Merge pull request #109 from siliataider/eliott-dev

add tests

backAgent/main.py

@@ -74,12 +74,12 @@ async def read_socket(websocket):
 
 
 # Run python backend as client
-async def connect_to_websocket():
-    uri = "wss://citymanagerjava.onrender.com/websocket-endpoint" 
-    async with websockets.connect(uri) as websocket:
-        while True:
-            # Envoyez et recevez des messages ici
-            await read_socket(websocket)
+# async def connect_to_websocket():
+#     uri = "wss://citymanagerjava.onrender.com/websocket-endpoint" 
+#     async with websockets.connect(uri) as websocket:
+#         while True:
+#             # Envoyez et recevez des messages ici
+#             await read_socket(websocket)
 
 # Run python backend as server
 async def listen_to_websocket():

backAgent/tests/test_agent.py

@@ -0,0 +1,91 @@
+import pytest
+import numpy as np
+from classes.Agent.AgentDQLearning import AgentDQLearning
+from classes.SimulationConditions import SimulationConditions
+from classes.Agent.AgentQLearning import AgentQLearning
+from classes.AgentEnvironment import AgentEnvironment
+from resources.variables import DISCOUNT_FACTOR, START_EXPLORATION_RATE
+
+@pytest.fixture
+def agent_q_learning():
+    env = AgentEnvironment(timestamp=8, weather=0)
+
+    # Créez une instance de AgentQLearning avec des valeurs fictives pour les paramètres nécessaires.
+    return AgentQLearning(num_states=108, num_actions=3, env=env, agent_id=1)
+
+@pytest.fixture
+def agent_dq_learning():
+    env = AgentEnvironment(timestamp=8, weather=0)
+
+    # Créez une instance de AgentQLearning avec des valeurs fictives pour les paramètres nécessaires.
+    return AgentDQLearning(num_states=108, num_actions=3, env=env, agent_id=1)
+
+
+@pytest.fixture
+def simulationConditions():
+    return SimulationConditions(exploration_rate=1)
+
+
+def test_choose_action_explotation_Q_learning(agent_q_learning):
+    # Testez le choix d'action en exploration
+    state_value = (8, 0, 0.5, 0.5, 0.5)
+    exploration_rate = 0
+    action = agent_q_learning.choose_action(state_value, exploration_rate)
+
+    #Table vide donc choisit la premiere action
+    assert action == 0
+
+
+def test_train_model_Q_learning(agent_q_learning):
+    # Testez le choix d'action en exploration
+    state_value = (8, 0, 0.5, 0.5, 0.5)
+    next_state_value = (9, 0, 0.6, 0.5, 0.4)
+    action = 0
+    reward = 15
+    learning_rate = 0.1
+    expected_array = [[0., 0., 0.], 
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [1.5, 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.],
+    [0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]
+
+    agent_q_learning.train_model(state_value, action, reward, next_state_value, learning_rate, DISCOUNT_FACTOR)
+    assert np.array_equal(agent_q_learning.q_table, np.array(expected_array))
+
+def test_train_agent(simulationConditions, agent_q_learning, monkeypatch):
+    agent_q_learning.env.state_value = (8, 0, 0.5, 0.5, 0.5)
+    simulationConditions.timestamp = 9
+    simulationConditions.weather = 0
+
+    def mock_action(arr):
+        return 0
+    monkeypatch.setattr(np.random, 'choice', mock_action)
+
+    agent_update, action = agent_q_learning.train(simulationConditions)
+
+    assert agent_q_learning.env.state_value == (9, 0, 0.475, 0.55, 0.5)
+
+

backAgent/tests/test_server_setup.py

@@ -6,13 +6,13 @@ def test_one_plus_one_equals_two():
     assert 1 + 1 == 2
 
 
-@pytest.mark.asyncio
-async def test_server_connection():
-    try:
-        # Attempt to connect to the WebSocket server
-        async with websockets.connect("wss://citymanagerpython.onrender.com") as websocket:
-            # If the connection is successful, pass the test
-            pass
-    except Exception as e:
-        # If the connection fails, fail the test
-        pytest.fail(f"WebSocket connection failed: {e}")
+# @pytest.mark.asyncio
+# async def test_server_connection():
+#     try:
+#         # Attempt to connect to the WebSocket server
+#         async with websockets.connect("wss://citymanagerpython.onrender.com") as websocket:
+#             # If the connection is successful, pass the test
+#             pass
+#     except Exception as e:
+#         # If the connection fails, fail the test
+#         pytest.fail(f"WebSocket connection failed: {e}")