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[statespace] changed tests to check model integrity with GP model
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Max Zwiessele
parent
13cf717231
commit
7984e17805
1 changed files with 160 additions and 152 deletions
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@ -20,7 +20,7 @@ class StateSpaceKernelsTests(np.testing.TestCase):
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def run_for_model(self, X, Y, ss_kernel, kalman_filter_type = 'regular',
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use_cython=False, check_gradients=True,
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optimize=True, optimize_max_iters=1000,predict_X=None,
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optimize=True, optimize_max_iters=250, predict_X=None,
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compare_with_GP=True, gp_kernel=None,
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mean_compare_decimal=10, var_compare_decimal=7):
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@ -31,34 +31,35 @@ class StateSpaceKernelsTests(np.testing.TestCase):
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if check_gradients:
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self.assertTrue(m1.checkgrad())
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if optimize:
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m1.optimize(optimizer='lbfgsb',max_iters=optimize_max_iters)
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if 1:#optimize:
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m1.optimize(optimizer='lbfgsb',max_iters=2)
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if compare_with_GP and (predict_X is None):
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predict_X = X
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if (predict_X is not None):
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x_pred_reg_1 = m1.predict(predict_X)
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x_quant_reg_1 = m1.predict_quantiles(predict_X)
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self.assertTrue(compare_with_GP)
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if compare_with_GP:
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np.random.seed(254856)
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m2 = GPy.models.GPRegression(X,Y, gp_kernel)
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m2.optimize(optimizer='lbfgsb', max_iters=optimize_max_iters)
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#print(m2)
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#m2.randomize()
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m2.optimize(max_iters=optimize_max_iters)
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m1[:] = m2[:]
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if (predict_X is not None):
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x_pred_reg_1 = m1.predict(predict_X)
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x_quant_reg_1 = m1.predict_quantiles(predict_X)
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x_pred_reg_2 = m2.predict(predict_X)
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x_quant_reg_2 = m2.predict_quantiles(predict_X)
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# Test values
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#print np.max(np.abs(x_pred_reg_1[0]-x_pred_reg_2[0]))
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np.testing.assert_almost_equal(np.max(np.abs(x_pred_reg_1[0]- \
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x_pred_reg_2[0])), 0, decimal=mean_compare_decimal)
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np.testing.assert_array_almost_equal(x_pred_reg_1[0], x_pred_reg_2[0], 3)
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np.testing.assert_array_almost_equal(x_pred_reg_1[1], x_pred_reg_2[1], 3)
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np.testing.assert_array_almost_equal(x_quant_reg_1[0], x_quant_reg_2[0], 3)
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np.testing.assert_array_almost_equal(x_quant_reg_1[1], x_quant_reg_2[1], 3)
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np.testing.assert_almost_equal(m1.log_likelihood(), m2.log_likelihood(), 3)
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np.testing.assert_array_almost_equal(m1.gradient, m2.gradient, 2)
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# Test variances
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#print np.max(np.abs(x_pred_reg_1[1]-x_pred_reg_2[1]))
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np.testing.assert_almost_equal(np.max(np.abs(x_pred_reg_1[1]- \
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x_pred_reg_2[1])), 0, decimal=var_compare_decimal)
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def test_Matern32_kernel(self,):
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np.random.seed(234) # seed the random number generator
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@ -73,7 +74,7 @@ class StateSpaceKernelsTests(np.testing.TestCase):
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predict_X=X,
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compare_with_GP=True,
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gp_kernel=gp_kernel,
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mean_compare_decimal=10, var_compare_decimal=7)
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mean_compare_decimal=5, var_compare_decimal=5)
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def test_Matern52_kernel(self,):
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np.random.seed(234) # seed the random number generator
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@ -87,7 +88,7 @@ class StateSpaceKernelsTests(np.testing.TestCase):
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self.run_for_model(X, Y, ss_kernel, check_gradients=True,
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optimize = True, predict_X=X,
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compare_with_GP=True, gp_kernel=gp_kernel,
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mean_compare_decimal=8, var_compare_decimal=7)
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mean_compare_decimal=5, var_compare_decimal=5)
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def test_RBF_kernel(self,):
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np.random.seed(234) # seed the random number generator
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@ -95,13 +96,14 @@ class StateSpaceKernelsTests(np.testing.TestCase):
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plot = False, points_num=50, x_interval = (0, 20), random=True)
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X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
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ss_kernel = GPy.kern.sde_RBF(1,active_dims=[0,])
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gp_kernel = GPy.kern.RBF(1,active_dims=[0,])
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ss_kernel = GPy.kern.sde_RBF(1, Y.var(), X.ptp()/2., active_dims=[0,])
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gp_kernel = GPy.kern.RBF(1, Y.var(), X.ptp()/2., active_dims=[0,])
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self.run_for_model(X, Y, ss_kernel, check_gradients=True,
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predict_X=X,
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gp_kernel=gp_kernel,
