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