- Added support for negative test in slim_predict.

- Re-organized the information that is displayed by the command-line programs.
- Fixed mini-help typos.

Makefile

@@ -10,10 +10,10 @@ gklib_path = not-set
 bcls_path  = not-set
 shared     = not-set
 with_mkl   = not-set
-cc         = /usr/bin/gcc
-cxx        = /usr/bin/g++
-#cc         = gcc-mp-4.9
-#cxx        = g++-mp-4.9
+cc         = not-set
+cxx        = not-set
+#cc         = /usr/bin/gcc
+#cxx        = /usr/bin/g++
 
 #===============================================================
 # There should be no need to modify beyond this point

include/slim.h

@@ -50,7 +50,7 @@ typedef void slim_t;
  * Constant definitions
  *-------------------------------------------------------------------------*/
 /* SLIM's version number */
-#define SLIM_VERSION "2.0pre1"
+#define SLIM_VERSION "2.0"
 
 /* The maximum length of the options[] array */
 #define SLIM_NOPTIONS 40

src/programs/cmdline_learn.c

@@ -73,24 +73,18 @@ static char helpstr[][512] = {
     "        csrnv   -  CSR format without ratings.",
     "        cluto   -  Format used by CLUTO.",
     "        ijv     -  One (row#, col#, val) per line.",
-    " "
+    " ",
     "   -binarize",
     "      Specifies that the ratings should be binarized.",
     " ",
     "   -l1r=double",
-    "      Specifies the L1 regularization parameter. The default value is "
-    "1.0.",
-    " ",
-    "   -ipmdlfile=string",
-    "      Specifies the file used to initialize the model.",
+    "      Specifies the L1 regularization parameter. The default value is 1.0.",
     " ",
     "   -l2r=double",
-    "      Specifies the L2 regularization parameter. The default value is "
-    "1.0.",
+    "      Specifies the L2 regularization parameter. The default value is 1.0.",
     " ",
     "   -nnbrs=int",
-    "      Selects FSLIM model and specifies the number of item nearest "
-    "neighbors",
+    "      Selects FSLIM model and specifies the number of item nearest neighbors",
     "      to be used. The default value is 0.",
     " ",
     "   -simtype=string",
@@ -116,12 +110,13 @@ static char helpstr[][512] = {
     " ",
     "   -nthreads=int",
     "      Specifies the number of threads to be used for estimation.",
-    "      The default value is maximum number of threads available in the "
-    "machine.",
+    "      The default value is maximum number of threads available in the machine.",
+    " ",
+    "   -ipmdlfile=string",
+    "      Specifies the file used to initialize the model.",
     " ",
     "   -dbglvl=int",
-    "      Specifies the debug level. The default value turns on info and "
-    "timing.",
+    "      Specifies the debug level. The default value turns on info and timing.",
     " ",
     "   -help",
     "      Prints this message.",

src/programs/cmdline_predict.c

@@ -36,17 +36,27 @@ static gk_StringMap_t ifmt_options[] = {
 static char helpstr[][512] = {
     " ",
     " Usage:",
-    "   slim_predict [options] model-file old-file [test-file]",
+    "   slim_predict [options] model-file old-file [test-file] [neg-file]",
     " ",
     " Parameters:",
     "   model-file",
     "       The file that stores the model that was generated by slim_learn.",
     " ",
     "   old-file",
-    "       The file that stores the historical information for each user.",
+    "       The file that stores the historical information for the users",
+    "       for which recommendations are generated.",
     " ",
     "   test-file",
     "       The file that stores the hidden items for each user.",
+    "       It is only used to evaluate the quality of the recommendations",
+    "       and it should contain a row for each of the users in the old-file.",
+    " ",
+    "   neg-file",
+    "       The file that stores the negative items for each user.",
+    "       It is used for evaluation purposes as follows: The hidden items",
+    "       and the negative items are predicted, and the nrcmds highest",
+    "       highest scoring items among them are returned as the recommendations.",
+    "       This is list is then used to evaluate the performance.",
     " ",
     " Options:",
     "   -ifmt=string",
@@ -55,7 +65,7 @@ static char helpstr[][512] = {
     "        csrnv   -  CSR format without ratings.",
     "        cluto   -  Format used by CLUTO.",
     "        ijv     -  One (row#, col#, val) per line.",
-    " "
+    " ",
     "   -binarize",
     "      Specifies that the ratings should be binarized.",
     " ",
@@ -98,6 +108,7 @@ params_t *parse_cmdline(int argc, char *argv[]) {
   params->binarize = 0;
   params->outfile = NULL;
   params->tstfile = NULL;
+  params->negfile = NULL;
   params->nrcmds = 10;
   params->dbglvl = 0;
 
