gp merge, grad dict is property of self + Y_metadata being passed through

GPy/core/gp.py

@@ -27,7 +27,7 @@ class GP(Model):
 
 
     """
-    def __init__(self, X, Y, kernel, likelihood, inference_method=None, Y_metadata=None, name='gp'):
+    def __init__(self, X, Y, kernel, likelihood, inference_method=None, name='gp', **Y_metadata):
         super(GP, self).__init__(name)
 
         assert X.ndim == 2
@@ -43,7 +43,7 @@ class GP(Model):
         _, self.output_dim = self.Y.shape
 
         if Y_metadata is not None:
-            self.Y_metadata = ObservableArray(Y_metadata)
+            self.Y_metadata = Y_metadata
         else:
             self.Y_metadata = None
 
@@ -56,7 +56,7 @@ class GP(Model):
 
         #find a sensible inference method
         if inference_method is None:
-            if isinstance(likelihood, likelihoods.Gaussian):
+            if isinstance(likelihood, likelihoods.Gaussian) or isinstance(likelihood, likelihoods.MixedNoise):
                 inference_method = exact_gaussian_inference.ExactGaussianInference()
             else:
                 inference_method = expectation_propagation
@@ -67,8 +67,8 @@ class GP(Model):
         self.add_parameter(self.likelihood)
 
     def parameters_changed(self):
-        self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y, Y_metadata=self.Y_metadata)
-        self.likelihood.update_gradients(np.diag(self.grad_dict['dL_dK']))
+        self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y, **self.Y_metadata)
+        self.likelihood.update_gradients(np.diag(self.grad_dict['dL_dK']), **self.Y_metadata)
         self.kern.update_gradients_full(self.grad_dict['dL_dK'], self.X)
 
     def log_likelihood(self):

GPy/inference/latent_function_inference/dtc.py

@@ -19,7 +19,7 @@ class DTC(object):
     def __init__(self):
         self.const_jitter = 1e-6
 
-    def inference(self, kern, X, X_variance, Z, likelihood, Y):
+    def inference(self, kern, X, Z, likelihood, Y):
         assert X_variance is None, "cannot use X_variance with DTC. Try varDTC."
 
         #TODO: MAX! fix this!
@@ -80,10 +80,6 @@ class DTC(object):
 
         grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':np.zeros_like(Knn), 'dL_dKnm':dL_dU.T}
 
-        #update gradients
-        kern.update_gradients_sparse(X=X, Z=Z, **grad_dict)
-        likelihood.update_gradients(dL_dR)
-
         #construct a posterior object
         post = Posterior(woodbury_inv=Kmmi-P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=L)
 

GPy/inference/latent_function_inference/exact_gaussian_inference.py

@@ -33,7 +33,7 @@ class ExactGaussianInference(object):
             #if Y in self.cache, return self.Cache[Y], else store Y in cache and return L.
             raise NotImplementedError, 'TODO' #TODO
 
-    def inference(self, kern, X, likelihood, Y, Y_metadata=None):
+    def inference(self, kern, X, likelihood, Y, **Y_metadata):
         """
         Returns a Posterior class containing essential quantities of the posterior
         """
@@ -41,7 +41,7 @@ class ExactGaussianInference(object):
 
         K = kern.K(X)
 
-        Wi, LW, LWi, W_logdet = pdinv(K + likelihood.covariance_matrix(Y, Y_metadata))
+        Wi, LW, LWi, W_logdet = pdinv(K + likelihood.covariance_matrix(Y, **Y_metadata))
 
         alpha, _ = dpotrs(LW, YYT_factor, lower=1)
 
@@ -50,5 +50,3 @@ class ExactGaussianInference(object):
         dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi)
 
         return Posterior(woodbury_chol=LW, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK}
-
-

GPy/inference/latent_function_inference/fitc.py

@@ -17,8 +17,7 @@ class FITC(object):
     def __init__(self):
         self.const_jitter = 1e-6
 
-    def inference(self, kern, X, X_variance, Z, likelihood, Y):
-        assert X_variance is None, "cannot use X_variance with FITC. Try varDTC."
+    def inference(self, kern, X, Z, likelihood, Y):
         
         #TODO: MAX! fix this!
         from ...util.misc import param_to_array
@@ -81,11 +80,7 @@ class FITC(object):
         dL_dU *= beta_star
         dL_dU -= 2.*KiU*dL_dR
 
-        grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':dL_dR, 'dL_dKnm':dL_dU.T}
-
-        #update gradients
-        kern.update_gradients_sparse(X=X, Z=Z, **grad_dict)
-        likelihood.update_gradients(dL_dR)
+        grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':dL_dR, 'dL_dKnm':dL_dU.T, 'partial_for_likelihood':dL_dR}
 
