Add testing for ddpg model

server/analysis/ddpg/ddpg.py

@@ -198,7 +198,6 @@ class DDPG(object):
         else:
             # Build Network
             self._build_network()
-            LOG.info('Finish Initializing Networks')
 
         self.replay_memory = PrioritizedReplayMemory(capacity=memory_size)
         self.noise = OUProcess(n_actions)

server/analysis/tests/test_ddpg.py

@@ -0,0 +1,44 @@
+#
+# OtterTune - test_ddpg.py
+#
+# Copyright (c) 2017-18, Carnegie Mellon University Database Group
+#
+
+import random
+import unittest
+from sklearn import datasets
+import numpy as np
+import torch
+from analysis.ddpg.ddpg import DDPG
+
+
+# test ddpg model
+class TestDDPG(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        torch.manual_seed(0)
+        random.seed(0)
+        np.random.seed(0)
+        super(TestDDPG, cls).setUpClass()
+        boston = datasets.load_boston()
+        data = boston['data']
+        X_train = data[0:500]
+        cls.X_test = data[500:]
+        y_train = boston['target'][0:500].reshape(500, 1)
+        cls.ddpg = DDPG(n_actions=1, n_states=13)
+        for i in range(500):
+            knob_data = np.array([random.random()])
+            prev_metric_data = X_train[i - 1]
+            metric_data = X_train[i]
+            reward = y_train[i - 1]
+            cls.ddpg.add_sample(prev_metric_data, knob_data, reward, metric_data, False)
+            if len(cls.ddpg.replay_memory) > 32:
+                cls.ddpg.update()
+
+    def test_ddpg_ypreds(self):
+        ypreds_round = [round(self.ddpg.choose_action(x)[0], 4) for x in self.X_test]
+        expected_ypreds = [0.1778, 0.1914, 0.2607, 0.4459, 0.5660, 0.3836]
+        self.assertEqual(ypreds_round, expected_ypreds)
+        for ypred_round, expected_ypred in zip(ypreds_round, expected_ypreds):
+            self.assertAlmostEqual(ypred_round, expected_ypred, places=6)

server/website/website/settings/constants.py

@@ -83,7 +83,7 @@ ACTOR_LEARNING_RATE = 0.001
 CRITIC_LEARNING_RATE = 0.001
 
 #  The impact of future reward on the decision
-GAMMA = 0.1
+GAMMA = 0.9
 
 #  The changing rate of the target network
 TAU = 0.002