@@ -145,7 +156,7 @@ params_t *parse_cmdline(int argc, char *argv[]) {
   }
 
   /* get the datafile */
-  if (argc - gk_optind < 1 || argc - gk_optind > 3) {
+  if (argc - gk_optind < 1 || argc - gk_optind > 4) {
     for (int i = 0; strlen(shorthelpstr[i]) > 0; i++)
       printf("%s\n", shorthelpstr[i]);
     exit(0);
@@ -159,11 +170,17 @@ params_t *parse_cmdline(int argc, char *argv[]) {
   if (!gk_fexists(params->trnfile))
     errexit("Input old file %s does not exist.\n", params->trnfile);
 
-  if (argc - gk_optind == 1) {
+  if (argc - gk_optind >= 1) {
     params->tstfile = gk_strdup(argv[gk_optind++]);
     if (!gk_fexists(params->tstfile))
       errexit("Input test file %s does not exist.\n", params->tstfile);
   }
 
+  if (argc - gk_optind >= 1) {
+    params->negfile = gk_strdup(argv[gk_optind++]);
+    if (!gk_fexists(params->negfile))
+      errexit("Input negative file %s does not exist.\n", params->negfile);
+  }
+
   return params;
 }

src/programs/slim_learn.c

@@ -33,12 +33,14 @@ int main(int argc, char *argv[]) {
       "------------------------------------------------------------------\n");
   printf("  trnfile: %s, nrows: %d, ncols: %d, nnz: %zd\n", params->trnfile,
          tmat->nrows, tmat->ncols, tmat->rowptr[tmat->nrows]);
-  printf("  l1r: %.2le, l2r: %.2le, optTol: %.2le, niters: %d\n", params->l1r,
-         params->l2r, params->optTol, params->niters);
-  printf("  binarize: %d, nnbrs: %d, nthreads: %d, dbglvl: %d\n",
-         params->binarize, params->nnbrs, params->nthreads, params->dbglvl);
-  printf("  simtype: %s, mdlfile: %s\n", slim_simtypenames[params->simtype],
-         params->mdlfile);
+  printf("  l1r: %.2le, l2r: %.2le, binarize: %s\n", params->l1r, 
+         params->l2r, (params->binarize == 0 ? "No" : "Yes"));
+  printf("  solver: %s, optTol: %.2le, niters: %d\n", 
+         slim_algonames[params->algo], params->optTol, params->niters);
+  printf("  mdlfile: %s, nthreads: %d, dbglvl: %d\n",
+         params->mdlfile, params->nthreads, params->dbglvl);
+  printf("  simtype: %s, nnbrs: %d\n", 
+         slim_simtypenames[params->simtype], params->nnbrs);
   printf("\nEstimating model...\n");
 
   /* free any user-supplied ratings if set to be ignored */

src/programs/slim_predict.c

@@ -14,14 +14,15 @@
 /*************************************************************************/
 int main(int argc, char *argv[]) {
   ssize_t zI;
-  int32_t iU, iR, nrcmds, nhits[3], ntrue[2];
+  int32_t i, iU, iR, nrcmds, ask_nrcmds, ncands, nhits[3], ntrue[2];
   int32_t nvalid, nvalid_head, nvalid_tail;
   float all_hr, head_hr, tail_hr;
   int is_tail_u, is_head_u;
   int32_t *rids, *rmarker, *fmarker;
+  gk_fkv_t *rcands, cand;
   float *rscores, hr[3], arhr, larhr, baseline;
   params_t *params;
-  gk_csr_t *oldmat, *tstmat = NULL, *model;
+  gk_csr_t *oldmat, *tstmat = NULL, *negmat = NULL, *model;
   int32_t ioptions[SLIM_NOPTIONS];
   FILE *fpout = NULL;
 
