Source code for GPy.testing.gpy_kernels_state_space_tests

# -*- coding: utf-8 -*-
# Copyright (c) 2015, Alex Grigorevskiy
# Licensed under the BSD 3-clause license (see LICENSE.txt)
"""
Testing state space related functions.
"""
import unittest
import numpy as np
import GPy
import GPy.models.state_space_model as SS_model
from .state_space_main_tests import generate_x_points, generate_sine_data, \
    generate_linear_data, generate_brownian_data, generate_linear_plus_sin
from nose import SkipTest

#from state_space_main_tests import generate_x_points, generate_sine_data, \
#    generate_linear_data, generate_brownian_data, generate_linear_plus_sin

[docs]class StateSpaceKernelsTests(np.testing.TestCase):
[docs] def setUp(self): pass
[docs] def run_for_model(self, X, Y, ss_kernel, kalman_filter_type = 'regular', use_cython=False, check_gradients=True, optimize=True, optimize_max_iters=250, predict_X=None, compare_with_GP=True, gp_kernel=None, mean_compare_decimal=10, var_compare_decimal=7): m1 = SS_model.StateSpace(X,Y, ss_kernel, kalman_filter_type=kalman_filter_type, use_cython=use_cython) m1.likelihood[:] = Y.var()/100. if check_gradients: self.assertTrue(m1.checkgrad()) if 1:#optimize: m1.optimize(optimizer='lbfgsb', max_iters=1) if compare_with_GP and (predict_X is None): predict_X = X self.assertTrue(compare_with_GP) if compare_with_GP: m2 = GPy.models.GPRegression(X,Y, gp_kernel) m2[:] = m1[:] if (predict_X is not None): x_pred_reg_1 = m1.predict(predict_X) x_quant_reg_1 = m1.predict_quantiles(predict_X) x_pred_reg_2 = m2.predict(predict_X) x_quant_reg_2 = m2.predict_quantiles(predict_X) np.testing.assert_array_almost_equal(x_pred_reg_1[0], x_pred_reg_2[0], mean_compare_decimal) np.testing.assert_array_almost_equal(x_pred_reg_1[1], x_pred_reg_2[1], var_compare_decimal) np.testing.assert_array_almost_equal(x_quant_reg_1[0], x_quant_reg_2[0], mean_compare_decimal) np.testing.assert_array_almost_equal(x_quant_reg_1[1], x_quant_reg_2[1], mean_compare_decimal) np.testing.assert_array_almost_equal(m1.gradient, m2.gradient, var_compare_decimal) np.testing.assert_almost_equal(m1.log_likelihood(), m2.log_likelihood(), var_compare_decimal)
[docs] def test_Matern32_kernel(self,): np.random.seed(234) # seed the random number generator (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=10.0, noise_var=2.0, plot = False, points_num=50, x_interval = (0, 20), random=True) X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) ss_kernel = GPy.kern.sde_Matern32(1,active_dims=[0,]) gp_kernel = GPy.kern.Matern32(1,active_dims=[0,]) self.run_for_model(X, Y, ss_kernel, check_gradients=True, predict_X=X, compare_with_GP=True, gp_kernel=gp_kernel, mean_compare_decimal=5, var_compare_decimal=5)
[docs] def test_Matern52_kernel(self,): np.random.seed(234) # seed the random number generator (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=10.0, noise_var=2.0, plot = False, points_num=50, x_interval = (0, 20), random=True) X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) ss_kernel = GPy.kern.sde_Matern52(1,active_dims=[0,]) gp_kernel = GPy.kern.Matern52(1,active_dims=[0,]) self.run_for_model(X, Y, ss_kernel, check_gradients=True, optimize = True, predict_X=X, compare_with_GP=True, gp_kernel=gp_kernel, mean_compare_decimal=5, var_compare_decimal=5)
[docs] def test_RBF_kernel(self,): #import pdb;pdb.set_trace() np.random.seed(234) # seed the random number generator (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=10.0, noise_var=2.0, plot = False, points_num=50, x_interval = (0, 20), random=True) X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) ss_kernel = GPy.kern.sde_RBF(1, 110., 1.5, active_dims=[0,], balance=True, approx_order=10) gp_kernel = GPy.kern.RBF(1, 110., 1.5, active_dims=[0,]) self.run_for_model(X, Y, ss_kernel, check_gradients=True, predict_X=X, gp_kernel=gp_kernel, optimize_max_iters=1000, mean_compare_decimal=2, var_compare_decimal=1)
