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/* Copyright (C) 1999 Greg Schohn - gcs@jprc.com */
/* "main" file for all of the svm related code - any svm stuff should
* pass through some function here */
#include <bow/svm.h>
#if !HAVE_SQRTF
#define sqrtf sqrt
#endif
#define BARREL_GET_MAX_NSV(barrel) (*((int *) &((GET_CDOC_ARRAY_EL(barrel,0))->normalizer)))
#define BARREL_GET_NCLASSES(barrel) (*((int *) &((GET_CDOC_ARRAY_EL(barrel,0))->prior)))
#define BARREL_GET_NMETA_DOCS(barrel) (*((int *) &((GET_CDOC_ARRAY_EL(barrel,1))->normalizer)))
#define KERNEL_TYPE 14001
#define WEIGHT_TYPE 14002
#define COST_TYPE 14003
#define EA_TYPE 14004
#define BSIZE_TYPE 14005
#define VOTE_TYPE 14006
#define CACHE_SIZE_ARG 14007
#define QUICK_SCORE 14008
#define DF_COUNTS_ARG 14009
#define REMOVE_MISCLASS_TYPE 14010
#define TF_TRANSFORM_TYPE 14011
#define USE_SMO_ARG 14012
#define CNAME_ARG 14013
#define LNAME_ARG 14014
#define DO_ACTIVE_LEARNING 14015
#define ACTIVE_LEARNING_CHUNK_SIZE_ARG 14016
#define AL_TEST_IN_TRAIN_ARG 14017
#define AL_BASELINE 14018
#define START_AT_ARG 14019
#define RANDOM_SEED_ARG 14020
#define SUPPRESS_SCORE_MAT_ARG 14021
#define INITIAL_AL_TSET_ARG 14022
#define TRANSDUCE_CLASS_ARG 14023
#define TRANS_CSTAR_ARG 14024
#define TRANS_NPOS_ARG 14025
#define SVM_BASENAME_ARG 14026
#define AL_WITH_TRANS_ARG 14027
#define TRANS_IGNORE_BIAS_ARG 14028
#define TRANS_HYP_REFRESH_ARG 14029
#define TRANS_SMART_VALS_ARG 14030
#define AGAINST_ALL 0
#define PAIRWISE 1
#define REMOVE_BOUND 1
#define REMOVE_WRONG 2
static int weight_type=RAW; /* 0=raw_freq, 1=tfidf, 2=infogain */
static int tf_transform_type=RAW; /* 0=raw, 1=log, 2?... */
static int vote_type=0;
static int cache_size=4000037;
static int quick_scoring=1;
static int do_active_learning=0;
static int test_in_train=0;
static int suppress_score_mat=0;
static int al_pick_random=0;
static int model_starting_no=0;
/* here's a C hack - it uses the actual of the enum to do the shift
* make sure when passing arguments, you know what the actuals are */
static int transduce_class=(1 << bow_doc_unlabeled);
static int transduce_class_overriding=0; /* gets set to 1 when args are
* passed to override */
static char *svml_basename=NULL;
FILE *svml_test_file=NULL;
#ifdef HAVE_LOQO
int svm_use_smo=0;
#else
int svm_use_smo=1;
#endif
double svm_epsilon_a=1E-12; /* for alpha's & there bounds */
double svm_epsilon_crit=INIT_KKT; /* for critical KT points */
double svm_C=1000.0; /* maximum cost */
int svm_bsize=4; /* sizeof working set */
int svm_kernel_type=0; /* 0=linear */
int svm_remove_misclassified=0;
int svm_weight_style;
int svm_nkc_calls;
int svm_trans_npos;
int svm_trans_nobias=0;
int svm_trans_hyp_refresh=40;
int svm_trans_smart_vals=1;
double svm_trans_cstar=200;
int svm_init_al_tset=8;
int svm_al_qsize;
int svm_al_do_trans=0;
int svm_random_seed=0; /* for al - gets filled in with time */
int svm_verbosity=0;
/* for tfidf scoring - they could (should?) be made into options... */
static int df_transform=LOG;
static int df_counts=bow_tfidf_occurrences;
/* these are dangerous optimizations for svm_score... - but they save a lot of time... */
/* dangerous because they waste a lot of memory (about the size of the original barrel)
* & if the vpc barrel gets played with, then its all wrong & there's no totally
* error proof way to do that without checking all of the barrel, which i don't do. */
struct model_bucket {
bow_wv **docs;
float **oweights; /* original weights (after norm & tf scaling)
note - this only matters when tf_transform is set &
some weight_per_model scheme is used */
/* note - these are regular vectors instead of wv's to save time
* (O(# qwv features) instead of O((# qwv features) + (# of features)) */
union {
float **sub_model; /* weights for submodels */
float *barrel; /* weights for the whole barrel */
} word_weights;
double *bvect;
int **indices;
int *sizes; /* length of each array */
double **weights;
double **W;
int **yvect;
bow_barrel *barrel;
int ndocs;
int nmodels;
};
static struct model_bucket model_cache = {NULL, NULL, {NULL}, NULL, NULL, NULL,
NULL, NULL, NULL, 0, 0};
double dprod(bow_wv *wv1, bow_wv *wv2);
double kernel_poly(bow_wv *wv1, bow_wv *wv2);
double kernel_rbf(bow_wv *wv1, bow_wv *wv2);
double kernel_sig(bow_wv *wv1, bow_wv *wv2);
/* by default use the dot product as the kernel */
static double (*kernel)(bow_wv *, bow_wv *) = dprod;
/* Command-line options specific to SVMs */
static struct argp_option svm_options[] = {
{0,0,0,0,
"Support Vector Machine options, --method=svm:", 50},
{"svm-active-learning-baseline", AL_BASELINE, "", 0,
"Incrementally add documents to the training set at random."},
{"svm-test-in-train", AL_TEST_IN_TRAIN_ARG, 0, 0,
"do active learning testing inside of the training... a hack "
"around making code 10 times more complicated."},
{"svm-al-transduce", AL_WITH_TRANS_ARG, 0, 0,
"do transduction over the unlabeled data during active learning."},
{"svm-bsize", BSIZE_TYPE, "", 0,
"maximum size to construct the subproblems."},
{"svm-cache-size", CACHE_SIZE_ARG, "", 0,
"Number of kernel evaluations to cache."},
{"svm-cost", COST_TYPE, "", 0,
"cost to bound the lagrange multipliers by (default 1000)."},
{"svm-df-counts", DF_COUNTS_ARG, "", 0,
"Set df_counts (0=occurrences, 1=words)."},
{"svm-active-learning", DO_ACTIVE_LEARNING, "", 0,
"Use active learning to query the labels & incrementally (by arg_size) build the barrels."},
{"svm-epsilon_a", EA_TYPE, "", 0,
"tolerance for the bounds of the lagrange multipliers (default 0.0001)."},
{"svm-kernel", KERNEL_TYPE, "", 0,
"type of kernel to use (0=linear, 1=polynomial, 2=gassian, 3=sigmoid, 4=fisher kernel)."},
{"svm-al_init_tsetsize", INITIAL_AL_TSET_ARG, "", 0,
"Number of random documents to start with in active learning."},
{"svm-quick-scoring", QUICK_SCORE, 0, 0,
"Turn quick scoring on."},
{"svm-rseed", RANDOM_SEED_ARG, "", 0,
"what random seed should be used in the test-in-train splits"},
{"svm-remove-misclassified", REMOVE_MISCLASS_TYPE, "", 0,
"Remove all of the misclassified examples and retrain (default none (0), 1=bound, 2=wrong."},
{"svm-start-at", START_AT_ARG, "", 0,
"which model should be the first generated."},
{"svm-suppress-score-matrix", SUPPRESS_SCORE_MAT_ARG, 0, 0,
"Do not print the scores of each test document at each AL iteration."},
{"svml-basename", SVM_BASENAME_ARG, "", OPTION_HIDDEN, ""},
{"svm-tf-transform", TF_TRANSFORM_TYPE, "", 0,
"0=raw, 1=log..."},
{"svm-transduce-class", TRANSDUCE_CLASS_ARG, "", 0,
"override default class(es) (int) to do transduction with "
"(default bow_doc_unlabeled)."},
{"svm-trans-cost", TRANS_CSTAR_ARG, "", 0,
"value to assign to C* (default 200)."},
{"svm-trans-hyp-refresh", TRANS_HYP_REFRESH_ARG, "", 0,
"how often the hyperplane should be recomputed during transduction. "
"Only applies to SMO. (default 40)"},
{"svm-trans-nobias", TRANS_IGNORE_BIAS_ARG, 0, 0,
"Do not use a bias when marking unlabeled documents. Use a "
"threshold of 0 to determine labels instead of some threshold to"
"mark a certain number of documents for each class."},
{"svm-trans-npos", TRANS_NPOS_ARG, "", 0,
"number of unlabeled documents to label as positive "
"(default: proportional to number of labeled positive docs)."},
{"svm-trans-smart-vals", TRANS_SMART_VALS_ARG, "", 0,
"use previous problem's as a starting point for the next. (default true)"},
{"svm-use-smo", USE_SMO_ARG, "", 0,
#ifdef HAVE_LOQO
"default 0 (don't use SMO)"
#else
"default 1 (use SMO) - PR_LOQO not compiled"
#endif
},
{"svm-vote", VOTE_TYPE, "", 0,
"Type of voting to use (0=singular, 1=pairwise; default 0)."},
{"svm-weight", WEIGHT_TYPE, "", 0,
"type of function to use to set the weights of the documents' words "
"(0=raw_frequency, 1=tfidf, 2=infogain."},
{0, 0}
};
union kern_param {
struct {
double const_co;
double lin_co;
double degree;
} poly ;
struct {
double gamma;
} rbf;
struct {
double const_co;
double lin_co;
} sig;
};
union kern_param kparm;
error_t svm_parse_opt (int key, char *arg, struct argp_state *state) {
switch (key) {
case START_AT_ARG:
model_starting_no = atoi(arg);
break;
case KERNEL_TYPE:
svm_kernel_type = atoi (arg);
if (svm_kernel_type > 4) {
fprintf(stderr, "Invalid value for -k, value must be between 0, 1, 2, 3, or 4.\n");
return ARGP_ERR_UNKNOWN;
}
switch (svm_kernel_type) {
case 0:
kernel = dprod;
break;
case 1:
kparm.poly.const_co = 1.0;
kparm.poly.lin_co = 1.0;
kparm.poly.degree = 4.0;
kernel = kernel_poly;
break;
case 2:
kparm.rbf.gamma = 1.0;
