end());
std::set<vid_t> intersection;
std::set_intersection(
pivot_edges.adjlist, pivot_edges.adjlist + pivot_edges.count,
edges.begin(), edges.end(),
std::inserter(intersection, intersection.begin()));
double intersection_size = (double)intersection.size();
//not enough user nodes rated both items, so the pairs of items are not compared.
if (intersection_size < (double)min_allowed_intersection)
return 0;
if (distance_metric == JACCARD){
uint set_a_size = v.num_edges(); //number of users connected to current item
uint set_b_size = acount(pivot); //number of users connected to current pivot
return intersection_size / (double)(set_a_size + set_b_size - intersection_size); //compute the distance
}
else if (distance_metric == AA){
double dist = 0;
for (std::set<vid_t>::iterator i= intersection.begin() ; i != intersection.end(); i++){
vid_t user = *i;
assert(latent_factors_inmem.size() == M && is_user(user));
assert(latent_factors_inmem[user].degree > 0);
dist += 1.0 / log(latent_factors_inmem[user].degree);
}
return dist;
}
else if (distance_metric == RA){
double dist = 0;
for (std::set<vid_t>::iterator i= intersection.begin() ; i != intersection.end(); i++){
vid_t user = *i;
assert(latent_factors_inmem.size() == M && is_user(user));
assert(latent_factors_inmem[user].degree > 0);
dist += 1.0 / latent_factors_inmem[user].degree;
}
return dist;
}
else if (distance_metric == ASYM_COSINE){
uint set_a_size = v.num_edges(); //number of users connected to current item
uint set_b_size = acount(pivot); //number of users connected to current pivot
return intersection_size / (pow(set_a_size,asym_cosine_alpha) * pow(set_b_size,1-asym_cosine_alpha));
}
return 0;
}
/*
* Vertex update function - computes the least square step
*/
void update(graphchi_vertex<VertexDataType, EdgeDataType> &vertex, graphchi_context &gcontext) {
if (gcontext.iteration == 0){
if (is_user(vertex.id())) { //user node. find the last rated item and store it
vertex_data_libfm user = latent_factors_inmem[vertex.id()];
int max_time = 0;
for(int e=0; e < vertex.num_outedges(); e++) {
const edge_data & edge = vertex.edge(e)->get_data();
if (edge.time >= max_time){
max_time = (int)(edge.time - time_offset);
*user.last_item = vertex.edge(e)->vertex_id() - M;
}
}
}
if (is_user(vertex.id()) && vertex.num_outedges() == 0)
logstream(LOG_WARNING)<<"Vertex: " << vertex.id() << " with no edges: " << std::endl;
return;
return;
}
//go over all user nodes
if (is_user(vertex.id())){
vertex_data_libfm user = latent_factors_inmem[vertex.id()];
assert(*user.last_item >= 0 && *user.last_item < (int)N);
vertex_data & last_item = latent_factors_inmem[M+N+K+(*user.last_item)];
for(int e=0; e < vertex.num_outedges(); e++) {
vertex_data_libfm movie(latent_factors_inmem[vertex.edge(e)->vertex_id()]);
float rui = vertex.edge(e)->get_data().weight;
double pui;
vec sum;
vertex_data & time = latent_factors_inmem[(int)vertex.edge(e)->get_data().time - time_offset];
float sqErr = libfm_predict(user, movie, time, rui, pui, &sum);
float eui = pui - rui;
globalMean -= libfm_rate * (eui + reg0 * globalMean);
*user.bias -= libfm_rate * (eui + libfm_regw * *user.bias);
*movie.bias -= libfm_rate * (eui + libfm_regw * *movie.bias);
time.bias -= libfm_rate * (eui + libfm_regw * time.bias);
assert(!std::isnan(time.bias));
last_item.bias -= libfm_rate * (eui + libfm_regw * last_item.bias);
for(int f = 0; f < D; f++){
// user
float grad = sum[f] - user.v[f];
user.v[f] -= libfm_rate * (eui * grad + libfm_regv * user.v[f]);
// item
grad = sum[f] - movie.v[f];
movie.v[f] -= libfm_rate * (eui * grad + libfm_regv * movie.v[f]);
// time
grad = sum[f] - time.pvec[f];
time.pvec[f] -= libfm_rate * (eui * grad + libfm_regv * time.pvec[f]);
// last item
grad = sum[f] - last_item.pvec[f];
last_item.pvec[f] -= libfm_rate * (eui * grad + libfm_regv * last_item.pvec[f]);
}
rmse_vec[omp_get_thread_num()] += sqErr;
}
}
};
Objid
db_renumber_object(Objid old)
{
Objid newbie;
Object *o;
db_priv_affected_callable_verb_lookup();
for (newbie = 0; newbie < old; newbie++) {
if (objects[newbie] == 0) {
/* Change the identity of the object. */
