Tests that the likelihoods that the LOO x-validation produces the right values by training on an equivalent dataset with those values left out

GPy/testing/loo_tests.py

@@ -0,0 +1,49 @@
+import numpy as np
+from scipy.stats import norm 
+import unittest
+import GPy
+
+class LOOTest(unittest.TestCase):
+    def test_LOO(self):
+        """
+        Tests if the log likelihoods for each element of the LOO x-validation
+        from the LOO method match the log likelihoods.
+        """
+
+        #simple model
+        X = np.arange(0,10,1)[:,None]
+        Y = np.sin(X/5.0)+np.random.randn(X.shape[0],X.shape[1])*0.1
+        k = GPy.kern.RBF(1)
+        m = GPy.models.GPRegression(X,Y,k)
+        m.optimize()
+
+        #variables to store the predictions of the GP model.
+        preds = []
+        variances = []
+        for it in range(len(X)):
+            #we create a new dataset with all but one element left out
+            Xloo = np.delete(X,it,0)
+            Yloo = np.delete(Y,it,0)
+            #the location we have left out (and that we want to predict)
+            predX = X[it].copy() 
+            #configure the model using the left-one-out data
+            k2 = GPy.kern.RBF(1)
+            m2 = GPy.models.GPRegression(Xloo,Yloo,k2)
+            #copy the hyperparameters over (these don't change between LOOs)
+            m2.param_array[:]=m.param_array[:].copy()
+            m2.update_model(True)
+
+            #make a prediction for the left-out data
+            pred, var = m2.predict(np.array(predX[:,None]))
+            preds.append(pred[0,0])
+            variances.append(var[0,0])
+        preds = np.array(preds)[:,None]
+        variances = np.array(variances)[:,None]
+
+
+        expected = np.log(norm.pdf(preds,Y,np.sqrt(variances)))
+        actual = m.LOO()
+
+        for e,a in zip(expected,actual):
+            print "OK"
+            assert np.isclose(e,a,atol=0.0001,rtol=0.0001), "%s, %s" % (e,a)