kernel = kernel_rbf;
break;
case 3:
kparm.sig.lin_co = 1.0;
kparm.sig.const_co = 0.0;
kernel = kernel_sig;
break;
case 4:
kernel = svm_kernel_fisher;
break;
}
break;
case AL_TEST_IN_TRAIN_ARG:
test_in_train = 1;
break;
case AL_WITH_TRANS_ARG:
svm_al_do_trans = 1;
break;
case BSIZE_TYPE:
svm_bsize = atoi(arg);
if (svm_bsize < 2) {
fprintf(stderr, "Invalid value for -b, value must be at least 2.\n");
return ARGP_ERR_UNKNOWN;
}
svm_bsize = ((svm_bsize+3)/4)*4;
break;
case CACHE_SIZE_ARG:
cache_size = atoi(arg);
if (cache_size < 2) {
fprintf(stderr, "Invalid value for --cache_size, value must be greater than 1\n");
return ARGP_ERR_UNKNOWN;
}
break;
case COST_TYPE:
svm_C = atof(arg);
break;
case DF_COUNTS_ARG:
key = atoi(arg);
if (key == 0) {
df_counts = bow_tfidf_occurrences;
} else if (key == 1) {
df_counts = bow_tfidf_words;
} else {
return ARGP_ERR_UNKNOWN;
}
break;
case EA_TYPE:
svm_epsilon_a = atof(arg);
break;
case AL_BASELINE:
test_in_train = 1;
al_pick_random = 1;
case DO_ACTIVE_LEARNING:
do_active_learning = 1;
svm_al_qsize = atoi(arg);
if (svm_al_qsize < 0) {
fprintf(stderr, "Bogus AL-query size\n");
return ARGP_ERR_UNKNOWN;
}
break;
case INITIAL_AL_TSET_ARG:
svm_init_al_tset = atoi(arg);
break;
case REMOVE_MISCLASS_TYPE:
svm_remove_misclassified = atoi(arg);
break;
case RANDOM_SEED_ARG:
svm_random_seed = atoi(arg);
break;
case QUICK_SCORE:
quick_scoring = 1;
break;
case SUPPRESS_SCORE_MAT_ARG:
suppress_score_mat = 1;
break;
case SVM_BASENAME_ARG:
svml_basename = arg;
break;
case TF_TRANSFORM_TYPE:
tf_transform_type = atoi(arg);
if ((tf_transform_type < 0) || (tf_transform_type > 1)) {
fprintf(stderr, "Invalid value for tf_transform_type, value must be 0 or 1\n");
return ARGP_ERR_UNKNOWN;
}
break;
case TRANSDUCE_CLASS_ARG:
{
int a;
a = atoi(arg);
if (a == bow_doc_train) {
fprintf(stderr,"Cannot do transduction on training set, ignoring \"%s\" option\n",arg);
} else {
if (!transduce_class) {
transduce_class_overriding = 1;
transduce_class = 0;
}
/* < 0 turns transduction off */
if (a > 0) {
transduce_class |= (1 << a);
}
}
}
break;
case TRANS_HYP_REFRESH_ARG:
svm_trans_hyp_refresh = atoi(arg);
if (svm_trans_hyp_refresh < 1) {
fprintf(stderr, "svm_trans_hyp_refresh (hyperplane refresh rate)"
" must be greater than 0\n");
}
break;
case TRANS_IGNORE_BIAS_ARG:
svm_trans_nobias = 1;
break;
case TRANS_NPOS_ARG:
svm_trans_npos = atoi(arg);
if (svm_trans_npos < 1) {
fprintf(stderr, "svm_trans_npos should be greater than 0.\n");
return ARGP_ERR_UNKNOWN;
}
break;
case TRANS_CSTAR_ARG:
svm_trans_cstar = atof(arg);
break;
case TRANS_SMART_VALS_ARG:
svm_trans_smart_vals = atoi(arg);
break;
case USE_SMO_ARG:
svm_use_smo = atoi(arg);
/* the epsilon is used is 2x as big as it would be in the loqo method */
if (svm_use_smo == 1) {
svm_epsilon_crit /= 2;
}
#ifndef HAVE_LOQO
if (svm_use_smo != 1) {
fprintf(stderr,"Cannot switch from SMO, no other solvers were built,\n"
"rebuild libbow with pr_loqo to use another algorithm.\n");
}
#endif
break;
case VOTE_TYPE:
vote_type = atoi(arg);
if ((vote_type < 0) || (vote_type > 1)) {
fprintf(stderr, "Invalid value for --vote, value must be 0 for linear or 1 for pairwise.\n");
return ARGP_ERR_UNKNOWN;
}
break;
case WEIGHT_TYPE:
weight_type = atoi(arg);
if ((weight_type < 0) || (weight_type > 3)) {
fprintf(stderr, "Invalid value for -w, value must be 0, 1, 2, or 3.\n");
return ARGP_ERR_UNKNOWN;
}
break;
default:
return ARGP_ERR_UNKNOWN;
}
return 0;
}
static const struct argp svm_argp = { svm_options, svm_parse_opt };
static struct argp_child svm_argp_child = {
&svm_argp, /* This child's argp structure */
0, /* flags for child */
0, /* optional header in help message */
0 /* arbitrary group number for ordering */
};
void svm_permute_data(int *permute_table, bow_wv **docs, int *yvect, int ndocs) {
int i, j;
for (i=0; i<ndocs; i++) {
permute_table[i] = i;
}
for (i=0; i<ndocs; i++) {
bow_wv *d;
int y;
j = random() % ndocs;
d = docs[j];
docs[j] = docs[i];
docs[i] = d;
y = yvect[j];
yvect[j] = yvect[i];
yvect[i] = y;
y = permute_table[j];
permute_table[j] = permute_table[i];
permute_table[i] = y;
}
}
void svm_unpermute_data(int *permute_table, bow_wv **docs, int *yvect, int ndocs) {
int i, j;
for (i=0; i<ndocs; ) {
bow_wv *d;
int y;
j = permute_table[i];
if (j == i) {
i++;
continue;
}
d = docs[j];
docs[j] = docs[i];
docs[i] = d;
y = yvect[j];
yvect[j] = yvect[i];
yvect[i] = y;
y = permute_table[j];
permute_table[j] = permute_table[i];
permute_table[i] = y;
}
}
/* Right now, the vectors it looks at are the raw freq vectors */
double dprod(bow_wv *wv1, bow_wv *wv2) {
double sum;
bow_we *v1, *v2;
int i1, i2;
i1 = i2 = 0;
sum = 0.0;
v1 = wv1->entry;
v2 = wv2->entry;
while ((i1 < wv1->num_entries) && (i2 < wv2->num_entries)) {
if(v1[i1].wi > v2[i2].wi) {
i2++;
}
else if (v1[i1].wi < v2[i2].wi) {
i1++;
}
else {
sum += (v1[i1].weight) * (v2[i2].weight);
i1++;
i2++;
}
}
return(sum);
}
/* dot product between a sparce & non-sparse vector */
double dprod_sd(bow_wv *wv, double *W) {
double sum;
bow_we *v;
int i;
i = 0;
sum = 0.0;
v = wv->entry;
while (i < wv->num_entries) {
sum += v[i].weight * W[v[i].wi];
i++;
}
return(sum);
}
/* this is a whole different function just because the kernel is the biggest bottleneck */
double ddprod(bow_wv *wv1, bow_wv *wv2) {
double tmp;
double sum;
bow_we *v1, *v2;
int i1, i2;
i1 = i2 = 0;
sum = 0.0;
v1 = wv1->entry;
v2 = wv2->entry;
while ((i1 < wv1->num_entries) && (i2 < wv2->num_entries)) {
if(v1[i1].wi > v2[i2].wi) {
i2++;
}
else if (v1[i1].wi < v2[i2].wi) {
i1++;
}
else {
tmp = (v1[i1].weight) - (v2[i2].weight);
sum += tmp*tmp;
i1++;
i2++;
}
}
return(sum);
}
/* End of command-line options specific to SVMs */
double kernel_poly(bow_wv *wv1, bow_wv *wv2) {
return (pow(kparm.poly.lin_co * dprod(wv1,wv2) +
kparm.poly.const_co, kparm.poly.degree));
}
double kernel_rbf(bow_wv *wv1, bow_wv *wv2) {
return (exp(-1*kparm.rbf.gamma * (ddprod(wv1,wv2))));
}
double kernel_sig(bow_wv *wv1, bow_wv *wv2) {
return(tanh(kparm.sig.lin_co * dprod(wv1,wv2)+kparm.sig.const_co));
}
static int rlength;
typedef struct _kc_el {
bow_wv *i, *j;
double val;
unsigned int age;
} kc_el;
static kc_el *harray;
static unsigned int max_age;
void kcache_init(int nwide) {
int i;
max_age = 1;
svm_nkc_calls = 0;
rlength = nwide;
if ((harray = (kc_el *) malloc(sizeof(kc_el)*cache_size)) == NULL) {
cache_size = cache_size/2;
fprintf(stderr, "Could not allocate space for the kernel cache.\n"
"Shrinking size to %d and trying again.\n", cache_size);
return (kcache_init(nwide));
}
for (i=0; i<cache_size; i++) {
harray[i].i = (bow_wv *) ~0;
harray[i].age = 0;
}
}
void kcache_clear() {
free(harray);
}
void kcache_age() {
max_age++;
}
#define NHASHES 3
static int sub_nkcc=0; /* this makes nkc_calls = actual calls / 100 */
double svm_kernel_cache(bow_wv *wv1, bow_wv *wv2) {
int h_index;
int k;
unsigned int min_age, min_from;
double d;
if (!((sub_nkcc++) % 100)) {
svm_nkc_calls ++;
}
min_age = ~((unsigned long) 0);
/* all of the kernels are symetric */
if (wv1>wv2) {
bow_wv *tmp;
tmp = wv2;
wv2 = wv1;
wv1 = tmp;
}
for (k=h_index=0; k<NHASHES; k++) {
h_index = ((((unsigned int)wv1)*rlength+((unsigned int)wv2))+h_index+19) % cache_size;
if ((harray[h_index].i == wv1) && (harray[h_index].j == wv2)) {
harray[h_index].age = max_age;
return (harray[h_index].val);
} else {
if (harray[h_index].age > 0) {
if (min_age > harray[h_index].age) {
min_age = harray[h_index].age;
min_from = h_index;
}
continue;
} else {
min_from = h_index;
break;
}
}
}
d = kernel(wv1,wv2);
harray[min_from].i = wv1;
harray[min_from].j = wv2;
harray[min_from].val = d;
harray[min_from].age = max_age;
return (d);
}
/* don't add the evaluation (useful if the items are getting deleted from a set) */
double svm_kernel_cache_lookup(bow_wv *wv1, bow_wv *wv2) {
int h_index;
int k;
/* all of the kernels are symetric */
if (wv1>wv2) {
bow_wv *tmp;
tmp = wv2;
wv2 = wv1;
wv1 = tmp;
}
for (k=h_index=0; k<NHASHES; k++) {
h_index = ((((unsigned int)wv1)*rlength+((unsigned int)wv2))+h_index+19) % cache_size;
if ((harray[h_index].i == wv1) && (harray[h_index].j == wv2)) {
return (harray[h_index].val);
}
}
return (kernel(wv1,wv2));
}
/* random qsort helpers */
int di_cmp(const void *v1, const void *v2) {
double d1, d2;
d1 = ((struct di *) v1)->d;
d2 = ((struct di *) v2)->d;
if (d1 < d2) {
return (-1);
} else if (d1 > d2) {
return (1);
} else {
return 0;
}
}
int i_cmp(const void *v1, const void *v2) {
int d1, d2;
d1 = *((int *) v1);
d2 = *((int *) v2);
if (d1 < d2) {
