Refactor word_ladder to include path tracking

Updated the word_ladder function to return the transformation path along with the number of steps. Enhanced the main block to display the results more clearly.

Author: Shreekant-Bharti

intermediate/word_ladder.py

@@ -1,38 +1,58 @@
 from collections import deque, defaultdict
 
 def word_ladder(beginWord, endWord, wordList):
+    """
+    Finds the shortest transformation sequence from beginWord to endWord such that:
+    - Only one letter can be changed at a time.
+    - Each transformed word must exist in the wordList.
+
+    Returns:
+        A tuple (steps, path) where:
+        - steps is the number of words in the shortest transformation sequence.
+        - path is a list of words representing the transformation.
+
+    Example:
+    >>> word_ladder("hit", "cog", ["hot","dot","dog","lot","log","cog"])
+    (5, ['hit', 'hot', 'dot', 'dog', 'cog'])
+    """
     wordSet = set(wordList)
     if endWord not in wordSet:
-        return 0
+        return 0, []
 
-    # Preprocess: create patterns like h*t, ho*
+    # Preprocess: create wildcard patterns (e.g., h*t, ho*)
     neighbors = defaultdict(list)
     for word in wordSet:
         for i in range(len(word)):
             pattern = word[:i] + "*" + word[i+1:]
             neighbors[pattern].append(word)
 
-    # BFS
-    queue = deque([(beginWord, 1)])
+    # BFS with path tracking
+    queue = deque([(beginWord, [beginWord])])
     visited = set([beginWord])
 
     while queue:
-        word, steps = queue.popleft()
+        word, path = queue.popleft()
         if word == endWord:
-            return steps
+            return len(path), path
 
         for i in range(len(word)):
             pattern = word[:i] + "*" + word[i+1:]
             for nei in neighbors.get(pattern, []):
                 if nei not in visited:
                     visited.add(nei)
-                    queue.append((nei, steps + 1))
+                    queue.append((nei, path + [nei]))
 
-    return 0
+    return 0, []
 
 
 if __name__ == "__main__":
     begin = "hit"
     end = "cog"
-    words = ["hot","dot","dog","lot","log","cog"]
-    print(word_ladder(begin, end, words))  # Output: 5
+    words = ["hot", "dot", "dog", "lot", "log", "cog"]
+
+    steps, path = word_ladder(begin, end, words)
+    if steps:
+        print(f"Shortest transformation length: {steps}")
+        print("Transformation path:", " → ".join(path))
+    else:
+        print("No transformation sequence found.")