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#include "search/v2/ranking_info.hpp"
#include "std/cmath.hpp"
#include "std/iomanip.hpp"
#include "std/limits.hpp"
#include "std/sstream.hpp"
namespace search
{
namespace v2
{
namespace
{
// See search/search_quality/scoring_model.py for details. In short,
// these coeffs correspond to coeffs in a linear model.
double const kDistanceToPivot = -0.7579760;
double const kRank = 0.9185310;
double const kFalseCats = -0.7996119;
double const kNameScore[NameScore::NAME_SCORE_COUNT] = {
-1.0000000 /* Zero */,
0.3585180 /* Substring Prefix */,
0.3198080 /* Substring */,
0.3216740 /* Full Match Prefix */,
0.3216740 /* Full Match */
};
double const kSearchType[SearchModel::SEARCH_TYPE_COUNT] = {
-0.1419479 /* POI */,
-0.1419479 /* Building */,
-0.1462099 /* Street */,
-0.1509122 /* Unclassified */,
-0.2591458 /* Village */,
-0.0451342 /* City */,
0.2515398 /* State */,
0.4918102 /* Country */
};
double TransformDistance(double distance)
{
return min(distance, RankingInfo::kMaxDistMeters) / RankingInfo::kMaxDistMeters;
}
} // namespace
// static
double const RankingInfo::kMaxDistMeters = 2e6;
// static
void RankingInfo::PrintCSVHeader(ostream & os)
{
os << "DistanceToPivot"
<< ",Rank"
<< ",NameScore"
<< ",SearchType"
<< ",PureCats"
<< ",FalseCats";
}
string DebugPrint(RankingInfo const & info)
{
ostringstream os;
os << "RankingInfo [";
os << "m_distanceToPivot:" << info.m_distanceToPivot << ",";
os << "m_rank:" << static_cast<int>(info.m_rank) << ",";
os << "m_nameScore:" << DebugPrint(info.m_nameScore) << ",";
os << "m_searchType:" << DebugPrint(info.m_searchType) << ",";
os << "m_pureCats:" << info.m_pureCats << ",";
os << "m_falseCats:" << info.m_falseCats;
os << "]";
return os.str();
}
void RankingInfo::ToCSV(ostream & os) const
{
os << fixed;
os << m_distanceToPivot << "," << static_cast<int>(m_rank) << "," << DebugPrint(m_nameScore)
<< "," << DebugPrint(m_searchType) << "," << m_pureCats << "," << m_falseCats;
}
double RankingInfo::GetLinearModelRank() const
{
// NOTE: this code must be consistent with scoring_model.py. Keep
// this in mind when you're going to change scoring_model.py or this
// code. We're working on automatic rank calculation code generator
// integrated in the build system.
double const distanceToPivot = TransformDistance(m_distanceToPivot);
double const rank = static_cast<double>(m_rank) / numeric_limits<uint8_t>::max();
auto nameScore = m_nameScore;
if (m_pureCats || m_falseCats)
{
// If the feature was matched only by categorial tokens, it's
// better for ranking to set name score to zero. For example,
// when we're looking for a "cafe", cafes "Cafe Pushkin" and
// "Lermontov" both match to the request, but must be ranked in
// accordance to their distances to the user position or viewport,
// in spite of "Cafe Pushkin" has a non-zero name rank.
nameScore = NAME_SCORE_ZERO;
}
return kDistanceToPivot * distanceToPivot + kRank * rank + kNameScore[nameScore] +
kSearchType[m_searchType] + m_falseCats * kFalseCats;
}
} // namespace v2
} // namespace search
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