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https://github.com/SheffieldML/GPy.git
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migrate minibatch_tests to pytest
This commit is contained in:
Martin Bubel
parent
4070fd68ff
commit
4685d10463
1 changed files with 81 additions and 66 deletions
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@ -3,13 +3,18 @@ Created on 4 Sep 2015
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@author: maxz
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"""
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import unittest
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import pytest
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import numpy as np
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import GPy
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try:
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import climin
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except ImportError:
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climin = None
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class BGPLVMTest(unittest.TestCase):
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def setUp(self):
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class TestBGPLVM:
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def setup(self):
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np.random.seed(12345)
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X, W = np.random.normal(0, 1, (100, 6)), np.random.normal(0, 1, (6, 13))
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Y = X.dot(W) + np.random.normal(0, 0.1, (X.shape[0], W.shape[1]))
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@ -19,6 +24,7 @@ class BGPLVMTest(unittest.TestCase):
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self.m_full = GPy.models.BayesianGPLVM(Y, self.Q)
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def test_lik_comparisons_m1_s0(self):
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self.setup()
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# Test if the different implementations give the exact same likelihood as the full model.
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# All of the following settings should give the same likelihood and gradients as the full model:
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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@ -32,6 +38,7 @@ class BGPLVMTest(unittest.TestCase):
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assert m.checkgrad()
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def test_predict_missing_data(self):
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self.setup()
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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self.Y,
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self.Q,
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@ -63,6 +70,7 @@ class BGPLVMTest(unittest.TestCase):
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np.testing.assert_allclose(var1[:, [0]], var2)
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def test_lik_comparisons_m0_s0(self):
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self.setup()
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# Test if the different implementations give the exact same likelihood as the full model.
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# All of the following settings should give the same likelihood and gradients as the full model:
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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@ -80,6 +88,7 @@ class BGPLVMTest(unittest.TestCase):
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assert m.checkgrad()
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def test_lik_comparisons_m1_s1(self):
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self.setup()
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# Test if the different implementations give the exact same likelihood as the full model.
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# All of the following settings should give the same likelihood and gradients as the full model:
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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@ -97,6 +106,7 @@ class BGPLVMTest(unittest.TestCase):
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assert m.checkgrad()
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def test_lik_comparisons_m0_s1(self):
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self.setup()
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# Test if the different implementations give the exact same likelihood as the full model.
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# All of the following settings should give the same likelihood and gradients as the full model:
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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@ -114,6 +124,7 @@ class BGPLVMTest(unittest.TestCase):
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assert m.checkgrad()
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def test_gradients_missingdata(self):
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self.seutp()
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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self.Y,
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self.Q,
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@ -124,6 +135,7 @@ class BGPLVMTest(unittest.TestCase):
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assert m.checkgrad()
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def test_gradients_missingdata_stochastics(self):
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self.setup()
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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self.Y, self.Q, missing_data=True, stochastic=True, batchsize=1
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)
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@ -134,6 +146,7 @@ class BGPLVMTest(unittest.TestCase):
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assert m.checkgrad()
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def test_gradients_stochastics(self):
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self.setup()
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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self.Y, self.Q, missing_data=False, stochastic=True, batchsize=1
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)
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@ -144,6 +157,7 @@ class BGPLVMTest(unittest.TestCase):
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assert m.checkgrad()
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def test_predict(self):
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self.setup()
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# Test if the different implementations give the exact same likelihood as the full model.
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# All of the following settings should give the same likelihood and gradients as the full model:
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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@ -161,8 +175,8 @@ class BGPLVMTest(unittest.TestCase):
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assert m.checkgrad()
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class SparseGPMinibatchTest(unittest.TestCase):
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def setUp(self):
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class TestSparseGPMinibatch:
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def setup(self):
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np.random.seed(12345)
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X, W = np.random.normal(0, 1, (100, 6)), np.random.normal(0, 1, (6, 13))
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Y = X.dot(W) + np.random.normal(0, 0.1, (X.shape[0], W.shape[1]))
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@ -174,6 +188,7 @@ class SparseGPMinibatchTest(unittest.TestCase):
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)
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def test_lik_comparisons_m1_s0(self):
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self.setup()
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# Test if the different implementations give the exact same likelihood as the full model.
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# All of the following settings should give the same likelihood and gradients as the full model:
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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@ -186,70 +201,68 @@ class SparseGPMinibatchTest(unittest.TestCase):
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np.testing.assert_allclose(m.gradient, self.m_full.gradient)
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assert m.checkgrad()
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@pytest.mark.skipif(climin is None, reason="climin not installed")
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def test_sparsegp_init(self):
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self.setup()
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# Test if the different implementations give the exact same likelihood as the full model.
