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https://github.com/SheffieldML/GPy.git
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244 lines
11 KiB
Python
244 lines
11 KiB
Python
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import unittest
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import numpy as np
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import GPy
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class GradientTests(unittest.TestCase):
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def setUp(self):
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######################################
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# # 1 dimensional example
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# sample inputs and outputs
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self.X1D = np.random.uniform(-3., 3., (20, 1))
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self.Y1D = np.sin(self.X1D) + np.random.randn(20, 1) * 0.05
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######################################
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# # 2 dimensional example
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# sample inputs and outputs
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self.X2D = np.random.uniform(-3., 3., (40, 2))
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self.Y2D = np.sin(self.X2D[:, 0:1]) * np.sin(self.X2D[:, 1:2]) + np.random.randn(40, 1) * 0.05
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def check_model(self, kern, model_type='GPRegression', dimension=1, uncertain_inputs=False):
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# Get the correct gradients
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if dimension == 1:
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X = self.X1D
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Y = self.Y1D
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else:
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X = self.X2D
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Y = self.Y2D
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# Get model type (GPRegression, SparseGPRegression, etc)
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model_fit = getattr(GPy.models, model_type)
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# noise = GPy.kern.white(dimension)
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kern = kern # + noise
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if uncertain_inputs:
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m = model_fit(X, Y, kernel=kern, X_variance=np.random.rand(X.shape[0], X.shape[1]))
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else:
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m = model_fit(X, Y, kernel=kern)
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m.randomize()
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# contrain all parameters to be positive
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self.assertTrue(m.checkgrad())
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def test_GPRegression_rbf_1d(self):
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''' Testing the GP regression with rbf kernel with white kernel on 1d data '''
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rbf = GPy.kern.rbf(1)
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self.check_model(rbf, model_type='GPRegression', dimension=1)
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def test_GPRegression_rbf_2D(self):
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''' Testing the GP regression with rbf kernel on 2d data '''
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rbf = GPy.kern.rbf(2)
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self.check_model(rbf, model_type='GPRegression', dimension=2)
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def test_GPRegression_rbf_ARD_2D(self):
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''' Testing the GP regression with rbf kernel on 2d data '''
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k = GPy.kern.rbf(2, ARD=True)
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self.check_model(k, model_type='GPRegression', dimension=2)
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def test_GPRegression_mlp_1d(self):
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''' Testing the GP regression with mlp kernel with white kernel on 1d data '''
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mlp = GPy.kern.mlp(1)
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self.check_model(mlp, model_type='GPRegression', dimension=1)
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def test_GPRegression_poly_1d(self):
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''' Testing the GP regression with polynomial kernel with white kernel on 1d data '''
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mlp = GPy.kern.poly(1, degree=5)
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self.check_model(mlp, model_type='GPRegression', dimension=1)
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def test_GPRegression_matern52_1D(self):
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''' Testing the GP regression with matern52 kernel on 1d data '''
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matern52 = GPy.kern.Matern52(1)
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self.check_model(matern52, model_type='GPRegression', dimension=1)
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def test_GPRegression_matern52_2D(self):
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''' Testing the GP regression with matern52 kernel on 2d data '''
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matern52 = GPy.kern.Matern52(2)
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self.check_model(matern52, model_type='GPRegression', dimension=2)
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def test_GPRegression_matern52_ARD_2D(self):
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''' Testing the GP regression with matern52 kernel on 2d data '''
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matern52 = GPy.kern.Matern52(2, ARD=True)
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self.check_model(matern52, model_type='GPRegression', dimension=2)
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def test_GPRegression_matern32_1D(self):
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''' Testing the GP regression with matern32 kernel on 1d data '''