return (-1);
} else if (d1 > d2) {
return (1);
} else {
return 0;
}
}
int d_cmp(const void *v1, const void *v2) {
double d1, d2;
d1 = *((double *) v1);
d2 = *((double *) v2);
if (d1 < d2) {
return (-1);
} else if (d1 > d2) {
return (1);
} else {
return 0;
}
}
int s_cmp(const void *v1, const void *v2) {
bow_score *s1, *s2;
s1 = ((bow_score *) v1);
s2 = ((bow_score *) v2);
if (s1->weight < s2->weight) {
return (1);
} else if (s1->weight > s2->weight) {
return (-1);
} else {
if (s1->di < s2->di) {
return (-1);
} else if (s1->di > s2->di) {
return (1);
} else {
return 0;
}
}
}
/* useful alternative to qsort or radix sort */
/* stick the top n values in the first n slots of arr */
void get_top_n(struct di *arr, int len, int n) {
double mind, tmpd;
int minfrom, tmpi;
int i,j;
if (len < n) {
return;
}
for (i=0; i<n && i<len; i++) {
mind = arr[i].d;
minfrom = i;
for (j=i+1; j<len; j++) {
if (arr[j].d < mind) {
mind = arr[j].d;
minfrom = j;
}
}
tmpi = arr[minfrom].i;
tmpd = arr[minfrom].d;
arr[minfrom].d = arr[i].d;
arr[minfrom].i = arr[i].i;
arr[i].d = tmpd;
arr[i].i = tmpi;
}
return;
}
/* takes in docs, creates an idf vector & then weights the document */
/* sets doc weights by using counts & normalizer */
static float *tfidf(bow_wv **docs, int ntrain) {
float idf_sum; /* sum of all the idf values */
int max_wi; /* the highest "word index" */
float *new_idf_vect;
int i, j;
bow_verbosify (bow_progress, "Setting weights over words: ");
max_wi = bow_num_words();
new_idf_vect = (float *) malloc(sizeof(float)*max_wi);
for (i=0; i<max_wi; i++) {
new_idf_vect[i] = 0.0;
}
idf_sum = 0.0;
/* First calculate document frequencies. */
for (i=0; i<ntrain; i++) {
for (j=0; j<docs[i]->num_entries; j++) {
if (df_counts == bow_tfidf_occurrences) {
/* Make DV be the number of documents in which word WI occurs
at least once. (We can't just set it to DV->LENGTH because
we have to check to make sure each document is part of the
model. */
new_idf_vect[docs[i]->entry[j].wi] ++;
} else if (df_counts == bow_tfidf_words) {
/* Make DV be the total number of times word WI appears
in any document. */
new_idf_vect[docs[i]->entry[j].wi] += docs[i]->entry[j].count;
} else {
bow_error ("Bad TFIDF parameter df_counts.");
}
}
}
for (i=0; i<max_wi; i++) {
/* Set IDF from DF. */
/* following what Thorsten alledgedly does */
if (new_idf_vect[i] >= 3.0) {
if (df_transform == LOG)
new_idf_vect[i] = log2f (ntrain / new_idf_vect[i]);
else if (df_transform == SQRT)
new_idf_vect[i] = sqrtf (ntrain / new_idf_vect[i]);
else if (df_transform == RAW)
new_idf_vect[i] = ntrain / new_idf_vect[i];
else {
new_idf_vect[i] = 0; /* to avoid gcc warning */
bow_error ("Bad TFIDF parameter df_transform.");
}
idf_sum += new_idf_vect[i];
} else {
new_idf_vect[i] = 0.0;
}
}
/* "normalize" the idf values */
for (i=0; i<max_wi; i++) {
/* Get the document vector for this word WI */
new_idf_vect[i] = max_wi*new_idf_vect[i]/idf_sum;
}
bow_verbosify (bow_progress, "\n");
return new_idf_vect;
}
/* next 2 fn's stolen from info-gain.c */
/* Return the entropy given counts for each type of element. */
static double entropy(float e1, float e2) {
double total = 0; /* How many elements we have in total */
double entropy = 0.0;
double fraction;
total = e1 + e2;
/* If we have no elements, then the entropy is zero. */
if (total == 0) {
return 0.0;
}
entropy = 0.0;
/* Now calculate the entropy */
fraction = e1 / total;
if (fraction != 0.0) {
entropy = -1 * fraction * log2f (fraction);
}
fraction = e2 / total;
if (fraction != 0.0) {
entropy -= fraction * log2f (fraction);
}
return entropy;
}
/* Return a malloc()'ed array containing an infomation-gain score for
each word index. */
float *infogain(bow_wv **docs, int *yvect, int ndocs) {
int grand_totals[2]; /* Totals for each class. */
double total_entropy; /* The entropy of the total collection. */
double with_word_entropy; /* The entropy of the set of docs with
the word in question. */
double without_word_entropy; /* The entropy of the set of docs without
the word in question. */
float grand_total = 0;
float with_word_total = 0;
float without_word_total = 0;
int i, j;
float *ret;
double sum;
int *fc[2]; /* tallies for all the words in class 1 & 2 */
int num_words;
bow_verbosify (bow_progress, "Calculating info gain... words :: ");
num_words = bow_num_words();
ret = bow_malloc (num_words*sizeof (float));
fc[0] = (int *) malloc(num_words*sizeof(double));
fc[1] = (int *) malloc(num_words*sizeof(double));
memset(fc[0], 0, num_words*sizeof(int));
memset(fc[1], 0, num_words*sizeof(int));
/* First set all the arrays to zero */
for(i = 0; i < 2; i++) {
grand_totals[i] = 0;
}
/* Now set up the grand totals. */
for (i = 0; i<ndocs; i++) {
if (yvect[i]) { /* if it is unlabeled, ignore it */
grand_totals[(yvect[i]+1)/2] ++;
/* this is only done incase some type of occurrence cnt should ever happen */
grand_total ++;
}
}
/* Calculate the total entropy */
total_entropy = entropy (grand_totals[0], grand_totals[1]);
sum = 0.0;
/* the fc[...] are like the with_word totals */
for (i=0; i<ndocs; i++) {
if (yvect[i]) {
int y = (yvect[i]+1)/2;
for (j=0; j<docs[i]->num_entries; j++) {
fc[y][docs[i]->entry[j].wi] ++;
}
}
}
for (i=0; i<num_words; i++) {
with_word_total = fc[0][i] + fc[1][i];
without_word_total = grand_total - with_word_total;
with_word_entropy = entropy((float)fc[0][i],(float)fc[1][i]);
without_word_entropy = entropy((float)(grand_totals[0] - fc[0][i]),
(float)(grand_totals[1] - fc[1][i]));
ret[i]=(float) (total_entropy -
(((double)with_word_total/(double)grand_total)*with_word_entropy) -
(((double)without_word_total/(double)grand_total)*without_word_entropy));
assert (ret[i] >= -1e-7);
sum += ret[i];
}
free(fc[0]);
free(fc[1]);
/* "normalize" in similar fashion to tfidf */
for (i=0; i<num_words; i++) {
/* Get the document vector for this word WI */
ret[i] = num_words*ret[i]/sum;
}
bow_verbosify (bow_progress, "\n");
return ret;
}
/* this sets the already transformed weights THEN does the normalizing... */
static void svm_set_barrel_weights(bow_wv **docs, int *yvect, int ndocs, float **weight_vect) {
int i,j;
/* the weights have yet to be set & since that's what we're using... */
if (svm_kernel_type == FISHER) {
svm_set_fisher_barrel_weights(docs, ndocs);
return;
} else if (weight_type == RAW) {
for (i=0; i<ndocs; i++) {
for (j=0; j<docs[i]->num_entries; j++) {
docs[i]->entry[j].weight *= docs[i]->normalizer;
}
}
return;
} else if (weight_type == TFIDF) {
*weight_vect = tfidf(docs, ndocs);
} else if (weight_type == INFOGAIN) {
*weight_vect = infogain(docs, yvect, ndocs);
}
/* Now loop through all the documents, setting their weights */
for (i=0; i<ndocs; i++) {
double sum = 0.0;
for (j=0; j<docs[i]->num_entries; j++) {
docs[i]->entry[j].weight *=
docs[i]->normalizer * (*weight_vect)[docs[i]->entry[j].wi];
sum += docs[i]->entry[j].weight;
}
if (sum >0.0) {
bow_wv_normalize_weights_by_summing(docs[i]);
for (j=0; j<docs[i]->num_entries; j++) {
docs[i]->entry[j].weight *= docs[i]->normalizer;
}
}
}
}
/* similar to barrel weights above, but this only works on 1 wv at a time */
/* will set weights from an already transformed oweights vector (if it was transformed),
* then normalize the weights */
static void svm_set_wv_weights(bow_wv *qwv, float *oweights, float *weight_vect) {
double sum;
int i;
sum = 0.0;
if (weight_type == TFIDF || weight_type == INFOGAIN) {
if (tf_transform_type) {
for (i=0; i<qwv->num_entries; i++) {
qwv->entry[i].weight =
weight_vect[qwv->entry[i].wi] * oweights[i];
sum += qwv->entry[i].weight;
}
} else {
for (i=0; i<qwv->num_entries; i++) {
/* since no transform was used - just use the raw count*/
qwv->entry[i].weight =
weight_vect[qwv->entry[i].wi] * ((float) qwv->entry[i].count);
sum += qwv->entry[i].weight;
}
}
} else {
for (i=0; i<qwv->num_entries && sum == 0.0; i++) {
sum += qwv->entry[i].weight;
}
}
if (sum > 0.0) {
bow_wv_normalize_weights_by_summing(qwv);
for (i=0; i<qwv->num_entries; i++) {
qwv->entry[i].weight *= qwv->normalizer;
}
}
}
/* the below comment is correct - but there are instances (& in some
* cases a substantial proportion) where some data may create an
* excellent starting point for the algorithms, even though so much has changed
* --- therefore, this should be changed to be more intelligent */
/* since removing bound support vectors is hard
* (since each bound support vector removed drastically
* changes the constraints) I don't bother to do it
* intuitively for each algorithm (that was tried &
