[latentfunctioninference] superclass LatentFunctionInference added, which contains a call just before and just after optimization

GPy/core/gp.py

@@ -10,7 +10,7 @@ from model import Model
 from parameterization import ObsAr
 from .. import likelihoods
 from ..likelihoods.gaussian import Gaussian
-from ..inference.latent_function_inference import exact_gaussian_inference, expectation_propagation
+from ..inference.latent_function_inference import exact_gaussian_inference, expectation_propagation, LatentFunctionInference
 from parameterization.variational import VariationalPosterior
 
 class GP(Model):
@@ -21,6 +21,7 @@ class GP(Model):
     :param Y: output observations
     :param kernel: a GPy kernel, defaults to rbf+white
     :param likelihood: a GPy likelihood
+    :param :class:`~GPy.inference.latent_function_inference.LatentFunctionInference` inference_method: The inference method to use for this GP
     :rtype: model object
 
     .. Note:: Multiple independent outputs are allowed using columns of Y
@@ -220,3 +221,20 @@ class GP(Model):
         """
         return self.kern.input_sensitivity()
 
+    def optimize(self, optimizer=None, start=None, **kwargs):
+        """
+        Optimize the model using self.log_likelihood and self.log_likelihood_gradient, as well as self.priors.
+        kwargs are passed to the optimizer. They can be:
+
+        :param max_f_eval: maximum number of function evaluations
+        :type max_f_eval: int
+        :messages: whether to display during optimisation
+        :type messages: bool
+        :param optimizer: which optimizer to use (defaults to self.preferred optimizer)
+        :type optimizer: string
+
+        TODO: valid args
+        """
+        self.inference_method.on_optimization_start()
+        super(GP, self).optimize(optimizer, start, **kwargs)
+        self.inference_method.on_optimization_end()

GPy/core/model.py

@@ -220,7 +220,7 @@ class Model(Parameterized):
         if self.is_fixed:
             raise RuntimeError, "Cannot optimize, when everything is fixed"
         if self.size == 0:
-            raise RuntimeError, "Model without parameters cannot be minimized"
+            raise RuntimeError, "Model without parameters cannot be optimized"
 
         if optimizer is None:
             optimizer = self.preferred_optimizer

GPy/inference/latent_function_inference/__init__.py

@@ -25,6 +25,20 @@ etc.
 
 """
 
+class LatentFunctionInference(object):
+    def on_optimization_start(self):
+        """
+        This function gets called, just before the optimization loop to start.
+        """
+        pass
+
+    def on_optimization_end(self):
+        """
+        This function gets called, just after the optimization loop ended.
+        """
+        pass
+
+
 from exact_gaussian_inference import ExactGaussianInference
 from laplace import Laplace
 from GPy.inference.latent_function_inference.var_dtc import VarDTC
@@ -38,11 +52,26 @@ from var_dtc_gpu import VarDTC_GPU
 # class FullLatentFunctionData(object):
 #
 #
-# class LatentFunctionInference(object):
+
-#     def inference(self, kern, X, likelihood, Y, Y_metadata=None):
+# class EMLikeLatentFunctionInference(LatentFunctionInference):
+#     def update_approximation(self):
+#         """
+#         This function gets called when the 
+#         """
+#     
+#     def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None):
 #         """
 #         Do inference on the latent functions given a covariance function `kern`,
-#         inputs and outputs `X` and `Y`, and a likelihood `likelihood`.
+#         inputs and outputs `X` and `Y`, inducing_inputs `Z`, and a likelihood `likelihood`.
+#         Additional metadata for the outputs `Y` can be given in `Y_metadata`.
+#         """
+#         raise NotImplementedError, "Abstract base class for full inference"
+# 
+# class VariationalLatentFunctionInference(LatentFunctionInference):
+#     def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None):
+#         """
+#         Do inference on the latent functions given a covariance function `kern`,
+#         inputs and outputs `X` and `Y`, inducing_inputs `Z`, and a likelihood `likelihood`.
 #         Additional metadata for the outputs `Y` can be given in `Y_metadata`.
 #         """
 #         raise NotImplementedError, "Abstract base class for full inference"

GPy/inference/latent_function_inference/dtc.py

@@ -4,9 +4,10 @@
 from posterior import Posterior
 from ...util.linalg import jitchol, tdot, dtrtrs, dpotri, pdinv
 import numpy as np
+from . import LatentFunctionInference
 log_2_pi = np.log(2*np.pi)
 
-class DTC(object):
+class DTC(LatentFunctionInference):
     """
     An object for inference when the likelihood is Gaussian, but we want to do sparse inference.
 

