Miguel 4.1 - Route Between Nodes [Python] (#88)

* Setting up graph-related data structures and operations

* Implement BFS and add test case

* Add type annotation for deque

* Initial solution file setup

* Implement route between nodes

* Fix bug and update test cases

* Update Python/chapter04/p01_route_between_nodes/miguelHx.py

Co-authored-by: lhchavez <[email protected]>

* Update Python/chapter04/p01_route_between_nodes/miguelHx.py

Co-authored-by: lhchavez <[email protected]>

* Update Python/chapter04/p01_route_between_nodes/miguelHx.py

Co-authored-by: lhchavez <[email protected]>

* Update Python/chapter04/p01_route_between_nodes/miguelHx.py

Co-authored-by: lhchavez <[email protected]>

* Use a set to track visited nodes instead of visited flag property on Node

* Remove code unused in solution

* Update Python/chapter04/p01_route_between_nodes/miguelHx.py

Co-authored-by: lhchavez <[email protected]>

* Delete graph search file

* Remove usage of visited as a property on Node

* Small optimization - terminate at destination

Co-authored-by: lhchavez <[email protected]>

Python/chapter04/p01_route_between_nodes/miguelHx.py

@@ -0,0 +1,180 @@
+"""Python Version 3.9.2
+4.1 - Route Between Nodes:
+Given a directed graph, design an algorithm to
+find out whether there is a route between two nodes.
+"""
+import unittest
+
+from collections import deque
+from dataclasses import dataclass
+from typing import List, Deque, Set
+
+
+@dataclass
+class Graph:
+    nodes: 'List[Node]'
+
+    def print_graph(self):
+        for node in self.nodes:
+            node.print_children()
+
+
+@dataclass
+class Node:
+    id: int
+    children: 'List[Node]'
+
+    def add_child(self, *nodes: 'Node'):
+        for node in nodes:
+            self.children.append(node)
+
+    def children_as_str(self) -> str:
+        return ', '.join(str(child.id) for child in self.children)
+
+    def print_children(self):
+        logging.debug('Adjacency list for node %s: %s', self.id, self.children_as_str())
+
+    def __str__(self):
+        return f'Node ({self.id}), children: {self.children_as_str()}'
+
+def bfs_search_exhaustive(root: Node) -> List[int]:
+    """Simple BFS.
+    takes in a root, returns a list
+    of ids of the sequence of visited
+    nodes. Goes through entire graph.
+
+    Args:
+        root (Node): starting node
+
+    Returns:
+        List[int]: List[int]: list of node IDs (i.e. [0, 1, 4])
+    """
+    visited_list: List[int] = [root.id]
+    visited: Set[int] = set([root.id])
+    queue: Deque[Node] = deque([root])
+    while queue:
+        node = queue.popleft()
+        # print(f'Visiting node ({node.id})')
+        for n in node.children:
+            if n.id not in visited:
+                queue.append(n)
+                visited_list.append(n.id)
+                visited.add(n.id)
+    return visited_list
+
+
+def bfs_search_for_dest(root: Node, dest: Node) -> List[int]:
+    """Simple BFS.
+    takes in a root, returns a list
+    of ids of the sequence of visited
+    nodes. Stops at destination node
+
+    Args:
+        root (Node): starting node
+
+    Returns:
+        List[int]: List[int]: list of node IDs (i.e. [0, 1, 4])
+    """
+    visited_list: List[int] = [root.id]
+    visited: Set[int] = set([root.id])
+    queue: Deque[Node] = deque([root])
+    while queue:
+        node = queue.popleft()
+        # print(f'Visiting node ({node.id})')
+        for n in node.children:
+            if n.id not in visited:
+                queue.append(n)
+                visited_list.append(n.id)
+                visited.add(n.id)
+            if n.id == dest.id:
+                # done searching
+                return visited_list
+    return visited_list
+
+def route_between_nodes(src: Node, dest: Node) -> bool:
+    """This function will return true if a path
+    is found between two nodes, false otherwise.
+    The idea is to perform a breadth first search
+    from src to dest. After obtaining a list of
+    nodes visited, we simply check to see if destination
+    node id is in there.
+
+    Runtime Complexity:
+        O(V + E) where V represents the number of
+        nodes in the graph and E represents the number
+        of edges in this graph.
+    Space Complexity:
+        O(V) where V represents the number of existing nodes
+        in the graph.
+
+    Args:
+        src (Node): from node
+        dest (Node): destination node
+
+    Returns:
+        bool: whether a path between src and dest exists
+    """
+    ids_visited: List[int] = bfs_search_for_dest(src, dest)
+    return dest.id in ids_visited
+
+
+class TestRouteBetweenNodes(unittest.TestCase):
+    def test_route_between_nodes(self):
+        n0 = Node(0, [])
+        n1 = Node(1, [])
+        n2 = Node(2, [])
+        n3 = Node(3, [])
+        n4 = Node(4, [])
+        n5 = Node(5, [])
+        n0.add_child(n1, n4, n5)
+        n1.add_child(n3, n4)
+        n2.add_child(n1)
+        n3.add_child(n2, n4)
+        # must remember to reset node visited properties
+        # before each fresh run
+        g = Graph([n0, n1, n2, n3, n4, n5])
+        # There is a route from node 0 to node 2
+        self.assertTrue(route_between_nodes(n0, n2))
+        # No route between node 1 and node 0
+        self.assertFalse(route_between_nodes(n1, n0))
+        # There is a route from node 2 to node 3
+        self.assertTrue(route_between_nodes(n2, n3))
+
+class TestMyGraphSearch(unittest.TestCase):
+
+    def test_basic_graph_creation(self):
+        n0 = Node(0, [])
+        n1 = Node(1, [])
+        n2 = Node(2, [])
+        n3 = Node(3, [])
+        n4 = Node(4, [])
+        n5 = Node(5, [])
+        n6 = Node(6, [])
+        n0.add_child(n1)
+        n1.add_child(n2)
+        n2.add_child(n0, n3)
+        n3.add_child(n2)
+        n4.add_child(n6)
+        n5.add_child(n4)
+        n6.add_child(n5)
+        nodes = [n0, n1, n2, n3, n4, n5, n6]
+        g = Graph(nodes)
+        # g.print_graph()
+
+    def test_basic_breadth_first_search_exhaustive(self):
+        n0 = Node(0, [])
+        n1 = Node(1, [])
+        n2 = Node(2, [])
+        n3 = Node(3, [])
+        n4 = Node(4, [])
+        n5 = Node(5, [])
+        n0.add_child(n1, n4, n5)
+        n1.add_child(n3, n4)
+        n2.add_child(n1)
+        n3.add_child(n2, n4)
+        result: List[int] = bfs_search_exhaustive(n0)
+        self.assertEqual(result, [0, 1, 4, 5, 3, 2])
+
+
+if __name__ == '__main__':
+    unittest.main()