# -*- 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 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()