* performance did not improve (see above)) - this
* function is nice because its modular & independent
* of any implementation. */
/* tvals is ignored, but the values filled in by the
* algorithm are not changed. */
int svm_remove_bound_examples(bow_wv **docs, int *yvect, double *weights,
double *b, double **W, int ndocs, double *tvals,
float *cvect, int *nsv) {
int nbound=0;
int *tdocs; /* trans table */
float *sub_cvect;
bow_wv **sub_docs;
int sub_ndocs=0;
int *sub_yvect;
int i,j,x;
sub_docs = (bow_wv **) alloca(sizeof(bow_wv *)*ndocs);
sub_yvect = (int *) alloca(sizeof(int)*ndocs);
tdocs = (int *) alloca(sizeof(int)*ndocs);
sub_cvect = (float *) alloca(sizeof(float)*ndocs);
if (svm_remove_misclassified==REMOVE_BOUND) {
for (i=nbound=sub_ndocs=0; i<ndocs; i++) {
if (weights[i] > cvect[i] - svm_epsilon_a) {
nbound ++;
} else {
sub_docs[sub_ndocs] = docs[i];
sub_yvect[sub_ndocs] = yvect[i];
tdocs[sub_ndocs] = i;
sub_ndocs++;
}
}
} else if (svm_remove_misclassified==REMOVE_WRONG) {
if (svm_kernel_type == 0) {
for (i=nbound=sub_ndocs=0; i<ndocs; i++) {
if (yvect[i]*evaluate_model_hyperplane(*W, *b, docs[i]) < 0.0) {
nbound ++;
} else {
sub_docs[sub_ndocs] = docs[i];
sub_yvect[sub_ndocs] = yvect[i];
tdocs[sub_ndocs] = i;
sub_ndocs++;
}
}
} else {
for (i=nbound=sub_ndocs=0; i<ndocs; i++) {
if (yvect[i]*evaluate_model_cache(docs, weights, yvect, *b, docs[i], *nsv) < 0.0) {
nbound ++;
} else {
sub_docs[sub_ndocs] = docs[i];
sub_yvect[sub_ndocs] = yvect[i];
tdocs[sub_ndocs] = i;
sub_ndocs++;
}
}
}
}
if (nbound) {
fprintf(stderr, "Removing %d bound examples\n",nbound);
fprintf(stdout, "Removing %d bound examples\n",nbound);
} else {
return 0;
}
/* prb not worthwile to resize arrays */
/* "unbound" everything & set weights & tvals... */
for (i=0; i<sub_ndocs; i++) {
tvals[i] = 0.0;
weights[i] = 0.0;
sub_cvect[i] = MAXFLOAT;
}
*nsv = 0;
if (svm_use_smo) {
x = smo(sub_docs, sub_yvect, weights, b, W, sub_ndocs, tvals, sub_cvect, nsv);
} else {
#ifdef HAVE_LOQO
x = build_svm_guts(sub_docs, sub_yvect, weights, b, W, sub_ndocs, tvals,
sub_cvect, nsv);
#else
bow_error("Must build rainbow with pr_loqo to use this solver!\n");
#endif
}
/* place the weights in the proper slots */
for (i=ndocs-1, j=sub_ndocs-1; i>0; i--) {
if (tdocs[j] == i) {
weights[i] = weights[j];
tvals[i] = tvals[j];
j--;
} else {
weights[i] = 0.0;
tvals[i] = 0.0;
}
}
return x;
}
/* returns whether or not x has changed */
inline int solve_svm(bow_wv **docs, int *yvect, double *weights, double *ab,
double **W, int ndocs, double *tvals, float *cvect,
int *nsv) {
int x;
if (svm_use_smo) {
x = smo(docs, yvect, weights, ab, W, ndocs, tvals, cvect, nsv);
} else {
#ifdef HAVE_LOQO
x = build_svm_guts(docs, yvect, weights, ab, W, ndocs, tvals, cvect, nsv);
#else
bow_error("Must build rainbow with pr_loqo to use this solver!\n");
#endif
}
if (svm_remove_misclassified) {
x |= svm_remove_bound_examples(docs,yvect,weights,ab,W,ndocs,tvals,
cvect,nsv);
}
return x;
}
/* returns if the weights have changed */
int svm_trans_or_chunk(bow_wv **docs, int *yvect, int *trans_yvect,
double *weights, double *tvals, double *ab,
double **W, int ntrans, int ndocs, int *nsv) {
if (ntrans) {
return (transduce_svm(docs, yvect, trans_yvect, weights, tvals, ab,
W, ndocs, ntrans, nsv));
} else {
int i;
float *cvect = (float *) alloca(sizeof(float)*ndocs);
for (i=0; i<ndocs; i++) {
cvect[i] = svm_C;
}
return(solve_svm(docs, yvect, weights, ab, W, ndocs, tvals, cvect, nsv));
}
}
/* cover for all the functions */
/* this function does a small amount of pre & post-processing for the
* algorithm independent stuff (like randomly permuting everything &
* outputting a hyperplane if possible) */
int tlf_svm(bow_wv **docs, int *yvect, double *weights, double *ab,
bow_wv **W_wv, int ntrans, int ndocs) {
int nlabeled;
int misclass;
int nsv;
int *permute_table;
double *tvals;
double *W=NULL;
int i,j;
struct tms t1, t2;
if (svm_random_seed) {
srandom(svm_random_seed);
} else {
svm_random_seed = (int) time(NULL);
srandom(svm_random_seed);
printf("random seed to chop test/train split: %d\n",svm_random_seed);
fprintf(stderr,"random seed to chop test/train split: %d\n",svm_random_seed);
}
permute_table = (int *) malloc(sizeof(int)*ndocs);
nlabeled = ndocs - ntrans;
/* permute each part, but don't mudge them together, because the
* solvers are going to expect all unlabeled data (data with a
* different C* to be in the latter half) */
svm_permute_data(permute_table, docs, yvect, nlabeled);
svm_permute_data(&(permute_table[nlabeled]), &(docs[nlabeled]), &(yvect[nlabeled]), ntrans);
/* lets try to reduce determinism... */
srandom((int) time(NULL));
times(&t1);
if (do_active_learning) {
if (test_in_train) {
nsv = al_svm_test_wrapper(docs, yvect, weights, ab, &W, ntrans, ndocs,
(suppress_score_mat ? 0 : 1),
al_pick_random, permute_table);
} else {
nsv = al_svm(docs, yvect, weights, ab, &W, ntrans, ndocs, al_pick_random);
}
} else {
/* initialize... */
tvals = (double *) alloca(sizeof(double)*ndocs);
nsv = 0;
for (i=0; i<ndocs; i++) {
weights[i] = 0.0;
tvals[i] = 0.0;
}
svm_trans_or_chunk(docs, yvect, NULL, weights, tvals, ab, &W, ntrans, ndocs, &nsv);
}
times(&t2);
fprintf(stderr,"user: %d, system:%d, kernel_calls:%d\n", (int)(t2.tms_utime-t1.tms_utime),
(int) (t2.tms_stime - t1.tms_stime), svm_nkc_calls);
printf("user: %d, system:%d, kernel_calls:%d\n", (int)(t2.tms_utime-t1.tms_utime),
(int) (t2.tms_stime - t1.tms_stime), svm_nkc_calls);
/* unpermute data */
svm_unpermute_data(permute_table, docs, yvect, nlabeled);
svm_unpermute_data(&(permute_table[nlabeled]), &(docs[nlabeled]), &(yvect[nlabeled]), ntrans);
free(permute_table);
if (svm_kernel_type == 0) {
*W_wv = svm_darray_to_wv(W);
free(W);
}
printf("support vectors: ");
for (i=j=0; j<nsv; i++) {
if (weights[i] > svm_epsilon_a) {
printf("%d(%f) ",i,weights[i]);
j++;
}
}
misclass = 0;
if (!svm_remove_misclassified) {
for (i=misclass=0; i<nlabeled; i++) {
if (weights[i] > svm_C-svm_epsilon_a) {
misclass++;
}
}
for (i=0; i<ntrans; i++) {
if (weights[nlabeled+i] > svm_trans_cstar-svm_epsilon_a) {
misclass++;
}
}
}
printf("\n%d support vectors (%d bounded)\n", nsv, misclass);
return nsv;
}
bow_wv *svm_darray_to_wv(double *W) {
bow_wv *W_wv;
int num_words, i, j;
num_words = bow_num_words();
for (i=j=0; i<num_words; i++) {
if (W[i] != 0.0)
j++;
}
W_wv = bow_wv_new(j);
for (i=j=0; j<W_wv->num_entries; i++) {
if (W[i] != 0.0) {
W_wv->entry[j].wi = i;
W_wv->entry[j].count = 1; /* just so that an assertion doesn't throw up later */
W_wv->entry[j].weight = W[i];
j++;
}
}
return (W_wv);
}
/* note - these 2 fn's are not MEANT to be inverses of each
* other - they don't need to be & shouldn't be! */
/* given a 'focus' value, this transforms x into some int
* this must be a BINARY function, outputting ONLY 1 & -1
* because that's what the SVM use for y. */
int map_class_to_y(int focus, int x) {
if (focus == x) {
return 1;
} else {
return (-1);
}
}
/* each pass over these things take up 2 labels... */
/* 1->1, -1->0 */
int map_y_to_class(int focus, int x) {
return ((focus*2)+((x+1)/2));
}
/* helper to do whatever transform on a wv & then normalize it... */
static void tf_transform(bow_wv *doc) {
int j;
for (j=0; j<doc->num_entries; j++) {
if (tf_transform_type == LOG) {
doc->entry[j].weight = log2f((float) (doc->entry[j].count + 1));
} else {
doc->entry[j].weight = (float) doc->entry[j].count;
}
}
}
/* sets counts & the normalizer too */
/* pulls from the barrel those docs that satisfy dec_fn & turns them into a doc array */
int make_doc_array(bow_barrel *barrel, bow_wv **docs, int *tdocs, int(*dec_fn)(bow_cdoc *)) {
bow_dv_heap *heap;
int ndocs;
bow_wv *wv_tmp1;
bow_wv *wv_tmp;
int j;
/* Create the Heap of vectors of all documents */
heap = bow_make_dv_heap_from_wi2dvf(barrel->wi2dvf);
for (ndocs=0; ; ndocs++) {
int t = bow_heap_next_wv(heap, barrel, &wv_tmp1, dec_fn);
if (t == -1) {
break;
} else {
tdocs[ndocs] = t;
}
wv_tmp = bow_wv_new(wv_tmp1->num_entries);
for (j=0; j<wv_tmp->num_entries; j++) {
wv_tmp->entry[j].wi = wv_tmp1->entry[j].wi;
wv_tmp->entry[j].count = wv_tmp1->entry[j].count;
}
tf_transform(wv_tmp);
bow_wv_normalize_weights_by_summing(wv_tmp);
docs[ndocs] = wv_tmp;
}
return ndocs;
}
/* C sucks - this is just a fn to pass to bow_heap_next_wv */
static int silly_currying_global_v1, silly_currying_global_v2;
int use_train_and_submodel(bow_cdoc *cdoc) {
return ((cdoc->type == bow_doc_train &&
(silly_currying_global_v1 == cdoc->class ||
silly_currying_global_v2 == cdoc->class)) ?
