-
Notifications
You must be signed in to change notification settings - Fork 5
/
Copy pathnaivebayes.c
1155 lines (1036 loc) · 33.9 KB
/
naivebayes.c
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
/* Weight-setting and scoring implementation for Naive-Bayes classification */
/* Copyright (C) 1997, 1998, 1999 Andrew McCallum
Written by: Andrew Kachites McCallum <[email protected]>
This file is part of the Bag-Of-Words Library, `libbow'.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Library General Public License
as published by the Free Software Foundation, version 2.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Library General Public License for more details.
You should have received a copy of the GNU Library General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111, USA */
#include <bow/libbow.h>
#include <math.h>
#include <argp/argp.h>
/* Command-line options specific to NaiveBayes */
/* Default value for option "naivebayes-m-est-m". When zero, then use
size-of-vocabulary instead. */
double naivebayes_argp_m_est_m = 0;
int naivebayes_score_returns_doc_pr;
int naivebayes_score_unsorted;
static int naivebayes_binary_scoring = 0;
static int naivebayes_normalize_log = 0;
static int naivebayes_rescale_scores = 1;
static int naivebayes_final_rescale_scores = 1;
static int naivebayes_return_log_pr = 0;
static int naivebayes_cross_entropy = 0;
double bow_naivebayes_anneal_temperature = 1;
/* icky globals for Good-Turing discounting */
static double **bow_naivebayes_goodturing_discounts = NULL;
static bow_barrel *bow_naivebayes_goodturing_barrel = NULL;
/* icky globals for Dirichlet smoothing */
double *bow_naivebayes_dirichlet_alphas = NULL;
double bow_naivebayes_dirichlet_total = 0;
/* The integer or single char used to represent this command-line option.
Make sure it is unique across all libbow and rainbow. */
#define NB_M_EST_M_KEY 3001
#define NB_BINARY_SCORE 3002
#define NB_NORMALIZE_LOG 3003
static struct argp_option naivebayes_options[] =
{
{0,0,0,0,
"Naive Bayes options, --method=naivebayes:", 30},
{"naivebayes-m-est-m", NB_M_EST_M_KEY, "M", 0,
"When using `m'-estimates for smoothing in NaiveBayes, use M as the "
"value for `m'. The default is the size of vocabulary."},
{"naivebayes-binary-scoring", NB_BINARY_SCORE, 0, 0,
"When using naivebayes, use hacky scoring to get good Precision-Recall "
"curves."},
{"naivebayes-normalize-log", NB_NORMALIZE_LOG, 0, 0,
"When using naivebayes, return -1/log(P(C|d), normalized to sum to one "
"instead of P(C|d). This results in values that are not so close to "
"zero and one."},
{0, 0}
};
error_t
naivebayes_parse_opt (int key, char *arg, struct argp_state *state)
{
switch (key)
{
case NB_M_EST_M_KEY:
naivebayes_argp_m_est_m = atof (arg);
break;
case NB_BINARY_SCORE:
naivebayes_binary_scoring = 1;
break;
case NB_NORMALIZE_LOG:
naivebayes_normalize_log = 1;
naivebayes_rescale_scores = 1;
naivebayes_final_rescale_scores = 1;
break;
default:
return ARGP_ERR_UNKNOWN;
}
return 0;
}
static const struct argp naivebayes_argp =
{
naivebayes_options,
naivebayes_parse_opt
};
static struct argp_child naivebayes_argp_child =
{
&naivebayes_argp, /* This child's argp structure */
0, /* flags for child */
0, /* optional header in help message */
0 /* arbitrary group number for ordering */
};
/* End of command-line options specific to NaiveBayes */
/* Defined in goodturing.c */
extern int simple_good_turing (int length, int *freq, double *disc);
void
bow_naivebayes_initialize_goodturing (bow_barrel *barrel)
{
int *counts =
bow_malloc (sizeof (int) * (bow_smoothing_goodturing_k + 1));
int len = bow_smoothing_goodturing_k + 1;
