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

GPy/core/model.py

@@ -349,7 +349,7 @@ class Model(Parameterized):
                 numerical_gradient = (f1 - f2) / (2 * step)
                 if np.all(gradient[xind] == 0): ratio = (f1 - f2) == gradient[xind]
                 else: ratio = (f1 - f2) / (2 * step * gradient[xind])
-                difference = np.abs((f1 - f2) / 2 / step - gradient[xind])
+                difference = np.abs(numerical_gradient - gradient[xind])
 
                 if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance:
                     formatted_name = "\033[92m {0} \033[0m".format(names[nind])

GPy/core/parameterization/parameter_core.py

@@ -281,7 +281,7 @@ class Gradcheckable(Pickleable, Parentable):
         """
         if self.has_parent():
             return self._highest_parent_._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance)
-        return self._checkgrad(self[''], verbose=verbose, step=step, tolerance=tolerance)
+        return self._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance)
 
     def _checkgrad(self, param, verbose=0, step=1e-6, tolerance=1e-3):
         """
@@ -372,8 +372,9 @@ class Indexable(Nameable, Observable):
         basically just sums up the parameter sizes which come before param.
         """
         if param.has_parent():
-            if param._parent_._get_original(param) in self.parameters:
-                return self._param_slices_[param._parent_._get_original(param)._parent_index_].start
+            p = param._parent_._get_original(param)
+            if p in self.parameters:
+                return reduce(lambda a,b: a + b.size, self.parameters[:p._parent_index_], 0)
             return self._offset_for(param._parent_) + param._parent_._offset_for(param)
         return 0
 
@@ -699,36 +700,10 @@ class OptimizationHandlable(Indexable):
 
     def _get_params_transformed(self):
         raise DeprecationWarning, "_get|set_params{_optimizer_copy_transformed} is deprecated, use self.optimizer array insetad!"
-#         # transformed parameters (apply un-transformation rules)
-#         p = self.param_array.copy()
-#         [np.put(p, ind, c.finv(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
-#         if self.has_parent() and self.constraints[__fixed__].size != 0:
-#             fixes = np.ones(self.size).astype(bool)
-#             fixes[self.constraints[__fixed__]] = FIXED
-#             return p[fixes]
-#         elif self._has_fixes():
-#             return p[self._fixes_]
-#         return p
 #
     def _set_params_transformed(self, p):
         raise DeprecationWarning, "_get|set_params{_optimizer_copy_transformed} is deprecated, use self.optimizer array insetad!"
 
-#         """
-#         Set parameters p, but make sure they get transformed before setting.
-#         This means, the optimizer sees p, whereas the model sees transformed(p),
-#         such that, the parameters the model sees are in the right domain.
-#         """
-#         if not(p is self.param_array):
-#             if self.has_parent() and self.constraints[__fixed__].size != 0:
-#                 fixes = np.ones(self.size).astype(bool)
-#                 fixes[self.constraints[__fixed__]] = FIXED
-#                 self.param_array.flat[fixes] = p
-#             elif self._has_fixes(): self.param_array.flat[self._fixes_] = p
-#             else: self.param_array.flat = p
-#         [np.put(self.param_array, ind, c.f(self.param_array.flat[ind]))
-#          for c, ind in self.constraints.iteritems() if c != __fixed__]
-#         self._trigger_params_changed()
-
     def _trigger_params_changed(self, trigger_parent=True):
         """
         First tell all children to update,
@@ -736,7 +711,7 @@ class OptimizationHandlable(Indexable):
 
         If trigger_parent is True, we will tell the parent, otherwise not.
         """
-        [p._trigger_params_changed(trigger_parent=False) for p in self.parameters]
+        [p._trigger_params_changed(trigger_parent=False) for p in self.parameters if not p.is_fixed]
         self.notify_observers(None, None if trigger_parent else -np.inf)
 
     def _size_transformed(self):
@@ -888,6 +863,25 @@ class Parameterizable(OptimizationHandlable):
             self._param_array_ = np.empty(self.size, dtype=np.float64)
         return self._param_array_
 
+    @property
+    def unfixed_param_array(self):
+        """
+        Array representing the parameters of this class.
+        There is only one copy of all parameters in memory, two during optimization.
+
+        !WARNING!: setting the parameter array MUST always be done in memory:
+        m.param_array[:] = m_copy.param_array
+        """
+        if self.__dict__.get('_param_array_', None) is None:
+            self._param_array_ = np.empty(self.size, dtype=np.float64)
+                    
+        if self.constraints[__fixed__].size !=0:
+            fixes = np.ones(self.size).astype(bool)
+            fixes[self.constraints[__fixed__]] = FIXED
+            return self._param_array_[fixes]
+        else:
+            return self._param_array_
+
     @param_array.setter
     def param_array(self, arr):
         self._param_array_ = arr

GPy/inference/latent_function_inference/expectation_propagation_dtc.py

@@ -56,7 +56,7 @@ class EPDTC(LatentFunctionInference):
         self._ep_approximation = None
 
     def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None):
-        num_data, output_dim = X.shape
+        num_data, output_dim = Y.shape
         assert output_dim ==1, "ep in 1D only (for now!)"
 
