test for dnn

server/analysis/tests/test_nn.py

@@ -0,0 +1,38 @@
+#
+# OtterTune - test_nn.py
+#
+# Copyright (c) 2017-18, Carnegie Mellon University Database Group
+#
+import unittest
+from tensorflow import set_random_seed
+from sklearn import datasets
+from analysis.nn_tf import NeuralNet
+
+
+# test neural network
+class TestNN(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        super(TestNN, 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)
+        set_random_seed(1)
+        cls.model = NeuralNet(n_input=X_test.shape[1],
+                              batch_size=X_test.shape[0])
+        cls.model.fit(X_train, y_train)
+        cls.nn_result = cls.model.predict(X_test)
+        cls.nn_recommend = cls.model.recommend(X_test)
+
+    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']
+        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']
+        self.assertEqual(recommends_round, expected_recommends)