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change the expected value in ddpg test

This commit is contained in:
yangdsh 2019-10-14 03:52:06 +00:00 committed by Dana Van Aken
parent f071a0e62c
commit 090387a176
2 changed files with 10 additions and 11 deletions
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19
server/analysis/tests/test_ddpg.py
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@ -17,27 +17,26 @@ class TestDDPG(unittest.TestCase):
@classmethod
def setUpClass(cls):
torch.manual_seed(0)
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
super(TestDDPG, cls).setUpClass()
boston = datasets.load_boston()
data = boston['data']
X_train = data[0:500]
cls.X_test = data[500:]
X_test = data[500:]
y_train = boston['target'][0:500].reshape(500, 1)
cls.ddpg = DDPG(n_actions=1, n_states=13)
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()
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]
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]
for ypred_round, expected_ypred in zip(ypreds_round, expected_ypreds):
self.assertAlmostEqual(ypred_round, expected_ypred, places=6)
expected_ypreds = ['0.3169', '0.3240', '0.3934', '0.5787', '0.6988', '0.5163']
self.assertEqual(self.ypreds_round, expected_ypreds)

2
server/website/website/tasks/async_tasks.py
View File

@ -299,7 +299,7 @@ def configuration_recommendation_ddpg(result_info): # pylint: disable=invalid-n
metric_scalar = MinMaxScaler().fit(metric_data.reshape(1, -1))
normalized_metric_data = metric_scalar.transform(metric_data.reshape(1, -1))[0]
cleaned_knob_data = clean_knob_data(agg_data['X_matrix'], agg_data['X_columnlabels'],
session)
session)
knob_labels = np.array(cleaned_knob_data[1]).flatten()
knob_num = len(knob_labels)
metric_num = len(metric_data)