1 : 0);
}
int use_transduction_docs(bow_cdoc *cdoc) {
return (((1 << cdoc->type) & transduce_class) ? 1 : 0);
}
/* helper fn for adding the data for a training example to the barrel */
int add_sv_barrel(bow_barrel *new_barrel,double *weights, int *yvect, int *tdocs,
double b, int model_no, int nsv) {
bow_cdoc cdoc_pos, cdoc_neg;
bow_wv *dummy_wv_neg;
bow_wv *dummy_wv_pos;
int n_meta_docs=0;
int ni, pi, i, j, num_words;
num_words = bow_num_words();
dummy_wv_pos = bow_wv_new(num_words);
dummy_wv_neg = bow_wv_new(num_words);
dummy_wv_pos->num_entries = dummy_wv_neg->num_entries = 0;
cdoc_pos.type = bow_doc_ignore;
cdoc_neg.normalizer = cdoc_pos.prior = 0.0;
cdoc_pos.filename = NULL;
cdoc_pos.class_probs = NULL;
cdoc_pos.class = 0;
cdoc_neg.type = bow_doc_ignore;
cdoc_neg.normalizer = cdoc_neg.prior = 0.0;
cdoc_neg.filename = NULL;
cdoc_neg.class_probs = NULL;
cdoc_neg.class = 0;
if (model_no == 0) {
/* insert an two empty docs into the barrel so that the
* ancillary data has a place to live */
cdoc_neg.word_count = 0;
bow_barrel_add_document(new_barrel, &cdoc_neg, dummy_wv_pos);
bow_barrel_add_document(new_barrel, &cdoc_neg, dummy_wv_pos);
n_meta_docs = 2;
}
cdoc_pos.normalizer = b;
cdoc_pos.class = map_y_to_class(model_no,(int) 1);
cdoc_neg.class = map_y_to_class(model_no,(int) -1);
ni = pi = 0;
for (i=j=0; j<nsv; i++) {
if (weights[i] > svm_epsilon_a) {
if (yvect[i] > 0) {
if (pi > num_words) {
dummy_wv_pos->num_entries = pi;
cdoc_pos.word_count = pi;
bow_barrel_add_document(new_barrel, &cdoc_pos, dummy_wv_pos);
pi = 0;
n_meta_docs++;
}
dummy_wv_pos->entry[pi].weight = (float) weights[i];
dummy_wv_pos->entry[pi].count = tdocs[i] + 1;
dummy_wv_pos->entry[pi].wi = pi;
pi++;
} else {
if (ni > num_words) {
dummy_wv_neg->num_entries = pi;
cdoc_neg.word_count = ni;
bow_barrel_add_document(new_barrel, &cdoc_neg, dummy_wv_pos);
ni = 0;
n_meta_docs++;
}
dummy_wv_neg->entry[ni].weight = (float) weights[i];
dummy_wv_neg->entry[ni].count = tdocs[i] + 1;
dummy_wv_neg->entry[ni].wi = ni;
ni++;
}
j++;
}
}
cdoc_pos.word_count = pi;
dummy_wv_pos->num_entries = pi;
bow_barrel_add_document(new_barrel, &cdoc_pos, dummy_wv_pos);
cdoc_neg.word_count = ni;
dummy_wv_neg->num_entries = ni;
bow_barrel_add_document(new_barrel, &cdoc_neg, dummy_wv_neg);
bow_wv_free(dummy_wv_pos);
bow_wv_free(dummy_wv_neg);
return (n_meta_docs+2);
}
bow_barrel *svm_vpc_merge(bow_barrel *src_barrel) {
double b;
int cto; /* for pairwise - works with npass */
bow_wv **docs; /* a doc major matrix */
int max_nsv; /* highest # of nsv's in a submodel */
int mdocs; /* the number of docs in the current submodel */
bow_wv **model_weights;
int n_meta_docs; /* # of documents that will go into the class barrel
* before the weight vectors will */
int nclasses;
int ndocs; /* total # of documents to be trained & transduced */
int ntrain; /* # of documents to be trained upon */
int ntrans; /* # of "unlabeled" docs to use in transduction */
bow_barrel *class_barrel;
int nloops; /* # of the current submodel being built */
int npass; /* tmp for making submodels from the src_barrel */
int nsv; /* # of support vectors for the current model */
int num_words;
bow_wv **sub_docs;
int *tdocs; /* trans table of indices in docs to indices
* in the original barrel */
int total_docs; /* total # of docs (some not for training) */
int *utdocs; /* trans table of the docs in our training set
* to those in the actually used in the models */
float *weight_vect;
double *weights; /* lagrange multipliers */
bow_wv **W; /* hyperplane for lin. folding */
int *yvect;
int i,j;
#ifndef HAVE_LOQO
if (svm_use_smo != 1) {
fprintf(stderr,"Can only use SMO, no other solvers were built,\n"
"rebuild libbow with pr_loqo to use another algorithm.\n");
}
#endif
#ifdef HAVE_FPSETMASK
fpsetmask(~(FP_X_INV | FP_X_DNML | FP_X_DZ | FP_X_OFL | FP_X_UFL | FP_X_IMP));
#endif
total_docs = src_barrel->cdocs->length;
nclasses = bow_barrel_num_classes(src_barrel);
weight_vect = NULL;
model_weights = NULL;
W = NULL;
yvect = NULL;
/* note - this OVER allocates - uses ALL, instead of just those for training */
docs = (bow_wv **) alloca(sizeof(bow_wv *)*total_docs);
tdocs = (int *) alloca(sizeof(int)*(total_docs+1));
mdocs = 0; /* to shut gcc up */
nsv = 0;
if (nclasses == 1) {
fprintf(stderr, "Cannot build SVM with only 1 class.\n");
fflush(stderr);
return NULL;
} else if (nclasses == 2) {
if (svm_kernel_type != FISHER) {
vote_type = PAIRWISE;
}
}
if (weight_type && svm_kernel_type == FISHER) {
weight_type = 0;
tf_transform_type = RAW;
}
if ((weight_type && vote_type == PAIRWISE) || weight_type == INFOGAIN) {
svm_weight_style = WEIGHTS_PER_MODEL;
} else if (weight_type) {
svm_weight_style = WEIGHTS_PER_BARREL;
} else {
svm_weight_style = NO_WEIGHTS;
}
if (svm_weight_style != WEIGHTS_PER_MODEL) {
ntrain = make_doc_array(src_barrel, docs, tdocs, bow_cdoc_is_train);
if (ntrain < 2) {
if (ntrain)
bow_wv_free(docs[0]);
fprintf(stderr, "Cannot build svm with less than 2 documents\n");
fflush(stderr);
return NULL;
}
/* append these trans docs to the arrays that were filled in above */
ntrans = make_doc_array(src_barrel, &(docs[ntrain]), &(tdocs[ntrain]),
use_transduction_docs);
ndocs = ntrain + ntrans;
utdocs = (int *) alloca(sizeof(int)*ndocs);
for (i=0; i<ndocs; i++) {
utdocs[i] = i;
}
sub_docs = docs;
mdocs = ndocs;
svm_set_barrel_weights(docs, NULL, ndocs, &weight_vect);
kcache_init(ndocs);
} else {
/* the ndocs value is the number of training documents that will
* actually be used - this is done now JUST to fill up the tdocs array. */
ntrain = make_doc_array(src_barrel, docs, tdocs, bow_cdoc_is_train);
if (ntrain < 2) {
if (ntrain)
bow_wv_free(docs[0]);
fprintf(stderr, "Cannot build svm with less than 2 documents\n");
fflush(stderr);
return NULL;
}
/* figure out the # of ntrans */
ntrans = make_doc_array(src_barrel, &(docs[ntrain]), &(tdocs[ntrain]),
use_transduction_docs);
ndocs = ntrain + ntrans;
/* since we don't need the docs for a while, free them */
for (i=0; i<ndocs; i++) {
bow_wv_free(docs[i]);
}
model_weights = (bow_wv **) malloc(sizeof(bow_wv *)*nclasses);
utdocs = (int *) alloca(sizeof(int)*ndocs);
/* the sub_docs vector will be rewritten with wv's to be used each iteration */
sub_docs = alloca(sizeof(bow_wv *)*ndocs);
}
/* build the naive bayes model for the kernel... */
if (svm_kernel_type == FISHER) {
/* this isn't too bad since the cache REALLY should be large enough
* to hold everything anyway (the cache doesn't get flushed) */
if (vote_type == PAIRWISE) {
fprintf(stderr, "Fisher kernel not implemented for pairwise models yet.\n");
return NULL;
}
svm_setup_fisher(src_barrel,docs,nclasses,ndocs);
weight_type = 0;
}
weights = (double *) alloca(sizeof(double)*ndocs);
yvect = (int *) alloca(sizeof(int)*ndocs);
/* put together the resultant barrel */
class_barrel = bow_barrel_new(src_barrel->wi2dvf->size, 2, sizeof(bow_cdoc),
src_barrel->cdocs->free_func);
class_barrel->method = src_barrel->method;
class_barrel->is_vpc = 1;
/* make a temp word array big enough to fill a whole strip of the wi2dvf table */
num_words = bow_num_words();
n_meta_docs = 0;
/* this is the beginning of the for loop */
max_nsv = -1;
nloops = 0;
npass = 0;
if (svm_kernel_type == 0) {
if (vote_type == PAIRWISE) {
W = (bow_wv **) malloc(sizeof(bow_wv *)*(nclasses-1)*nclasses/2);
} else {
W = (bow_wv **) malloc(sizeof(bow_wv *)*nclasses);
}
}
for (npass=0, cto=1; 1; ) {
/* initialize & pull together the classes for the npass'th model... */
if (vote_type == PAIRWISE) {
if (cto == nclasses) {
npass ++;
if (npass == nclasses-1) {