@@ -34,6 +35,8 @@ int main(int argc, char *argv[]) {
   oldmat = gk_csr_Read(params->trnfile, params->ifmt, params->readvals, 0);
   if (params->tstfile)
     tstmat = gk_csr_Read(params->tstfile, params->ifmt, params->readvals, 0);
+  if (params->negfile)
+    negmat = gk_csr_Read(params->negfile, params->ifmt, params->readvals, 0);
 
   printf(
       "------------------------------------------------------------------\n");
@@ -47,6 +50,9 @@ int main(int argc, char *argv[]) {
   if (tstmat)
     printf("  tstfile: %s, nrows: %d, ncols: %d, nnz: %zd\n", params->tstfile,
            tstmat->nrows, tstmat->ncols, tstmat->rowptr[tstmat->nrows]);
+  if (negmat)
+    printf("  negfile: %s, nrows: %d, ncols: %d, nnz: %zd\n", params->negfile,
+           negmat->nrows, negmat->ncols, negmat->rowptr[negmat->nrows]);
   if (params->outfile)
     printf("  outfile: %s\n",
            (params->outfile ? params->outfile : "No output"));
@@ -62,18 +68,25 @@ int main(int argc, char *argv[]) {
     gk_free((void **)&oldmat->rowval, LTERM);
     if (tstmat)
       gk_free((void **)&tstmat->rowval, LTERM);
+    if (negmat)
+      gk_free((void **)&negmat->rowval, LTERM);
   }
 
   SLIM_iSetDefaults(ioptions);
   ioptions[SLIM_OPTION_DBGLVL] = params->dbglvl;
 
-  /* predict for each row in oldmat */
   if (params->outfile)
     fpout = gk_fopen(params->outfile, "w", "outfile");
 
-  rids = gk_i32malloc(params->nrcmds, "rids");
-  rscores = gk_fmalloc(params->nrcmds, "rscores");
+  /* if we are using a negative test, ask for a score for all non-supplied items */
+  ask_nrcmds = (negmat ? model->nrows : params->nrcmds);
+
+  /* allocate neccessary arrays */
+  rids    = gk_i32malloc(ask_nrcmds, "rids");
+  rscores = gk_fmalloc(ask_nrcmds, "rscores");
   rmarker = (tstmat ? gk_i32smalloc(model->ncols, -1, "rmarker") : NULL);
+  rcands  = (negmat ? gk_fkvmalloc(model->ncols, "rcands") : NULL);
+
   // get head and tail columns, mark 0 for head items and 1 for items in tail
   fmarker = (tstmat ? SLIM_DetermineHeadAndTail(
                           oldmat->nrows, gk_max(oldmat->ncols, tstmat->ncols),
@@ -84,12 +97,72 @@ int main(int argc, char *argv[]) {
   arhr = 0.0;
   nvalid = nvalid_head = nvalid_tail = 0;
 
