merged. ish.

GPy/kern/_src/prod.py

@@ -6,6 +6,20 @@ from kern import CombinationKernel
 from ...util.caching import Cache_this
 import itertools
 
+
+def numpy_invalid_op_as_exception(func):
+    """
+    A decorator that allows catching numpy invalid operations
+    as exceptions (the default behaviour is raising warnings).
+    """
+    def func_wrapper(*args, **kwargs):
+        np.seterr(invalid='raise')
+        result = func(*args, **kwargs)
+        np.seterr(invalid='warn')
+        return result
+    return func_wrapper
+
+
 class Prod(CombinationKernel):
     """
     Computes the product of 2 kernels
@@ -46,28 +60,30 @@ class Prod(CombinationKernel):
             self.parts[0].update_gradients_full(dL_dK*self.parts[1].K(X,X2), X, X2)
             self.parts[1].update_gradients_full(dL_dK*self.parts[0].K(X,X2), X, X2)
         else:
-            k = self.K(X,X2)*dL_dK
+            for combination in itertools.combinations(self.parts, len(self.parts) - 1):
-            for p in self.parts:
+                prod = reduce(np.multiply, [p.K(X, X2) for p in combination])
-                p.update_gradients_full(k/p.K(X,X2),X,X2)
+                to_update = list(set(self.parts) - set(combination))[0]
+                to_update.update_gradients_full(dL_dK * prod, X, X2)
 
     def update_gradients_diag(self, dL_dKdiag, X):
         if len(self.parts)==2:
             self.parts[0].update_gradients_diag(dL_dKdiag*self.parts[1].Kdiag(X), X)
             self.parts[1].update_gradients_diag(dL_dKdiag*self.parts[0].Kdiag(X), X)
         else:
-            k = self.Kdiag(X)*dL_dKdiag
+            for combination in itertools.combinations(self.parts, len(self.parts) - 1):
-            for p in self.parts:
+                prod = reduce(np.multiply, [p.Kdiag(X) for p in combination])
-                p.update_gradients_diag(k/p.Kdiag(X),X)
+                to_update = list(set(self.parts) - set(combination))[0]
+                to_update.update_gradients_diag(dL_dKdiag * prod, X)
 
     def gradients_X(self, dL_dK, X, X2=None):
         target = np.zeros(X.shape)
         if len(self.parts)==2:
             target += self.parts[0].gradients_X(dL_dK*self.parts[1].K(X, X2), X, X2)
             target += self.parts[1].gradients_X(dL_dK*self.parts[0].K(X, X2), X, X2)
-        else:
+            for combination in itertools.combinations(self.parts, len(self.parts) - 1):
-            k = self.K(X,X2)*dL_dK
+                prod = reduce(np.multiply, [p.K(X, X2) for p in combination])
-            for p in self.parts:
+                to_update = list(set(self.parts) - set(combination))[0]
-                target += p.gradients_X(k/p.K(X,X2),X,X2)
+                target += to_update.gradients_X(dL_dK * prod, X, X2)
         return target
 
     def gradients_X_diag(self, dL_dKdiag, X):
@@ -80,3 +96,5 @@ class Prod(CombinationKernel):
             for p in self.parts:
                 target += p.gradients_X_diag(k/p.Kdiag(X),X)
         return target
+
+

GPy/models/sparse_gp_minibatch.py

@@ -43,10 +43,11 @@ class SparseGPMiniBatch(SparseGP):
     def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None,
                  name='sparse gp', Y_metadata=None, normalizer=False,
                  missing_data=False, stochastic=False, batchsize=1):
+        
         # pick a sensible inference method
         if inference_method is None:
             if isinstance(likelihood, likelihoods.Gaussian):
-                inference_method = var_dtc.VarDTC(limit=1 if not self.missing_data else Y.shape[1])
+                inference_method = var_dtc.VarDTC(limit=1 if not missing_data else Y.shape[1])
             else:
                 #inference_method = ??
                 raise NotImplementedError, "what to do what to do?"

