Merge branch 'devel' of github.com:SheffieldML/GPy into devel

GPy/core/model.py

@@ -453,7 +453,12 @@ class Model(Parameterized):
 
         if not verbose:
             # just check the global ratio
-            dx = step * np.sign(np.random.uniform(-1, 1, x.size))
+
+            #choose a random direction to find the linear approximation in
+            if x.size==2:
+                dx = step * np.ones(2) # random direction for 2 parameters can fail dure to symmetry
+            else:
+                dx = step * np.sign(np.random.uniform(-1, 1, x.size))
 
             # evaulate around the point x
             f1, g1 = self.objective_and_gradients(x + dx)

GPy/examples/laplace_approximations.py

@@ -2,7 +2,7 @@ import GPy
 import numpy as np
 import matplotlib.pyplot as plt
 from GPy.util import datasets
-np.random.seed(1)
+#np.random.seed(1)
 
 def student_t_approx():
     """
@@ -106,7 +106,7 @@ def student_t_approx():
     corrupt_stu_t_likelihood = GPy.likelihoods.Laplace(Yc.copy(), t_distribution)
     m = GPy.models.GPRegression(X, Yc.copy(), kernel4, likelihood=corrupt_stu_t_likelihood)
     m.ensure_default_constraints()
-    m.constrain_positive('t_noise')
+    m.constrain_bounded('t_noise', 1e-6, 10.)
     m.constrain_fixed('white', 1e-4)
     m.randomize()
     for a in range(1):

GPy/likelihoods/laplace.py

@@ -65,11 +65,10 @@ class Laplace(likelihood):
 
         self.old_Ki_f = None
 
-    def predictive_values(self, mu, var, full_cov):
+    def predictive_values(self,mu,var,full_cov,**noise_args):
         if full_cov:
-            raise NotImplementedError("Cannot make correlated predictions\
+            raise NotImplementedError, "Cannot make correlated predictions with an EP likelihood"
-                    with an Laplace likelihood")
+        return self.noise_model.predictive_values(mu,var,**noise_args)
-        return self.noise_model.predictive_values(mu, var)
 
     def log_predictive_density(self, y_test, mu_star, var_star):
         """
@@ -209,6 +208,7 @@ class Laplace(likelihood):
                    - 0.5*self.f_Ki_f
                    + 0.5*self.y_Wi_Ki_i_y
                   )
+        #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)
 
         #Convert to float as its (1, 1) and Z must be a scalar
         self.Z = np.float64(Z_tilde)
@@ -349,7 +349,8 @@ class Laplace(likelihood):
             #Find the stepsize that minimizes the objective function using a brent line search
             #The tolerance and maxiter matter for speed! Seems to be best to keep them low and make more full
             #steps than get this exact then make a step, if B was bigger it might be the other way around though
-            new_obj = sp.optimize.minimize_scalar(i_o, method='brent', tol=1e-4, options={'maxiter':5}).fun
+            #new_obj = sp.optimize.minimize_scalar(i_o, method='brent', tol=1e-4, options={'maxiter':5}).fun
+            new_obj = sp.optimize.brent(i_o, tol=1e-4, maxiter=10)
             f = self.tmp_f.copy()
             Ki_f = self.tmp_Ki_f.copy()
 
@@ -380,8 +381,8 @@ class Laplace(likelihood):
 
             #difference = abs(new_obj - old_obj)
             #old_obj = new_obj.copy()
-            #difference = np.abs(np.sum(f - f_old))
+            difference = np.abs(np.sum(f - f_old))
-            difference = np.abs(np.sum(Ki_f - old_Ki_f))
+            #difference = np.abs(np.sum(Ki_f - old_Ki_f))
             old_Ki_f = Ki_f.copy()
             i += 1
 

GPy/testing/likelihoods_tests.py

@@ -6,6 +6,8 @@ import functools
 import inspect
 from GPy.likelihoods.noise_models import gp_transformations
 from functools import partial
+#np.random.seed(300)
+np.random.seed(7)
 
 def dparam_partial(inst_func, *args):
     """
@@ -144,7 +146,7 @@ class TestNoiseModels(object):
                             "model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
                             "grad_params": {
                                 "names": ["t_noise"],
-                                "vals": [1],
+                                "vals": [1.0],
                                 "constraints": [constrain_positive]
                                 },
                             "laplace": True
@@ -158,6 +160,15 @@ class TestNoiseModels(object):
                                 },
                             "laplace": True
                             },
+                        "Student_t_large_var": {
+                            "model": GPy.likelihoods.student_t(deg_free=5, sigma2=self.var),
+                            "grad_params": {
+                                "names": ["t_noise"],
+                                "vals": [10.0],
+                                "constraints": [constrain_positive]
+                                },
+                            "laplace": True
+                            },
                         "Student_t_approx_gauss": {
                             "model": GPy.likelihoods.student_t(deg_free=1000, sigma2=self.var),
                             "grad_params": {
@@ -315,9 +326,11 @@ class TestNoiseModels(object):
     def t_logpdf(self, model, Y, f):
         print "\n{}".format(inspect.stack()[0][3])
         print model
+        print model._get_params()
         np.testing.assert_almost_equal(
-                               np.log(model.pdf(f.copy(), Y.copy())),
+                               model.pdf(f.copy(), Y.copy()),
-                               model.logpdf(f.copy(), Y.copy()))
+                               np.exp(model.logpdf(f.copy(), Y.copy()))
+                               )
 
