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modify ddpg test 

change the expectation values in dnn test
This commit is contained in:
yangdsh 2019-10-15 00:30:12 +00:00 committed by Dana Van Aken
parent dcb3152ec5
commit ab17aafac4
2 changed files with 33 additions and 21 deletions
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44
server/analysis/tests/test_ddpg.py
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@ -6,13 +6,17 @@
import random
import unittest
from sklearn import datasets
import numpy as np
import torch
from analysis.ddpg.ddpg import DDPG
# test ddpg model
# test ddpg model:
# The enviroment has 1-dim state and 1-dim action, the reward is calculated as follows:
# if state < 0.5, taking action < 0.5 gets reward 1, taking action >= 0.5 gets reward 0
# if state >= 0.5, taking action >= 0.5 gets reward 1, taking action < 0.5 gets reward 0
# Train 500 iterations and test for 500 iterations
# If the average reward during test is larger than 0.9, this test passes
class TestDDPG(unittest.TestCase):
@classmethod
@ -21,22 +25,26 @@ class TestDDPG(unittest.TestCase):
np.random.seed(0)
torch.manual_seed(0)
super(TestDDPG, cls).setUpClass()
boston = datasets.load_boston()
data = boston['data']
X_train = data[0:500]
X_test = data[500:]
y_train = boston['target'][0:500].reshape(500, 1)
ddpg = DDPG(n_actions=1, n_states=13)
for i in range(500):
cls.ddpg = DDPG(n_actions=1, n_states=1, gamma=0)
for _ in range(700):
knob_data = np.array([random.random()])
prev_metric_data = X_train[i - 1]
metric_data = X_train[i]
reward = y_train[i - 1]
ddpg.add_sample(prev_metric_data, knob_data, reward, metric_data, False)
if len(ddpg.replay_memory) > 32:
ddpg.update()
cls.ypreds_round = ['%.4f' % ddpg.choose_action(x)[0] for x in X_test]
prev_metric_data = np.array([random.random()])
metric_data = np.array([random.random()])
reward = 1.0 if (prev_metric_data[0] - 0.5) * (knob_data[0] - 0.5) > 0 else 0.0
reward = np.array([reward])
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):
expected_ypreds = ['0.3169', '0.3240', '0.3934', '0.5787', '0.6988', '0.5163']
self.assertEqual(self.ypreds_round, expected_ypreds)
total_reward = 0.0
for _ in range(500):
prev_metric_data = np.array([random.random()])
knob_data = self.ddpg.choose_action(prev_metric_data)
reward = 1.0 if (prev_metric_data[0] - 0.5) * (knob_data[0] - 0.5) > 0 else 0.0
total_reward += reward
self.assertGreater(total_reward / 500, 0.9)
if __name__ == '__main__':
unittest.main()

10
server/analysis/tests/test_nn.py
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@ -3,7 +3,9 @@
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
import random
import unittest
import numpy as np
from tensorflow import set_random_seed
from sklearn import datasets
from analysis.nn_tf import NeuralNet
@ -20,7 +22,9 @@ class TestNN(unittest.TestCase):
X_train = data[0:500]
X_test = data[500:]
y_train = boston['target'][0:500].reshape(500, 1)
set_random_seed(1)
random.seed(0)
np.random.seed(0)
set_random_seed(0)
cls.model = NeuralNet(n_input=X_test.shape[1],
batch_size=X_test.shape[0])
cls.model.fit(X_train, y_train)
@ -29,10 +33,10 @@ class TestNN(unittest.TestCase):
def test_nn_ypreds(self):
ypreds_round = ['%.3f' % x[0] for x in self.nn_result]
expected_ypreds = ['20.503', '22.158', '22.158', '25.692', '24.536', '23.637']
expected_ypreds = ['21.279', '22.668', '23.115', '27.228', '25.892', '23.967']
self.assertEqual(ypreds_round, expected_ypreds)
def test_nn_yrecommend(self):
recommends_round = ['%.3f' % x[0] for x in self.nn_recommend.minl]
expected_recommends = ['14.229', '22.158', '22.158', '23.591', '23.591', '23.593']
expected_recommends = ['21.279', '21.279', '21.279', '21.279', '21.279', '21.279']
self.assertEqual(recommends_round, expected_recommends)