dlhweight.cc

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/* Copyright (C) 2013, 2014 Aarsh Shah
 * Copyright (C) 2016,2017,2019 Olly Betts
 *
 * This program is free software; you can redistribute it and/or
 * modify it under the terms of the GNU General Public License as
 * published by the Free Software Foundation; either version 2 of the
 * License, or (at your option) any later version.
 *
 * This program 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 General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, see
 * <https://www.gnu.org/licenses/>.
 */
 
#include <config.h>
 
#include "xapian/weight.h"
 
#include "xapian/error.h"
 
#include <algorithm>
#include <cmath>
 
using namespace std;
 
namespace Xapian {
 
DLHWeight *
DLHWeight::clone() const
{
    return new DLHWeight();
}
 
void
DLHWeight::init(double factor)
{
    if (factor == 0.0) {
        // This object is for the term-independent contribution, and that's
        // always zero for this scheme.
        return;
    }
 
    double wdf_upper = get_wdf_upper_bound();
    if (wdf_upper == 0) {
        upper_bound = 0.0;
        return;
    }
 
    const double wdf_lower = 1.0;
    double len_upper = get_doclength_upper_bound();
    double len_lower = get_doclength_lower_bound();
 
    double F = get_collection_freq();
 
    // Calculate constant values to be used in get_sumpart().
    log_constant = get_total_length() / F;
    wqf_product_factor = get_wqf() * factor;
 
    // Calculate values for the upper bound.
 
    // w <= l, so if the allowed ranges overlap, max w/l is 1.0.
    double max_wdf_over_l = wdf_upper < len_lower ? wdf_upper / len_lower : 1.0;
 
    // First term A: w/(w+.5)*log2(w/l*L) where L=total_len/coll_freq
    // Assume log >= 0:
    //   w/(w+.5) = 1-1/(2w+1) and w >= 1 so max A at w=w_max
    //   log2(w/l*L) maximised when w/l maximised
    //   so max A at w=w_max, l=max(l_min, w_max)
    // If log < 0 => then A <= 0, so max A <= 0 and we want to minimise the
    // factor outside the log.
    double logged_expr = max_wdf_over_l * log_constant;
    double w_for_A = logged_expr > 1.0 ? wdf_upper : wdf_lower;
    double A = w_for_A / (w_for_A + 0.5) * log2(logged_expr);
 
    // Second term B:
    //
    // (l-w)*log2(1-w/l)
    //
    // The log is negative, and w <= l so B <= 0 and its maximum is the value
    // as close to zero as possible.  So smaller (l-w) is better and smaller
    // w/l is better.
    //
    // This function is ill defined at w=l, but -> 0 as w -> l.
    //
    // If w=l is valid (i.e. len_lower > wdf_upper) then B = 0.
    double B = 0;
    if (len_lower > wdf_upper) {
        // If not, then minimising l-w gives us a candidate (i.e. w=wdf_upper
        // and l=len_lower).
        //
        // The function is also 0 at w = 0 (there must be a local mimina at
        // some value of w between 0 and l), so the other candidate is at
        // w=wdf_lower.
        //
        // We need to find the optimum value of l in this case, so
        // differentiate the formula by l:
        //
        // d/dl: log2(1-w/l) + (l-w)*(1-w/l)/(l*log(2))
        //     = (log(1-w/l) + (1-w/l)²)/log(2)
        //     = (log(x) + x²)/log(2)     [x=1-w/l]
        //
        // which is 0 at approx x=0.65291864
        //
        // x=1-w/l <=> l*(1-x)=w <=> l=w/(1-x) <=> l ~= 0.34708136*w
        //
        // but l >= w so we can't attain that (and the log isn't valid there).
        //
        // Gradient at (without loss of generality) l=2*w is:
        //       (log(0.5) + 0.25)/log(2)
        // which is < 0 so want to minimise l, i.e. l=len_lower, so the other
        // candidate is w=wdf_lower and l=len_lower.
        //
        // So evaluate both candidates and pick the larger:
        double B1 = (len_lower - wdf_lower) * log2(1.0 - wdf_lower / len_lower);
        double B2 = (len_lower - wdf_upper) * log2(1.0 - wdf_upper / len_lower);
        B = max(B1, B2);
    }
 
    /* An upper bound of the term used in the third log can be obtained by:
     *
     * 0.5 * log2(2.0 * M_PI * wdf * (1 - wdf / len))
     *
     * An upper bound on wdf * (1 - wdf / len) (and hence on the log, since
     * log is a monotonically increasing function on positive numbers) can
     * be obtained by plugging in the upper bound of the length and
     * differentiating the term w.r.t wdf which gives the value of wdf at which
     * the function attains maximum value - at wdf = len_upper / 2 (or if the
     * wdf can't be that large, at wdf = wdf_upper): */
    double wdf_var = min(wdf_upper, len_upper / 2.0);
    double max_product = wdf_var * (1.0 - wdf_var / len_upper);
#if 0
    /* An upper bound can also be obtained by taking the minimum and maximum
     * wdf value in the formula as shown (which isn't useful now as it's always
     * >= the bound above, but could be useful if we tracked bounds on wdf/len):
     */
    double min_wdf_to_len = wdf_lower / len_upper;
    double max_product_2 = wdf_upper * (1.0 - min_wdf_to_len);
    /* Take the minimum of the two upper bounds. */
    max_product = min(max_product, max_product_2);
#endif
    double C = 0.5 * log2(2.0 * M_PI * max_product) / (wdf_lower + 0.5);
    upper_bound = A + B + C;
 
    if (rare(upper_bound < 0.0))
        upper_bound = 0.0;
    else
        upper_bound *= wqf_product_factor;
}
 
string
DLHWeight::name() const
{
    return "dlh";
}
 
string
DLHWeight::serialise() const
{
    return string();
}
 
DLHWeight *
DLHWeight::unserialise(const string& s) const
{
    if (rare(!s.empty()))
        throw Xapian::SerialisationError("Extra data in DLHWeight::unserialise()");
    return new DLHWeight();
}
 
double
DLHWeight::get_sumpart(Xapian::termcount wdf, Xapian::termcount len,
                       Xapian::termcount, Xapian::termcount) const
{
    if (wdf == 0 || wdf == len) return 0.0;
 
    double wdf_to_len = double(wdf) / len;
    double one_minus_wdf_to_len = 1.0 - wdf_to_len;
 
    double wt = wdf * log2(wdf_to_len * log_constant) +
                (len - wdf) * log2(one_minus_wdf_to_len) +
                0.5 * log2(2.0 * M_PI * wdf * one_minus_wdf_to_len);
    if (rare(wt <= 0.0)) return 0.0;
 
    return wqf_product_factor * wt / (wdf + 0.5);
}
 
double
DLHWeight::get_maxpart() const
{
    return upper_bound;
}
 
DLHWeight *
DLHWeight::create_from_parameters(const char * p) const
{
    if (*p != '\0')
        throw InvalidArgumentError("No parameters are required for DLHWeight");
    return new Xapian::DLHWeight();
}
 
}