break;
}
cto = npass+1;
}
if (svm_weight_style == WEIGHTS_PER_MODEL) {
silly_currying_global_v1 = npass;
silly_currying_global_v2 = cto;
/* this gets called here since the doctype labels are in the barrel */
/* utdocs is filled with actual indices, not indices of the train set */
mdocs = make_doc_array(src_barrel, sub_docs, utdocs, use_train_and_submodel);
/* put the labels in for the labeled docs. */
for (i=0; i<mdocs; i++) {
bow_cdoc *cdoc = (GET_CDOC_ARRAY_EL(src_barrel,utdocs[i]));
yvect[i] = map_class_to_y(npass, cdoc->class);
}
/* even though this set of docs is always the same (since all of the
* unlabeled data is used for each pairwise document [this is not
* suggested to with a barrel w/ more than 2 classes] is used) we
* still grab it, since the starting position for the unlabeled data
* isn't known beforehand (its a slight hack) */
ntrans = make_doc_array(src_barrel, &(sub_docs[mdocs]),
&(utdocs[mdocs]), use_transduction_docs);
/* this says that it is unlabelled */
for (i=0; i<ntrans; i++) {
yvect[i+mdocs] = 0;
}
mdocs = mdocs + ntrans;
/* utdocs holds the barrel indices we're interested in the sub-model
* indices - so we need to remap utdocs */
for (i=j=0; j<mdocs; i++) {
if (tdocs[i] < utdocs[j]) {
continue;
} else {
utdocs[j] = i;
j++;
}
}
} else {
for (i=j=0; i<ntrain; i++) {
bow_cdoc *cdoc = (GET_CDOC_ARRAY_EL(src_barrel,tdocs[i]));
if ((cdoc->class == npass) || (cdoc->class == cto)) {
sub_docs[j] = docs[i];
yvect[j] = map_class_to_y(npass, cdoc->class);
utdocs[j] = i;
j++;
}
}
for (i=0; i<ntrans; j++,i++) {
sub_docs[j] = docs[i+ntrain];
utdocs[j] = i+ntrain;
yvect[j] = 0;
}
mdocs = j;
}
} else {
if (npass == nclasses) {
break;
}
/* all docs should be included - the yvect will do the proper mapping */
for (i=0; i<ntrain; i++) {
bow_cdoc *cdoc = (GET_CDOC_ARRAY_EL(src_barrel,tdocs[i]));
/* this map will be extended to make the barrel handle more than 2 classes */
yvect[i] = map_class_to_y(npass, cdoc->class);
}
for (i=0; i<ntrans; i++) {
yvect[i+ntrain] = 0;
}
if (svm_weight_style == WEIGHTS_PER_MODEL) {
for (i=0; i<mdocs; i++) {
/* the weight values are not correct - they include the last values */
/* make_doc_array does this for pairwise voting */
tf_transform(docs[i]);
}
}
}
if (svm_weight_style == WEIGHTS_PER_MODEL) {
svm_set_barrel_weights(sub_docs, yvect, mdocs, &weight_vect);
model_weights[nloops] = bow_wv_new(num_words);
for (i=j=0; i<num_words; i++) {
if (weight_vect[i] != 0.0) {
model_weights[nloops]->entry[j].wi = i;
model_weights[nloops]->entry[j].count = 1;
model_weights[nloops]->entry[j].weight = weight_vect[i];
j++;
}
}
free(weight_vect);
model_weights[nloops]->num_entries = j;
}
if (mdocs < 2) {
bow_error("Cannot create SVM with only 1 document!\n");
}
fprintf(stderr,"Learning %dth model\n",nloops);
if (svml_basename) {
char *tmp;
FILE *f = stdout;
tmp = malloc(sizeof(char)*(20+strlen(svml_basename)));
sprintf(tmp,"train_%d_%s",nloops,svml_basename);
f = fopen (tmp, "w");
for (i=0; i<mdocs; i++) {
fprintf(f,"%d ", yvect[i]);
for (j=0; j<sub_docs[i]->num_entries; j++) {
fprintf (f,"%d:%f ",1+sub_docs[i]->entry[j].wi, sub_docs[i]->entry[j].weight);
}
fprintf(f,"\n");
}
fclose(f);
/* set up the test output file */
sprintf(tmp,"test_%s",svml_basename);
svml_test_file = fopen (tmp, "w");
free(tmp);
nsv = 0;
W[nloops] = bow_wv_new(0);
} else {
/* only useful with test-in-train - ONLY build models after a certain point
* (like when the previously acquired data runs out) */
if ((!test_in_train) || ((test_in_train) && (nloops >= model_starting_no))) {
nsv = tlf_svm(sub_docs,yvect,weights,&b,&(W[nloops]),ntrans,mdocs);
}
}
if (vote_type == PAIRWISE && weight_type) {
for (i=0; i<mdocs; i++) {
bow_wv_free(sub_docs[i]);
}
}
if (max_nsv < nsv) {
max_nsv = nsv;
}
/* now we need to drop the significant classes into the barrel */
if (!test_in_train) {
n_meta_docs += add_sv_barrel(class_barrel, weights, yvect, utdocs, b, nloops, nsv);
}
if (vote_type == PAIRWISE) {
cto++;
} else {
npass ++;
}
nloops++;
}
if (test_in_train) {
exit(0);
}
if (svm_kernel_type == 0) {
bow_cdoc cdoc;
cdoc.filename = NULL;
cdoc.class_probs = NULL;
cdoc.type = bow_doc_ignore;
cdoc.class = 1;
for (i=0; i<nloops; i++) {
cdoc.word_count = W[i]->num_entries;
bow_barrel_add_document(class_barrel, &cdoc, W[i]);
bow_wv_free(W[i]);
}
free(W);
}
/* if it was per model, the cache would need to be alloc-ed & de-alloced locally */
if (svm_weight_style != WEIGHTS_PER_MODEL) {
kcache_clear();
}
/* place the model weights into the barrel */
if (svm_weight_style == WEIGHTS_PER_MODEL) {
bow_cdoc cdoc;
cdoc.filename = NULL;
cdoc.class_probs = NULL;
cdoc.type = bow_doc_ignore;
cdoc.class = 1; /* this is fine since all of the docs are class 0 & we
* know how many meta docs there are */
for (i=0; i<nloops; i++) {
cdoc.word_count = model_weights[i]->num_entries;
bow_barrel_add_document(class_barrel, &cdoc, model_weights[i]);
bow_wv_free(model_weights[i]);
}
free(model_weights);
}
/* the docs were freed before just to save memory - now we need them again
* & the optimizer's done, so a lot of memory is no longer being used */
if (svm_weight_style == WEIGHTS_PER_MODEL && vote_type == PAIRWISE) {
make_doc_array(src_barrel, docs, tdocs, bow_cdoc_is_train);
/* append these trans docs to the arrays that were filled in above */
make_doc_array(src_barrel, &(docs[ntrain]), &(tdocs[ntrain]),
use_transduction_docs);
}
/* now add all of the documents from the doc barrel to the class barrel */
for (i=0; i<ndocs; i++) {
/* add the i'th document to the class_barrel */
/* first we need to make a new cdoc */
bow_cdoc cdoc;
memcpy(&cdoc, GET_CDOC_ARRAY_EL(src_barrel, tdocs[i]), sizeof(bow_cdoc));
cdoc.filename = strdup(cdoc.filename);
cdoc.class = 0;
bow_barrel_add_document(class_barrel, &cdoc, docs[i]);
}
/* this has to be done after all possible dv's have been created */
if (!((vote_type == PAIRWISE && weight_type) || weight_type == INFOGAIN)
&& weight_type) { /* if no weights are used at all this isn't nec. */
bow_dv *dv;
j = bow_num_words();
for (i=0; i<j; i++) {
dv = bow_wi2dvf_dv (class_barrel->wi2dvf, i);
if (dv) {
dv->idf = weight_vect[i];
}
}
free(weight_vect);
}
if (vote_type == PAIRWISE) {
BARREL_GET_MAX_NSV(class_barrel) = max_nsv;
} else {
BARREL_GET_MAX_NSV(class_barrel) = -1*max_nsv;
}
BARREL_GET_NCLASSES(class_barrel) = nclasses;
BARREL_GET_NMETA_DOCS(class_barrel) = n_meta_docs;
class_barrel->classnames = bow_int4str_new(0);
for (i=0; i<nclasses; i++) {
/* drop a class label in */
bow_str2int(class_barrel->classnames, bow_int2str(src_barrel->classnames, i));
}
for (i=0; i<ndocs; i++) {
bow_wv_free(docs[i]);
}
return class_barrel;
}
inline double evaluate_model(bow_wv **docs, double *weights, int *yvect, double b,
bow_wv *query_wv, int nsv) {
double sum,tmp;
int i,j;
for (i=j=0, sum=0.0; j<nsv; i++) {
if (weights[i] != 0.0) {
tmp = kernel(docs[i],query_wv);
sum += yvect[i]*weights[i]*tmp;
j++;
}
}
return (sum - b);
}
/* similar to above, but to only for when the cache should be used */
inline double evaluate_model_cache(bow_wv **docs, double *weights, int *yvect, double b,
bow_wv *query_wv, int nsv) {
double sum,tmp;
int i,j;
for (i=j=0, sum=0.0; j<nsv; i++) {
if (weights[i] != 0.0) {
tmp = svm_kernel_cache(docs[i],query_wv);
sum += yvect[i]*weights[i]*tmp;
j++;
}
}
return (sum - b);
}
inline double evaluate_model_hyperplane(double *W, double b, bow_wv *query_wv) {
return (dprod_sd(query_wv,W)-b);
}
/* this & setup_docs are for "caching" the barrel into its wv form */
static void clear_model_cache () {