int k;
int ci;
int wi;
int max_wi;
int dvi;
bow_dv *dv;
int zero_count;
int total_words = 0;
if (NULL != bow_naivebayes_goodturing_discounts)
{
for (k = 0; k < bow_barrel_num_classes(barrel) ; k++)
bow_free (bow_naivebayes_goodturing_discounts[k]);
bow_free (bow_naivebayes_goodturing_discounts);
}
bow_naivebayes_goodturing_barrel = barrel;
bow_naivebayes_goodturing_discounts = bow_malloc (sizeof (double *) *
bow_barrel_num_classes(barrel));
for (k = 0; k < bow_barrel_num_classes(barrel) ; k++)
{
bow_naivebayes_goodturing_discounts[k] =
bow_malloc (sizeof (double) * len);
}
max_wi = MIN (barrel->wi2dvf->size, bow_num_words ());
for (ci = 0; ci < bow_barrel_num_classes(barrel); ci ++)
{
bow_cdoc *cdoc = bow_array_entry_at_index (barrel->cdocs, ci);
total_words = 0;
for (k = 0; k < len ; k++)
{
bow_naivebayes_goodturing_discounts[ci][k] = 0.0;
counts[k] = 0;
}
zero_count = barrel->wi2dvf->num_words - cdoc->normalizer;
counts[0] = zero_count;
for (wi = 0; wi < max_wi; wi++)
{
dv = bow_wi2dvf_dv (barrel->wi2dvf, wi);
if (!dv)
continue;
dvi = 0;
/* Find the index of entry for this class. */
while (dvi < dv->length && dv->entry[dvi].di < ci)
dvi++;
if (dvi < dv->length && dv->entry[dvi].di == ci)
{
/* There is an entry in DV for class CI.
Note it if it's in the interesting range */
if (dv->entry[dvi].count > 0 &&
dv->entry[dvi].count < len)
{
counts[dv->entry[dvi].count]++;
total_words += dv->entry[dvi].count;
}
}
}
bow_verbosify(bow_progress, "Class %d:\n", ci);
for (k = 0; k < len; k++)
{
bow_verbosify(bow_progress, "(%d %d)", k, counts[k]);
}
bow_verbosify(bow_progress, "\n");
/* Calculate all the discount factors */
if (0 != simple_good_turing(len, counts,
&(bow_naivebayes_goodturing_discounts[ci][0])))
bow_error("Simple Good-Turing calculation error.");
/* Distribute the weight of the zero mass evenly */
bow_naivebayes_goodturing_discounts[ci][0] =
bow_naivebayes_goodturing_discounts[ci][0] * total_words /
(cdoc->word_count * zero_count);
for (k = 0; k < len; k++)
{
bow_verbosify(bow_progress, "(%d %f)", k,
bow_naivebayes_goodturing_discounts[ci][k] );
}
bow_verbosify(bow_progress, "\n");
}
}
void
bow_naivebayes_load_dirichlet_alphas ()
{
int max_wi = bow_num_words ();
FILE *fp;
float x;
char s[256];
int wi;
if (bow_naivebayes_dirichlet_alphas)
bow_free (bow_naivebayes_dirichlet_alphas);
bow_naivebayes_dirichlet_alphas = bow_malloc (sizeof (double) * max_wi);
for (wi = 0; wi < max_wi; wi++)
bow_naivebayes_dirichlet_alphas[wi] = 0.0;
fp = fopen (bow_smoothing_dirichlet_filename, "r");
while (fscanf(fp, "%f %s", &x, s)==2)
{
wi = bow_word2int (s);
assert (wi != -1);
bow_naivebayes_dirichlet_alphas[wi] = x * bow_smoothing_dirichlet_weight;
}
fclose (fp);
}
/* load up the alphas */
void
bow_naivebayes_initialize_dirichlet_smoothing (bow_barrel *barrel)
{
int max_wi = MIN (barrel->wi2dvf->size, bow_num_words ());
int wi;
bow_naivebayes_dirichlet_total = 0;
/* make sure all the alphas are > 0 and calculate the sum */
for (wi = 0; wi < max_wi; wi++)
{
bow_dv *dv = bow_wi2dvf_dv (barrel->wi2dvf, wi);
if (dv)
{
bow_naivebayes_dirichlet_total += bow_naivebayes_dirichlet_alphas[wi];
assert (bow_naivebayes_dirichlet_alphas[wi] > 0);
}
}
}
/* Return the probability of word WI in class CI.
If LOO_CLASS is non-negative, then we are doing
leave-out-one-document evaulation. LOO_CLASS is the index
of the class from which the document has been removed.
LOO_WI_COUNT is the number of WI'th words that are in the document
LOO_W_COUNT is the total number of words in the docment
The last two argments help this function avoid searching for
the right entry in the DV from the beginning each time.
