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/*
Copyright (c) 2008 - Chris Buckley.
Permission is granted for use and modification of this file for
research, non-commercial purposes.
*/
#include "common.h"
#include "sysfunc.h"
#include "trec_eval.h"
#include "functions.h"
#include "trec_format.h"
static int
te_calc_Rprec_mult (const EPI *epi, const REL_INFO *rel_info,
const RESULTS *results, const TREC_MEAS *tm,
TREC_EVAL *eval);
static double Rprec_cutoff_array[] = {
0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0};
static PARAMS default_Rprec_cutoffs = {
NULL, sizeof (Rprec_cutoff_array) / sizeof (Rprec_cutoff_array[0]),
&Rprec_cutoff_array[0]};
/* See trec_eval.h for definition of TREC_MEAS */
TREC_MEAS te_meas_Rprec_mult =
{"Rprec_mult",
" Precision measured at multiples of R (num_rel).\n\
This is an attempt to measure topics at the same multiple milestones\n\
in a retrieval (see explanation of R-prec), in order to determine\n\
whether methods are precision oriented or recall oriented. If method A\n\
dominates method B at the low multiples but performs less well at the\n\
high multiples then it is precision oriented (compared to B).\n\
Default param: -m Rprec_mult.0.2,0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8,2.0 ...\n",
te_init_meas_a_float_cut_float,
te_calc_Rprec_mult,
te_acc_meas_a_cut,
te_calc_avg_meas_a_cut,
te_print_single_meas_a_cut,
te_print_final_meas_a_cut,
(void *) &default_Rprec_cutoffs, -1};
static int
te_calc_Rprec_mult (const EPI *epi, const REL_INFO *rel_info,
const RESULTS *results, const TREC_MEAS *tm,
TREC_EVAL *eval)
{
double *cutoff_percents = (double *) tm->meas_params->param_values;
long *cutoffs; /* cutoffs expressed in num ret docs instead of percents*/
long current_cut; /* current index into cutoffs */
RES_RELS rr;
long rel_so_far;
long i;
double precis, int_precis;
if (UNDEF == te_form_res_rels (epi, rel_info, results, &rr))
return (UNDEF);
/* translate percentage of rels as given in the measure params, to
an actual cutoff number of docs. */
if (NULL == (cutoffs = Malloc (tm->meas_params->num_params, long)))
return (UNDEF);
for (i = 0; i < tm->meas_params->num_params; i++)
cutoffs[i] = (long)(cutoff_percents[i] * rr.num_rel +0.9);
precis = (double) rr.num_rel_ret / (double) rr.num_ret;
int_precis = precis;
current_cut = tm->meas_params->num_params - 1;
while (current_cut >= 0 && cutoffs[current_cut] > rr.num_ret) {
eval->values[tm->eval_index + current_cut].value =
(double) rr.num_rel_ret / (double) cutoffs[current_cut];
current_cut--;
}
/* Loop over all retrieved docs in reverse order. */
rel_so_far = rr.num_rel_ret;
for (i = rr.num_ret; i > 0 && rel_so_far > 0; i--) {
precis = (double) rel_so_far / (double) i;
if (int_precis < precis)
int_precis = precis;
while (current_cut >= 0 && i == cutoffs[current_cut]) {
eval->values[tm->eval_index + current_cut].value = precis;
current_cut--;
}
if (rr.results_rel_list[i-1] >= epi->relevance_level) {
rel_so_far--;
}
}
(void) Free (cutoffs);
return (1);
}
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