[search] Changed the name scoring scheme.

search/intermediate_result.cpp

@@ -64,7 +64,8 @@ void ProcessMetadata(FeatureType const & ft, Result::Metadata & meta)
     meta.m_hotelRating = rating;
 
     int pricing;
-    strings::to_int(src.Get(feature::Metadata::FMD_PRICE_RATE), pricing);
+    if (!strings::to_int(src.Get(feature::Metadata::FMD_PRICE_RATE), pricing))
+      pricing = 0;
     string pricingStr;
     CHECK_GREATER_OR_EQUAL(pricing, 0, ("Pricing must be positive!"));
     for (auto i = 0; i < pricing; i++)
@@ -292,11 +293,12 @@ bool PreResult2::IsStreet() const
 string PreResult2::DebugPrint() const
 {
   stringstream ss;
-  ss << "{ IntermediateResult: " <<
-        "Name: " << m_str <<
-        "; Type: " << GetBestType() <<
-        "; Rank: " << static_cast<int>(m_info.m_rank) <<
-        "; Distance: " << m_distance << " }";
+  ss << "IntermediateResult [ "
+     << "Name: " << m_str
+     << "; Type: " << GetBestType()
+     << "; Ranking info: " << search::DebugPrint(m_info)
+     << "; Linear model rank: " << m_info.GetLinearModelRank()
+     << " ]";
   return ss.str();
 }
 

search/locality_scorer.cpp

@@ -19,7 +19,7 @@ namespace
 {
 bool IsAlmostFullMatch(NameScore score)
 {
-  return score == NAME_SCORE_FULL_MATCH_PREFIX || score == NAME_SCORE_FULL_MATCH;
+  return score == NAME_SCORE_PREFIX || score == NAME_SCORE_FULL_MATCH;
 }
 }  // namespace
 

search/ranking_info.cpp

@@ -11,19 +11,26 @@ namespace
 {
 // See search/search_quality/scoring_model.py for details.  In short,
 // these coeffs correspond to coeffs in a linear model.
-double const kDistanceToPivot = -1.0000000;
-double const kRank = 0.7165246;
-double const kFalseCats = -0.3833900;
+double const kDistanceToPivot = -0.37897824370302247;
+double const kRank = 1.0;
+double const kFalseCats = -0.05775625793967508;
+
 double const kNameScore[NameScore::NAME_SCORE_COUNT] = {
-    -0.1069757 /* Zero */, -0.0250079 /* Substring Prefix */, 0.0447104 /* Substring */,
-    0.0872732 /* Full Match Prefix */, 0.0872732 /* Full Match */
+     -0.11436302557264734 /* Zero */
+    , 0.014295634567960331 /* Substring */
+    , 0.046219090910780115 /* Prefix */
+    , 0.05384830009390816 /* Full Match */
 };
 
 double const kSearchType[SearchModel::SEARCH_TYPE_COUNT] = {
-    -0.3884116 /* POI */,     -0.3884116 /* Building */,
-    -0.3214653 /* Street */,  -0.3357469 /* Unclassified */,
-    -0.4341714 /* Village */, 0.2721947 /* City */,
-    0.4708555 /* State */,    0.7367450 /* Country */
+      -0.09164609318265761 /* POI */
+    , -0.09164609318265761 /* Building */
+    , -0.0805969548653964 /* Street */
+    , -0.030493728520630793 /* Unclassified */
+    , -0.19242203325862917 /* Village */
+    , -0.10945592241057521 /* City */
+    , 0.19250143015921584 /* State */
+    , 0.31211330207867427 /* Country */
 };
 
 double TransformDistance(double distance)