         #construct a posterior object
         post = Posterior(woodbury_inv=Kmmi-P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=L)

GPy/kern/_src/coregionalize.py

@@ -34,8 +34,8 @@ class Coregionalize(Kern):
 
     .. note: see coregionalization examples in GPy.examples.regression for some usage.
     """
-    def __init__(self, output_dim, rank=1, W=None, kappa=None, name='coregion'):
-        super(Coregionalize, self).__init__(input_dim=1, name=name)
+    def __init__(self, input_dim, output_dim, rank=1, W=None, kappa=None, name='coregion'):
+        super(Coregionalize, self).__init__(input_dim, name=name)
         self.output_dim = output_dim
         self.rank = rank
         if self.rank>output_dim:

GPy/kern/_src/prod.py

@@ -40,7 +40,7 @@ class Prod(CombinationKernel):
     def update_gradients_full(self, dL_dK, X):
         for k1,k2 in itertools.combinations(self.parts, 2):
             k1._sliced_X = k1._sliced_X2 = k2._sliced_X = k2._sliced_X2 = True
-            k1.update_gradients_full(dL_dK*k2.K(X, X)
+            k1.update_gradients_full(dL_dK*k2.K(X, X))
             self.k2.update_gradients_full(dL_dK*self.k1.K(X[:,self.slice1]), X[:,self.slice2])
 
     def gradients_X(self, dL_dK, X, X2=None):

GPy/likelihoods/__init__.py

@@ -5,3 +5,4 @@ from gamma import Gamma
 from poisson import Poisson
 from student_t import StudentT
 from likelihood import Likelihood
+from mixed_noise import MixedNoise

GPy/likelihoods/gaussian.py

@@ -49,7 +49,7 @@ class Gaussian(Likelihood):
         if isinstance(gp_link, link_functions.Identity):
             self.log_concave = True
 
-    def covariance_matrix(self, Y, Y_metadata=None):
+    def covariance_matrix(self, Y, **Y_metadata):
         return np.eye(Y.shape[0]) * self.variance
 
     def update_gradients(self, partial):

GPy/likelihoods/mixed_noise.py

@@ -0,0 +1,58 @@
+import numpy as np
+from scipy import stats, special
+from GPy.util.univariate_Gaussian import std_norm_pdf, std_norm_cdf
+import link_functions
+from likelihood import Likelihood
+from ..core.parameterization import Param
+from ..core.parameterization.transformations import Logexp
+from ..core.parameterization import Parameterized
+import itertools
+
+class MixedNoise(Likelihood):
+    def __init__(self, likelihoods_list, noise_index, variance = None, name='mixed_noise'):
+
+        Nlike = len(likelihoods_list)
+        self.order = np.unique(noise_index)
+
+        assert self.order.size == Nlike
+
+        if variance is None:
+            variance = np.ones(Nlike)
+        else:
+            assert variance.size == Nlike
+
+        super(Likelihood, self).__init__(name=name)
+
+        self.add_parameters(*likelihoods_list)
+        self.likelihoods_list = likelihoods_list
+        self.noise_index = noise_index
+        self.log_concave = False
+        self.likelihoods_indices = [noise_index.flatten()==j for j in self.order]
+
+    def covariance_matrix(self, Y, noise_index, **Y_metadata):
+        variance = np.zeros(Y.shape[0])
+        for lik, ind in itertools.izip(self.likelihoods_list, self.likelihoods_indices):
+            variance[ind] = lik.variance
+        return np.diag(variance)
+
+    def update_gradients(self, partial, noise_index, **Y_metadata):
+        [lik.update_gradients(partial[ind]) for lik,ind in itertools.izip(self.likelihoods_list, self.likelihoods_indices)]
+
+    def predictive_values(self, mu, var, full_cov=False, noise_index=None, **Y_metadata):
+        _variance = np.array([ self.likelihoods_list[j].variance for j in noise_index ])
+        if full_cov:
+            var += np.eye(var.shape[0])*_variance
+            d = 2*np.sqrt(np.diag(var))
+            low, up = mu - d, mu + d
+        else:
+            var += _variance
+            d = 2*np.sqrt(var)
+            low, up = mu - d, mu + d
+        return mu, var, low, up
+
+    def predictive_variance(self, mu, sigma, noise_index, predictive_mean=None, **Y_metadata):
+        if isinstance(noise_index,int):
+            _variance = self.variance[noise_index]
+        else:
+            _variance = np.array([ self.variance[j] for j in noise_index ])[:,None]
+        return _variance + sigma**2

