Added Johnson's algorithm for all-pairs shortest paths

graphs/johnson.py

@@ -0,0 +1,115 @@
+import heapq
+from collections.abc import Hashable
+
+Node = Hashable
+edge = tuple[Node, Node, float]
+adjacency = dict[Node, list[tuple[Node, float]]]
+
+
+def _collect_nodes_and_edges(graph: adjacency) -> tuple[list[Node], list[edge]]:
+    nodes = set()
+    edges: list[edge] = []
+    for u, neighbors in graph.items():
+        nodes.add(u)
+        for v, w in neighbors:
+            nodes.add(v)
+            edges.append((u, v, w))
+    return list(nodes), edges
+
+
+def _bellman_ford(nodes: list[Node], edges: list[edge]) -> dict[Node, float]:
+    """
+    Bellman-Ford relaxation to compute potentials h[v] for all vertices.
+    Raises ValueError if a negative weight cycle exists.
+    """
+    dist: dict[Node, float] = dict.fromkeys(nodes, 0.0)
+    n = len(nodes)
+
+    for _ in range(n - 1):
+        updated = False
+        for u, v, w in edges:
+            if dist[u] + w < dist[v]:
+                dist[v] = dist[u] + w
+                updated = True
+        if not updated:
+            break
+    else:
+        for u, v, w in edges:
+            if dist[u] + w < dist[v]:
+                raise ValueError("Negative weight cycle detected")
+    return dist
+
+
+def _dijkstra(
+    start: Node,
+    nodes: list[Node],
+    graph: adjacency,
+    potentials: dict[Node, float],
+) -> dict[Node, float]:
+    """
+    Dijkstra over reweighted graph, using potentials h to make weights non-negative.
+    Returns distances from start in the reweighted space.
+    """
+    inf = float("inf")
+    dist: dict[Node, float] = dict.fromkeys(nodes, inf)
+    dist[start] = 0.0
+    heap: list[tuple[float, Node]] = [(0.0, start)]
+
+    while heap:
+        d_u, u = heapq.heappop(heap)
+        if d_u > dist[u]:
+            continue
+        for v, w in graph.get(u, []):
+            w_prime = w + potentials[u] - potentials[v]
+            if w_prime < 0:
+                raise ValueError(
+                    "Negative edge weight after reweighting: numeric error"
+                )
+            new_dist = d_u + w_prime
+            if new_dist < dist[v]:
+                dist[v] = new_dist
+                heapq.heappush(heap, (new_dist, v))
+    return dist
+
+
+def johnson(graph: adjacency) -> dict[Node, dict[Node, float]]:
+    """
+    Compute all-pairs shortest paths using Johnson's algorithm.
+
+    Args:
+        graph: adjacency list {u: [(v, weight), ...], ...}
+
+    Returns:
+        dict of dicts: dist[u][v] = shortest distance from u to v
+
+    Raises:
+        ValueError: if a negative weight cycle is detected
+
+    Example:
+    >>> g = {
+    ...     0: [(1, 3), (2, 8), (4, -4)],
+    ...     1: [(3, 1), (4, 7)],
+    ...     2: [(1, 4)],
+    ...     3: [(0, 2), (2, -5)],
+    ...     4: [(3, 6)],
+    ... }
+    >>> round(johnson(g)[0][3], 2)
+    2.0
+    """
+    nodes, edges = _collect_nodes_and_edges(graph)
+    potentials = _bellman_ford(nodes, edges)
+
+    all_pairs: dict[Node, dict[Node, float]] = {}
+    inf = float("inf")
+    for s in nodes:
+        dist_reweighted = _dijkstra(s, nodes, graph, potentials)
+        dists_orig: dict[Node, float] = {}
+        for v in nodes:
+            d_prime = dist_reweighted[v]
+            if d_prime < inf:
+                dists_orig[v] = d_prime - potentials[s] + potentials[v]
+            else:
+                dists_orig[v] = inf
+        all_pairs[s] = dists_orig
+
+    return all_pairs

graphs/tests/test_johnson.py

@@ -0,0 +1,24 @@
+import math
+
+import pytest
+
+from graphs.johnson import johnson
+
+
+def test_johnson_basic():
+    g = {
+        0: [(1, 3), (2, 8), (4, -4)],
+        1: [(3, 1), (4, 7)],
+        2: [(1, 4)],
+        3: [(0, 2), (2, -5)],
+        4: [(3, 6)],
+    }
+    dist = johnson(g)
+    assert math.isclose(dist[0][3], 2.0, abs_tol=1e-9)
+    assert math.isclose(dist[3][2], -5.0, abs_tol=1e-9)
+
+
+def test_johnson_negative_cycle():
+    g2 = {0: [(1, 1)], 1: [(0, -3)]}
+    with pytest.raises(ValueError):
+        johnson(g2)