X-Git-Url: http://jsfdemo.indexdata.com/?a=blobdiff_plain;f=src%2Frelevance.c;h=a002a7b631eec74f076d89b28821febe9e1f8e9c;hb=446f32183265d59ee79e2859376c598fa24408e0;hp=7603a7a928c2422941e9f5340fe89b55d266cf51;hpb=beda9709478c02182ceadb5f0526c32d8986c039;p=pazpar2-moved-to-github.git

diff --git a/src/relevance.c b/src/relevance.c
index 7603a7a..a002a7b 100644
--- a/src/relevance.c
+++ b/src/relevance.c
@@ -1,5 +1,5 @@
 /* This file is part of Pazpar2.
-   Copyright (C) 2006-2009 Index Data
+   Copyright (C) 2006-2012 Index Data
 
 Pazpar2 is free software; you can redistribute it and/or modify it under
 the terms of the GNU General Public License as published by the Free
@@ -21,119 +21,239 @@ Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
 #include <config.h>
 #endif
 
+#include <assert.h>
 #include <math.h>
 #include <stdlib.h>
 
 #include "relevance.h"
-#include "pazpar2.h"
+#include "session.h"
 
 struct relevance
 {
     int *doc_frequency_vec;
+    int *term_frequency_vec_tmp;
     int vec_len;
     struct word_entry *entries;
-    pp2_charset_t pct;
+    pp2_charset_token_t prt;
+    int rank_cluster;
+    int follow_boost;
+    int lead_boost;
+    int length_divide;
     NMEM nmem;
 };
 
-
 struct word_entry {
     const char *norm_str;
+    const char *display_str;
     int termno;
+    int follow_boost;
+    char *ccl_field;
     struct word_entry *next;
 };
 
-static void add_word_entry(NMEM nmem, 
-                           struct word_entry **entries,
-                           const char *norm_str,
-                           int term_no)
+static int word_entry_match(struct relevance *r, const char *norm_str,
+                            const char *rank, int *mult)
 {
-    struct word_entry *ne = nmem_malloc(nmem, sizeof(*ne));
-    ne->norm_str = nmem_strdup(nmem, norm_str);
-    ne->termno = term_no;
-    
-    ne->next = *entries;
-    *entries = ne;
-}
-
-
-int word_entry_match(struct word_entry *entries, const char *norm_str)
-{
-    for (; entries; entries = entries->next)
+    int i = 1;
+    struct word_entry *entries = r->entries;
+    for (; entries; entries = entries->next, i++)
     {
-        if (!strcmp(norm_str, entries->norm_str))
+        if (*norm_str && !strcmp(norm_str, entries->norm_str))
+        {
+            int extra = r->follow_boost;
+            struct word_entry *e_follow = entries;
+            const char *cp = 0;
+            int no_read = 0;
+            sscanf(rank, "%d%n", mult, &no_read);
+            rank += no_read;
+            while (*rank == ' ')
+                rank++;
+            if (no_read > 0 && (cp = strchr(rank, ' ')))
+            {
+                if ((cp - rank) == strlen(entries->ccl_field) &&
+                    memcmp(entries->ccl_field, rank, cp - rank) == 0)
+                    *mult = atoi(cp + 1);
+            }
+            (*mult) += entries->follow_boost;
+            while ((e_follow = e_follow->next) != 0 && extra > 0)
+            {
+                e_follow->follow_boost = extra--;
+            }
             return entries->termno;
+        }
+        entries->follow_boost = 0;
     }
     return 0;
 }
 
-static struct word_entry *build_word_entries(pp2_charset_t pct, NMEM nmem,
-                                             const char **terms)
+void relevance_countwords(struct relevance *r, struct record_cluster *cluster,
+                          const char *words, const char *rank,
+                          const char *name)
 {
-    int termno = 1; /* >0 signals THERE is an entry */
-    struct word_entry *entries = 0;
-    const char **p = terms;
+    int *mult = r->term_frequency_vec_tmp;
+    const char *norm_str;
+    int i, length = 0;
+    int lead_mult = r->lead_boost;
+    struct word_entry *e;
+    WRBUF w = cluster->relevance_explain1;
 
-    for (; *p; p++)
+    pp2_charset_token_first(r->prt, words, 0);
+    for (e = r->entries, i = 1; i < r->vec_len; i++, e = e->next)
     {
-        pp2_relevance_token_t prt = pp2_relevance_tokenize(pct, *p);
-        const char *norm_str;
-
-        while ((norm_str = pp2_relevance_token_next(prt)))
-            add_word_entry(nmem, &entries, norm_str, termno);
+        mult[i] = 0;
+        e->follow_boost = 0;
+    }
 