         Kmm = kern.K(Z)

GPy/inference/optimization/__init__.py

@@ -1,2 +1,3 @@
 from scg import SCG
 from optimization import *
+from hmc import HMC,HMC_shortcut

GPy/inference/optimization/hmc.py

@@ -0,0 +1,157 @@
+"""HMC implementation"""
+
+import numpy as np
+
+
+class HMC:
+    def __init__(self,model,M=None,stepsize=1e-1):
+        self.model = model
+        self.stepsize = stepsize
+        self.p = np.empty_like(model.optimizer_array.copy())
+        if M is None:
+            self.M = np.eye(self.p.size)
+        else:
+            self.M = M
+        self.Minv = np.linalg.inv(self.M)
+
+    def sample(self, m_iters=1000, hmc_iters=20):
+        params = np.empty((m_iters,self.p.size))
+        for i in xrange(m_iters):
+            self.p[:] = np.random.multivariate_normal(np.zeros(self.p.size),self.M)
+            H_old = self._computeH()
+            theta_old = self.model.optimizer_array.copy()
+            params[i] = self.model.unfixed_param_array
+            #Matropolis
+            self._update(hmc_iters)
+            H_new = self._computeH()
+
+            if H_old>H_new:
+                k = 1.
+            else:
+                k = np.exp(H_old-H_new)
+            if np.random.rand()<k:
+                params[i] = self.model.unfixed_param_array
+            else:
+                self.model.optimizer_array = theta_old
+        return params
+
+    def _update(self, hmc_iters):
+        for i in xrange(hmc_iters):
+            self.p[:] += -self.stepsize/2.*self.model._transform_gradients(self.model.objective_function_gradients())
+            self.model.optimizer_array = self.model.optimizer_array + self.stepsize*np.dot(self.Minv, self.p)
+            self.p[:] += -self.stepsize/2.*self.model._transform_gradients(self.model.objective_function_gradients())
+
+    def _computeH(self,):
+        return self.model.objective_function()+self.p.size*np.log(2*np.pi)/2.+np.log(np.linalg.det(self.M))/2.+np.dot(self.p, np.dot(self.Minv,self.p[:,None]))/2.
+
+class HMC_shortcut:
+    def __init__(self,model,M=None,stepsize_range=[1e-6, 1e-1],groupsize=5, Hstd_th=[1e-5, 3.]):
+        self.model = model
+        self.stepsize_range = np.log(stepsize_range)
+        self.p = np.empty_like(model.optimizer_array.copy())
+        self.groupsize = groupsize
+        self.Hstd_th = Hstd_th
+        if M is None:
+            self.M = np.eye(self.p.size)
+        else:
+            self.M = M
+        self.Minv = np.linalg.inv(self.M)
+
+    def sample(self, m_iters=1000, hmc_iters=20):
+        params = np.empty((m_iters,self.p.size))
+        for i in xrange(m_iters):
+            # sample a stepsize from the uniform distribution
+            stepsize = np.exp(np.random.rand()*(self.stepsize_range[1]-self.stepsize_range[0])+self.stepsize_range[0])
+            self.p[:] = np.random.multivariate_normal(np.zeros(self.p.size),self.M)
+            H_old = self._computeH()
+            params[i] = self.model.unfixed_param_array
+            theta_old = self.model.optimizer_array.copy()
+            #Matropolis
+            self._update(hmc_iters, stepsize)
+            H_new = self._computeH()
+
+            if H_old>H_new:
+                k = 1.
+            else:
+                k = np.exp(H_old-H_new)
+            if np.random.rand()<k:
+                params[i] = self.model.unfixed_param_array
+            else:
+                self.model.optimizer_array = theta_old
+        return params
+
+    def _update(self, hmc_iters, stepsize):
+        theta_buf = np.empty((2*hmc_iters+1,self.model.optimizer_array.size))
+        p_buf = np.empty((2*hmc_iters+1,self.p.size))
+        H_buf = np.empty((2*hmc_iters+1,))
+        # Set initial position
+        theta_buf[hmc_iters] = self.model.optimizer_array
+        p_buf[hmc_iters] = self.p
+        H_buf[hmc_iters] = self._computeH()
+
+        reversal = []
+        pos = 1
+        i=0
+        while i<hmc_iters:
+            self.p[:] += -stepsize/2.*self.model._transform_gradients(self.model.objective_function_gradients())
+            self.model.optimizer_array = self.model.optimizer_array + stepsize*np.dot(self.Minv, self.p)
+            self.p[:] += -stepsize/2.*self.model._transform_gradients(self.model.objective_function_gradients())
+
+            theta_buf[hmc_iters+pos] = self.model.optimizer_array
+            p_buf[hmc_iters+pos] = self.p
+            H_buf[hmc_iters+pos] = self._computeH()
+            i+=1
+
+            if i<self.groupsize:
+                pos += 1
+                continue
+            else:
+                if len(reversal)==0:
+                    Hlist = range(hmc_iters+pos,hmc_iters+pos-self.groupsize,-1)
+                    if self._testH(H_buf[Hlist]):
+                        pos += 1
+                    else:
+                        # Reverse the trajectory for the 1st time
+                        reversal.append(pos)
+                        if hmc_iters-i>pos:
+                            pos = -1
+                            i += pos
+                            self.model.optimizer_array = theta_buf[hmc_iters]
+                            self.p[:] = -p_buf[hmc_iters]
+                        else:
+                            pos_new = pos-hmc_iters+i
+                            self.model.optimizer_array = theta_buf[hmc_iters+pos_new]
+                            self.p[:] = -p_buf[hmc_iters+pos_new]
+                            break
+                else:
+                    Hlist = range(hmc_iters+pos,hmc_iters+pos+self.groupsize)
+#                    print Hlist
+#                    print self._testH(H_buf[Hlist])
+
+                    if self._testH(H_buf[Hlist]):
+                        pos += -1
+                    else:
+                        # Reverse the trajectory for the 2nd time
+                        r = (hmc_iters - i)%((reversal[0]-pos)*2)
+                        if r>(reversal[0]-pos):
+                            pos_new = 2*reversal[0] - r - pos
+                        else:
+                            pos_new = pos + r
+                        self.model.optimizer_array = theta_buf[hmc_iters+pos_new]
+                        self.p[:] = p_buf[hmc_iters+pos_new] # the sign of momentum might be wrong!
+#                        print reversal[0],pos,pos_new
+#                        print H_buf
+                        break
+
+    def _testH(self, Hlist):
+        Hstd = np.std(Hlist)
+#        print Hlist
+#        print Hstd
+        if Hstd<self.Hstd_th[0] or Hstd>self.Hstd_th[1]:
+            return False
+        else:
+            return True
+
+    def _computeH(self,):
+        return self.model.objective_function()+self.p.size*np.log(2*np.pi)/2.+np.log(np.linalg.det(self.M))/2.+np.dot(self.p, np.dot(self.Minv,self.p[:,None]))/2.
+