GPy/models/warped_gp.py

@@ -1,7 +1,6 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-
 import numpy as np
 from ..util.warping_functions import *
 from ..core import GP
@@ -10,14 +9,16 @@ from GPy.util.warping_functions import TanhWarpingFunction_d
 from GPy import kern
 
 class WarpedGP(GP):
-    def __init__(self, X, Y, kernel=None, warping_function=None, warping_terms=3, normalize_X=False, normalize_Y=False):
+    def __init__(self, X, Y, kernel=None, warping_function=None, warping_terms=3):
 
         if kernel is None:
-            kernel = kern.rbf(X.shape[1])
+            kernel = kern.RBF(X.shape[1])
 
         if warping_function == None:
             self.warping_function = TanhWarpingFunction_d(warping_terms)
             self.warping_params = (np.random.randn(self.warping_function.n_terms * 3 + 1,) * 1)
+        else:
+            self.warping_function = warping_function
 
         self.scale_data = False
         if self.scale_data:
@@ -25,10 +26,10 @@ class WarpedGP(GP):
         self.has_uncertain_inputs = False
         self.Y_untransformed = Y.copy()
         self.predict_in_warped_space = False
-        likelihood = likelihoods.Gaussian(self.transform_data(), normalize=normalize_Y)
+        likelihood = likelihoods.Gaussian()
 
-        GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
+        GP.__init__(self, X, self.transform_data(), likelihood=likelihood, kernel=kernel)
-        self._set_params(self._get_params())
+        self.link_parameter(self.warping_function)
 
     def _scale_data(self, Y):
         self._Ymax = Y.max()
@@ -38,62 +39,55 @@ class WarpedGP(GP):
     def _unscale_data(self, Y):
         return (Y + 0.5) * (self._Ymax - self._Ymin) + self._Ymin
 
-    def _set_params(self, x):
+    def parameters_changed(self):
-        self.warping_params = x[:self.warping_function.num_parameters]
+        self.Y[:] = self.transform_data()
-        Y = self.transform_data()
+        super(WarpedGP, self).parameters_changed()
-        self.likelihood.set_data(Y)
-        GP._set_params(self, x[self.warping_function.num_parameters:].copy())
 
-    def _get_params(self):
+        Kiy = self.posterior.woodbury_vector.flatten()
-        return np.hstack((self.warping_params.flatten().copy(), GP._get_params(self).copy()))
 
-    def _get_param_names(self):
+        grad_y = self.warping_function.fgrad_y(self.Y_untransformed)
-        warping_names = self.warping_function._get_param_names()
+        grad_y_psi, grad_psi = self.warping_function.fgrad_y_psi(self.Y_untransformed,
-        param_names = GP._get_param_names(self)
-        return warping_names + param_names
-
-    def transform_data(self):
-        Y = self.warping_function.f(self.Y_untransformed.copy(), self.warping_params).copy()
-        return Y
-
-    def log_likelihood(self):
-        ll = GP.log_likelihood(self)
-        jacobian = self.warping_function.fgrad_y(self.Y_untransformed, self.warping_params)
-        return ll + np.log(jacobian).sum()
-
-    def _log_likelihood_gradients(self):
-        ll_grads = GP._log_likelihood_gradients(self)
-        alpha = np.dot(self.Ki, self.likelihood.Y.flatten())
-        warping_grads = self.warping_function_gradients(alpha)
-
-        warping_grads = np.append(warping_grads[:, :-1].flatten(), warping_grads[0, -1])
-        return np.hstack((warping_grads.flatten(), ll_grads.flatten()))
-
-    def warping_function_gradients(self, Kiy):
-        grad_y = self.warping_function.fgrad_y(self.Y_untransformed, self.warping_params)
-        grad_y_psi, grad_psi = self.warping_function.fgrad_y_psi(self.Y_untransformed, self.warping_params,
                                                                  return_covar_chain=True)
         djac_dpsi = ((1.0 / grad_y[:, :, None, None]) * grad_y_psi).sum(axis=0).sum(axis=0)
         dquad_dpsi = (Kiy[:, None, None, None] * grad_psi).sum(axis=0).sum(axis=0)
 
-        return -dquad_dpsi + djac_dpsi
+        warping_grads = -dquad_dpsi + djac_dpsi
+
+        self.warping_function.psi.gradient[:] = warping_grads[:, :-1]
+        self.warping_function.d.gradient[:] = warping_grads[0, -1]
+
+
+    def transform_data(self):
+        Y = self.warping_function.f(self.Y_untransformed.copy()).copy()
+        return Y
+
+    def log_likelihood(self):
+        ll = GP.log_likelihood(self)
+        jacobian = self.warping_function.fgrad_y(self.Y_untransformed)
+        return ll + np.log(jacobian).sum()
 
     def plot_warping(self):
-        self.warping_function.plot(self.warping_params, self.Y_untransformed.min(), self.Y_untransformed.max())
+        self.warping_function.plot(self.Y_untransformed.min(), self.Y_untransformed.max())
 