LAST_DV is a pointer to the DV to use.
LAST_DVI is a pointer to the index into the LAST_DV that is
guaranteed to have class index less than CI.
*/
double
bow_naivebayes_pr_wi_ci (bow_barrel *barrel,
int wi, int ci,
int loo_class,
float loo_wi_count, float loo_w_count,
bow_dv **last_dv, int *last_dvi)
{
bow_dv *dv;
bow_cdoc *cdoc;
double num_wi_ci; /* the number of times wi occurs in class */
double num_w_ci; /* the number of words in class. */
int dvi;
double m_est_m;
double m_est_p;
double pr_w_c;
cdoc = bow_array_entry_at_index (barrel->cdocs, ci);
if (last_dv && *last_dv)
{
dv = *last_dv;
dvi = *last_dvi;
/* No, not always true. assert (dv->entry[dvi].di <= ci); */
}
else
{
dv = bow_wi2dvf_dv (barrel->wi2dvf, wi);
dvi = 0;
if (last_dv)
*last_dv = dv;
}
/* If the model doesn't know about this word, return 0. */
if (!dv)
return -1.0;
/* Find the index of entry for this class. */
while (dvi < dv->length && dv->entry[dvi].di < ci)
dvi++;
/* Remember this index value for future calls to this function */
if (last_dvi)
*last_dvi = dvi;
if (dvi < dv->length && dv->entry[dvi].di == ci)
{
/* There is an entry in DV for class CI. */
num_wi_ci = dv->entry[dvi].weight;
}
else
{
/* There is no entry in DV for class CI. */
num_wi_ci = 0;
if (loo_class == ci)
bow_error ("There should be data for WI,CI");
}
num_w_ci = cdoc->word_count;
#if 0
fprintf (stdout, "count-%-25s %f\n",
bow_int2word (wi), num_wi_ci);
#endif
if (loo_class == ci)
{
num_wi_ci -= loo_wi_count;
num_w_ci -= loo_w_count;
if (!(num_wi_ci >= 0 && num_w_ci >= 0))
bow_error ("foo %g %g\n", num_wi_ci, num_w_ci);
}
if (bow_event_model == bow_event_document)
{
/* This corresponds to adding two training pseudo-data points:
one that has all features, and one that has no features. */
pr_w_c = ((num_wi_ci + 1)
/ (num_w_ci + 2));
}
else if (bow_smoothing_method == bow_smoothing_laplace
|| bow_smoothing_method == bow_smoothing_mestimate)
{
/* xxx This is not exactly right, because
BARREL->WI2DVF->NUM_WORDS might have changed with the
removal of QUERY_WV's document. */
if (/* naivebayes_argp_m_est_m == 0
|| */ bow_smoothing_method == bow_smoothing_laplace)
m_est_m = barrel->wi2dvf->num_words;
else
m_est_m = naivebayes_argp_m_est_m;
m_est_p = 1.0 / barrel->wi2dvf->num_words;
pr_w_c = ((num_wi_ci + m_est_m * m_est_p)
/ (num_w_ci + m_est_m));
}
else if (bow_smoothing_method == bow_smoothing_wittenbell)
{
/* Here CDOC->NORMALIZER is the number of unique terms in the class */
if (num_wi_ci > 0)
pr_w_c =
(num_wi_ci / (num_w_ci + cdoc->normalizer));
else
{
if (cdoc->word_count)
/* There is training data for this class */
pr_w_c =
(cdoc->normalizer
/ ((num_w_ci + cdoc->normalizer)
* (barrel->wi2dvf->num_words - cdoc->normalizer)));
else
/* There no training data for this class */
pr_w_c = 1.0 / barrel->wi2dvf->num_words;
}
}
else if (bow_smoothing_method == bow_smoothing_goodturing)
{
assert(barrel == bow_naivebayes_goodturing_barrel);
/* don't adjust if above k */
if (num_wi_ci > bow_smoothing_goodturing_k)
pr_w_c = num_wi_ci / num_w_ci;
/* if zero, just grab the stored weight */
else if (num_wi_ci == 0)
pr_w_c = bow_naivebayes_goodturing_discounts[ci][0];
/* else adjust by discount factor */
else
pr_w_c = bow_naivebayes_goodturing_discounts[ci][(int) num_wi_ci] *
num_wi_ci / num_w_ci;
}
else if (bow_smoothing_method == bow_smoothing_dirichlet)
{
pr_w_c = (num_wi_ci + bow_naivebayes_dirichlet_alphas[wi]) /
