45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
#
|
|
# 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)
|