search/ranking_utils.cpp

@@ -65,9 +65,8 @@ string DebugPrint(NameScore score)
   switch (score)
   {
   case NAME_SCORE_ZERO: return "Zero";
-  case NAME_SCORE_SUBSTRING_PREFIX: return "Substring Prefix";
   case NAME_SCORE_SUBSTRING: return "Substring";
-  case NAME_SCORE_FULL_MATCH_PREFIX: return "Full Match Prefix";
+  case NAME_SCORE_PREFIX: return "Prefix";
   case NAME_SCORE_FULL_MATCH: return "Full Match";
   case NAME_SCORE_COUNT: return "Count";
   }

search/ranking_utils.hpp

@@ -30,10 +30,9 @@ bool PrefixMatch(QueryParams::Token const & token, strings::UniString const & te
 enum NameScore
 {
   NAME_SCORE_ZERO = 0,
-  NAME_SCORE_SUBSTRING_PREFIX = 1,
-  NAME_SCORE_SUBSTRING = 2,
-  NAME_SCORE_FULL_MATCH_PREFIX = 3,
-  NAME_SCORE_FULL_MATCH = 4,
+  NAME_SCORE_SUBSTRING = 1,
+  NAME_SCORE_PREFIX = 2,
+  NAME_SCORE_FULL_MATCH = 3,
 
   NAME_SCORE_COUNT
 };
@@ -44,8 +43,8 @@ bool IsStopWord(strings::UniString const & s);
 // Normalizes, simplifies and splits string, removes stop-words.
 void PrepareStringForMatching(std::string const & name, std::vector<strings::UniString> & tokens);
 
-template <typename TSlice>
-NameScore GetNameScore(std::string const & name, TSlice const & slice)
+template <typename Slice>
+NameScore GetNameScore(std::string const & name, Slice const & slice)
 {
   if (slice.Empty())
     return NAME_SCORE_ZERO;
@@ -55,8 +54,8 @@ NameScore GetNameScore(std::string const & name, TSlice const & slice)
   return GetNameScore(tokens, slice);
 }
 
-template <typename TSlice>
-NameScore GetNameScore(std::vector<strings::UniString> const & tokens, TSlice const & slice)
+template <typename Slice>
+NameScore GetNameScore(std::vector<strings::UniString> const & tokens, Slice const & slice)
 {
   if (slice.Empty())
     return NAME_SCORE_ZERO;
@@ -75,18 +74,19 @@ NameScore GetNameScore(std::vector<strings::UniString> const & tokens, TSlice co
     if (!match)
       continue;
 
-    if (impl::FullMatch(slice.Get(m - 1), tokens[offset + m - 1]))
-    {
-      if (m == n)
-        return NAME_SCORE_FULL_MATCH;
-      score = max(score, NAME_SCORE_SUBSTRING);
-    }
-    if (lastTokenIsPrefix && impl::PrefixMatch(slice.Get(m - 1), tokens[offset + m - 1]))
-    {
-      if (m == n)
-        return NAME_SCORE_FULL_MATCH_PREFIX;
-      score = max(score, NAME_SCORE_SUBSTRING_PREFIX);
-    }
+    bool const fullMatch = impl::FullMatch(slice.Get(m - 1), tokens[offset + m - 1]);
+    bool const prefixMatch =
+        lastTokenIsPrefix && impl::PrefixMatch(slice.Get(m - 1), tokens[offset + m - 1]);
+    if (!fullMatch && !prefixMatch)
+      continue;
+
+    if (m == n && fullMatch)
+      return NAME_SCORE_FULL_MATCH;
+
+    if (offset == 0)
+      score = max(score, NAME_SCORE_PREFIX);
+
+    score = max(score, NAME_SCORE_SUBSTRING);
   }
   return score;
 }

search/search_quality/scoring_model.py

@@ -16,7 +16,7 @@ import sys
 MAX_DISTANCE_METERS = 2e6
 MAX_RANK = 255
 RELEVANCES = {'Irrelevant': 0, 'Relevant': 1, 'Vital': 3}
-NAME_SCORES = ['Zero', 'Substring Prefix', 'Substring', 'Full Match Prefix', 'Full Match']
+NAME_SCORES = ['Zero', 'Substring', 'Prefix', 'Full Match']
 SEARCH_TYPES = ['POI', 'Building', 'Street', 'Unclassified', 'Village', 'City', 'State', 'Country']
 