o = objects[newbie] = objects[old];
objects[old] = 0;
objects[newbie]->id = newbie;
/* Fix up the parent/children hierarchy */
{
Objid oid, *oidp;
if (o->parent != NOTHING) {
oidp = &objects[o->parent]->child;
while (*oidp != old && *oidp != NOTHING)
oidp = &objects[*oidp]->sibling;
if (*oidp == NOTHING)
panic("Object not in parent's children list");
*oidp = newbie;
}
for (oid = o->child;
oid != NOTHING;
oid = objects[oid]->sibling)
objects[oid]->parent = newbie;
}
/* Fix up the location/contents hierarchy */
{
Objid oid, *oidp;
if (o->location != NOTHING) {
oidp = &objects[o->location]->contents;
while (*oidp != old && *oidp != NOTHING)
oidp = &objects[*oidp]->next;
if (*oidp == NOTHING)
panic("Object not in location's contents list");
*oidp = newbie;
}
for (oid = o->contents;
oid != NOTHING;
oid = objects[oid]->next)
objects[oid]->location = newbie;
}
/* Fix up the list of users, if necessary */
if (is_user(newbie)) {
int i;
for (i = 1; i <= all_users.v.list[0].v.num; i++)
if (all_users.v.list[i].v.obj == old) {
all_users.v.list[i].v.obj = newbie;
break;
}
}
/* Fix the owners of verbs, properties and objects */
{
Objid oid;
for (oid = 0; oid < num_objects; oid++) {
Object *o = objects[oid];
Verbdef *v;
Pval *p;
int i, count;
if (!o)
continue;
if (o->owner == newbie)
o->owner = NOTHING;
else if (o->owner == old)
o->owner = newbie;
for (v = o->verbdefs; v; v = v->next)
if (v->owner == newbie)
v->owner = NOTHING;
else if (v->owner == old)
v->owner = newbie;
count = dbpriv_count_properties(oid);
p = o->propval;
for (i = 0; i < count; i++)
if (p[i].owner == newbie)
p[i].owner = NOTHING;
else if (p[i].owner == old)
p[i].owner = newbie;
}
}
return newbie;
}
}
/* There are no recycled objects less than `old', so keep its number. */
return old;
}
/**
* add weighted ratings for each linked item
*
*/
double compute_ratings(graphchi_vertex<uint32_t, edge_data> &item, vid_t user_pivot, float edge_weight) {
assert(is_pivot(user_pivot));
if (!allow_zeros)
assert(edge_weight != 0);
else if (edge_weight == 0) {
zero_edges++;
return 0;
}
dense_adj &pivot_edges = adjs[user_pivot - pivot_st];
if (!get_val(pivot_edges.edges, item.id())) {
if (debug)
Rcpp::Rcerr<<"Skipping item pivot pair since not connected!" << item.id() << std::endl;
return 0;
}
int num_edges = item.num_edges();
if (debug)
Rcpp::Rcerr<<"Found " << num_edges << " edges from item : " << item.id() << std::endl;
//if there are not enough neighboring user nodes to those two items there is no need
//to actually count the intersection
if (num_edges < min_allowed_intersection || nnz(pivot_edges.edges) < min_allowed_intersection) {
if (debug)
Rcpp::Rcerr<<"skipping item pivot pair since < min_allowed_intersection" << std::endl;
return 0;
}
for(int i=0; i < num_edges; i++) {
vid_t other_item = item.edge(i)->vertex_id();
assert(other_item - M >= 0);
bool up = item.id() < other_item;
if (debug)
Rcpp::Rcerr<<"Checking now edge: " << other_item << std::endl;
if (is_user(other_item)) {
if (debug)
Rcpp::Rcerr<<"skipping edge to user " << other_item << std::endl;
continue;
}
if (!undirected && ((up && item.edge(i)->get_data().up_weight == 0) ||
(!up && item.edge(i)->get_data().down_weight == 0))) {
if (debug)
Rcpp::Rcerr<<"skipping edge with wrong direction to " << other_item << std::endl;
continue;
}
if (get_val(pivot_edges.edges, other_item)) {
if (debug)
Rcpp::Rcerr<<"skipping edge to " << other_item << " because alrteady connected to pivot" << std::endl;
continue;
}
assert(get_val(pivot_edges.edges, item.id()) != 0);
float weight = std::max(item.edge(i)->get_data().down_weight, item.edge(i)->get_data().up_weight);
if (!allow_zeros)
assert(weight != 0);
else if (weight == 0) continue;
pivot_edges.mymutex.lock();
set_val(pivot_edges.ratings, other_item-M, get_val(pivot_edges.ratings, other_item-M) + edge_weight * pow(weight,Q));
pivot_edges.mymutex.unlock();
if (debug)
Rcpp::Rcerr<<"Adding weight: " << weight << " to item: " << other_item-M+1 << " for user: "******"Finished user pivot " << user_pivot << std::endl;
return 0;
}
/**
* Vertex update function.
