Adding knobs on HQP provider.

chrome/browser/history/scored_history_match.cc

Index: chrome/browser/history/scored_history_match.cc
diff --git a/chrome/browser/history/scored_history_match.cc b/chrome/browser/history/scored_history_match.cc
index 1f1184c859273b31a94a901238661ce72bd018d8..0032d49242ceda2b08ac18b597378b2c41cd0d44 100644
--- a/chrome/browser/history/scored_history_match.cc
+++ b/chrome/browser/history/scored_history_match.cc
@@ -14,6 +14,8 @@
 
 #include "base/logging.h"
 #include "base/metrics/histogram.h"
+#include "base/strings/string_number_conversions.h"
+#include "base/strings/string_split.h"
 #include "base/strings/string_util.h"
 #include "base/strings/utf_string_conversions.h"
 #include "chrome/browser/autocomplete/history_url_provider.h"
@@ -61,6 +63,9 @@ ScoredHistoryMatch::ScoredHistoryMatch(
       can_inline_(false) {
   Init();
 
+  // Initialize the HQP scoring params.
+  InitializeHQPExperimentalParams();
+
   GURL gurl = row.url();
   if (!gurl.is_valid())
     return;
@@ -154,7 +159,8 @@ ScoredHistoryMatch::ScoredHistoryMatch(
       terms.size(), url, terms_to_word_starts_offsets, word_starts);
   const float frequency_score = GetFrequency(
       now, (history_client && history_client->IsBookmarked(gurl)), visits);
-  raw_score_ = GetFinalRelevancyScore(topicality_score, frequency_score);
+  raw_score_ = GetFinalRelevancyScore(topicality_score, frequency_score,
+                                      hqp_relevance_buckets_);
   raw_score_ =
       (raw_score_ <= kint32max) ? static_cast<int>(raw_score_) : kint32max;
 
@@ -438,7 +444,15 @@ float ScoredHistoryMatch::GetTopicalityScore(
   // TODO(mpearson): If there are multiple terms, consider taking the
   // geometric mean of per-term scores rather than the arithmetic mean.
 
-  return topicality_score / num_terms;
+  float final_topicality_score = topicality_score / num_terms;
+
+  // Demote all the URLs if the topicality score is less than threshold.
+  if (hqp_experimental_scoring_enabled_ &&
+      (final_topicality_score < topicality_threshold_)) {
+    return 0.0;
+  }
+
+  return final_topicality_score;
 }
 
 // static
@@ -542,8 +556,10 @@ float ScoredHistoryMatch::GetFrequency(const base::Time& now,
 }
 
