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https://github.com/SheffieldML/GPy.git
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Merge branch 'devel' of github.com:SheffieldML/GPy into devel
This commit is contained in:
Max Zwiessele
commit
d0a9995c38
7 changed files with 57 additions and 24 deletions
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@ -453,7 +453,12 @@ class Model(Parameterized):
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if not verbose:
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# just check the global ratio
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dx = step * np.sign(np.random.uniform(-1, 1, x.size))
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#choose a random direction to find the linear approximation in
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if x.size==2:
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dx = step * np.ones(2) # random direction for 2 parameters can fail dure to symmetry
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else:
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dx = step * np.sign(np.random.uniform(-1, 1, x.size))
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# evaulate around the point x
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f1, g1 = self.objective_and_gradients(x + dx)
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@ -2,7 +2,7 @@ import GPy
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import numpy as np
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import matplotlib.pyplot as plt
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from GPy.util import datasets
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np.random.seed(1)
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#np.random.seed(1)
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def student_t_approx():
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"""
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@ -106,7 +106,7 @@ def student_t_approx():
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corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution)
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m = GPy.models.GPRegression(X, Yc.copy(), kernel4, likelihood=corrupt_stu_t_likelihood)
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m.ensure_default_constraints()
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m.constrain_positive('t_noise')
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m.constrain_bounded('t_noise', 1e-6, 10.)
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m.constrain_fixed('white', 1e-4)
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m.randomize()
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for a in range(1):
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@ -65,11 +65,10 @@ class Laplace(likelihood):
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self.old_Ki_f = None
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def predictive_values(self, mu, var, full_cov):
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def predictive_values(self,mu,var,full_cov,**noise_args):
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if full_cov:
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raise NotImplementedError("Cannot make correlated predictions\
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with an Laplace likelihood")
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return self.noise_model.predictive_values(mu, var)
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raise NotImplementedError, "Cannot make correlated predictions with an EP likelihood"
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return self.noise_model.predictive_values(mu,var,**noise_args)
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def log_predictive_density(self, y_test, mu_star, var_star):
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"""
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@ -209,6 +208,7 @@ class Laplace(likelihood):
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- 0.5*self.f_Ki_f
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+ 0.5*self.y_Wi_Ki_i_y
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)
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#print "Term, {}, {}, {}, {}, {}".format(self.lik, - 0.5*self.ln_B_det, + 0.5*self.ln_det_Wi_K, - 0.5*self.f_Ki_f, + 0.5*self.y_Wi_Ki_i_y)
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#Convert to float as its (1, 1) and Z must be a scalar
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self.Z = np.float64(Z_tilde)
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@ -349,7 +349,8 @@ class Laplace(likelihood):
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#Find the stepsize that minimizes the objective function using a brent line search
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#The tolerance and maxiter matter for speed! Seems to be best to keep them low and make more full
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#steps than get this exact then make a step, if B was bigger it might be the other way around though
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new_obj = sp.optimize.minimize_scalar(i_o, method='brent', tol=1e-4, options={'maxiter':5}).fun
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#new_obj = sp.optimize.minimize_scalar(i_o, method='brent', tol=1e-4, options={'maxiter':5}).fun
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new_obj = sp.optimize.brent(i_o, tol=1e-4, maxiter=10)
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f = self.tmp_f.copy()
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Ki_f = self.tmp_Ki_f.copy()
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@ -380,8 +381,8 @@ class Laplace(likelihood):
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#difference = abs(new_obj - old_obj)
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#old_obj = new_obj.copy()
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#difference = np.abs(np.sum(f - f_old))
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difference = np.abs(np.sum(Ki_f - old_Ki_f))
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difference = np.abs(np.sum(f - f_old))
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#difference = np.abs(np.sum(Ki_f - old_Ki_f))
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old_Ki_f = Ki_f.copy()
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i += 1
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@ -6,6 +6,8 @@ import functools
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import inspect
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from GPy.likelihoods.noise_models import gp_transformations
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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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def dparam_partial(inst_func, *args):
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"""
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@ -144,7 +146,7 @@ class TestNoiseModels(object):
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"model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
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"grad_params": {
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"names": ["t_noise"],
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"vals": [1],
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"vals": [1.0],
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"constraints": [constrain_positive]
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},
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"laplace": True
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@ -158,6 +160,15 @@ class TestNoiseModels(object):
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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.student_t(deg_free=5, sigma2=self.var),
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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": [constrain_positive]
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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.student_t(deg_free=1000, sigma2=self.var),
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"grad_params": {