-        pp2_relevance_token_destroy(prt);
+    assert(rank);
+    while ((norm_str = pp2_charset_token_next(r->prt)))
+    {
+        int local_mult = 0;
+        int res = word_entry_match(r, norm_str, rank, &local_mult);
+        if (res)
+        {
+            assert(res < r->vec_len);
+            mult[res] += local_mult + lead_mult;
+        }
+        if (lead_mult > 0)
+            --lead_mult;
+        length++;
+    }
 
-        termno++;
+    for (e = r->entries, i = 1; i < r->vec_len; i++, e = e->next)
+    {
+        if (length == 0 || mult[i] == 0)
+            continue;
+        wrbuf_printf(w, "%s: field=%s vecf[%d] += mult(%d)",
+                     e->display_str, name, i, mult[i]);
+        switch (r->length_divide)
+        {
+        case 0:
+            wrbuf_printf(w, ";\n");
+            cluster->term_frequency_vecf[i] += (double) mult[i];
+            break;
+        case 1:
+            wrbuf_printf(w, " / log2(1+length(%d));\n", length);
+            cluster->term_frequency_vecf[i] +=
+                (double) mult[i] / log2(1 + length);
+            break;
+        case 2:
+            wrbuf_printf(w, " / length(%d);\n", length);
+            cluster->term_frequency_vecf[i] += (double) mult[i] / length;
+        }
+        cluster->term_frequency_vec[i] += mult[i];
     }
-    return entries;
+
+    cluster->term_frequency_vec[0] += length;
 }
 
-void relevance_countwords(struct relevance *r, struct record_cluster *cluster,
-        const char *words, int multiplier)
+static void pull_terms(struct relevance *res, struct ccl_rpn_node *n)
 {
-    pp2_relevance_token_t prt = pp2_relevance_tokenize(r->pct, words);
-    
-    const char *norm_str;
-    
-    while ((norm_str = pp2_relevance_token_next(prt)))
+    char **words;
+    int numwords;
+    char *ccl_field;
+    int i;
+
+    switch (n->kind)
     {
-        int res = word_entry_match(r->entries, norm_str);
-        if (res)
-            cluster->term_frequency_vec[res] += multiplier;
-        cluster->term_frequency_vec[0]++;
+    case CCL_RPN_AND:
+    case CCL_RPN_OR:
+    case CCL_RPN_NOT:
+    case CCL_RPN_PROX:
+        pull_terms(res, n->u.p[0]);
+        pull_terms(res, n->u.p[1]);
+        break;
+    case CCL_RPN_TERM:
+        nmem_strsplit(res->nmem, " ", n->u.t.term, &words, &numwords);
+        for (i = 0; i < numwords; i++)
+        {
+            const char *norm_str;
+
+            ccl_field = nmem_strdup_null(res->nmem, n->u.t.qual);
+
+            pp2_charset_token_first(res->prt, words[i], 0);
+            while ((norm_str = pp2_charset_token_next(res->prt)))
+            {
+                struct word_entry **e = &res->entries;
+                while (*e)
+                    e = &(*e)->next;
+                *e = nmem_malloc(res->nmem, sizeof(**e));
+                (*e)->norm_str = nmem_strdup(res->nmem, norm_str);
+                (*e)->ccl_field = ccl_field;
+                (*e)->termno = res->vec_len++;
+                (*e)->display_str = nmem_strdup(res->nmem, words[i]);
+                (*e)->next = 0;
+            }
+        }
+        break;
+    default:
+        break;
     }
-    pp2_relevance_token_destroy(prt);
 }
 
-struct relevance *relevance_create(pp2_charset_t pct,
-                                   NMEM nmem, const char **terms)
+struct relevance *relevance_create_ccl(pp2_charset_fact_t pft,
+                                       struct ccl_rpn_node *query,
+                                       int rank_cluster,
+                                       int follow_boost, int lead_boost,
+                                       int length_divide)
 {
-    struct relevance *res = nmem_malloc(nmem, sizeof(struct relevance));
-    const char **p;
+    NMEM nmem = nmem_create();
+    struct relevance *res = nmem_malloc(nmem, sizeof(*res));
     int i;
 
-    for (p = terms, i = 0; *p; p++, i++)
-        ;
-    res->vec_len = ++i;
-    res->doc_frequency_vec = nmem_malloc(nmem, res->vec_len * sizeof(int));
-    memset(res->doc_frequency_vec, 0, res->vec_len * sizeof(int));
     res->nmem = nmem;
-    res->entries = build_word_entries(pct, nmem, terms);
-    res->pct = pct;
+    res->entries = 0;
+    res->vec_len = 1;
+    res->rank_cluster = rank_cluster;
+    res->follow_boost = follow_boost;
+    res->lead_boost = lead_boost;
+    res->length_divide = length_divide;
+    res->prt = pp2_charset_token_create(pft, "relevance");
+
+    pull_terms(res, query);
+
+    res->doc_frequency_vec = nmem_malloc(nmem, res->vec_len * sizeof(int));
+    for (i = 0; i < res->vec_len; i++)
+        res->doc_frequency_vec[i] = 0;
+
+    // worker array
+    res->term_frequency_vec_tmp =
+        nmem_malloc(res->nmem,
+                    res->vec_len * sizeof(*res->term_frequency_vec_tmp));
     return res;
 }
 
