fix SetPosition

add tests for get_position and set_position

python-package/lightgbm/basic.py

@@ -1507,8 +1507,6 @@ def __init__(
             sum(group) = n_samples.
             For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
             where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
-        position : numpy 1-D array, pandas Series or None, optional (default=None)
-            Position of items used in unbiased learning-to-rank task.
         init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None, optional (default=None)
             Init score for Dataset.
         feature_name : list of str, or 'auto', optional (default="auto")
@@ -1528,6 +1526,8 @@ def __init__(
             Other parameters for Dataset.
         free_raw_data : bool, optional (default=True)
             If True, raw data is freed after constructing inner Dataset.
+        position : numpy 1-D array, pandas Series or None, optional (default=None)
+            Position of items used in unbiased learning-to-rank task.
         """
         self._handle: Optional[_DatasetHandle] = None
         self.data = data
@@ -2263,12 +2263,12 @@ def create_valid(
             sum(group) = n_samples.
             For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
             where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
-        position : numpy 1-D array, pandas Series or None, optional (default=None)
-            Position of items used in unbiased learning-to-rank task.
         init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None, optional (default=None)
             Init score for Dataset.
         params : dict or None, optional (default=None)
             Other parameters for validation Dataset.
+        position : numpy 1-D array, pandas Series or None, optional (default=None)
+            Position of items used in unbiased learning-to-rank task.
 
         Returns
         -------

src/io/metadata.cpp

@@ -540,19 +540,21 @@ void Metadata::SetPosition(const data_size_t* positions, data_size_t len) {
 
   position_load_from_file_ = false;
 
-  #pragma omp parallel for schedule(static, 512) if (num_positions_ >= 1024)
+  position_ids_.clear();
+  std::unordered_map<data_size_t, int> map_id2pos;
   for (data_size_t i = 0; i < num_positions_; ++i) {
-    positions_[i] = positions[i];
+    if (map_id2pos.count(positions[i]) == 0) {
+      int pos = static_cast<int>(map_id2pos.size());
+      map_id2pos[positions[i]] = pos;
+      position_ids_.push_back(std::to_string(positions[i]));
+    }
   }
 
-  position_ids_.clear();
-  std::set<int> position_set;
-  for (int position : positions_) {
-    position_set.insert(position);
-  }
-  Log::Debug("number of unique positions found = %ld", position_set.size());
-  for (int position : position_set) {
-    position_ids_.push_back(std::to_string(position));
+  Log::Debug("number of unique positions found = %ld", position_ids_.size());
+
+  #pragma omp parallel for schedule(static, 512) if (num_positions_ >= 1024)
+  for (data_size_t i = 0; i < num_positions_; ++i) {
+    positions_[i] = map_id2pos.at(positions[i]);
   }
 }