(num_w_ci + bow_naivebayes_dirichlet_total);
}
else
{
bow_error ("Naivebayes does not implement smoothing method %d",
bow_smoothing_method);
pr_w_c = 0; /* to avoid gcc warning */
}
#if 0
if (pr_w_c <= 0)
bow_error ("A negative word probability was calculated. "
"This can happen if you are using\n"
"--test-files-loo and the test files are "
"not being lexed in the same way as they\n"
"were when the model was built");
assert (pr_w_c > 0 && pr_w_c <= 1);
#endif
return pr_w_c;
}
double
bow_naivebayes_total_word_count_for_ci (bow_barrel *class_barrel, int ci)
{
double ret = 0;
int max_wi, wi, dvi;
bow_dv *dv;
max_wi = MIN (class_barrel->wi2dvf->size, bow_num_words());
for (wi = 0; wi < max_wi; wi++)
{
dv = bow_wi2dvf_dv (class_barrel->wi2dvf, wi);
for (dvi = 0; dv && dvi < dv->length; dvi++)
if (dv->entry[dvi].di == ci)
ret += dv->entry[dvi].weight;
}
return ret;
}
void
bow_naivebayes_print_word_probabilities_for_class (bow_barrel *barrel,
const char *classname)
{
int wi;
int ci = bow_str2int_no_add (barrel->classnames, classname);
double pr_w;
assert (ci >= 0);
for (wi = 0; wi < barrel->wi2dvf->size; wi++)
{
pr_w = bow_naivebayes_pr_wi_ci (barrel, wi, ci, -1, 0, 0, NULL, NULL);
if (pr_w >= 0)
printf ("%20.18f %s\n", pr_w,
bow_int2word (wi));
}
printf ("%-30s %10.8f\n", "total_count",
bow_naivebayes_total_word_count_for_ci (barrel, ci));
}
bow_wa *
bow_naivebayes_new_odds_ratio_for_ci (bow_barrel *barrel,
int the_ci)
{
bow_wa *ret;
int wi;
int ci;
int max_wi;
bow_cdoc *cdoc;
double pr_wi_c;
double pr_wi_not_c;
double class_prior_ratio;
double pr_wi;
double pr_not_wi;
double ig;
bow_dv *dv;
int dvi;
cdoc = bow_array_entry_at_index (barrel->cdocs, the_ci);
class_prior_ratio = cdoc->prior / (1.0 - cdoc->prior);
max_wi = MIN (barrel->wi2dvf->size, bow_num_words());
ret = bow_wa_new (max_wi+2);
for (wi = 0; wi < max_wi; wi++)
{
dv = bow_wi2dvf_dv (barrel->wi2dvf, wi);
/* If the model doesn't know about this word, skip it. */
if (dv == NULL)
continue;
pr_wi_c = 0;
pr_wi_not_c = 0;
for (ci = 0, dvi = 0; ci < barrel->cdocs->length; ci++)
{
if (the_ci == ci)
pr_wi_c = bow_naivebayes_pr_wi_ci (barrel, wi, ci, -1, 0, 0,
&dv, &dvi);
else
pr_wi_not_c += bow_naivebayes_pr_wi_ci (barrel, wi, ci, -1, 0, 0,
&dv, &dvi);
}
pr_wi = pr_wi_c + pr_wi_not_c;
pr_not_wi = (1 - pr_wi);
#if 0
ig = (-(pr_wi * log (pr_wi) + pr_not_wi * log (pr_not_wi))
+ ((pr_wi_c * log (pr_wi_c) + (1-pr_wi_c) * log (1-pr_wi_c))));
#endif
ig = pr_wi_c * log (pr_wi_c / pr_wi_not_c);
bow_wa_append (ret, wi, ig);
}
bow_wa_sort (ret);
return ret;
}
/* Print the top N words by odds ratio for each class. */
void
bow_naivebayes_print_odds_ratio_for_all_classes (bow_barrel *barrel, int n)
{
int ci;
bow_cdoc *cdoc;
bow_wa *wa;
for (ci = 0; ci < barrel->cdocs->length; ci++)
{
cdoc = bow_array_entry_at_index (barrel->cdocs, ci);
wa = bow_naivebayes_new_odds_ratio_for_ci (barrel, ci);
fprintf (stderr, "%s [%d words]\n", cdoc->filename, cdoc->word_count);
bow_wa_fprintf (wa, stderr, n);
bow_wa_free (wa);
}
}
void
bow_naivebayes_print_odds_ratio_for_class (bow_barrel *barrel,
const char *classname)
{
int wi;
int the_ci;
int ci;
int max_wi;
bow_cdoc *cdoc;
double pr_wi_c;
double pr_wi_not_c;
double class_prior_ratio;
bow_dv *dv;
int dvi;
the_ci = bow_str2int_no_add (barrel->classnames, classname);
if (the_ci == -1)
bow_error ("%s: Classname `%s' not found",
__PRETTY_FUNCTION__, classname);
cdoc = bow_array_entry_at_index (barrel->cdocs, the_ci);