-    def predict(self, Xnew, which_parts='all', full_cov=False, pred_init=None):
+    def predict(self, Xnew, which_parts='all', pred_init=None):
         # normalize X values
-        Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
+        # Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
-        mu, var = GP._raw_predict(self, Xnew, full_cov=full_cov, which_parts=which_parts)
+        mu, var = GP._raw_predict(self, Xnew)
 
         # now push through likelihood
-        mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov)
+        mean, var = self.likelihood.predictive_values(mu, var)
 
         if self.predict_in_warped_space:
-            mean = self.warping_function.f_inv(mean, self.warping_params, y=pred_init)
+            mean = self.warping_function.f_inv(mean,  y=pred_init)
-            var = self.warping_function.f_inv(var, self.warping_params)
+            var = self.warping_function.f_inv(var)
 
         if self.scale_data:
             mean = self._unscale_data(mean)
 
-        return mean, var, _025pm, _975pm
+        return mean, var
+
+if __name__ == '__main__':
+    X = np.random.randn(100, 1)
+    Y = np.sin(X) + np.random.randn(100, 1)*0.05
+
+    m = WarpedGP(X, Y)

GPy/plotting/matplot_dep/maps.py

@@ -6,7 +6,11 @@ try:
     from matplotlib.patches import Polygon
     from matplotlib.collections import PatchCollection
     #from matplotlib import cm
+    try:
+        __IPYTHON__
         pb.ion()
+    except NameError:
+        pass
 except:
     pass
 import re

GPy/testing/kernel_tests.py

@@ -401,11 +401,27 @@ class Coregionalize_weave_test(unittest.TestCase):
     GPy.util.config.config.set('weave', 'working', 'False')
 
 
+class KernelTestsProductWithZeroValues(unittest.TestCase):
+
+    def setUp(self):
+        self.X = np.array([[0,1],[1,0]])
+        self.k = GPy.kern.Linear(2) * GPy.kern.Bias(2)
+
+    def test_zero_valued_kernel_full(self):
+        self.k.update_gradients_full(1, self.X)
+        self.assertFalse(np.isnan(self.k['linear.variances'].gradient),
+                         "Gradient resulted in NaN")
+
+    def test_zero_valued_kernel_gradients_X(self):
+        target = self.k.gradients_X(1, self.X)
+        self.assertFalse(np.any(np.isnan(target)),
+                         "Gradient resulted in NaN")
 
 
 if __name__ == "__main__":
     print "Running unit tests, please be (very) patient..."
     unittest.main()
+
 #     np.random.seed(0)
 #     N0 = 3
 #     N1 = 9

GPy/util/linalg.py

@@ -96,16 +96,21 @@ def jitchol(A, maxtries=5):
         num_tries = 1
         while num_tries <= maxtries and np.isfinite(jitter):
             try:
+                print jitter
                 L = linalg.cholesky(A + np.eye(A.shape[0]) * jitter, lower=True)
-                logging.warning('Added {} rounds of jitter, jitter of {:.10e}\n'.format(num_tries, jitter))
                 return L
             except:
                 jitter *= 10
+            finally:
                 num_tries += 1
-    import traceback
-    logging.warning('\n'.join(['Added {} rounds of jitter, jitter of {:.10e}'.format(num_tries-1, jitter),
-                                '  in '+traceback.format_list(traceback.extract_stack(limit=2)[-2:-1])[0][2:]]))
         raise linalg.LinAlgError, "not positive definite, even with jitter."
+    import traceback
+    try: raise
+    except:
+        logging.warning('\n'.join(['Added jitter of {:.10e}'.format(jitter),
+            '  in '+traceback.format_list(traceback.extract_stack(limit=2)[-2:-1])[0][2:]]))
+    import ipdb;ipdb.set_trace()
+    return L
 
 # def dtrtri(L, lower=1):
 #     """

GPy/util/warping_functions.py

@@ -1,17 +1,18 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-
 import numpy as np
+from GPy.core.parameterization import Parameterized, Param
+from ..core.parameterization.transformations import Logexp
 
-class WarpingFunction(object):
+class WarpingFunction(Parameterized):
     """
     abstract function for warping
     z = f(y)
     """
 