*/
void update(CE_Graph_vertex<VertexDataType, EdgeDataType> &v, CE_Graph_context &gcontext) {
if (debug)
printf("Entered iteration %d with %d\n", gcontext.iteration, v.id());
//in the zero iteration compute the mean
if (gcontext.iteration == 0){
if (is_item(v.id())){
for(int i=0; i<v.num_edges(); i++) {
CE_Graph_edge<float> * e = v.edge(i);
vid_t user = e->vertexid;
mean[user] += e->get_data() / (float)N;
}
}
}
//at the first iteration compute the stddev of each item from the mean
else if (gcontext.iteration == 1){
if (is_item(v.id())){
dense_adj item_edges;
for(int i=0; i < v.num_edges(); i++)
set_new(item_edges.edges, v.edge(i)->vertexid, v.edge(i)->get_data());
stddev[v.id() - M] = sum(minus(item_edges.edges, mean).array().pow(2)) / (M-1.0);
if (debug)
std::cout<<"item: " << v.id() - M+1 << " stddev: " << stddev[v.id() - M] << std::endl;
}
}
/* even iteration numbers:
* 1) load a subset of items into memory (pivots)
* 2) Find which subset of items needs to compared to the users
*/
else if (gcontext.iteration % 2 == 0) {
if (adjcontainer->is_pivot(v.id()) && is_item(v.id())){
adjcontainer->load_edges_into_memory(v);
if (debug)
printf("Loading pivot %d intro memory\n", v.id());
}
else if (is_user(v.id())){
//check if this user is connected to any pivot item
bool has_pivot = false;
int pivot = -1;
for(int i=0; i<v.num_edges(); i++) {
CE_Graph_edge<float> * e = v.edge(i);
//assert(is_item(e->vertexid));
if (adjcontainer->is_pivot(e->vertexid) && relevant_items[e->vertexid-M]) {
has_pivot = true;
pivot = e->vertexid;
break;
}
}
if (debug)
printf("user %d is linked to pivot %d\n", v.id(), pivot);
if (!has_pivot) //this user is not connected to any of the pivot item nodes and thus
//it is not relevant at this point
return;
//this user is connected to a pivot items, thus all connected items should be compared
for(int i=0; i<v.num_edges(); i++) {
CE_Graph_edge<float> * e = v.edge(i);
//assert(v.id() != e->vertexid);
relevant_items[e->vertexid - M] = true;
}
}//is_user
} //iteration % 2 = 1
/* odd iteration number:
* 1) For any item connected to a pivot item
* compute itersection
*/
else {
if (!relevant_items[v.id() - M]){
return;
}
for (vid_t i=adjcontainer->pivot_st; i< adjcontainer->pivot_en; i++){
//since metric is symmetric, compare only to pivots which are smaller than this item id
if (i >= v.id() || (!relevant_items[i-M]))
continue;
double dist = adjcontainer->calc_distance(v, i, distance_metric);
item_pairs_compared++;
if (item_pairs_compared % 1000000 == 0)
logstream(LOG_INFO)<< std::setw(10) << mytimer.current_time() << ") " << std::setw(10) << item_pairs_compared << " pairs compared " << std::endl;
if (debug)
printf("comparing %d to pivot %d distance is %lg\n", i - M + 1, v.id() - M + 1, dist);
if (dist != 0){
fprintf(out_files[omp_get_thread_num()], "%u %u %.12lg\n", v.id()-M+1, i-M+1, (double)dist);//write item similarity to file
//where the output format is:
//[item A] [ item B ] [ distance ]
written_pairs++;
}
}
}//end of iteration % 2 == 1
}//end of update function