class_prior_ratio = cdoc->prior / (1.0 - cdoc->prior);
max_wi = MIN (barrel->wi2dvf->size, bow_num_words());
for (wi = 0; wi < max_wi; wi++)
{
dv = bow_wi2dvf_dv (barrel->wi2dvf, wi);
/* If the model doesn't know about this word, skip it. */
if (dv == NULL)
continue;
pr_wi_c = 0;
pr_wi_not_c = 0;
for (ci = 0, dvi = 0; ci < bow_barrel_num_classes (barrel); ci++)
{
if (the_ci == ci)
pr_wi_c = bow_naivebayes_pr_wi_ci (barrel, wi, ci, -1, 0, 0,
&dv, &dvi);
else
pr_wi_not_c += bow_naivebayes_pr_wi_ci (barrel, wi, ci, -1, 0, 0,
&dv, &dvi);
}
printf ("%.10f %s\n",
pr_wi_c * log (pr_wi_c / pr_wi_not_c),
bow_int2word (wi));
}
}
/* Get the total number of terms in each class; store this in
CDOC->WORD_COUNT. */
void
bow_naivebayes_set_cdoc_word_count_from_wi2dvf_weights (bow_barrel *barrel)
{
int ci;
bow_cdoc *cdoc;
int wi, max_wi;
bow_dv *dv;
int dvi;
int num_classes = bow_barrel_num_classes (barrel);
double num_words_per_ci[num_classes];
for (ci = 0; ci < num_classes; ci++)
num_words_per_ci[ci] = 0;
max_wi = MIN (barrel->wi2dvf->size, bow_num_words());
for (wi = 0; wi < max_wi; wi++)
{
dv = bow_wi2dvf_dv (barrel->wi2dvf, wi);
if (dv == NULL)
continue;
for (dvi = 0; dvi < dv->length; dvi++)
{
cdoc = bow_array_entry_at_index (barrel->cdocs,
dv->entry[dvi].di);
ci = dv->entry[dvi].di;
assert (ci < num_classes);
num_words_per_ci[ci] += dv->entry[dvi].weight;
}
}
for (ci = 0; ci < barrel->cdocs->length; ci++)
{
cdoc = bow_array_entry_at_index (barrel->cdocs, ci);
cdoc->word_count = (int) rint (num_words_per_ci[ci]);
}
}
/* Function to assign `Naive Bayes'-style weights to each element of
each document vector. */
void
bow_naivebayes_set_weights (bow_barrel *barrel)
{
int ci;
bow_cdoc *cdoc;
int wi; /* a "word index" into WI2DVF */
int max_wi; /* the highest "word index" in WI2DVF. */
bow_dv *dv; /* the "document vector" at index WI */
int dvi; /* an index into the DV */
int weight_setting_num_words = 0;
double *pr_all_w_c = alloca (barrel->cdocs->length * sizeof (double));
double pr_w_c;
int total_num_words = 0;
/* Gather the word count here instead of directly of in CDOC->WORD_COUNT
so we avoid round-off error with each increment. Remember,
CDOC->WORD_COUNT is a int! */
float num_words_per_ci[bow_barrel_num_classes (barrel)];
int barrel_is_empty = 0;
/* We assume that we have already called BOW_BARREL_NEW_VPC() on
BARREL, so BARREL already has one-document-per-class. */
#if 0
assert (!strcmp (barrel->method->name, "naivebayes")
|| !strcmp (barrel->method->name, "crossentropy")
|| !strcmp (barrel->method->name, "active"));
#endif
max_wi = MIN (barrel->wi2dvf->size, bow_num_words());
/* The CDOC->PRIOR should have been set in bow_barrel_new_vpc();
verify it. */
/* Get the total number of unique terms in each class; store this in
CDOC->NORMALIZER. */
for (ci = 0; ci < barrel->cdocs->length; ci++)
{
cdoc = bow_array_entry_at_index (barrel->cdocs, ci);
assert (cdoc->prior >= 0);
pr_all_w_c[ci] = 0;
cdoc->normalizer = 0;
num_words_per_ci[ci] = 0;
}
/* Set the CDOC->WORD_COUNT for each class. If we are using a
document (binomial) model, then we'll just use the value of
WORD_COUNT set in bow_barrel_new_vpc(), which is the total number
of *documents* in the class, not the number of words. */
/* Calculate P(w); store this in DV->IDF. */
if (bow_event_model != bow_event_document)
{
/* Get the total number of terms in each class; store this in
