refactor(graphs): remove benchmark baseline and microbenchmark from kahns_algorithm_topo

Author: Kanika0306

graphs/kahns_algorithm_topo.py

@@ -65,77 +65,7 @@ def topological_sort(graph: dict[int, list[int]]) -> list[int] | None:
     return topo_order  # valid topological ordering
 
 
-def _topological_sort_list_queue(graph: dict[int, list[int]]) -> list[int] | None:
-    """
-    Pre-optimization implementation of Kahn's topological sort using list.pop(0).
-
-    Used as a baseline for benchmark comparison against deque.popleft().
-    """
-    indegree = [0] * len(graph)
-    queue = []
-    topo_order = []
-    processed_vertices_count = 0
-
-    for values in graph.values():
-        for i in values:
-            indegree[i] += 1
-
-    for i in range(len(indegree)):
-        if indegree[i] == 0:
-            queue.append(i)
-
-    while queue:
-        vertex = queue.pop(0)
-        processed_vertices_count += 1
-        topo_order.append(vertex)
-
-        for neighbor in graph[vertex]:
-            indegree[neighbor] -= 1
-            if indegree[neighbor] == 0:
-                queue.append(neighbor)
-
-    if processed_vertices_count != len(graph):
-        return None
-    return topo_order
-
-
-def benchmark() -> None:
-    """
-    Benchmark comparing topological_sort() (using deque.popleft) against
-    the pre-optimization baseline _topological_sort_list_queue() (using list.pop(0)).
-
-    Demonstrates the performance improvement of O(1) queue operations in
-    Kahn's algorithm on a graph with a large number of zero-indegree vertices.
-    """
-    from timeit import timeit
-
-    num_sources = 30_000
-    graph = {i: [num_sources] for i in range(num_sources)}
-    graph[num_sources] = []
-
-    # Verify correctness: both implementations produce valid topological sorts
-    old_result = _topological_sort_list_queue(graph)
-    new_result = topological_sort(graph)
-    assert old_result is not None and new_result is not None
-    assert len(old_result) == len(new_result) == num_sources + 1
-    assert set(old_result) == set(new_result)
-
-    runs = 5
-    old_time = timeit(lambda: _topological_sort_list_queue(graph), number=runs)
-    new_time = timeit(lambda: topological_sort(graph), number=runs)
-
-    print(
-        f"Benchmark results for topological_sort with {num_sources} vertices "
-        f"over {runs} runs:"
-    )
-    print(f"Pre-optimization (list.pop(0)): {old_time:.5f} seconds")
-    print(f"Current (deque.popleft):       {new_time:.5f} seconds")
-    if new_time > 0:
-        print(f"Speedup ratio:                 {old_time / new_time:.2f}x faster")
-
-
 if __name__ == "__main__":
     import doctest
 
     doctest.testmod()
-    benchmark()