+
+  /* predict for each row in oldmat */
   for (iU = 0; iU < oldmat->nrows; iU++) {
     nrcmds = SLIM_GetTopN(
         model, oldmat->rowptr[iU + 1] - oldmat->rowptr[iU],
         oldmat->rowind + oldmat->rowptr[iU],
-        (oldmat->rowval ? oldmat->rowval + oldmat->rowptr[iU] : NULL), ioptions,
-        params->nrcmds, rids, rscores);
+        (oldmat->rowval ? oldmat->rowval + oldmat->rowptr[iU] : NULL), 
+        ioptions, ask_nrcmds, rids, rscores);
+
+    /* if negative test items, select the params->nrcmds from neg+pos test */
+    if (negmat && nrcmds != SLIM_ERROR) {
+      for (zI = tstmat->rowptr[iU]; zI < tstmat->rowptr[iU + 1]; zI++) 
+        rmarker[tstmat->rowind[zI]] = -2;
+      for (zI = negmat->rowptr[iU]; zI < negmat->rowptr[iU + 1]; zI++) 
+        rmarker[negmat->rowind[zI]] = -2;
+
+      /* select the neg+pos that were in the recommended list */
+      for (ncands=0, iR=0; iR<nrcmds; iR++) {
+        if (rmarker[rids[iR]] == -2) {
+          rmarker[rids[iR]] = -3;
+          rcands[ncands].val = rids[iR];
+          rcands[ncands].key = rscores[iR];
+          ncands++;
+        }
+      }
+
+      //printf("u: %5d, ncands: %5d, ", iU, ncands);
+
+      /* add the neg+pos that were not in the recommended list */
+      for (zI = tstmat->rowptr[iU]; zI < tstmat->rowptr[iU + 1]; zI++) {
+        if (rmarker[tstmat->rowind[zI]] != -3) {
+          rcands[ncands].val = tstmat->rowind[zI];
+          rcands[ncands].key = 0.0;
+          ncands++;
+        }
+        rmarker[tstmat->rowind[zI]] = -1;
+      }
+      for (zI = negmat->rowptr[iU]; zI < negmat->rowptr[iU + 1]; zI++) {
+        if (rmarker[negmat->rowind[zI]] != -3) {
+          rcands[ncands].val = negmat->rowind[zI];
+          rcands[ncands].key = 0.0;
+          ncands++;
+        }
+        rmarker[negmat->rowind[zI]] = -1;
+      }
+      //printf("ncands: %5d,", ncands);
+
+
+      /* shuffle prior to sorting */
+      for (iR=0; iR<ncands; iR++) {
+        i = gk_irandInRange(ncands);
+        gk_SWAP(rcands[iR], rcands[i], cand);
+      }
+      for (iR=0; iR<ncands; iR++) {
+        i = gk_irandInRange(ncands);
+        gk_SWAP(rcands[iR], rcands[i], cand);
+      }
+
+      gk_fkvsortd(ncands, rcands);
+      nrcmds = gk_min(nrcmds, params->nrcmds);
+      for (iR=0; iR<nrcmds; iR++) {
+        rids[iR]    = rcands[iR].val;
+        rscores[iR] = rcands[iR].key;
+      }
+      //printf(" nrcmds: %5d,", nrcmds);
+    }
 
     nvalid += (nrcmds != SLIM_ERROR ? 1 : 0);
     is_tail_u = is_head_u = 0;
@@ -140,6 +213,7 @@ int main(int argc, char *argv[]) {
           larhr += 1.0 / (1.0 + iR);
         }
       }
+      //printf(" hit: %d\n", nhits[2]);
 
       // head hit rate in test data
       hr[0] += (nhits[0] > 0 ? 1.0 * nhits[0] / ntrue[0] : 0.0);
@@ -171,7 +245,7 @@ int main(int argc, char *argv[]) {
       "------------------------------------------------------------------\n");
 
   /* clean up */
-  gk_free((void **)&rids, &rscores, &rmarker, &fmarker, LTERM);
+  gk_free((void **)&rids, &rscores, &rmarker, &fmarker, &rcands, LTERM);
   SLIM_FreeModel((slim_t **)&model);
   gk_csr_Free(&oldmat);
   if (tstmat)

src/programs/struct.h

@@ -17,6 +17,7 @@ the University of Minnesota
 typedef struct {
   char *trnfile;   /*!< the file of historical preferences */
   char *tstfile;   /*!< the file to validate the recommendations */
+  char *negfile;   /*!< the file containing the negative test instances */
   char *l12file;   /*!< the file that contains the regularization values over
                       which to search */
   char *mdlfile;   /*!< the model file during prediction */