GPy/kern/_src/add.py

@@ -10,7 +10,7 @@ class Add(CombinationKernel):
     """
     Add given list of kernels together.
     propagates gradients through.
-    
+
     This kernel will take over the active dims of it's subkernels passed in.
     """
     def __init__(self, subkerns, name='add'):
@@ -40,7 +40,7 @@ class Add(CombinationKernel):
         return reduce(np.add, (p.Kdiag(X) for p in which_parts))
 
     def update_gradients_full(self, dL_dK, X, X2=None):
-        [p.update_gradients_full(dL_dK, X, X2) for p in self.parts]
+        [p.update_gradients_full(dL_dK, X, X2) for p in self.parts if not p.is_fixed]
 
     def update_gradients_diag(self, dL_dK, X):
         [p.update_gradients_diag(dL_dK, X) for p in self.parts]

GPy/models/bayesian_gplvm.py

@@ -84,6 +84,22 @@ class BayesianGPLVM(SparseGP):
 
         self.X.mean.gradient, self.X.variance.gradient = self.kern.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, dL_dpsi0=self.grad_dict['dL_dpsi0'], dL_dpsi1=self.grad_dict['dL_dpsi1'], dL_dpsi2=self.grad_dict['dL_dpsi2'])
 
+        # This is testing code -------------------------
+#         i = np.random.randint(self.X.shape[0])
+#         X_ = self.X.mean
+#         which = np.sqrt(((X_ - X_[i:i+1])**2).sum(1)).argsort()>(max(0, self.X.shape[0]-51))
+#         _, _, grad_dict = self.inference_method.inference(self.kern, self.X[which], self.Z, self.likelihood, self.Y[which], self.Y_metadata)
+#         grad = self.kern.gradients_qX_expectations(variational_posterior=self.X[which], Z=self.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2'])
+#
+#         self.X.mean.gradient[:] = 0
+#         self.X.variance.gradient[:] = 0
+#         self.X.mean.gradient[which] = grad[0]
+#         self.X.variance.gradient[which] = grad[1]
+
+        # update for the KL divergence
+#         self.variational_prior.update_gradients_KL(self.X, which)
+        # -----------------------------------------------
+
         # update for the KL divergence
         self.variational_prior.update_gradients_KL(self.X)
 

GPy/plotting/matplot_dep/models_plots.py

@@ -150,7 +150,11 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
         if plot_raw:
             m, _ = model._raw_predict(Xgrid)
         else:
-            m, _ = model.predict(Xgrid)
+            if isinstance(model,GPCoregionalizedRegression) or isinstance(model,SparseGPCoregionalizedRegression):
+                meta = {'output_index': Xgrid[:,-1:].astype(np.int)}
+            else:
+                meta = None
+            m, v = model.predict(Xgrid, full_cov=False, Y_metadata=meta)
         for d in which_data_ycols:
             m_d = m[:,d].reshape(resolution, resolution).T
             plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)

setup.py

@@ -1,13 +1,13 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
-#import os
+import os
 from setuptools import setup
 
 # Version number
 version = '0.4.6'
 
-from pkg_resources import Requirement, resource_string
+from pkg_resources import Requirement
 def read(fname):
     return open(os.path.join(os.path.dirname(__file__), fname)).read()