     @with_setup(setUp, tearDown)
     def t_dlogpdf_df(self, model, Y, f):
@@ -363,7 +376,7 @@ class TestNoiseModels(object):
         assert (
                 dparam_checkgrad(model.logpdf, model.dlogpdf_dtheta,
                     params, args=(f, Y), constraints=param_constraints,
-                    randomize=False, verbose=True)
+                    randomize=True, verbose=True)
                 )
 
     @with_setup(setUp, tearDown)
@@ -373,7 +386,7 @@ class TestNoiseModels(object):
         assert (
                 dparam_checkgrad(model.dlogpdf_df, model.dlogpdf_df_dtheta,
                     params, args=(f, Y), constraints=param_constraints,
-                    randomize=False, verbose=True)
+                    randomize=True, verbose=True)
                 )
 
     @with_setup(setUp, tearDown)
@@ -383,7 +396,7 @@ class TestNoiseModels(object):
         assert (
                 dparam_checkgrad(model.d2logpdf_df2, model.d2logpdf_df2_dtheta,
                     params, args=(f, Y), constraints=param_constraints,
-                    randomize=False, verbose=True)
+                    randomize=True, verbose=True)
                 )
 
     ################
@@ -478,7 +491,7 @@ class TestNoiseModels(object):
         print "\n{}".format(inspect.stack()[0][3])
         #Normalize
         Y = Y/Y.max()
-        white_var = 0.001
+        white_var = 1e-6
         kernel = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
         laplace_likelihood = GPy.likelihoods.Laplace(Y.copy(), model)
         m = GPy.models.GPRegression(X.copy(), Y.copy(), kernel, likelihood=laplace_likelihood)
@@ -490,12 +503,13 @@ class TestNoiseModels(object):
             m[name] = param_vals[param_num]
             constraints[param_num](name, m)
 
+        print m
         m.randomize()
-        m.optimize(max_iters=8)
+        #m.optimize(max_iters=8)
         print m
         m.checkgrad(verbose=1, step=step)
-        if not m.checkgrad(step=step):
+        #if not m.checkgrad(step=step):
-            m.checkgrad(verbose=1, step=step)
+            #m.checkgrad(verbose=1, step=step)
             #import ipdb; ipdb.set_trace()
             #NOTE this test appears to be stochastic for some likelihoods (student t?)
             # appears to all be working in test mode right now...
@@ -509,7 +523,7 @@ class TestNoiseModels(object):
         print "\n{}".format(inspect.stack()[0][3])
         #Normalize
         Y = Y/Y.max()
-        white_var = 0.001
+        white_var = 1e-6
         kernel = GPy.kern.rbf(X.shape[1]) + GPy.kern.white(X.shape[1])
         ep_likelihood = GPy.likelihoods.EP(Y.copy(), model)
         m = GPy.models.GPRegression(X.copy(), Y.copy(), kernel, likelihood=ep_likelihood)

GPy/util/__init__.py

@@ -14,6 +14,15 @@ import visualize
 import decorators
 import classification
 import latent_space_visualizations
-import symbolic
+
+try:
+    import sympy
+    _sympy_available = True
+    del sympy
+except ImportError as e:
+    _sympy_available = False
+
+if _sympy_available:
+    import symbolic
 
 import netpbmfile

GPy/util/linalg.py

@@ -12,6 +12,7 @@ import ctypes
 from ctypes import byref, c_char, c_int, c_double # TODO
 # import scipy.lib.lapack
 import scipy
+import warnings
 
 if np.all(np.float64((scipy.__version__).split('.')[:2]) >= np.array([0, 12])):
     import scipy.linalg.lapack as lapack
@@ -25,6 +26,9 @@ try:
     assert hasattr(_blaslib, 'dsyr_')
 except AssertionError:
     _blas_available = False
+except AttributeError as e:
+    _blas_available = False
+    warnings.warn("warning: caught this exception:" + str(e))
 
 def dtrtrs(A, B, lower=0, trans=0, unitdiag=0):
     """

setup.py

@@ -18,9 +18,9 @@ setup(name = 'GPy',
       license = "BSD 3-clause",
       keywords = "machine-learning gaussian-processes kernels",
       url = "http://sheffieldml.github.com/GPy/",
-      packages = ['GPy', 'GPy.core', 'GPy.kern', 'GPy.util', 'GPy.models', 'GPy.inference', 'GPy.examples', 'GPy.likelihoods', 'GPy.testing', 'GPy.util.latent_space_visualizations', 'GPy.util.latent_space_visualizations.controllers', 'GPy.likelihoods.noise_models', 'GPy.kern.parts', 'GPy.mappings'],
+      packages = ['GPy', 'GPy.core', 'GPy.kern', 'GPy.util', 'GPy._models', 'GPy.inference', 'GPy.examples', 'GPy.likelihoods', 'GPy.testing', 'GPy.util.latent_space_visualizations', 'GPy.util.latent_space_visualizations.controllers', 'GPy.likelihoods.noise_models', 'GPy.kern.parts', 'GPy.mappings'],
       package_dir={'GPy': 'GPy'},
-      package_data = {'GPy': ['GPy/examples']},
+      package_data = {'GPy': ['GPy/examples', 'gpy_config.cfg']},
       py_modules = ['GPy.__init__'],
       long_description=read('README.md'),
       install_requires=['numpy>=1.6', 'scipy>=0.9','matplotlib>=1.1', 'nose'],