feat: add dijkstra tests

Author: codejsha

python-algorithm/algorithm/graph/test/test_dijkstra.py

@@ -0,0 +1,46 @@
+import networkx
+import pytest
+
+from algorithm.graph.test.graph_data_utils import create_weighted_city_graph
+
+city_graph = create_weighted_city_graph()
+
+
+@pytest.mark.benchmark(group='graph_dijkstra')
+@pytest.mark.parametrize(
+    argnames='graph, source, target, expected_path',
+    argvalues=[
+        (city_graph, 'Los Angeles', 'Boston', ['Los Angeles', 'Riverside', 'Chicago', 'Detroit', 'Boston']),
+    ],
+    ids=['los_angeles_to_boston'])
+def test_graph_dijkstra_path(benchmark, graph, source, target, expected_path):
+    """
+    Find the shortest path between two nodes in a graph using Dijkstra's algorithm.
+    :param benchmark: benchmark fixture
+    :param graph: city graph of the United States
+    :param source: source city node
+    :param target: target city node
+    :param expected_path: expected shortest path
+    """
+    shortest_path = benchmark(networkx.dijkstra_path, graph, source, target)
+    assert shortest_path == expected_path
+
+
+@pytest.mark.benchmark(group='graph_dijkstra')
+@pytest.mark.parametrize(
+    argnames='graph, source, target, expected_distance',
+    argvalues=[
+        (city_graph, 'Los Angeles', 'Boston', 2605),
+    ],
+    ids=['los_angeles_to_boston'])
+def test_graph_dijkstra_distance(benchmark, graph, source, target, expected_distance):
+    """
+    Find the shortest distance between two nodes in a graph using Dijkstra's algorithm.
+    :param benchmark: benchmark fixture
+    :param graph: city graph of the United States
+    :param source: source city node
+    :param target: target city node
+    :param expected_distance: expected shortest distance (weight)
+    """
+    total_weight = benchmark(networkx.dijkstra_path_length, graph, source, target)
+    assert total_weight == expected_distance