GPy/models/__init__.py

@@ -13,6 +13,6 @@ from warped_gp import WarpedGP
 from bayesian_gplvm import BayesianGPLVM
 from mrd import MRD
 from gradient_checker import GradientChecker
-from gp_multioutput_regression import GPMultioutputRegression
-from sparse_gp_multioutput_regression import SparseGPMultioutputRegression
 from ss_gplvm import SSGPLVM
+from gp_coregionalized_regression import GPCoregionalizedRegression
+#.py file not included!!! #from sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression

GPy/models/gp_coregionalized_regression.py

@@ -0,0 +1,44 @@
+# Copyright (c) 2012 - 2014 the GPy Austhors (see AUTHORS.txt)
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import numpy as np
+from ..core import GP
+from .. import likelihoods
+from .. import kern
+from .. import util
+
+class GPCoregionalizedRegression(GP):
+    """
+    Gaussian Process model for heteroscedastic multioutput regression
+
+    This is a thin wrapper around the models.GP class, with a set of sensible defaults
+
+    :param X_list: list of input observations corresponding to each output
+    :type X_list: list of numpy arrays
+    :param Y_list: list of observed values related to the different noise models
+    :type Y_list: list of numpy arrays
+    :param kernel: a GPy kernel, defaults to RBF ** Coregionalized
+    :type kernel: None | GPy.kernel defaults
+    :likelihoods_list: a list of likelihoods, defaults to list of Gaussian likelihoods
+    :type likelihoods_list: None | a list GPy.likelihoods
+    :param name: model name
+    :type name: string
+    :param W_rank: number tuples of the corregionalization parameters 'W' (see coregionalize kernel documentation)
+    :type W_rank: integer
+    :param kernel_name: name of the kernel
+    :type kernel_name: string
+    """
+    def __init__(self, X_list, Y_list, kernel=None, likelihoods_list=None, name='GPCR',W_rank=1,kernel_name='X'):
+
+        #Input and Output
+        X,Y,self.noise_index = util.multioutput.build_XY(X_list,Y_list)
+        Ny = len(Y_list)
+
+        #Kernel
+        if kernel is None:
+            kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=GPy.kern.rbf(X.shape[1]-1), W_rank=1,name=kernel_name)
+
+        #Likelihood
+        likelihood = util.multioutput.build_likelihood(Y_list,self.noise_index,likelihoods_list)
+
+        super(GPCoregionalizedRegression, self).__init__(X,Y,kernel,likelihood, noise_index=self.noise_index)

GPy/plotting/matplot_dep/models_plots.py

@@ -86,7 +86,14 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
             upper = m + 2*np.sqrt(v)
             Y = Y
         else:
-            m, v, lower, upper = model.predict(Xgrid)
+            if 'noise_index' in model.Y_metadata.keys():
+                if np.unique(model.Y_metadata['noise_index'][which_data_rows]).size > 1:
+                    print "Data slices choosen have different noise models. Just one will be used."
+                noise_index = np.repeat(model.Y_metadata['noise_index'][which_data_rows][0], Xgrid.shape[0])[:,None]
+                m, v, lower, upper = model.predict(Xgrid,full_cov=False,noise_index=noise_index)
+            else:
+                noise_index = None
+                m, v, lower, upper = model.predict(Xgrid,full_cov=False)
             Y = Y
         for d in which_data_ycols:
             plots['gpplot'] = gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], ax=ax, edgecol=linecol, fillcol=fillcol)

GPy/util/__init__.py

@@ -14,6 +14,7 @@ import subarray_and_sorting
 import caching
 import diag
 import initialization
+import multioutput
 
 try:
     import sympy

GPy/util/multioutput.py

@@ -1,12 +1,17 @@
 import numpy as np
 import warnings
-from .. import kern
+import GPy
 
-def build_XY(input_list,output_list=None,index=None):
+
+def get_slices(input_list):
     num_outputs = len(input_list)
     _s = [0] + [ _x.shape[0] for _x in input_list ]
     _s = np.cumsum(_s)
     slices = [slice(a,b) for a,b in zip(_s[:-1],_s[1:])]
+    return slices
+
+def build_XY(input_list,output_list=None,index=None):
+    num_outputs = len(input_list)
     if output_list is not None:
         assert num_outputs == len(output_list)
         Y = np.vstack(output_list)
@@ -15,42 +20,82 @@ def build_XY(input_list,output_list=None,index=None):
 
     if index is not None:
         assert len(index) == num_outputs
-        I = np.vstack( [j*np.ones((_x.shape[0],1)) for _x,j in zip(input_list,index)] )
+        I = np.hstack( [np.repeat(j,_x.shape[0]) for _x,j in zip(input_list,index)] )
     else:
-        I = np.vstack( [j*np.ones((_x.shape[0],1)) for _x,j in zip(input_list,range(num_outputs))] )
+        I = np.hstack( [np.repeat(j,_x.shape[0]) for _x,j in zip(input_list,range(num_outputs))] )
 