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# All of the following settings should give the same likelihood and gradients as the full model:
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try:
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np.random.seed(1234)
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Z = self.X[np.random.choice(self.X.shape[0], replace=False, size=10)].copy()
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Q = Z.shape[1]
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m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(
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self.X,
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self.Y,
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Z,
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GPy.kern.RBF(Q) + GPy.kern.Matern32(Q) + GPy.kern.Bias(Q),
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GPy.likelihoods.Gaussian(),
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missing_data=True,
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stochastic=False,
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)
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assert m.checkgrad()
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m.optimize("adadelta", max_iters=10)
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assert m.checkgrad()
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np.random.seed(1234)
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Z = self.X[np.random.choice(self.X.shape[0], replace=False, size=10)].copy()
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Q = Z.shape[1]
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m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(
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self.X,
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self.Y,
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Z,
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GPy.kern.RBF(Q) + GPy.kern.Matern32(Q) + GPy.kern.Bias(Q),
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GPy.likelihoods.Gaussian(),
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missing_data=True,
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stochastic=False,
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)
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assert m.checkgrad()
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m.optimize("adadelta", max_iters=10)
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assert m.checkgrad()
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m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(
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self.X,
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self.Y,
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Z,
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GPy.kern.RBF(Q) + GPy.kern.Matern32(Q) + GPy.kern.Bias(Q),
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GPy.likelihoods.Gaussian(),
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missing_data=True,
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stochastic=True,
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)
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assert m.checkgrad()
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m.optimize("rprop", max_iters=10)
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assert m.checkgrad()
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m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(
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self.X,
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self.Y,
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Z,
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GPy.kern.RBF(Q) + GPy.kern.Matern32(Q) + GPy.kern.Bias(Q),
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GPy.likelihoods.Gaussian(),
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missing_data=True,
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stochastic=True,
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)
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assert m.checkgrad()
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m.optimize("rprop", max_iters=10)
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assert m.checkgrad()
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m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(
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self.X,
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self.Y,
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Z,
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GPy.kern.RBF(Q) + GPy.kern.Matern32(Q) + GPy.kern.Bias(Q),
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GPy.likelihoods.Gaussian(),
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missing_data=False,
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stochastic=False,
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)
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assert m.checkgrad()
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m.optimize("rprop", max_iters=10)
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assert m.checkgrad()
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m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(
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self.X,
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self.Y,
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Z,
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GPy.kern.RBF(Q) + GPy.kern.Matern32(Q) + GPy.kern.Bias(Q),
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GPy.likelihoods.Gaussian(),
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missing_data=False,
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stochastic=False,
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)
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assert m.checkgrad()
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m.optimize("rprop", max_iters=10)
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assert m.checkgrad()
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m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(
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self.X,
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self.Y,
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Z,
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GPy.kern.RBF(Q) + GPy.kern.Matern32(Q) + GPy.kern.Bias(Q),
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GPy.likelihoods.Gaussian(),
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missing_data=False,
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stochastic=True,
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)
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assert m.checkgrad()
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m.optimize("adadelta", max_iters=10)
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assert m.checkgrad()
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except ImportError:
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from nose import SkipTest
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raise SkipTest("climin not installed, skipping stochastic gradients")
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m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(
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self.X,
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self.Y,
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Z,
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GPy.kern.RBF(Q) + GPy.kern.Matern32(Q) + GPy.kern.Bias(Q),
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GPy.likelihoods.Gaussian(),
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missing_data=False,
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stochastic=True,
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)
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assert m.checkgrad()
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m.optimize("adadelta", max_iters=10)
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assert m.checkgrad()
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def test_predict_missing_data(self):
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self.setup()
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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self.Y,
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self.Q,
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@ -277,6 +290,7 @@ class SparseGPMinibatchTest(unittest.TestCase):
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np.testing.assert_allclose(var1[:, :, i], var2)
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def test_lik_comparisons_m0_s0(self):
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self.setup()
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# Test if the different implementations give the exact same likelihood as the full model.
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# All of the following settings should give the same likelihood and gradients as the full model:
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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@ -290,6 +304,7 @@ class SparseGPMinibatchTest(unittest.TestCase):
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assert m.checkgrad()
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def test_lik_comparisons_m1_s1(self):
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self.setup()
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# Test if the different implementations give the exact same likelihood as the full model.
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# All of the following settings should give the same likelihood and gradients as the full model:
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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@ -308,6 +323,7 @@ class SparseGPMinibatchTest(unittest.TestCase):
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assert m.checkgrad()
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def test_lik_comparisons_m0_s1(self):
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self.setup()
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# Test if the different implementations give the exact same likelihood as the full model.
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# All of the following settings should give the same likelihood and gradients as the full model:
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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@ -326,6 +342,7 @@ class SparseGPMinibatchTest(unittest.TestCase):
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assert m.checkgrad()
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def test_gradients_missingdata(self):
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self.setup()
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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self.Y,
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self.Q,
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@ -337,6 +354,7 @@ class SparseGPMinibatchTest(unittest.TestCase):
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assert m.checkgrad()
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def test_gradients_missingdata_stochastics(self):
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self.setup()
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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self.Y,
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self.Q,
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@ -357,6 +375,7 @@ class SparseGPMinibatchTest(unittest.TestCase):
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assert m.checkgrad()
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def test_gradients_stochastics(self):
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self.setup()
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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self.Y,
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self.Q,
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@ -377,6 +396,7 @@ class SparseGPMinibatchTest(unittest.TestCase):
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assert m.checkgrad()
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def test_predict(self):
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self.setup()
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# Test if the different implementations give the exact same likelihood as the full model.
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# All of the following settings should give the same likelihood and gradients as the full model:
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m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(
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@ -393,8 +413,3 @@ class SparseGPMinibatchTest(unittest.TestCase):
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)
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np.testing.assert_allclose(m.gradient, self.m_full.gradient)
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assert m.checkgrad()
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if __name__ == "__main__":
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# import sys;sys.argv = ['', 'Test.testName']
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unittest.main()
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