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Fixed likelihood tests
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Alan Saul
parent
632a702532
commit
3b5a86ec84
1 changed files with 29 additions and 30 deletions
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@ -10,6 +10,7 @@ from functools import partial
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#np.random.seed(300)
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#np.random.seed(7)
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np.seterr(divide='raise')
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def dparam_partial(inst_func, *args):
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"""
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If we have a instance method that needs to be called but that doesn't
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@ -149,9 +150,9 @@ class TestNoiseModels(object):
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noise_models = {"Student_t_default": {
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"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"names": [".*t_noise"],
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"vals": [self.var],
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"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
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"constraints": [(".*t_noise", constrain_positive), (".*deg_free", constrain_fixed)]
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#"constraints": [("t_noise", constrain_positive), ("deg_free", partial(constrain_fixed, value=5))]
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},
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"laplace": True
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@ -159,63 +160,63 @@ class TestNoiseModels(object):
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"Student_t_1_var": {
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"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"names": [".*t_noise"],
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"vals": [1.0],
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"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
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"constraints": [(".*t_noise", constrain_positive), (".*deg_free", constrain_fixed)]
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},
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"laplace": True
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},
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"Student_t_small_deg_free": {
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"model": GPy.likelihoods.StudentT(deg_free=1.5, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"names": [".*t_noise"],
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"vals": [self.var],
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"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
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"constraints": [(".*t_noise", constrain_positive), (".*deg_free", constrain_fixed)]
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},
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"laplace": True
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},
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"Student_t_small_var": {
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"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"vals": [0.0001],
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"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
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"names": [".*t_noise"],
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"vals": [0.001],
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"constraints": [(".*t_noise", constrain_positive), (".*deg_free", constrain_fixed)]
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},
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"laplace": True
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},
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"Student_t_large_var": {
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"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"names": [".*t_noise"],
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"vals": [10.0],
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"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
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"constraints": [(".*t_noise", constrain_positive), (".*deg_free", constrain_fixed)]
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},
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"laplace": True
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},
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"Student_t_approx_gauss": {
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"model": GPy.likelihoods.StudentT(deg_free=1000, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"names": [".*t_noise"],
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"vals": [self.var],
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"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
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"constraints": [(".*t_noise", constrain_positive), (".*deg_free", constrain_fixed)]
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},
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"laplace": True
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},
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"Student_t_log": {
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"model": GPy.likelihoods.StudentT(gp_link=link_functions.Log(), deg_free=5, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"names": [".*t_noise"],
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"vals": [self.var],
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"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_fixed)]
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"constraints": [(".*t_noise", constrain_positive), (".*deg_free", constrain_fixed)]
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},
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"laplace": True
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},
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"Gaussian_default": {
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"model": GPy.likelihoods.Gaussian(variance=self.var),
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"grad_params": {
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"names": ["variance"],
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"names": [".*variance"],
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"vals": [self.var],
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"constraints": [("variance", constrain_positive)]
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"constraints": [(".*variance", constrain_positive)]
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},
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"laplace": True,
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"ep": False # FIXME: Should be True when we have it working again
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@ -515,10 +516,10 @@ class TestNoiseModels(object):
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#Normalize
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Y = Y/Y.max()
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white_var = 1e-6
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kernel = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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kernel = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
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laplace_likelihood = GPy.inference.latent_function_inference.Laplace()
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m = GPy.core.GP(X.copy(), Y.copy(), kernel, likelihood=model, inference_method=laplace_likelihood)
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m['white'].constrain_fixed(white_var)
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m['.*white'].constrain_fixed(white_var)
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#Set constraints
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for constrain_param, constraint in constraints:
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@ -552,10 +553,10 @@ class TestNoiseModels(object):
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#Normalize
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Y = Y/Y.max()
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white_var = 1e-6
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kernel = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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kernel = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
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ep_inf = GPy.inference.latent_function_inference.EP()
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m = GPy.core.GP(X.copy(), Y.copy(), kernel=kernel, likelihood=model, inference_method=ep_inf)
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m['white'].constrain_fixed(white_var)
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m['.*white'].constrain_fixed(white_var)
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for param_num in range(len(param_names)):
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name = param_names[param_num]
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@ -631,26 +632,24 @@ class LaplaceTests(unittest.TestCase):
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Y = Y/Y.max()
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#Yc = Y.copy()
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#Yc[75:80] += 1
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kernel1 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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kernel1 = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
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#FIXME: Make sure you can copy kernels when params is fixed
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#kernel2 = kernel1.copy()
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kernel2 = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
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kernel2 = GPy.kern.RBF(X.shape[1]) + GPy.kern.White(X.shape[1])
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gauss_distr1 = GPy.likelihoods.Gaussian(variance=initial_var_guess)
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exact_inf = GPy.inference.latent_function_inference.ExactGaussianInference()
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m1 = GPy.core.GP(X, Y.copy(), kernel=kernel1, likelihood=gauss_distr1, inference_method=exact_inf)
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m1['white'].constrain_fixed(1e-6)
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m1['variance'] = initial_var_guess
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m1['variance'].constrain_bounded(1e-4, 10)
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m1['rbf'].constrain_bounded(1e-4, 10)
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m1['.*white'].constrain_fixed(1e-6)
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m1['.*rbf.variance'] = initial_var_guess
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m1['.*rbf.variance'].constrain_bounded(1e-4, 10)
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m1.randomize()
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gauss_distr2 = GPy.likelihoods.Gaussian(variance=initial_var_guess)
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laplace_inf = GPy.inference.latent_function_inference.Laplace()
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m2 = GPy.core.GP(X, Y.copy(), kernel=kernel2, likelihood=gauss_distr2, inference_method=laplace_inf)
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m2['white'].constrain_fixed(1e-6)
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m2['rbf'].constrain_bounded(1e-4, 10)
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m2['variance'].constrain_bounded(1e-4, 10)
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m2['.*white'].constrain_fixed(1e-6)
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m2['.*rbf.variance'].constrain_bounded(1e-4, 10)
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m2.randomize()
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if debug:
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