+void relevance_destroy(struct relevance **rp)
+{
+    if (*rp)
+    {
+        pp2_charset_token_destroy((*rp)->prt);
+        nmem_destroy((*rp)->nmem);
+        *rp = 0;
+    }
+}
+
 void relevance_newrec(struct relevance *r, struct record_cluster *rec)
 {
     if (!rec->term_frequency_vec)
     {
-        rec->term_frequency_vec = nmem_malloc(r->nmem, r->vec_len * sizeof(int));
-        memset(rec->term_frequency_vec, 0, r->vec_len * sizeof(int));
+        int i;
+
+        // term frequency [1,..] . [0] is total length of all fields
+        rec->term_frequency_vec =
+            nmem_malloc(r->nmem,
+                        r->vec_len * sizeof(*rec->term_frequency_vec));
+        for (i = 0; i < r->vec_len; i++)
+            rec->term_frequency_vec[i] = 0;
+
+        // term frequency divided by length of field [1,...]
+        rec->term_frequency_vecf =
+            nmem_malloc(r->nmem,
+                        r->vec_len * sizeof(*rec->term_frequency_vecf));
+        for (i = 0; i < r->vec_len; i++)
+            rec->term_frequency_vecf[i] = 0.0;
     }
 }
 
-
 void relevance_donerecord(struct relevance *r, struct record_cluster *cluster)
 {
     int i;
@@ -151,7 +271,7 @@ void relevance_prepare_read(struct relevance *rel, struct reclist *reclist)
     int i;
     float *idfvec = xmalloc(rel->vec_len * sizeof(float));
 
-    reclist_rewind(reclist);
+    reclist_enter(reclist);
     // Calculate document frequency vector for each term.
     for (i = 1; i < rel->vec_len; i++)
     {
@@ -159,37 +279,55 @@ void relevance_prepare_read(struct relevance *rel, struct reclist *reclist)
             idfvec[i] = 0;
         else
         {
-            // This conditional may be terribly wrong
-            // It was there to address the situation where vec[0] == vec[i]
-            // which leads to idfvec[i] == 0... not sure about this
-            // Traditional TF-IDF may assume that a word that occurs in every
-            // record is irrelevant, but this is actually something we will
-            // see a lot
-            if ((idfvec[i] = log((float) rel->doc_frequency_vec[0] /
-                            rel->doc_frequency_vec[i])) < 0.0000001)
-                idfvec[i] = 1;
+            /* add one to nominator idf(t,D) to ensure a value > 0 */
+            idfvec[i] = log((float) (1 + rel->doc_frequency_vec[0]) /
+                            rel->doc_frequency_vec[i]);
         }
     }
     // Calculate relevance for each document
-
     while (1)
     {
-        int t;
         int relevance = 0;
+        WRBUF w;
+        struct word_entry *e = rel->entries;
         struct record_cluster *rec = reclist_read_record(reclist);
         if (!rec)
             break;
-        for (t = 1; t < rel->vec_len; t++)
+        w = rec->relevance_explain2;
+        wrbuf_rewind(w);
+        for (i = 1; i < rel->vec_len; i++)
+        {
+            float termfreq = (float) rec->term_frequency_vecf[i];
+            int add = 100000 * termfreq * idfvec[i];
+
+            wrbuf_printf(w, "idf[%d] = log(((1 + total(%d))/termoccur(%d));\n",
+                         i, rel->doc_frequency_vec[0],
+                         rel->doc_frequency_vec[i]);
+            wrbuf_printf(w, "%s: relevance += 100000 * vecf[%d](%f) * "
+                         "idf[%d](%f) (%d);\n",
+                         e->display_str, i, termfreq, i, idfvec[i], add);
+            relevance += add;
+            e = e->next;
+        }
+        if (!rel->rank_cluster)
+        {
+            struct record *record;
+            int cluster_size = 0;
+
+            for (record = rec->records; record; record = record->next)
+                cluster_size++;
+
+            wrbuf_printf(w, "score = relevance(%d)/cluster_size(%d);\n",
+                         relevance, cluster_size);
+            relevance /= cluster_size;
+        }
+        else
         {
-            float termfreq;
-            if (!rec->term_frequency_vec[0])
-                break;
-            termfreq = (float) rec->term_frequency_vec[t] / rec->term_frequency_vec[0];
-            relevance += 100000 * (termfreq * idfvec[t] + 0.0000005);  
+            wrbuf_printf(w, "score = relevance(%d);\n", relevance);
         }
-        rec->relevance = relevance;
+        rec->relevance_score = relevance;
     }
-    reclist_rewind(reclist);
+    reclist_leave(reclist);
     xfree(idfvec);
 }