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Have most of the likelihood testing working, laplace likelihood parameters need fixing, some of the signs are wrong I believe
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Alan Saul
parent
625943ef27
commit
0f263d2ff2
5 changed files with 122 additions and 71 deletions
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@ -8,7 +8,7 @@ from GPy.likelihoods import link_functions
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from ..core.parameterization import Param
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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.random.seed(7)
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def dparam_partial(inst_func, *args):
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"""
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@ -41,25 +41,27 @@ def dparam_checkgrad(func, dfunc, params, params_names, args, constraints=None,
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The number of parameters and N is the number of data
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Need to take a slice out from f and a slice out of df
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"""
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#print "\n{} likelihood: {} vs {}".format(func.im_self.__class__.__name__,
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#func.__name__, dfunc.__name__)
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print "\n{} likelihood: {} vs {}".format(func.im_self.__class__.__name__,
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func.__name__, dfunc.__name__)
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partial_f = dparam_partial(func, *args)
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partial_df = dparam_partial(dfunc, *args)
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gradchecking = True
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zipped_params = zip(params, params_names)
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for param_val, param_name in zipped_params:
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fnum = np.atleast_1d(partial_f(param_val, param_name)).shape[0]
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dfnum = np.atleast_1d(partial_df(param_val, param_name)).shape[0]
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for param_ind, (param_val, param_name) in enumerate(zipped_params):
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#Check one parameter at a time, make sure it is 2d (as some gradients only return arrays) then strip out the parameter
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fnum = np.atleast_2d(partial_f(param_val, param_name))[:, param_ind].shape[0]
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dfnum = np.atleast_2d(partial_df(param_val, param_name))[:, param_ind].shape[0]
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for fixed_val in range(dfnum):
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#dlik and dlik_dvar gives back 1 value for each
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f_ind = min(fnum, fixed_val+1) - 1
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print "fnum: {} dfnum: {} f_ind: {} fixed_val: {}".format(fnum, dfnum, f_ind, fixed_val)
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#Make grad checker with this param moving, note that set_params is NOT being called
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#The parameter is being set directly with __setattr__
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grad = GradientChecker(lambda p_val: np.atleast_1d(partial_f(p_val, param_name))[f_ind],
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lambda p_val: np.atleast_1d(partial_df(p_val, param_name))[fixed_val],
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#Check only the parameter and function value we wish to check at a time
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grad = GradientChecker(lambda p_val: np.atleast_2d(partial_f(p_val, param_name))[f_ind, param_ind],
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lambda p_val: np.atleast_2d(partial_df(p_val, param_name))[fixed_val, param_ind],
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param_val, [param_name])
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#This is not general for more than one param...
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if constraints is not None:
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for constrain_param, constraint in constraints:
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if grad.grep_param_names(constrain_param):
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@ -115,8 +117,8 @@ class TestNoiseModels(object):
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####################################################
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# Constraint wrappers so we can just list them off #
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####################################################
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def constrain_fixed(regex, model, value):
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model[regex].constrain_fixed(value)
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def constrain_fixed(regex, model):
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model[regex].constrain_fixed()
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def constrain_negative(regex, model):
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model[regex].constrain_negative()
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@ -149,7 +151,7 @@ class TestNoiseModels(object):
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"grad_params": {
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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_positive)]
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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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@ -159,7 +161,7 @@ class TestNoiseModels(object):
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"grad_params": {
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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_positive)]
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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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@ -168,7 +170,7 @@ class TestNoiseModels(object):
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"grad_params": {
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"names": ["t_noise"],
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"vals": [0.01],
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"constraints": [("t_noise", constrain_positive), ("deg_free", constrain_positive)]
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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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@ -177,7 +179,7 @@ class TestNoiseModels(object):
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"grad_params": {
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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_positive)]
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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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@ -186,7 +188,7 @@ class TestNoiseModels(object):
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"grad_params": {
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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_positive)]
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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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@ -195,7 +197,7 @@ class TestNoiseModels(object):
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"grad_params": {
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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_positive)]
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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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@ -542,8 +544,8 @@ class TestNoiseModels(object):
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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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ep_likelihood = GPy.likelihoods.EP(Y.copy(), model)
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m = GPy.core.GP(X.copy(), Y.copy(), kernel, likelihood=ep_likelihood)
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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.ensure_default_constraints()
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m['white'].constrain_fixed(white_var)
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@ -622,7 +624,9 @@ class LaplaceTests(unittest.TestCase):
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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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kernel2 = kernel1.copy()
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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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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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@ -686,7 +690,7 @@ class LaplaceTests(unittest.TestCase):
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#Check Y's are the same
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np.testing.assert_almost_equal(Y, m2.likelihood.Y, decimal=5)
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np.testing.assert_almost_equal(m1.Y, m2.Y, decimal=5)
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#Check marginals are the same
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np.testing.assert_almost_equal(m1.log_likelihood(), m2.log_likelihood(), decimal=2)
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#Check marginals are the same with random
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