[docs] def test_periodic_kernel(self,): np.random.seed(322) # seed the random number generator (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=10.0, noise_var=2.0, plot = False, points_num=50, x_interval = (0, 20), random=True) X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) ss_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,]) ss_kernel.lengthscale.constrain_bounded(0.27, 1000) ss_kernel.period.constrain_bounded(0.17, 100) gp_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,]) gp_kernel.lengthscale.constrain_bounded(0.27, 1000) gp_kernel.period.constrain_bounded(0.17, 100) self.run_for_model(X, Y, ss_kernel, check_gradients=True, predict_X=X, gp_kernel=gp_kernel, mean_compare_decimal=3, var_compare_decimal=3)
[docs] def test_quasi_periodic_kernel(self,): np.random.seed(329) # seed the random number generator (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=10.0, noise_var=2.0, plot = False, points_num=50, x_interval = (0, 20), random=True) X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) ss_kernel = GPy.kern.sde_Matern32(1)*GPy.kern.sde_StdPeriodic(1,active_dims=[0,]) ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) ss_kernel.std_periodic.period.constrain_bounded(0.15, 100) gp_kernel = GPy.kern.Matern32(1)*GPy.kern.StdPeriodic(1,active_dims=[0,]) gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) gp_kernel.std_periodic.period.constrain_bounded(0.15, 100) self.run_for_model(X, Y, ss_kernel, check_gradients=True, predict_X=X, gp_kernel=gp_kernel, mean_compare_decimal=1, var_compare_decimal=2)
[docs] def test_linear_kernel(self,): np.random.seed(234) # seed the random number generator (X,Y) = generate_linear_data(x_points=None, tangent=2.0, add_term=20.0, noise_var=2.0, plot = False, points_num=50, x_interval = (0, 20), random=True) X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + GPy.kern.sde_Bias(1, active_dims=[0,]) gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) self.run_for_model(X, Y, ss_kernel, check_gradients= False, predict_X=X, gp_kernel=gp_kernel, mean_compare_decimal=5, var_compare_decimal=5)
[docs] def test_brownian_kernel(self,): np.random.seed(234) # seed the random number generator (X,Y) = generate_brownian_data(x_points=None, kernel_var=2.0, noise_var = 0.1, plot = False, points_num=50, x_interval = (0, 20), random=True) X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) ss_kernel = GPy.kern.sde_Brownian() gp_kernel = GPy.kern.Brownian() self.run_for_model(X, Y, ss_kernel, check_gradients=True, predict_X=X, gp_kernel=gp_kernel, mean_compare_decimal=4, var_compare_decimal=4)
[docs] def test_exponential_kernel(self,): np.random.seed(12345) # seed the random number generator (X,Y) = generate_linear_data(x_points=None, tangent=1.0, add_term=20.0, noise_var=2.0, plot = False, points_num=10, x_interval = (0, 20), random=True) X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) ss_kernel = GPy.kern.sde_Exponential(1, Y.var(), X.ptp()/2., active_dims=[0,]) gp_kernel = GPy.kern.Exponential(1, Y.var(), X.ptp()/2., active_dims=[0,]) Y -= Y.mean() self.run_for_model(X, Y, ss_kernel, check_gradients=True, predict_X=X, gp_kernel=gp_kernel, optimize_max_iters=1000, mean_compare_decimal=2, var_compare_decimal=2)
[docs] def test_kernel_addition_svd(self,): #np.random.seed(329) # seed the random number generator np.random.seed(42) (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0, plot = False, points_num=100, x_interval = (0, 40), random=True) (X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0, plot = False, points_num=100, x_interval = (0, 40), random=True) # Sine data <- Y = Y + Y1 Y -= Y.mean() X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) def get_new_kernels(): ss_kernel = GPy.kern.sde_Linear(1, X, variances=1) + GPy.kern.sde_StdPeriodic(1, period=5.0, variance=300, lengthscale=3, active_dims=[0,]) #ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) #ss_kernel.std_periodic.period.constrain_bounded(3, 8) gp_kernel = GPy.kern.Linear(1, variances=1) + GPy.kern.StdPeriodic(1, period=5.0, variance=300, lengthscale=3, active_dims=[0,]) #gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) #gp_kernel.std_periodic.period.constrain_bounded(3, 8) return ss_kernel, gp_kernel # Cython is available only with svd. ss_kernel, gp_kernel = get_new_kernels() self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'svd', use_cython=True, optimize_max_iters=10, check_gradients=False, predict_X=X, gp_kernel=gp_kernel, mean_compare_decimal=3, var_compare_decimal=3) ss_kernel, gp_kernel = get_new_kernels() self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'svd', use_cython=False, optimize_max_iters=10, check_gradients=False, predict_X=X, gp_kernel=gp_kernel, mean_compare_decimal=3, var_compare_decimal=3)
[docs] def test_kernel_addition_regular(self,): #np.random.seed(329) # seed the random number generator np.random.seed(42) (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0, plot = False, points_num=100, x_interval = (0, 40), random=True) (X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0, plot = False, points_num=100, x_interval = (0, 40), random=True) # Sine data <- Y = Y + Y1 Y -= Y.mean() X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) def get_new_kernels(): ss_kernel = GPy.kern.sde_Linear(1, X, variances=1) + GPy.kern.sde_StdPeriodic(1, period=5.0, variance=300, lengthscale=3, active_dims=[0,]) #ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) #ss_kernel.std_periodic.period.constrain_bounded(3, 8) gp_kernel = GPy.kern.Linear(1, variances=1) + GPy.kern.StdPeriodic(1, period=5.0, variance=300, lengthscale=3, active_dims=[0,]) #gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) #gp_kernel.std_periodic.period.constrain_bounded(3, 8) return ss_kernel, gp_kernel ss_kernel, gp_kernel = get_new_kernels() try: self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'regular', use_cython=False, optimize_max_iters=10, check_gradients=True, predict_X=X, gp_kernel=gp_kernel, mean_compare_decimal=2, var_compare_decimal=2) except AssertionError: raise SkipTest("Skipping Regular kalman filter for kernel addition, because it is not stable (normal situation) for this data.")
[docs] def test_kernel_multiplication(self,): np.random.seed(329) # seed the random number generator (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=10.0, noise_var=2.0, plot = False, points_num=50, x_interval = (0, 20), random=True) X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) def get_new_kernels(): ss_kernel = GPy.kern.sde_Matern32(1)*GPy.kern.sde_Matern52(1) gp_kernel = GPy.kern.Matern32(1)*GPy.kern.sde_Matern52(1) return ss_kernel, gp_kernel ss_kernel, gp_kernel = get_new_kernels() #import ipdb;ipdb.set_trace() self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'svd', use_cython=True, optimize_max_iters=10, check_gradients=True, predict_X=X, gp_kernel=gp_kernel, mean_compare_decimal=2, var_compare_decimal=2) ss_kernel, gp_kernel = get_new_kernels() self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'regular', use_cython=False, optimize_max_iters=10, check_gradients=True, predict_X=X, gp_kernel=gp_kernel, mean_compare_decimal=2, var_compare_decimal=2) ss_kernel, gp_kernel = get_new_kernels() self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'svd', use_cython=False, optimize_max_iters=10, check_gradients=True, predict_X=X, gp_kernel=gp_kernel, mean_compare_decimal=2, var_compare_decimal=2)