CDOC->WORD_COUNT. */
/* Calculate the total number of unique words, and make sure it is
the same as BARREL->WI2DVF->NUM_WORDS. */
int num_unique_words = 0;
for (wi = 0; wi < max_wi; wi++)
{
dv = bow_wi2dvf_dv (barrel->wi2dvf, wi);
if (dv == NULL)
continue;
num_unique_words++;
dv->idf = 0.0;
for (dvi = 0; dvi < dv->length; dvi++)
{
cdoc = bow_array_entry_at_index (barrel->cdocs,
dv->entry[dvi].di);
ci = dv->entry[dvi].di;
num_words_per_ci[ci] += dv->entry[dvi].weight;
cdoc->normalizer++;
dv->idf += dv->entry[dvi].weight;
total_num_words += dv->entry[dvi].weight;
}
}
for (ci = 0; ci < barrel->cdocs->length; ci++)
{
cdoc = bow_array_entry_at_index (barrel->cdocs, ci);
cdoc->word_count = (int) rint (num_words_per_ci[ci]);
}
assert (num_unique_words == barrel->wi2dvf->num_words);
/* Normalize the DV->IDF to sum to one across all words, so it is
P(w). */
if (total_num_words)
{
for (wi = 0; wi < max_wi; wi++)
{
dv = bow_wi2dvf_dv (barrel->wi2dvf, wi);
if (dv == NULL)
continue;
dv->idf /= total_num_words;
}
}
else
{
barrel_is_empty = 1;
bow_verbosify (bow_progress, "Zero words in class barrel\n");
}
}
/* initialize smoothing methods, if necessary */
if (bow_smoothing_method == bow_smoothing_goodturing)
bow_naivebayes_initialize_goodturing (barrel);
else if (bow_smoothing_method == bow_smoothing_dirichlet)
{
bow_naivebayes_load_dirichlet_alphas ();
bow_naivebayes_initialize_dirichlet_smoothing (barrel);
}
if (bow_event_model != bow_event_document && !barrel_is_empty)
{
/* Now loop through all the classes, verifying the
the probability of all in each class sums to one. */
total_num_words = 0;
for (wi = 0; wi < max_wi; wi++)
{
dv = bow_wi2dvf_dv (barrel->wi2dvf, wi);
/* If the model doesn't know about this word, skip it. */
if (dv == NULL)
continue;
for (ci = 0; ci < barrel->cdocs->length; ci++)
{
pr_w_c = bow_naivebayes_pr_wi_ci (barrel, wi, ci, -1, 0, 0,
NULL, NULL);
cdoc = bow_array_entry_at_index (barrel->cdocs, ci);
assert (pr_w_c <= 1);
pr_all_w_c[ci] += pr_w_c;
}
weight_setting_num_words++;
}
for (ci = 0; ci < barrel->cdocs->length; ci++)
{
/* Is this too much round-off error to expect? */
assert (pr_all_w_c[ci] < 1.01 && pr_all_w_c[ci] > 0.99);
}
}
#if 0
fprintf (stderr, "wi2dvf num_words %d, weight-setting num_words %d\n",
barrel->wi2dvf->num_words, weight_setting_num_words);
#endif
}
#define IMPOSSIBLE_SCORE_FOR_ZERO_CLASS_PRIOR 999.99
int
bow_naivebayes_score (bow_barrel *barrel, bow_wv *query_wv,
bow_score *bscores, int bscores_len,
int loo_class)
{
double *scores; /* will become prob(class), indexed over CI */
int ci; /* a "class index" (document index) */
int wvi; /* an index into the entries of QUERY_WV. */
int dvi; /* an index into a "document vector" */
double pr_w_c; /* P(w|C), prob a word is in a class */
double log_pr_tf; /* log(P(w|C)^TF), ditto, log() of it */
double rescaler; /* Rescale SCORES by this after each word */
double new_score; /* a temporary holder */
int num_scores = 0; /* number of entries placed in SCORES */
int num_words_in_query = 0;
double pr_w_d; /* P(w|d) */
double h_w_d; /* entropy of P(W|d) */
int wi;
int hi;
int max_wi;
double query_wv_total_weight;
/* Binomial event model with LOO processing doesn't work yet. */
assert (bow_event_model != bow_event_document
|| loo_class == -1);
max_wi = MIN (barrel->wi2dvf->size, bow_num_words());
/* Allocate space to store scores for *all* classes (documents) */