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matern32 = GPy.kern.Matern32(1)
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self.check_model(matern32, model_type='GPRegression', dimension=1)
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def test_GPRegression_matern32_2D(self):
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''' Testing the GP regression with matern32 kernel on 2d data '''
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matern32 = GPy.kern.Matern32(2)
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self.check_model(matern32, model_type='GPRegression', dimension=2)
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def test_GPRegression_matern32_ARD_2D(self):
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''' Testing the GP regression with matern32 kernel on 2d data '''
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matern32 = GPy.kern.Matern32(2, ARD=True)
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self.check_model(matern32, model_type='GPRegression', dimension=2)
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def test_GPRegression_exponential_1D(self):
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''' Testing the GP regression with exponential kernel on 1d data '''
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exponential = GPy.kern.exponential(1)
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self.check_model(exponential, model_type='GPRegression', dimension=1)
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def test_GPRegression_exponential_2D(self):
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''' Testing the GP regression with exponential kernel on 2d data '''
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exponential = GPy.kern.exponential(2)
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self.check_model(exponential, model_type='GPRegression', dimension=2)
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def test_GPRegression_exponential_ARD_2D(self):
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''' Testing the GP regression with exponential kernel on 2d data '''
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exponential = GPy.kern.exponential(2, ARD=True)
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self.check_model(exponential, model_type='GPRegression', dimension=2)
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def test_GPRegression_bias_kern_1D(self):
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''' Testing the GP regression with bias kernel on 1d data '''
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bias = GPy.kern.bias(1)
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self.check_model(bias, model_type='GPRegression', dimension=1)
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def test_GPRegression_bias_kern_2D(self):
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''' Testing the GP regression with bias kernel on 2d data '''
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bias = GPy.kern.bias(2)
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self.check_model(bias, model_type='GPRegression', dimension=2)
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def test_GPRegression_linear_kern_1D_ARD(self):
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''' Testing the GP regression with linear kernel on 1d data '''
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linear = GPy.kern.linear(1, ARD=True)
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self.check_model(linear, model_type='GPRegression', dimension=1)
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def test_GPRegression_linear_kern_2D_ARD(self):
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''' Testing the GP regression with linear kernel on 2d data '''
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linear = GPy.kern.linear(2, ARD=True)
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self.check_model(linear, model_type='GPRegression', dimension=2)
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def test_GPRegression_linear_kern_1D(self):
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''' Testing the GP regression with linear kernel on 1d data '''
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linear = GPy.kern.linear(1)
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self.check_model(linear, model_type='GPRegression', dimension=1)
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def test_GPRegression_linear_kern_2D(self):
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''' Testing the GP regression with linear kernel on 2d data '''
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linear = GPy.kern.linear(2)
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self.check_model(linear, model_type='GPRegression', dimension=2)
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def test_SparseGPRegression_rbf_white_kern_1d(self):
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''' Testing the sparse GP regression with rbf kernel with white kernel on 1d data '''
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rbf = GPy.kern.rbf(1)
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self.check_model(rbf, model_type='SparseGPRegression', dimension=1)
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def test_SparseGPRegression_rbf_white_kern_2D(self):
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''' Testing the sparse GP regression with rbf kernel on 2d data '''
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rbf = GPy.kern.rbf(2)
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self.check_model(rbf, model_type='SparseGPRegression', dimension=2)
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def test_SparseGPRegression_rbf_linear_white_kern_1D(self):
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''' Testing the sparse GP regression with rbf kernel on 2d data '''
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rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1)
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self.check_model(rbflin, model_type='SparseGPRegression', dimension=1)
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def test_SparseGPRegression_rbf_linear_white_kern_2D(self):
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''' Testing the sparse GP regression with rbf kernel on 2d data '''
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rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
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self.check_model(rbflin, model_type='SparseGPRegression', dimension=2)