[docs] def test_forecast_regular(self,): # Generate data -> np.random.seed(339) # seed the random number generator #import pdb; pdb.set_trace() (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0, plot = False, points_num=100, x_interval = (0, 40), random=True) (X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0, plot = False, points_num=100, x_interval = (0, 40), random=True) Y = Y + Y1 X_train = X[X <= 20] Y_train = Y[X <= 20] X_test = X[X > 20] Y_test = Y[X > 20] X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) X_train.shape = (X_train.shape[0],1); Y_train.shape = (Y_train.shape[0],1) X_test.shape = (X_test.shape[0],1); Y_test.shape = (Y_test.shape[0],1) # Generate data <- #import pdb; pdb.set_trace() periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,]) gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) gp_kernel.std_periodic.period.constrain_bounded(0.15, 100) periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,]) ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \ GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) ss_kernel.std_periodic.period.constrain_bounded(0.15, 100) self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'regular', use_cython=False, optimize_max_iters=30, check_gradients=True, predict_X=X_test, gp_kernel=gp_kernel, mean_compare_decimal=2, var_compare_decimal=2)
[docs] def test_forecast_svd(self,): # Generate data -> np.random.seed(339) # seed the random number generator #import pdb; pdb.set_trace() (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0, plot = False, points_num=100, x_interval = (0, 40), random=True) (X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0, plot = False, points_num=100, x_interval = (0, 40), random=True) Y = Y + Y1 X_train = X[X <= 20] Y_train = Y[X <= 20] X_test = X[X > 20] Y_test = Y[X > 20] X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) X_train.shape = (X_train.shape[0],1); Y_train.shape = (Y_train.shape[0],1) X_test.shape = (X_test.shape[0],1); Y_test.shape = (Y_test.shape[0],1) # Generate data <- #import pdb; pdb.set_trace() periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,]) gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) gp_kernel.std_periodic.period.constrain_bounded(0.15, 100) periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,]) ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \ GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) ss_kernel.std_periodic.period.constrain_bounded(0.15, 100) self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd', use_cython=False, optimize_max_iters=30, check_gradients=False, predict_X=X_test, gp_kernel=gp_kernel, mean_compare_decimal=2, var_compare_decimal=2)
[docs] def test_forecast_svd_cython(self,): # Generate data -> np.random.seed(339) # seed the random number generator #import pdb; pdb.set_trace() (X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0, plot = False, points_num=100, x_interval = (0, 40), random=True) (X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0, plot = False, points_num=100, x_interval = (0, 40), random=True) Y = Y + Y1 X_train = X[X <= 20] Y_train = Y[X <= 20] X_test = X[X > 20] Y_test = Y[X > 20] X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1) X_train.shape = (X_train.shape[0],1); Y_train.shape = (Y_train.shape[0],1) X_test.shape = (X_test.shape[0],1); Y_test.shape = (Y_test.shape[0],1) # Generate data <- #import pdb; pdb.set_trace() periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,]) gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) gp_kernel.std_periodic.period.constrain_bounded(0.15, 100) periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,]) ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \ GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) ss_kernel.std_periodic.period.constrain_bounded(0.15, 100) self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd', use_cython=True, optimize_max_iters=30, check_gradients=False, predict_X=X_test, gp_kernel=gp_kernel, mean_compare_decimal=2, var_compare_decimal=2)
if __name__ == "__main__": print("Running state-space inference tests...") unittest.main() #tt = StateSpaceKernelsTests('test_RBF_kernel') #import pdb; pdb.set_trace() #tt.test_Matern32_kernel() #tt.test_Matern52_kernel() #tt.test_RBF_kernel() #tt.test_periodic_kernel() #tt.test_quasi_periodic_kernel() #tt.test_linear_kernel() #tt.test_brownian_kernel() #tt.test_exponential_kernel() #tt.test_kernel_addition() #tt.test_kernel_multiplication() #tt.test_forecast()