-    def __init__(self):
+    def __init__(self, name):
-        raise NotImplementedError
+        super(WarpingFunction, self).__init__(name=name)
 
     def f(self,y,psi):
         """function transformation
@@ -34,9 +35,10 @@ class WarpingFunction(object):
     def _get_param_names(self):
         raise NotImplementedError
 
-    def plot(self, psi, xmin, xmax):
+    def plot(self,  xmin, xmax):
+        psi = self.psi
         y = np.arange(xmin, xmax, 0.01)
-        f_y = self.f(y, psi)
+        f_y = self.f(y)
         from matplotlib import pyplot as plt
         plt.figure()
         plt.plot(y, f_y)
@@ -50,6 +52,7 @@ class TanhWarpingFunction(WarpingFunction):
         """n_terms specifies the number of tanh terms to be used"""
         self.n_terms = n_terms
         self.num_parameters = 3 * self.n_terms
+        super(TanhWarpingFunction, self).__init__(name='warp_tanh')
 
     def f(self,y,psi):
         """
@@ -163,8 +166,18 @@ class TanhWarpingFunction_d(WarpingFunction):
         """n_terms specifies the number of tanh terms to be used"""
         self.n_terms = n_terms
         self.num_parameters = 3 * self.n_terms + 1
+        self.psi = np.ones((self.n_terms, 3))
 
-    def f(self,y,psi):
+        super(TanhWarpingFunction_d, self).__init__(name='warp_tanh')
+        self.psi = Param('psi', self.psi)
+        self.psi[:, :2].constrain_positive()
+
+        self.d = Param('%s' % ('d'), 1.0, Logexp())
+        self.link_parameter(self.psi)
+        self.link_parameter(self.d)
+
+
+    def f(self,y):
         """
         Transform y with f using parameter vector psi
         psi = [[a,b,c]]
@@ -175,9 +188,9 @@ class TanhWarpingFunction_d(WarpingFunction):
         #1. check that number of params is consistent
         # assert psi.shape[0] == self.n_terms, 'inconsistent parameter dimensions'
         # assert psi.shape[1] == 4, 'inconsistent parameter dimensions'
-        mpsi = psi.copy()
+
-        d = psi[-1]
+        d = self.d
-        mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)
+        mpsi = self.psi
 
         #3. transform data
         z = d*y.copy()
@@ -187,7 +200,7 @@ class TanhWarpingFunction_d(WarpingFunction):
         return z
 
 
-    def f_inv(self, z, psi, max_iterations=1000, y=None):
+    def f_inv(self, z, max_iterations=1000, y=None):
         """
         calculate the numerical inverse of f
 
@@ -203,7 +216,7 @@ class TanhWarpingFunction_d(WarpingFunction):
         update = np.inf
 
         while it == 0 or (np.abs(update).sum() > 1e-10 and it < max_iterations):
-            update = (self.f(y, psi) - z)/self.fgrad_y(y, psi)
+            update = (self.f(y) - z)/self.fgrad_y(y)
             y -= update
             it += 1
         if it == max_iterations:
@@ -212,7 +225,7 @@ class TanhWarpingFunction_d(WarpingFunction):
         return y
 
 
-    def fgrad_y(self, y, psi, return_precalc = False):
+    def fgrad_y(self, y,return_precalc = False):
         """
         gradient of f w.r.t to y ([N x 1])
 
@@ -221,9 +234,8 @@ class TanhWarpingFunction_d(WarpingFunction):
         """
 
 
-        mpsi = psi.copy()
+        d = self.d
-        d = psi[-1]
+        mpsi = self.psi
-        mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)
 
         # vectorized version
 
@@ -240,7 +252,7 @@ class TanhWarpingFunction_d(WarpingFunction):
         return GRAD
 
 
-    def fgrad_y_psi(self, y, psi, return_covar_chain = False):
+    def fgrad_y_psi(self, y, return_covar_chain = False):
         """
         gradient of f w.r.t to y and psi
 
@@ -248,10 +260,10 @@ class TanhWarpingFunction_d(WarpingFunction):
 
         """
 
-        mpsi = psi.copy()
-        mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)
 
-        w, s, r, d = self.fgrad_y(y, psi, return_precalc = True)
+        mpsi = self.psi
+
+        w, s, r, d = self.fgrad_y(y, return_precalc = True)
 
         gradients = np.zeros((y.shape[0], y.shape[1], len(mpsi), 4))
         for i in range(len(mpsi)):