 FEATURES = ['DistanceToPivot', 'Rank', 'FalseCats'] + NAME_SCORES + SEARCH_TYPES
@@ -25,8 +25,6 @@ FEATURES = ['DistanceToPivot', 'Rank', 'FalseCats'] + NAME_SCORES + SEARCH_TYPES
 def transform_name_score(value, categories_match):
     if categories_match == 1:
         return 'Zero'
-    elif value == 'Full Match Prefix':
-        return 'Full Match'
     else:
         return value
 
@@ -40,10 +38,6 @@ def normalize_data(data):
 
     cats = data['PureCats'].combine(data['FalseCats'], max)
 
-    # Full prefix match is unified with a full match as these features
-    # are collinear. But we need both of them as they're also used in
-    # locality sorting.
-    #
     # TODO (@y, @m): do forward/backward/subset selection of features
     # instead of this merging.  It would be great to conduct PCA on
     # the features too.
@@ -277,7 +271,6 @@ def main(args):
 
     # Following code restores coeffs for merged features.
     ws[FEATURES.index('Building')] = ws[FEATURES.index('POI')]
-    ws[FEATURES.index('Full Match Prefix')] = ws[FEATURES.index('Full Match')]
 
     ndcgs = compute_ndcgs_for_ws(data, ws)
 

search/search_tests/ranking_tests.cpp

@@ -45,12 +45,14 @@ UNIT_TEST(NameTest_Smoke)
   TEST_EQUAL(GetScore("New York", "Central Park, New York, US", TokenRange(2, 4)),
              NAME_SCORE_FULL_MATCH, ());
   TEST_EQUAL(GetScore("New York", "York", TokenRange(0, 1)), NAME_SCORE_SUBSTRING, ());
-  TEST_EQUAL(GetScore("Moscow", "Red Square Mosc", TokenRange(2, 3)), NAME_SCORE_FULL_MATCH_PREFIX,
-             ());
+  TEST_EQUAL(GetScore("Moscow", "Red Square Mosc", TokenRange(2, 3)), NAME_SCORE_PREFIX, ());
   TEST_EQUAL(GetScore("Moscow", "Red Square Moscow", TokenRange(2, 3)), NAME_SCORE_FULL_MATCH, ());
-  TEST_EQUAL(GetScore("San Francisco", "Fran", TokenRange(0, 1)), NAME_SCORE_SUBSTRING_PREFIX, ());
+  TEST_EQUAL(GetScore("San Francisco", "Fran", TokenRange(0, 1)), NAME_SCORE_SUBSTRING, ());
   TEST_EQUAL(GetScore("San Francisco", "Fran ", TokenRange(0, 1)), NAME_SCORE_ZERO, ());
-  TEST_EQUAL(GetScore("Лермонтовъ", "Лермонтов", TokenRange(0, 1)), NAME_SCORE_FULL_MATCH_PREFIX,
-             ());
+  TEST_EQUAL(GetScore("San Francisco", "Sa", TokenRange(0, 1)), NAME_SCORE_PREFIX, ());
+  TEST_EQUAL(GetScore("San Francisco", "San ", TokenRange(0, 1)), NAME_SCORE_PREFIX, ());
+  TEST_EQUAL(GetScore("Лермонтовъ", "Лермонтов", TokenRange(0, 1)), NAME_SCORE_PREFIX, ());
+  TEST_EQUAL(GetScore("фото на документы", "фото", TokenRange(0, 1)), NAME_SCORE_PREFIX, ());
+  TEST_EQUAL(GetScore("фотоателье", "фото", TokenRange(0, 1)), NAME_SCORE_PREFIX, ());
 }
 }  // namespace