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fix: splitting forecast tests into 3 to circumvent 10 minute stop of travis

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mzwiessele 2017-06-19 13:31:14 +01:00
parent 5074a94f1d
commit 2820ddfdf7
1 changed files with 81 additions and 20 deletions
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101
GPy/testing/gpy_kernels_state_space_tests.py
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@ -306,11 +306,7 @@ class StateSpaceKernelsTests(np.testing.TestCase):
gp_kernel=gp_kernel, gp_kernel=gp_kernel,
mean_compare_decimal=2, var_compare_decimal=2) mean_compare_decimal=2, var_compare_decimal=2)
def test_forecast(self,): def test_forecast_regular(self,):
"""
Test time-series forecasting.
"""
# Generate data -> # Generate data ->
np.random.seed(339) # seed the random number generator np.random.seed(339) # seed the random number generator
#import pdb; pdb.set_trace() #import pdb; pdb.set_trace()
@ -334,37 +330,102 @@ class StateSpaceKernelsTests(np.testing.TestCase):
#import pdb; pdb.set_trace() #import pdb; pdb.set_trace()
def get_new_kernels(): periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
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 = 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.lengthscale.constrain_bounded(0.25, 1000) gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,]) periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \ ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000) ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.std_periodic.period.constrain_bounded(0.15, 100) ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
return ss_kernel, gp_kernel
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'regular', self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'regular',
use_cython=False, optimize_max_iters=30, check_gradients=True, use_cython=False, optimize_max_iters=30, check_gradients=True,
predict_X=X_test, predict_X=X_test,
gp_kernel=gp_kernel, gp_kernel=gp_kernel,
mean_compare_decimal=2, var_compare_decimal=2) mean_compare_decimal=2, var_compare_decimal=2)
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)
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd', self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd',
use_cython=False, optimize_max_iters=30, check_gradients=False, use_cython=False, optimize_max_iters=30, check_gradients=False,
predict_X=X_test, predict_X=X_test,
gp_kernel=gp_kernel, gp_kernel=gp_kernel,
mean_compare_decimal=2, var_compare_decimal=2) mean_compare_decimal=2, var_compare_decimal=2)
ss_kernel, gp_kernel = get_new_kernels() 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', self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd',
use_cython=True, optimize_max_iters=30, check_gradients=False, use_cython=True, optimize_max_iters=30, check_gradients=False,
predict_X=X_test, predict_X=X_test,