int i;
if (model_cache.barrel) {
for (i=0; i<model_cache.ndocs; i++) {
bow_wv_free(model_cache.docs[i]);
}
for (i=0; i<model_cache.nmodels; i++) {
free(model_cache.indices[i]);
free(model_cache.weights[i]);
free(model_cache.yvect[i]);
if (svm_weight_style == WEIGHTS_PER_MODEL) {
free(model_cache.word_weights.sub_model[i]);
}
if (svm_kernel_type == 0) {
free(model_cache.W[i]);
}
}
free(model_cache.docs);
free(model_cache.indices);
free(model_cache.weights);
free(model_cache.yvect);
free(model_cache.bvect);
free(model_cache.sizes);
if (svm_weight_style == WEIGHTS_PER_MODEL) {
free(model_cache.word_weights.sub_model);
} else if (svm_weight_style == WEIGHTS_PER_BARREL) {
free(model_cache.word_weights.barrel);
}
if (svm_kernel_type == 0) {
free(model_cache.W);
}
}
model_cache.barrel = NULL;
}
/* this fn fills *sub_docs with the m-th submodel (it pulls the docs
* from the cache that setup_docs fills & then sets whatever weights
* are necessary) */
/* the query vector should already be normalized */
void make_sub_model(int m, int weight_style, bow_wv ***sub_docs) {
bow_wv **docs;
int *indices;
float *weights;
bow_we *v2;
int i,j;
docs = *sub_docs;
for(j=0; j<model_cache.sizes[m]; j++) {
docs[j] = model_cache.docs[model_cache.indices[m][j]];
}
if (weight_style) {
indices = model_cache.indices[m];
weights = model_cache.word_weights.sub_model[m];
for (i=0; i<model_cache.sizes[m]; i++) {
int n = docs[i]->num_entries;
int di = indices[i];
v2 = docs[i]->entry;
for (j=0; j<n; j++) {
v2[j].weight = weights[v2[j].wi] * model_cache.oweights[di][j];
}
}
}
}
static void setup_docs(bow_barrel *barrel, int nclasses, int nmodels) {
bow_cdoc *cdoc;
int classnum, c_old;
bow_wv *dtmp;
bow_dv_heap *heap;
int ndocs;
int nmeta_docs;
int nwords;
int total_words;
int h,i,j,k,l;
nmeta_docs = BARREL_GET_NMETA_DOCS(barrel);
ndocs = barrel->cdocs->length - nmeta_docs;
total_words = bow_num_words();
clear_model_cache();
model_cache.docs = (bow_wv **) malloc(sizeof(bow_wv *)*ndocs);
model_cache.indices = (int **) malloc(sizeof(int *)*nmodels);
model_cache.weights = (double **) malloc(sizeof(double *)*nmodels);
model_cache.yvect = (int **) malloc(sizeof(int *)*nmodels);
model_cache.bvect = (double *) malloc(sizeof(double)*nmodels);
model_cache.sizes = (int *) malloc(sizeof(int)*nmodels);
if (weight_type) {
if (vote_type == PAIRWISE || weight_type == INFOGAIN) {
svm_weight_style = WEIGHTS_PER_MODEL;
model_cache.word_weights.sub_model = (float **) malloc(sizeof(float *)*nmodels);
if (tf_transform_type)
model_cache.oweights = (float **) malloc(sizeof(float *)*ndocs);
} else {
svm_weight_style = WEIGHTS_PER_BARREL;
model_cache.word_weights.barrel = (float *) malloc(sizeof(float *)*total_words);
}
} else {
svm_weight_style = NO_WEIGHTS;
}
if (svm_kernel_type == 0) {
model_cache.W = (double **) malloc(sizeof(double *)*nmodels);
} else {
model_cache.W = NULL;
}
/* Create the Heap of vectors of all documents */
heap = bow_make_dv_heap_from_wi2dvf(barrel->wi2dvf);
/* throw away the first 2 - they hold only ancillary info
* (see the macros at the top of the file) */
bow_heap_next_wv(heap, barrel, &dtmp, bow_cdoc_yes);
bow_heap_next_wv(heap, barrel, &dtmp, bow_cdoc_yes);
/* grab the meta documents first & setup the arrays */
for (h=0,l=2; h<nmodels; h++) {
classnum=c_old=-1;
for (nwords=j=0,k=-1; l<nmeta_docs; l++) { /* only go thru for 2 different classes */
cdoc = bow_cdocs_di2doc (barrel->cdocs, l);
/* if this isn't what the last one was, */
if ((cdoc->class != classnum) && (c_old != cdoc->class) && (k==1)) {
break;
}
bow_heap_next_wv(heap, barrel, &dtmp, bow_cdoc_yes);
if ((cdoc->class != classnum) && (c_old != cdoc->class)) {
if (k==-1) {
/* do the stuff that needs done once for each model */
model_cache.bvect[h] = cdoc->normalizer;
nwords = dtmp->num_entries;
model_cache.indices[h] = (int *) malloc(sizeof(int)*nwords);
model_cache.weights[h] = (double *) malloc(sizeof(double)*nwords);
model_cache.yvect[h] = (int *) malloc(sizeof(int)*nwords);
} else { /* in an already initialized model, but we need to grow arrays */
nwords += dtmp->num_entries;
model_cache.indices[h] = (int *) realloc(model_cache.indices[h], sizeof(int)*(nwords));
model_cache.weights[h] = (double *) realloc(model_cache.weights[h], sizeof(double)*nwords);
model_cache.yvect[h] = (int *) realloc(model_cache.yvect[h], sizeof(int)*nwords);
}
k++;
c_old = classnum;
classnum = cdoc->class;
} else { /* already seen this class - need to grow some arrays */
nwords += dtmp->num_entries;
model_cache.indices[h] = (int *) realloc(model_cache.indices[h], sizeof(int)*(nwords));
model_cache.weights[h] = (double *) realloc(model_cache.weights[h], sizeof(double)*nwords);
model_cache.yvect[h] = (int *) realloc(model_cache.yvect[h], sizeof(int)*nwords);
}
for (i=0; j<nwords; j++,i++) {
model_cache.indices[h][j] = dtmp->entry[i].count - 1;
model_cache.weights[h][j] = dtmp->entry[i].weight;
model_cache.yvect[h][j] = ((k == 0) ? 1.0 : -1.0);
}
}
model_cache.sizes[h] = nwords;
}
/* if there are cached hyperplanes, lets grab them... */
if (svm_kernel_type == 0) {
for (i=0; i<nmodels; i++) {
bow_heap_next_wv(heap, barrel, &dtmp, bow_cdoc_yes);
model_cache.W[i] = (double *) malloc(total_words*sizeof(double));
for (h=j=0; j<dtmp->num_entries; h++) {
if (h == dtmp->entry[j].wi) {
model_cache.W[i][h] = dtmp->entry[j].weight;
j++;
} else {
model_cache.W[i][h] = 0.0;
}
}
for (; h<total_words; h++) {
model_cache.W[i][h] = 0.0;
}
}
#ifdef DEBUG
for (j=0; j<total_words; j++) {
tmp = model_cache.W[0][j] + model_cache.W[1][j];
assert(tmp >= -1*svm_epsilon_crit && tmp <= svm_epsilon_crit);
}
#endif
}
/* any kind of pairwise weights needs its own set of weights, since the domain
* for each model is different... Info-gain also needs it since items relevant
* & useful in one model may be of no use in another (since there are always only
* 2 classes...) */
if (svm_weight_style == WEIGHTS_PER_MODEL) {
for (h=0; h<nmodels; h++) {
bow_heap_next_wv(heap, barrel, &dtmp, bow_cdoc_yes);
model_cache.word_weights.sub_model[h] = (float *) malloc(sizeof(float)*total_words);
for (i=j=0; i<total_words; i++) {
if ((j < dtmp->num_entries) && (dtmp->entry[j].wi == i)) {
model_cache.word_weights.sub_model[h][i] = dtmp->entry[j].weight;
j++;
} else {
model_cache.word_weights.sub_model[h][i] = 0.0;
}
}
}
} else if (svm_weight_style == WEIGHTS_PER_BARREL) {
bow_dv *dv;
for (h=0; h<total_words; h++) {
dv = bow_wi2dvf_dv (barrel->wi2dvf, h);
if (dv) {
model_cache.word_weights.barrel[h] = dv->idf;
} else {
model_cache.word_weights.barrel[h] = 0.0;
}
}
}
/* the rest of the documents are just the training documents - keep
* grabbing them until they're gone */
for (h=0; heap->length; h++) {
bow_heap_next_wv(heap, barrel, &dtmp, bow_cdoc_yes);
model_cache.docs[h] = bow_wv_new(dtmp->num_entries);
for (j=0; j<dtmp->num_entries; j++) {
model_cache.docs[h]->entry[j].wi = dtmp->entry[j].wi;
model_cache.docs[h]->entry[j].count = dtmp->entry[j].count;
}
/*
if (svm_kernel_type == FISHER) {
for (j=0; j<model_cache.docs[h]->num_entries; j++) {
model_cache.docs[h]->entry[j].weight = (float) model_cache.docs[h]->entry[j].count;
}
model_cache.docs[h]->normalizer = 1.0;
continue;
}*/
tf_transform(model_cache.docs[h]);
/* this means that the weights will change with every model &
* therefore we need to keep track of what they were initially (after the tf_transform) */
if (svm_weight_style == WEIGHTS_PER_MODEL && tf_transform_type) {
model_cache.oweights[h] = (float *) malloc(sizeof(float)*dtmp->num_entries);
for (j=0; j<model_cache.docs[h]->num_entries; j++) {