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def test_SparseGPRegression_rbf_linear_white_kern_2D_uncertain_inputs(self):
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''' Testing the sparse GP regression with rbf, linear kernel on 2d data with uncertain inputs'''
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rbflin = GPy.kern.rbf(2) + GPy.kern.linear(2)
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self.check_model(rbflin, model_type='SparseGPRegression', dimension=2, uncertain_inputs=1)
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def test_SparseGPRegression_rbf_linear_white_kern_1D_uncertain_inputs(self):
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''' Testing the sparse GP regression with rbf, linear kernel on 1d data with uncertain inputs'''
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rbflin = GPy.kern.rbf(1) + GPy.kern.linear(1)
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self.check_model(rbflin, model_type='SparseGPRegression', dimension=1, uncertain_inputs=1)
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def test_GPLVM_rbf_bias_white_kern_2D(self):
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""" Testing GPLVM with rbf + bias kernel """
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N, input_dim, D = 50, 1, 2
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X = np.random.rand(N, input_dim)
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k = GPy.kern.rbf(input_dim, 0.5, 0.9 * np.ones((1,))) + GPy.kern.bias(input_dim, 0.1) + GPy.kern.white(input_dim, 0.05)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T
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m = GPy.models.GPLVM(Y, input_dim, kernel=k)
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self.assertTrue(m.checkgrad())
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def test_GPLVM_rbf_linear_white_kern_2D(self):
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""" Testing GPLVM with rbf + bias kernel """
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N, input_dim, D = 50, 1, 2
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X = np.random.rand(N, input_dim)
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k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim, 0.1) + GPy.kern.white(input_dim, 0.05)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N), K, input_dim).T
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m = GPy.models.GPLVM(Y, input_dim, init='PCA', kernel=k)
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self.assertTrue(m.checkgrad())
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def test_GP_EP_probit(self):
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N = 20
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X = np.hstack([np.random.normal(5, 2, N / 2), np.random.normal(10, 2, N / 2)])[:, None]
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Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None]
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kernel = GPy.kern.rbf(1)
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m = GPy.models.GPClassification(X,Y,kernel=kernel)
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m.update_likelihood_approximation()
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self.assertTrue(m.checkgrad())
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def test_sparse_EP_DTC_probit(self):
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N = 20
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X = np.hstack([np.random.normal(5, 2, N / 2), np.random.normal(10, 2, N / 2)])[:, None]
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Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None]
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Z = np.linspace(0, 15, 4)[:, None]
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kernel = GPy.kern.rbf(1)
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m = GPy.models.SparseGPClassification(X,Y,kernel=kernel,Z=Z)
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#distribution = GPy.likelihoods.likelihood_functions.Binomial()
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#likelihood = GPy.likelihoods.EP(Y, distribution)
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#m = GPy.core.SparseGP(X, likelihood, kernel, Z)
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#m.ensure_default_constraints()
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m.update_likelihood_approximation()
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self.assertTrue(m.checkgrad())
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def test_generalized_FITC(self):
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N = 20
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X = np.hstack([np.random.rand(N / 2) + 1, np.random.rand(N / 2) - 1])[:, None]
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k = GPy.kern.rbf(1) + GPy.kern.white(1)
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Y = np.hstack([np.ones(N/2),np.zeros(N/2)])[:,None]
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m = GPy.models.FITCClassification(X, Y, kernel = k)
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m.update_likelihood_approximation()
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self.assertTrue(m.checkgrad())
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def multioutput_regression_1D(self):
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X1 = np.random.rand(50, 1) * 8
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X2 = np.random.rand(30, 1) * 5
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X = np.vstack((X1, X2))
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Y1 = np.sin(X1) + np.random.randn(*X1.shape) * 0.05
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Y2 = -np.sin(X2) + np.random.randn(*X2.shape) * 0.05
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Y = np.vstack((Y1, Y2))
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k1 = GPy.kern.rbf(1)
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m = GPy.models.GPMultioutputRegression(X_list=[X1,X2],Y_list=[Y1,Y2],kernel_list=[k1])
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m.constrain_fixed('.*rbf_var', 1.)
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self.assertTrue(m.checkgrad())
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if __name__ == "__main__":
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print "Running unit tests, please be (very) patient..."
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unittest.main()
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