scores = alloca (barrel->cdocs->length * sizeof (double));
/* Instead of multiplying probabilities, we will sum up
log-probabilities, (so we don't loose floating point resolution),
and then take the exponent of them to get probabilities back. */
/* Initialize the SCORES to the class prior probabilities. */
if (bow_print_word_scores)
printf ("%s\n",
"(CLASS PRIOR PROBABILIES)");
for (hi = 0; hi < bscores_len; hi++)
bscores[hi].name = NULL;
/* Initialize log-probabilities to 0 (unless class prior is zero) */
for (ci = 0; ci < barrel->cdocs->length; ci++)
{
bow_cdoc *cdoc;
cdoc = bow_array_entry_at_index (barrel->cdocs, ci);
if (cdoc->prior == 0)
scores[ci] = IMPOSSIBLE_SCORE_FOR_ZERO_CLASS_PRIOR;
else
scores[ci] = 0;
}
/* If we are doing leave-one-out evaluation, get the total number of
words in this query. */
if (1 || loo_class >= 0 || naivebayes_cross_entropy)
{
bow_dv *dv;
num_words_in_query = 0;
for (wvi = 0; wvi < query_wv->num_entries; wvi++)
{
/* Only count those words that are in the model's vocabulary. */
dv = bow_wi2dvf_dv (barrel->wi2dvf, query_wv->entry[wvi].wi);
if (dv)
num_words_in_query += query_wv->entry[wvi].count;
}
}
/* Set the weights of the QUERY_WV, according to the event model. */
for (wvi = 0; wvi < query_wv->num_entries; wvi++)
{
if (bow_event_model == bow_event_document_then_word)
query_wv->entry[wvi].weight =
bow_event_document_then_word_document_length
* ((float)query_wv->entry[wvi].count) / num_words_in_query;
else
query_wv->entry[wvi].weight = query_wv->entry[wvi].count;
}
if (bow_event_model == bow_event_document_then_word)
query_wv_total_weight = bow_event_document_then_word_document_length;
else
query_wv_total_weight = num_words_in_query;
/* Put contribution of the words into SCORES. If we are using the
document event model, then loop over all words in the vocabulary,
otherwise, just loop over all the words in the QUERY_WV
document. */
h_w_d = 0;
for (wvi = 0, wi = 0;
((bow_event_model == bow_event_document)
? (wi < max_wi)
: (wvi < query_wv->num_entries));
((bow_event_model == bow_event_document)
? (wi++)
: (wvi++)))
{
bow_dv *dv; /* the "document vector" for the word WI */
/* Get information about this word. */
/* Align WI and WVI in ways that depend on whether we are looping
over all words in the vocabulary or over words in the query. */
if (bow_event_model == bow_event_document)
{
if (query_wv->entry[wvi].wi < wi
&& wvi < query_wv->num_entries)
{
assert (query_wv->entry[wvi].wi == wi-1);
wvi++;
}
}
else
{
wi = query_wv->entry[wvi].wi;
}
dv = bow_wi2dvf_dv (barrel->wi2dvf, wi);
/* If the model doesn't know about this word, skip it. */
if (!dv)
continue;
if (wi == query_wv->entry[wvi].wi && query_wv->num_entries)
{
pr_w_d = ((double)query_wv->entry[wvi].count) / num_words_in_query;
h_w_d -= pr_w_d * log (pr_w_d);
}
if (bow_print_word_scores)
printf ("%-30s (queryweight=%.8f)\n",
bow_int2word (wi),
query_wv->entry[wvi].weight * query_wv->normalizer);
rescaler = DBL_MAX;
/* Loop over all classes, putting this word's (WI's)
contribution into SCORES. */
for (ci = 0, dvi = 0; ci < barrel->cdocs->length; ci++)
{
if (scores[ci] == IMPOSSIBLE_SCORE_FOR_ZERO_CLASS_PRIOR)
continue;
pr_w_c = bow_naivebayes_pr_wi_ci (barrel, wi, ci,
loo_class,
query_wv->entry[wvi].weight,
query_wv_total_weight,
&dv, &dvi);
/* If this is a word that does not occur in the document,
then use the probability it does not occur in the class.