model_cache.oweights[h][j] = model_cache.docs[h]->entry[j].weight;
}
} else {
/* otherwise, the weights should be set now... */
if (svm_weight_style == NO_WEIGHTS) {
bow_wv_normalize_weights_by_summing(model_cache.docs[h]);
for (j=0; j<model_cache.docs[h]->num_entries; j++) {
model_cache.docs[h]->entry[j].weight *= model_cache.docs[h]->normalizer;
}
} else {
for (j=0; j<model_cache.docs[h]->num_entries; j++) {
model_cache.docs[h]->entry[j].weight *=
model_cache.word_weights.barrel[model_cache.docs[h]->entry[j].wi];
}
bow_wv_normalize_weights_by_summing(model_cache.docs[h]);
for (j=0; j<model_cache.docs[h]->num_entries; j++) {
model_cache.docs[h]->entry[j].weight *= model_cache.docs[h]->normalizer;
}
}
}
/* the oweights (original weights) in the svm_wv now has the proper,
* tf_transformed & normalized value. */
}
model_cache.barrel = barrel;
model_cache.ndocs = h;
model_cache.nmodels = nmodels;
}
int svm_score(bow_barrel *barrel, bow_wv *query_wv, bow_score *bscores,
int bscores_len, int loo_class) {
int ci;
int max_nsv;
double *model_vals;
bow_score *myscores;
float *base_qwv_weights;
int nclasses;
int nmodels;
int ntied;
int num_scores;
int set_weights;
bow_wv **sub_docs;
int voting_scheme;
int i, ii, j, k;
/* This should be initialized in case BSCORES_LEN is larger than the number
* of classes in the barrel */
for (ci=0; ci < bscores_len; ci++) {
bscores[ci].weight = 0.0;
bscores[ci].di = 0;
bscores[ci].name = "default";
}
base_qwv_weights = NULL;
max_nsv = BARREL_GET_MAX_NSV(barrel);
nclasses = BARREL_GET_NCLASSES(barrel);
if (max_nsv < 0) {
max_nsv *= -1;
nmodels = nclasses;
voting_scheme = AGAINST_ALL;
} else {
nmodels = nclasses*(nclasses-1)/2;
voting_scheme = PAIRWISE;
}
if (model_cache.barrel != barrel) {
setup_docs(barrel, nclasses, nmodels);
}
set_weights = svm_weight_style;
tf_transform(query_wv);
if (svm_weight_style == WEIGHTS_PER_BARREL) {
svm_set_wv_weights(query_wv, NULL, model_cache.word_weights.barrel);
}
if ((svm_weight_style == NO_WEIGHTS) || (svm_weight_style == WEIGHTS_PER_BARREL)) {
bow_wv_normalize_weights_by_summing(query_wv);
for (i=0; i<query_wv->num_entries; i++) {
query_wv->entry[i].weight *= query_wv->normalizer;
}
set_weights = 0;
} else if (tf_transform_type) {
base_qwv_weights = (float *) malloc(sizeof(float)*query_wv->num_entries);
for (i=0; i<query_wv->num_entries; i++) {
base_qwv_weights[i] = query_wv->entry[i].weight;
}
}
model_vals = (double *) alloca(sizeof(double)*nmodels);
sub_docs = (bow_wv **) malloc(sizeof(bow_wv *)*model_cache.ndocs);
/* classify all of our models */
if (svm_kernel_type == 0) {
for (i=0; i<nmodels; i++) {
if (set_weights) {
if (tf_transform_type) {
svm_set_wv_weights(query_wv, base_qwv_weights, model_cache.word_weights.sub_model[i]);
} else {
svm_set_wv_weights(query_wv, NULL, model_cache.word_weights.sub_model[i]);
}
}
if (svml_test_file) {
for (j=0; j<query_wv->num_entries; j++) {
fprintf (svml_test_file,"%d:%f ",1+query_wv->entry[j].wi, query_wv->entry[j].weight);
}
fprintf(svml_test_file,"\n");
model_vals[i] = 1;
} else {
model_vals[i] =
evaluate_model_hyperplane(model_cache.W[i], model_cache.bvect[i], query_wv);
}
}
} else {
for (i=0; i<nmodels; i++) {
make_sub_model(i, set_weights, &sub_docs);
if (svm_weight_style == WEIGHTS_PER_MODEL) {
svm_set_wv_weights(query_wv, base_qwv_weights, model_cache.word_weights.sub_model[i]);
}
if (svml_test_file) {
for (j=0; j<query_wv->num_entries; j++) {
fprintf (svml_test_file,"%d:%f ",1+query_wv->entry[j].wi, query_wv->entry[j].weight);
}
fprintf(svml_test_file,"\n");
model_vals[i] = 1;
} else {
model_vals[i] =
evaluate_model(sub_docs, model_cache.weights[i], model_cache.yvect[i],
model_cache.bvect[i], query_wv, model_cache.sizes[i]);
}
}
}
if (base_qwv_weights)
free(base_qwv_weights);
free(sub_docs);
if (!quick_scoring) {
clear_model_cache();
}
/* now I have the outputs for each of the models, if its a linear model,
* i'm done. If its pairwise, then I need to put together votes */
if (voting_scheme == PAIRWISE && nclasses > 2) {
myscores = (bow_score *) alloca(sizeof(bow_score)*nclasses);
for (i=0; i<nclasses; i++) {
myscores[i].di = i;
myscores[i].name = "default";
myscores[i].weight = 0.0;
}
for (i=ii=0; i<nclasses-1; i++, ii+=j) {
for (j=0; j<nclasses-i-1; j++) {
if (model_vals[j+ii] > 0) {
myscores[i].weight += 1.0;
} else {
myscores[j+i+1].weight += 1.0;
}
}
}
/* check for ties */
qsort(myscores, nclasses, sizeof(bow_score), s_cmp);
for (ntied=i=1; i<nclasses; i++) {
if (myscores[i].weight == myscores[0].weight) {
ntied++;
} else {
break;
}
}
/* break ties */
if (ntied > 1) {
struct di *div;
div = (struct di*) alloca(sizeof(struct di)*ntied);
for (i=0; i<ntied; i++) {
div[i].d = 0.0;
div[i].i = myscores[i].di;
}
fprintf(stderr,"Warning, %d way tie.\n",ntied);
fflush(stdout);
for (i=ii=k=0; k<ntied-1; i++, ii+=(nclasses-i)) {
if (i == myscores[k].di) {
k++;
for (j=k; j<ntied; j++) {
if (model_vals[ii+myscores[j].di-i-1] > 0) {
myscores[k-1].weight += 0.2;
div[k-1].d += model_vals[ii+myscores[j].di-i-1];
} else {
myscores[j].weight += 0.2;
div[j].d += -model_vals[ii+myscores[j].di-i-1];
}
}
}
}
qsort(myscores, ntied, sizeof(bow_score), s_cmp);
k = ntied;
for (ntied=i=1; i<k; i++) {
if (myscores[i].weight == myscores[0].weight) {
ntied++;
} else {
break;
}
}
if (ntied > 1) {
fprintf(stderr,"Warning, taking largest pairwise value to break %d-way tie\n", ntied);
fflush(stdout);
for (i=0; i<ntied; i++) {
for (j=0; 1; j++) {
if (myscores[i].di == div[j].i) {
myscores[i].weight += div[j].d/1000;
break;
}
}
}
qsort(myscores, ntied, sizeof(bow_score), s_cmp);
}
}
memcpy(bscores, myscores, nclasses*sizeof(bow_score));
return nclasses;
} else {
if (nclasses == 2) {
model_vals[1] = -1*model_vals[0];
}
/* Put SCORES into BSCORES in sorted order */
/* Each round, find the best remainaing score and put it into bscores */
for (num_scores=ci=0; ci < nclasses; ci++) {
if (num_scores < bscores_len
|| bscores[num_scores-1].weight < model_vals[ci]) {
int dsi;
/* We are going to put this score and class index into SCORES
* because either 1) there is an empty space in SCORES, or 2)
* SCORES[CI] is larger than the smallest score currently there */
if (num_scores < bscores_len)
num_scores++;
dsi = num_scores - 1;
/* Shift down all the entries that are smaller than SCORES[CI] */
for (; dsi > 0 && bscores[dsi-1].weight < model_vals[ci]; dsi--) {
bscores[dsi].weight = bscores[dsi-1].weight;
bscores[dsi].name = bscores[dsi-1].name;
bscores[dsi].di = bscores[dsi-1].di;
}
bscores[dsi].weight = model_vals[ci];
bscores[dsi].di = ci;
bscores[dsi].name = "default";
}
}
return num_scores;
}
}
/* since the class_probs field of the cdocs is used & is not a ptr,
* the value needs to be nullified before the std free fn is invoked */
void svm_barrel_free(bow_barrel *barrel) {
BARREL_GET_MAX_NSV(barrel) = 0;
BARREL_GET_NMETA_DOCS(barrel) = 0;
return (bow_barrel_free(barrel));
}
rainbow_method rainbow_method_svm = {
"svm",
NULL,
NULL,
NULL,
svm_vpc_merge, /* note: this is written esp. for svms, hence
* the reason the first 3 fns are undefined */
NULL,
svm_score,
bow_wv_set_weights_to_count, /* any similarity metric will work... */
NULL,
svm_barrel_free,
NULL
};
bow_method bow_method_svm = { "svm" };
void _register_method_svm () __attribute__ ((constructor));
void _register_method_svm () {
bow_method_register_with_name ((bow_method*)&rainbow_method_svm, "svm",
sizeof(rainbow_method), &svm_argp_child);
bow_argp_add_child (&svm_argp_child);
}
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