From 00f514b4517727fddb97831c7154066351aea117 Mon Sep 17 00:00:00 2001
From: Max Zwiessele <ibinbei@gmail.com>
Date: Fri, 4 Sep 2015 10:38:17 +0100
Subject: [PATCH] [spgp] minibatch testing

---
 GPy/testing/bgplvm_minibatch_tests.py | 76 +++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 GPy/testing/bgplvm_minibatch_tests.py

diff --git a/GPy/testing/bgplvm_minibatch_tests.py b/GPy/testing/bgplvm_minibatch_tests.py
new file mode 100644
index 00000000..c5be8b0c
--- /dev/null
+++ b/GPy/testing/bgplvm_minibatch_tests.py
@@ -0,0 +1,76 @@
+'''
+Created on 4 Sep 2015
+
+@author: maxz
+'''
+import unittest
+import numpy as np
+import GPy
+
+class BGPLVMTest(unittest.TestCase):
+
+
+    def setUp(self):
+        np.random.seed(12345)
+        X, W = np.random.normal(0,1,(100,6)), np.random.normal(0,1,(6,13))
+        Y = X.dot(W) + np.random.normal(0, .1, (X.shape[0], W.shape[1]))
+        self.inan = np.random.binomial(1, .1, Y.shape).astype(bool)
+        self.X, self.W, self.Y = X,W,Y
+        self.Q = 3
+        self.m_full = GPy.models.BayesianGPLVM(Y, self.Q)
+
+    def test_lik_comparisons_m1_s0(self):
+        # Test if the different implementations give the exact same likelihood as the full model.
+        # All of the following settings should give the same likelihood and gradients as the full model:
+        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=True, stochastic=False)
+        m[:] = self.m_full[:]
+        np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
+        np.testing.assert_allclose(m.gradient, self.m_full.gradient)
+        assert(m.checkgrad())
+
+    def test_lik_comparisons_m0_s0(self):
+        # Test if the different implementations give the exact same likelihood as the full model.
+        # All of the following settings should give the same likelihood and gradients as the full model:
+        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=False, stochastic=False)
+        m[:] = self.m_full[:]
+        np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
+        np.testing.assert_allclose(m.gradient, self.m_full.gradient)
+        assert(m.checkgrad())
+
+    def test_lik_comparisons_m1_s1(self):
+        # Test if the different implementations give the exact same likelihood as the full model.
+        # All of the following settings should give the same likelihood and gradients as the full model:
+        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=True, stochastic=True, batchsize=self.Y.shape[1])
+        m[:] = self.m_full[:]
+        np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
+        np.testing.assert_allclose(m.gradient, self.m_full.gradient)
+        assert(m.checkgrad())
+
+    def test_lik_comparisons_m0_s1(self):
+        # Test if the different implementations give the exact same likelihood as the full model.
+        # All of the following settings should give the same likelihood and gradients as the full model:
+        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=False, stochastic=True, batchsize=self.Y.shape[1])
+        m[:] = self.m_full[:]
+        np.testing.assert_almost_equal(m.log_likelihood(), self.m_full.log_likelihood(), 7)
+        np.testing.assert_allclose(m.gradient, self.m_full.gradient)
+        assert(m.checkgrad())
+
+    def test_gradients_missingdata(self):
+        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=True, stochastic=False, batchsize=self.Y.shape[1])
+        assert(m.checkgrad())
+
+    def test_gradients_missingdata_stochastics(self):
+        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=True, stochastic=True, batchsize=1)
+        assert(m.checkgrad())
+        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=True, stochastic=True, batchsize=4)
+        assert(m.checkgrad())
+
+    def test_gradients_stochastics(self):
+        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=False, stochastic=True, batchsize=1)
+        assert(m.checkgrad())
+        m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, missing_data=False, stochastic=True, batchsize=4)
+        assert(m.checkgrad())
+
+if __name__ == "__main__":
+    #import sys;sys.argv = ['', 'Test.testName']
+    unittest.main()
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