This occurs only if we are using the document event model. */
if (query_wv->num_entries == 0 || wi != query_wv->entry[wvi].wi)
pr_w_c = 1.0 - pr_w_c;
assert (pr_w_c > 0 && pr_w_c <= 1);
/* Put the probability in log-space */
log_pr_tf = log (pr_w_c);
assert (log_pr_tf > -FLT_MAX + 1.0e5);
/* Take into consideration the number of times it occurs in
the query document */
if (bow_event_model != bow_event_document)
log_pr_tf *= query_wv->entry[wvi].weight;
assert (log_pr_tf > -FLT_MAX + 1.0e5);
scores[ci] += log_pr_tf;
if (bow_print_word_scores)
{
bow_cdoc *cdoc = bow_array_entry_at_index (barrel->cdocs, ci);
printf (" %8.2e %7.2f %-40s %10.9f\n",
pr_w_c,
log_pr_tf,
(strrchr (cdoc->filename, '/') ? : cdoc->filename),
scores[ci]);
}
/* Keep track of the minimum score updated for this word. */
if (rescaler > scores[ci])
rescaler = scores[ci];
}
/* Loop over all classes, re-scaling SCORES so that they
don't get so small we loose floating point resolution.
This scaling always keeps all SCORES positive. */
if (naivebayes_rescale_scores && rescaler < 0 &&
!naivebayes_score_returns_doc_pr)
{
for (ci = 0; ci < barrel->cdocs->length; ci++)
{
/* Add to SCORES to bring them close to zero. RESCALER is
expected to often be less than zero here. */
/* xxx If this doesn't work, we could keep track of the min
and the max, and sum by their average. */
if (scores[ci] != IMPOSSIBLE_SCORE_FOR_ZERO_CLASS_PRIOR)
scores[ci] += -rescaler;
assert (scores[ci] > -DBL_MAX + 1.0e5
&& scores[ci] < DBL_MAX - 1.0e5);
}
}
}
/* Now SCORES[] contains a (unnormalized) log-probability of the
document for each class. */
/* Anneal the probability */
if (bow_naivebayes_anneal_temperature != 1)
{
for (ci = 0; ci < barrel->cdocs->length; ci++)
if (scores[ci] != IMPOSSIBLE_SCORE_FOR_ZERO_CLASS_PRIOR)
{
#if 0
scores[ci] /= (query_wv_total_weight
+ bow_naivebayes_anneal_temperature);
#elif 0
scores[ci] /= 1 + log (query_wv_total_weight + 1);
#elif 0
scores[ci] /= ((pow (query_wv_total_weight, 0.9) + 1) / 3);
#elif 1
scores[ci] /= ((query_wv_total_weight + 1) / 7);
#else
scores[ci] /= bow_naivebayes_anneal_temperature;
#endif
assert (scores[ci] > -FLT_MAX + 1.0e5);
}
}
/* Incorporate the class prior */
if (!naivebayes_score_returns_doc_pr && !bow_uniform_class_priors)
{
for (ci = 0; ci < barrel->cdocs->length; ci++)
{
bow_cdoc *cdoc;
cdoc = bow_array_entry_at_index (barrel->cdocs, ci);
assert (cdoc->prior >= 0.0f && cdoc->prior <= 1.0f);
if (cdoc->prior == 0)
assert (scores[ci] == IMPOSSIBLE_SCORE_FOR_ZERO_CLASS_PRIOR);
else
scores[ci] += log (cdoc->prior);
assert (scores[ci] > -FLT_MAX + 1.0e5);