     X = np.vstack(input_list)
-    X = np.hstack([X,I])
-    return X,Y,slices
+    X = np.hstack([X,I[:,None]])
 
-def build_lcm(input_dim, num_outputs, CK = [], NC = [], W_columns=1,W=None,kappa=None):
-    #TODO build_icm or build_lcm
+    return X,Y,I[:,None]#slices
+
+def build_likelihood(Y_list,noise_index,likelihoods_list=None):
+    Ny = len(Y_list)
+    if likelihoods_list is None:
+       likelihoods_list = [GPy.likelihoods.Gaussian(name="Gaussian_noise_%s" %j) for y,j in zip(Y_list,range(Ny))]
+    else:
+        assert len(likelihoods_list) == Ny
+    likelihood = GPy.likelihoods.mixed_noise.MixedNoise(likelihoods_list=likelihoods_list, noise_index=noise_index)
+    return likelihood
+
+
+def ICM(input_dim, num_outputs, kernel, W_rank=1,W=None,kappa=None,name='X'):
     """
-    Builds a kernel for a linear coregionalization model
+    Builds a kernel for an Intrinsic Coregionalization Model
 
     :input_dim: Input dimensionality
     :num_outputs: Number of outputs
-    :param CK: List of coregionalized kernels (i.e., this will be multiplied by a coregionalize kernel).
-    :param K: List of kernels that will be added up together with CK, but won't be multiplied by a coregionalize kernel
-    :param W_columns: number tuples of the corregionalization parameters 'coregion_W'
-    :type W_columns: integer
+    :param kernel: kernel that will be multiplied by the coregionalize kernel (matrix B).
+    :type kernel: a GPy kernel
+    :param W_rank: number tuples of the corregionalization parameters 'W'
+    :type W_rank: integer
     """
-
-    for k in CK:
-        if k.input_dim <> input_dim:
-            k.input_dim = input_dim
+    if kernel.input_dim <> input_dim:
+        kernel.input_dim = input_dim
         warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.")
 
-    for k in NC:
-        if k.input_dim <> input_dim + 1:
-            k.input_dim = input_dim + 1
-            warnings.warn("kernel's input dimension overwritten to fit input_dim parameter.")
+    #K = kernel.prod(GPy.kern.Coregionalize(input_dim, num_outputs,W_rank,W,kappa,name='B'),tensor=True,name=name)
+    K = kernel.prod(GPy.kern.Coregionalize(input_dim, num_outputs,W_rank,W,kappa,name='B'),name=name)
+    K['.*variance'] = 1.
+    K['.*variance'].fix()
+    return K
 
-    kernel = CK[0].prod(kern.Coregionalize(num_outputs,W_columns,W,kappa),tensor=True)
-    for k in CK[1:]:
-        k_coreg = kern.Coregionalize(num_outputs,W_columns,W,kappa)
-        kernel += k.prod(k_coreg,tensor=True)
-    for k in NC:
-        kernel += k
 
-    return kernel
+def LCM(input_dim, num_outputs, kernels_list, W_rank=1,name='X'):
+    """
+    Builds a kernel for an Linear Coregionalization Model
+
+    :input_dim: Input dimensionality
+    :num_outputs: Number of outputs
+    :param kernel: kernel that will be multiplied by the coregionalize kernel (matrix B).
+    :type kernel: a GPy kernel
+    :param W_rank: number tuples of the corregionalization parameters 'W'
+    :type W_rank: integer
+    """
+    Nk = len(kernels_list)
+    K = ICM(input_dim,num_outputs,kernels_list[0],W_rank,name='%s%s' %(name,0))
+    j = 1
+    for kernel in kernels_list[1:]:
+        K += ICM(input_dim,num_outputs,kernel,W_rank,name='%s%s' %(name,j))
+    return K
+
+
+def Private(input_dim, num_outputs, kernel, output, kappa=None,name='X'):
+    """
+    Builds a kernel for an Intrinsic Coregionalization Model
+
+    :input_dim: Input dimensionality
+    :num_outputs: Number of outputs
+    :param kernel: kernel that will be multiplied by the coregionalize kernel (matrix B).
+    :type kernel: a GPy kernel
+    :param W_rank: number tuples of the corregionalization parameters 'W'
+    :type W_rank: integer
+    """
+    K = ICM(input_dim,num_outputs,kernel,W_rank=1,kappa=kappa,name=name)
+    K.B.W.fix(0)
+    _range = range(num_outputs)
+    _range.pop(output)
+    for j in _range:
+        K.B.kappa[j] = 0
+        K.B.kappa[j].fix()
+    return K