fix old gpr model, add noise variance

server/analysis/tests/test_gpr.py

@@ -4,10 +4,11 @@
 # Copyright (c) 2017-18, Carnegie Mellon University Database Group
 #
 import unittest
+import numpy as np
 from sklearn import datasets
 from analysis.gp import GPRNP
 from analysis.gp_tf import GPR
-
+from analysis.gp_tf import GPRGD
 
 # test numpy version GPR
 class TestGPRNP(unittest.TestCase):
@@ -31,7 +32,7 @@ class TestGPRNP(unittest.TestCase):
 
     def test_gprnp_sigmas(self):
         sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
-        expected_sigmas = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+        expected_sigmas = [1.4142, 1.4142, 1.4142, 1.4142, 1.4142, 1.4142]
         self.assertEqual(sigmas_round, expected_sigmas)
 
 
@@ -57,5 +58,33 @@ class TestGPRTF(unittest.TestCase):
 
     def test_gprnp_sigmas(self):
         sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
-        expected_sigmas = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+        expected_sigmas = [1.4142, 1.4142, 1.4142, 1.4142, 1.4142, 1.4142]
+        self.assertEqual(sigmas_round, expected_sigmas)
+
+
+# test Tensorflow GPRGD model
+class TestGPRGD(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        super(TestGPRGD, 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)
+        Xmin = np.min(X_train, 0)
+        Xmax = np.max(X_train, 0)
+        cls.model = GPRGD(length_scale=1.0, magnitude=1.0, max_iter=1, learning_rate=0)
+        cls.model.fit(X_train, y_train, Xmin, Xmax, ridge=1.0)
+        cls.gpr_result = cls.model.predict(X_test)
+
+    def test_gprnp_ypreds(self):
+        ypreds_round = [round(x[0], 4) for x in self.gpr_result.ypreds]
+        expected_ypreds = [0.0181, 0.0014, 0.0006, 0.0015, 0.0039, 0.0014]
+        self.assertEqual(ypreds_round, expected_ypreds)
+
+    def test_gprnp_sigmas(self):
+        sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
+        expected_sigmas = [1.4142, 1.4142, 1.4142, 1.4142, 1.4142, 1.4142]
         self.assertEqual(sigmas_round, expected_sigmas)