Added to_csr and to_csc

rtrec/utils/interactions.py

@@ -2,7 +2,7 @@
 from typing import List, Optional, Any
 import time, math
 
-from scipy.sparse import csr_matrix
+from scipy.sparse import csr_matrix, csc_matrix
 
 class UserItemInteractions:
     def __init__(self, min_value: int = -5, max_value: int = 10, decay_in_days: Optional[int] = None, **kwargs: Any) -> None:
@@ -15,6 +15,7 @@ def __init__(self, min_value: int = -5, max_value: int = 10, decay_in_days: Opti
             decay_rate (Optional[float]): Rate at which interactions decay over time.
                                           If None, no decay is applied.
         """
+        # Store interactions as a dictionary of dictionaries in shape {user_id: {item_id: (value, timestamp)}}
         self.interactions: defaultdict[int, dict[int, tuple[float, float]]] = defaultdict(dict)
         self.empty = {}
         self.all_item_ids = set()
@@ -174,13 +175,28 @@ def to_csr(self) -> csr_matrix:
         rows, cols, data = [], [], []
         max_row, max_col = 0, 0
 
-        for row, inner_dict in self.interactions.items():
-            for col, (value, tstamp) in inner_dict.items():
-                rows.append(row)
-                cols.append(col)
-                data.append(self._apply_decay(value, tstamp))
-                max_row = max(max_row, row)
-                max_col = max(max_col, col)
+        for user, inner_dict in self.interactions.items():
+            for item, (rating, tstamp) in inner_dict.items():
+                rows.append(user)
+                cols.append(item)
+                data.append(self._apply_decay(rating, tstamp))
+                max_row = max(max_row, user)
+                max_col = max(max_col, item)
 
         # Create the csr_matrix
         return csr_matrix((data, (rows, cols)), shape=(max_row, max_col))
+
+    def to_csc(self) -> csc_matrix:
+        rows, cols, data = [], [], []
+        max_row, max_col = 0, 0
+
+        for user, inner_dict in self.interactions.items():
+            for item, (rating, tstamp) in inner_dict.items():
+                rows.append(user)
+                cols.append(item)
+                data.append(self._apply_decay(rating, tstamp))
+                max_row = max(max_row, user)
+                max_col = max(max_col, item)
+
+        # Create the csc_matrix
+        return csc_matrix((data, (rows, cols)), shape=(max_row, max_col))