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@ -315,9 +326,11 @@ class TestNoiseModels(object):
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def t_logpdf(self, model, Y, f):
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print "\n{}".format(inspect.stack()[0][3])
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print model
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print model._get_params()
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np.testing.assert_almost_equal(
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np.log(model.pdf(f.copy(), Y.copy())),
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model.logpdf(f.copy(), Y.copy()))
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model.pdf(f.copy(), Y.copy()),
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np.exp(model.logpdf(f.copy(), Y.copy()))
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)
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@with_setup(setUp, tearDown)
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def t_dlogpdf_df(self, model, Y, f):
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@ -363,7 +376,7 @@ class TestNoiseModels(object):
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assert (
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dparam_checkgrad(model.logpdf, model.dlogpdf_dtheta,
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params, args=(f, Y), constraints=param_constraints,
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randomize=False, verbose=True)
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randomize=True, verbose=True)
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)
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@with_setup(setUp, tearDown)
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@ -373,7 +386,7 @@ class TestNoiseModels(object):
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assert (
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dparam_checkgrad(model.dlogpdf_df, model.dlogpdf_df_dtheta,
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params, args=(f, Y), constraints=param_constraints,
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randomize=False, verbose=True)
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randomize=True, verbose=True)
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)
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@with_setup(setUp, tearDown)
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@ -383,7 +396,7 @@ class TestNoiseModels(object):
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assert (
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dparam_checkgrad(model.d2logpdf_df2, model.d2logpdf_df2_dtheta,
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params, args=(f, Y), constraints=param_constraints,
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randomize=False, verbose=True)
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randomize=True, verbose=True)
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)
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################
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@ -478,7 +491,7 @@ class TestNoiseModels(object):
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print "\n{}".format(inspect.stack()[0][3])
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#Normalize
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Y = Y/Y.max()
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white_var = 0.001
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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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laplace_likelihood = GPy.likelihoods.Laplace(Y.copy(), model)
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m = GPy.models.GPRegression(X.copy(), Y.copy(), kernel, likelihood=laplace_likelihood)
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@ -490,12 +503,13 @@ class TestNoiseModels(object):
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m[name] = param_vals[param_num]
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constraints[param_num](name, m)
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print m
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m.randomize()
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m.optimize(max_iters=8)
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#m.optimize(max_iters=8)
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print m
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m.checkgrad(verbose=1, step=step)
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if not m.checkgrad(step=step):
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m.checkgrad(verbose=1, step=step)
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#if not m.checkgrad(step=step):
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#m.checkgrad(verbose=1, step=step)
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#import ipdb; ipdb.set_trace()
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#NOTE this test appears to be stochastic for some likelihoods (student t?)
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# appears to all be working in test mode right now...
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@ -509,7 +523,7 @@ class TestNoiseModels(object):
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print "\n{}".format(inspect.stack()[0][3])
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#Normalize
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Y = Y/Y.max()
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white_var = 0.001
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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.models.GPRegression(X.copy(), Y.copy(), kernel, likelihood=ep_likelihood)
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@ -14,6 +14,15 @@ import visualize
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import decorators
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import classification
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import latent_space_visualizations
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import symbolic
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try:
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import sympy
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_sympy_available = True
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del sympy
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except ImportError as e:
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_sympy_available = False
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if _sympy_available:
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import symbolic
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import netpbmfile
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@ -12,6 +12,7 @@ import ctypes
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from ctypes import byref, c_char, c_int, c_double # TODO
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# import scipy.lib.lapack
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import scipy
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import warnings
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if np.all(np.float64((scipy.__version__).split('.')[:2]) >= np.array([0, 12])):
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import scipy.linalg.lapack as lapack
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@ -25,6 +26,9 @@ try:
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assert hasattr(_blaslib, 'dsyr_')
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except AssertionError:
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_blas_available = False
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except AttributeError as e:
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_blas_available = False
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warnings.warn("warning: caught this exception:" + str(e))
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def dtrtrs(A, B, lower=0, trans=0, unitdiag=0):
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"""
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