[mypy] Type annotations for graphs directory (#5798)

* Type annotations for `breadth_first_search.py`

* Type annotations for `breadth_first_search_2.py`

* Remove from excluded in mypy.ini

* Add doctest.testmod()

* Type annotations for `graphs/check_cycle.py`

* Type annotations for `graphs/greedy_min_vertex_cover.py`

* Remove from excluded in mypy.ini

graphs/breadth_first_search.py

@@ -53,7 +53,7 @@ class Graph:
         visited = set()
 
         # create a first in first out queue to store all the vertices for BFS
-        queue = Queue()
+        queue: Queue = Queue()
 
         # mark the source node as visited and enqueue it
         visited.add(start_vertex)

graphs/breadth_first_search_2.py

@@ -32,7 +32,7 @@ def breadth_first_search(graph: dict, start: str) -> set[str]:
     'ABCDEF'
     """
     explored = {start}
-    queue = Queue()
+    queue: Queue = Queue()
     queue.put(start)
     while not queue.empty():
         v = queue.get()
@@ -44,4 +44,7 @@ def breadth_first_search(graph: dict, start: str) -> set[str]:
 
 
 if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()
     print(breadth_first_search(G, "A"))

graphs/check_cycle.py

@@ -6,16 +6,15 @@ Program to check if a cycle is present in a given graph
 def check_cycle(graph: dict) -> bool:
     """
     Returns True if graph is cyclic else False
-
     >>> check_cycle(graph={0:[], 1:[0, 3], 2:[0, 4], 3:[5], 4:[5], 5:[]})
     False
     >>> check_cycle(graph={0:[1, 2], 1:[2], 2:[0, 3], 3:[3]})
     True
     """
     # Keep track of visited nodes
-    visited = set()
+    visited: set[int] = set()
     # To detect a back edge, keep track of vertices currently in the recursion stack
-    rec_stk = set()
+    rec_stk: set[int] = set()
     for node in graph:
         if node not in visited:
             if depth_first_search(graph, node, visited, rec_stk):
@@ -27,7 +26,6 @@ def depth_first_search(graph: dict, vertex: int, visited: set, rec_stk: set) ->
     """
     Recur for all neighbours.
     If any neighbour is visited and in rec_stk then graph is cyclic.
-
     >>> graph = {0:[], 1:[0, 3], 2:[0, 4], 3:[5], 4:[5], 5:[]}
     >>> vertex, visited, rec_stk = 0, set(), set()
     >>> depth_first_search(graph, vertex, visited, rec_stk)

graphs/greedy_min_vertex_cover.py

@@ -2,7 +2,6 @@
 * Author: Manuel Di Lullo (https://github.com/manueldilullo)
 * Description: Approximization algorithm for minimum vertex cover problem.
                Greedy Approach. Uses graphs represented with an adjacency list
-
 URL: https://mathworld.wolfram.com/MinimumVertexCover.html
 URL: https://cs.stackexchange.com/questions/129017/greedy-algorithm-for-vertex-cover
 """
@@ -10,7 +9,7 @@ URL: https://cs.stackexchange.com/questions/129017/greedy-algorithm-for-vertex-c
 import heapq
 
 
-def greedy_min_vertex_cover(graph: dict) -> set:
+def greedy_min_vertex_cover(graph: dict) -> set[int]:
     """
     Greedy APX Algorithm for min Vertex Cover
     @input: graph (graph stored in an adjacency list where each vertex
@@ -21,7 +20,7 @@ def greedy_min_vertex_cover(graph: dict) -> set:
     {0, 1, 2, 4}
     """
     # queue used to store nodes and their rank
-    queue = []
+    queue: list[list] = []
 
     # for each node and his adjacency list add them and the rank of the node to queue
     # using heapq module the queue will be filled like a Priority Queue
@@ -61,5 +60,5 @@ if __name__ == "__main__":
 
     doctest.testmod()
 
-    # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
-    # print(f"Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}")
+    graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
+    print(f"Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}")

mypy.ini

@@ -2,4 +2,4 @@
 ignore_missing_imports = True
 install_types = True
 non_interactive = True
-exclude = (graphs/breadth_first_search.py|graphs/breadth_first_search_2.py|graphs/check_cycle.py|graphs/greedy_min_vertex_cover.py|matrix_operation.py|other/least_recently_used.py|other/lfu_cache.py|other/lru_cache.py|searches/simulated_annealing.py|searches/ternary_search.py)
+exclude = (matrix_operation.py|other/least_recently_used.py|other/lfu_cache.py|other/lru_cache.py|searches/simulated_annealing.py|searches/ternary_search.py)