1 files changed, 93 insertions, 0 deletions
diff --git a/search/ranking_info.cpp b/search/ranking_info.cpp
new file mode 100644
index 0000000000..163208b652
--- /dev/null
+++ b/search/ranking_info.cpp
@@ -0,0 +1,93 @@
+#include "search/ranking_info.hpp"
+
+#include "std/cmath.hpp"
+#include "std/iomanip.hpp"
+#include "std/limits.hpp"
+#include "std/sstream.hpp"
+
+namespace search
+{
+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 search