 // static
-float ScoredHistoryMatch::GetFinalRelevancyScore(float topicality_score,
-                                                 float frequency_score) {
+float ScoredHistoryMatch::GetFinalRelevancyScore(
+    float topicality_score,
+    float frequency_score,
+    std::vector<ScoreMaxRelevance>& hqp_relevance_buckets) {
   if (topicality_score == 0)
     return 0;
   // Here's how to interpret intermediate_score: Suppose the omnibox
@@ -559,29 +575,92 @@ float ScoredHistoryMatch::GetFinalRelevancyScore(float topicality_score,
   // - a typed visit once a week -> 11.77
   // - a typed visit every three days -> 14.12
   // - at least ten typed visits today -> 20.0 (maximum score)
-  const float intermediate_score = topicality_score * frequency_score;
+  //
   // The below code maps intermediate_score to the range [0, 1399].
+  // For example:
+  // HQP default scoring buckets: "1.5:600,12.0:1300,20.0:1399"
+  // We will linearly interpolate the scores between:
+  //      0 to 1.5    --> 400 to 600
+  //    1.5 to 12.0   --> 600 to 1300
+  //    12.0 to 20.0  --> 1300 to 1399
+  //       >= 20.0    --> 1399
+  //
   // The score maxes out at 1400 (i.e., cannot beat a good inline result).
-  if (intermediate_score <= 1) {
-    // Linearly extrapolate between 0 and 1.5 so 0 has a score of 400
-    // and 1.5 has a score of 600.
-    const float slope = (600 - 400) / (1.5f - 0.0f);
-    return 400 + slope * intermediate_score;
+  //
+  // If experimental scoring is enabled, then the score buckets will be like:
+  // HQP experimental scoring buckets: "1.5:600,5.0:900,12.0:1100,20.0:1300"
+  const float intermediate_score = topicality_score * frequency_score;
+
+  double base_intermediate_score = 0.0;
+  int base_hqp_score = 400;
+  double max_intermediate_score = base_intermediate_score;
+  int max_hqp_score = base_hqp_score;
+
+  // Find the threshold where intermediate score is greater than bucket.
+  for (size_t i = 0; i < hqp_relevance_buckets.size(); ++i) {
+    ScoreMaxRelevance hqp_bucket = hqp_relevance_buckets[i];
+    max_intermediate_score = hqp_bucket.first;
+    max_hqp_score = hqp_bucket.second;
+    if (intermediate_score <= max_intermediate_score) {
+      const float slope = (
+          (max_hqp_score - base_hqp_score) /
+          (max_intermediate_score - base_intermediate_score));
+      const int final_hqp_score = (base_hqp_score +
+                                   (slope * (intermediate_score -
+                                             base_intermediate_score)));
+      return std::min(final_hqp_score, max_hqp_score);
+    }
+    base_intermediate_score = max_intermediate_score;
+    base_hqp_score = max_hqp_score;
   }
-  if (intermediate_score <= 12.0) {
-    // Linearly extrapolate up to 12 so 12 has a score of 1300.
-    const float slope = (1300 - 600) / (12.0f - 1.5f);
-    return 600 + slope * (intermediate_score - 1.5);
+  // It will reach this stage when the score is > highest bucket score or
+  // when buckets are not specified. Return max_hqp_score.
+  return max_hqp_score;
+}
+
+void ScoredHistoryMatch::InitializeHQPExperimentalParams() {
+  // Initialize the hqp experiment params.
+  hqp_experimental_scoring_enabled_ = false;
+  topicality_threshold_ = -1;
+  // These are default HQP scoring params.
+  // See GetFinalRelevancyScore() for details.
+  std::string hqp_relevance_buckets_str = "1.5:600,12.0:1300,20.0:1399";
+
+  // Fetch the experiment params if they are any.
+  hqp_experimental_scoring_enabled_ =
+    OmniboxFieldTrial::HQPExperimentalScoringEnabled();
+
+  if (hqp_experimental_scoring_enabled_) {
+    // Add the topicality threshold from experiment params.
+    float hqp_experimental_topicality_threhold =
+      OmniboxFieldTrial::HQPExperimentalTopicalityThreshold();
+    if (hqp_experimental_topicality_threhold > 0)
+      topicality_threshold_ =  hqp_experimental_topicality_threhold;

[Bart N., 2015/02/11 01:30:15]

Extra space after =.

[Ashok vardhan, 2015/02/11 19:06:54]

Done.

+
+    // Add the HQP experimental scoring buckets.
+    std::string hqp_experimental_scoring_buckets =
+      OmniboxFieldTrial::HQPExperimentalScoringBuckets();
+    if (!hqp_experimental_scoring_buckets.empty()) {

[Bart N., 2015/02/11 01:30:15]

You should be consistent with how you use {} around single line "if" statements
(check the line above).

[Ashok vardhan, 2015/02/11 19:06:54]

Done.

+      hqp_relevance_buckets_str = hqp_experimental_scoring_buckets;
+    }
+  }
+
+  // Parse the hqp_relevance_buckets_str string once and store them in vector
+  // which is easy to access.
+  base::StringPairs kv_pairs;
+  if (base::SplitStringIntoKeyValuePairs(hqp_relevance_buckets_str,
+                                         ':', ',', &kv_pairs)) {
+    for (base::StringPairs::const_iterator it = kv_pairs.begin();
+         it != kv_pairs.end(); ++it) {
+      ScoreMaxRelevance bucket;
+      base::StringToDouble(it->first, &bucket.first);
+      base::StringToInt(it->second, &bucket.second);
+      hqp_relevance_buckets_.push_back(bucket);
+    }
   }
-  // Linearly extrapolate so a score of 20 (or more) has a score of 1399.
-  // (Scores above 20 are possible for URLs that have multiple term hits
-  // in the URL and/or title and that are visited practically all
-  // the time using typed visits.  We don't attempt to distinguish
-  // between these very good results.)
-  const float slope = (1399 - 1300) / (20.0f - 12.0f);
-  return std::min(1399.0, 1300 + slope * (intermediate_score - 12.0));
 }
 
+// static
 void ScoredHistoryMatch::Init() {
   if (initialized_)
     return;
@@ -602,6 +681,7 @@ void ScoredHistoryMatch::Init() {
   bookmark_value_ = OmniboxFieldTrial::HQPBookmarkValue();
   allow_tld_matches_ = OmniboxFieldTrial::HQPAllowMatchInTLDValue();
   allow_scheme_matches_ = OmniboxFieldTrial::HQPAllowMatchInSchemeValue();
+
   initialized_ = true;
 }