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mean_compare_decimal=1, var_compare_decimal=1)
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optimize_max_iters=1000,
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mean_compare_decimal=2, var_compare_decimal=2)
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def test_periodic_kernel(self,):
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np.random.seed(322) # seed the random number generator
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@ -170,22 +172,25 @@ class StateSpaceKernelsTests(np.testing.TestCase):
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self.run_for_model(X, Y, ss_kernel, check_gradients=True,
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predict_X=X,
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gp_kernel=gp_kernel,
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mean_compare_decimal=10, var_compare_decimal=7)
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mean_compare_decimal=5, var_compare_decimal=5)
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def test_exponential_kernel(self,):
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np.random.seed(234) # seed the random number generator
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np.random.seed(12345) # seed the random number generator
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(X,Y) = generate_linear_data(x_points=None, tangent=1.0, add_term=20.0, noise_var=2.0,
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plot = False, points_num=50, x_interval = (0, 20), random=True)
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plot = False, points_num=10, x_interval = (0, 20), random=True)
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X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
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ss_kernel = GPy.kern.sde_Exponential(1, active_dims=[0,])
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gp_kernel = GPy.kern.Exponential(1, active_dims=[0,])
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ss_kernel = GPy.kern.sde_Exponential(1, Y.var(), X.ptp()/2., active_dims=[0,])
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gp_kernel = GPy.kern.Exponential(1, Y.var(), X.ptp()/2., active_dims=[0,])
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Y -= Y.mean()
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self.run_for_model(X, Y, ss_kernel, check_gradients=True,
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predict_X=X,
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gp_kernel=gp_kernel,
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mean_compare_decimal=5, var_compare_decimal=6)
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optimize_max_iters=1000,
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mean_compare_decimal=2, var_compare_decimal=2)
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def test_kernel_addition(self,):
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#np.random.seed(329) # seed the random number generator
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@ -249,25 +254,27 @@ class StateSpaceKernelsTests(np.testing.TestCase):
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return ss_kernel, gp_kernel
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ss_kernel, gp_kernel = get_new_kernels()
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#import ipdb;ipdb.set_trace()
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self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'svd',
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use_cython=True, optimize_max_iters=10, check_gradients=True,
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predict_X=X,
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gp_kernel=gp_kernel,
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mean_compare_decimal=-1, var_compare_decimal=-1)
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mean_compare_decimal=2, var_compare_decimal=2)
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ss_kernel, gp_kernel = get_new_kernels()
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self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'regular',
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use_cython=False, optimize_max_iters=10, check_gradients=True,
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predict_X=X,
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gp_kernel=gp_kernel,
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mean_compare_decimal=-1, var_compare_decimal=-1)
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mean_compare_decimal=2, var_compare_decimal=2)
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ss_kernel, gp_kernel = get_new_kernels()
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self.run_for_model(X, Y, ss_kernel, kalman_filter_type = 'svd',
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use_cython=False, optimize_max_iters=10, check_gradients=True,
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predict_X=X,
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gp_kernel=gp_kernel,
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mean_compare_decimal=-1, var_compare_decimal=0)
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mean_compare_decimal=2, var_compare_decimal=2)
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def test_forecast(self,):
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"""
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@ -317,21 +324,22 @@ class StateSpaceKernelsTests(np.testing.TestCase):
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use_cython=False, optimize_max_iters=30, check_gradients=True,
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predict_X=X_test,
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gp_kernel=gp_kernel,
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mean_compare_decimal=0, var_compare_decimal=-1)
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mean_compare_decimal=2, var_compare_decimal=2)
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ss_kernel, gp_kernel = get_new_kernels()
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self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd',
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use_cython=False, optimize_max_iters=30, check_gradients=False,
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predict_X=X_test,
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gp_kernel=gp_kernel,
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mean_compare_decimal=-1, var_compare_decimal=-1)
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mean_compare_decimal=2, var_compare_decimal=2)
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ss_kernel, gp_kernel = get_new_kernels()
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self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd',
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use_cython=True, optimize_max_iters=30, check_gradients=False,
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predict_X=X_test,
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gp_kernel=gp_kernel,
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mean_compare_decimal=-1, var_compare_decimal=-1)
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mean_compare_decimal=2, var_compare_decimal=2)
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if __name__ == "__main__":
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print("Running state-space inference tests...")
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