GPy/inference/latent_function_inference/exact_gaussian_inference.py

@@ -5,10 +5,11 @@ from posterior import Posterior
 from ...util.linalg import pdinv, dpotrs, tdot
 from ...util import diag
 import numpy as np
+from . import LatentFunctionInference
 log_2_pi = np.log(2*np.pi)
 
 
-class ExactGaussianInference(object):
+class ExactGaussianInference(LatentFunctionInference):
     """
     An object for inference when the likelihood is Gaussian.
 

GPy/inference/latent_function_inference/expectation_propagation.py

@@ -1,9 +1,10 @@
 import numpy as np
 from ...util.linalg import pdinv,jitchol,DSYR,tdot,dtrtrs, dpotrs
 from posterior import Posterior
+from . import LatentFunctionInference
 log_2_pi = np.log(2*np.pi)
 
-class EP(object):
+class EP(LatentFunctionInference):
     def __init__(self, epsilon=1e-6, eta=1., delta=1.):
         """
         The expectation-propagation algorithm.

GPy/inference/latent_function_inference/fitc.py

@@ -5,9 +5,10 @@ from posterior import Posterior
 from ...util.linalg import jitchol, tdot, dtrtrs, dpotri, pdinv
 from ...util import diag
 import numpy as np
+from . import LatentFunctionInference
 log_2_pi = np.log(2*np.pi)
 
-class FITC(object):
+class FITC(LatentFunctionInference):
     """
     An object for inference when the likelihood is Gaussian, but we want to do sparse inference.
 

GPy/inference/latent_function_inference/laplace.py

@@ -16,8 +16,9 @@ from ...util.misc import param_to_array
 from posterior import Posterior
 import warnings
 from scipy import optimize
+from . import LatentFunctionInference
 
-class Laplace(object):
+class Laplace(LatentFunctionInference):
 
     def __init__(self):
         """

GPy/inference/latent_function_inference/var_dtc.py

@@ -7,9 +7,10 @@ from ...util import diag
 from ...core.parameterization.variational import VariationalPosterior
 import numpy as np
 from ...util.misc import param_to_array
+from . import LatentFunctionInference
 log_2_pi = np.log(2*np.pi)
 
-class VarDTC(object):
+class VarDTC(LatentFunctionInference):
     """
     An object for inference when the likelihood is Gaussian, but we want to do sparse inference.
 
@@ -190,7 +191,7 @@ class VarDTC(object):
         post = Posterior(woodbury_inv=woodbury_inv, woodbury_vector=woodbury_vector, K=Kmm, mean=None, cov=None, K_chol=Lm)
         return post, log_marginal, grad_dict
 
-class VarDTCMissingData(object):
+class VarDTCMissingData(LatentFunctionInference):
     const_jitter = 1e-6
     def __init__(self, limit=1, inan=None):
         from ...util.caching import Cacher

GPy/inference/latent_function_inference/var_dtc_gpu.py

@@ -7,6 +7,7 @@ from ...util import diag
 from ...core.parameterization.variational import VariationalPosterior
 import numpy as np
 from ...util.misc import param_to_array
+from . import LatentFunctionInference
 log_2_pi = np.log(2*np.pi)
 
 from ...util import gpu_init
@@ -19,7 +20,7 @@ try:
 except:
     pass
 
-class VarDTC_GPU(object):
+class VarDTC_GPU(LatentFunctionInference):
     """
     An object for inference when the likelihood is Gaussian, but we want to do sparse inference.
 

GPy/inference/latent_function_inference/var_dtc_parallel.py

@@ -7,9 +7,10 @@ from ...util import diag
 from ...core.parameterization.variational import VariationalPosterior
 import numpy as np
 from ...util.misc import param_to_array
+from . import LatentFunctionInference
 log_2_pi = np.log(2*np.pi)
 
-class VarDTC_minibatch(object):
+class VarDTC_minibatch(LatentFunctionInference):
     """
     An object for inference when the likelihood is Gaussian, but we want to do sparse inference.