added robust pickling, switches to old behaviour, if get/setstate not implemented

GPy/models/bayesian_gplvm.py

@@ -48,16 +48,16 @@ class BayesianGPLVM(SparseGP, GPLVM):
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs)
         self.ensure_default_constraints()
 
-    def __getstate__(self):
+    def getstate(self):
         """
         Get the current state of the class,
         here just all the indices, rest can get recomputed
         """
-        return SparseGP.__getstate__(self) + [self.init]
+        return SparseGP.getstate(self) + [self.init]
 
-    def __setstate__(self, state):
+    def setstate(self, state):
         self.init = state.pop()
-        SparseGP.__setstate__(self, state)
+        SparseGP.setstate(self, state)
 
     def _get_param_names(self):
         X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])

GPy/models/gp_regression.py

@@ -25,11 +25,20 @@ class GPRegression(GP):
 
     """
 
-    def __init__(self,X,Y,kernel=None,normalize_X=False,normalize_Y=False):
+    def __init__(self, X, Y, kernel=None, normalize_X=False, normalize_Y=False):
         if kernel is None:
             kernel = kern.rbf(X.shape[1])
 
-        likelihood = likelihoods.Gaussian(Y,normalize=normalize_Y)
+        likelihood = likelihoods.Gaussian(Y, normalize=normalize_Y)
 
         GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
         self.ensure_default_constraints()
+
+    def getstate(self):
+        return GP.getstate(self)
+
+
+    def setstate(self, state):
+        return GP.setstate(self, state)
+
+    pass

GPy/models/mrd.py

@@ -84,8 +84,8 @@ class MRD(Model):
         Model.__init__(self)
         self.ensure_default_constraints()
 
-    def __getstate__(self):
-        return Model.__getstate__(self) + [self.names,
+    def getstate(self):
+        return Model.getstate(self) + [self.names,
                 self.bgplvms,
                 self.gref,
                 self.nparams,
@@ -95,7 +95,7 @@ class MRD(Model):
                 self.NQ,
                 self.MQ]
 
-    def __setstate__(self, state):
+    def setstate(self, state):
         self.MQ = state.pop()
         self.NQ = state.pop()
         self.num_data = state.pop()
@@ -105,7 +105,7 @@ class MRD(Model):
         self.gref = state.pop()
         self.bgplvms = state.pop()
         self.names = state.pop()
-        Model.__setstate__(self, state)
+        Model.setstate(self, state)
 
     @property
     def X(self):

GPy/models/sparse_gp_classification.py

@@ -28,7 +28,7 @@ class SparseGPClassification(SparseGP):
 
     def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, num_inducing=10):
         if kernel is None:
-            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
+            kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1], 1e-3)
 
         if likelihood is None:
             distribution = likelihoods.likelihood_functions.Binomial()
@@ -41,7 +41,16 @@ class SparseGPClassification(SparseGP):
             i = np.random.permutation(X.shape[0])[:num_inducing]
             Z = X[i].copy()
         else:
-            assert Z.shape[1]==X.shape[1]
+            assert Z.shape[1] == X.shape[1]
 
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
         self.ensure_default_constraints()
+
+    def getstate(self):
+        return SparseGP.getstate(self)
+
+
+    def setstate(self, state):
+        return SparseGP.setstate(self, state)
+
+    pass

GPy/models/sparse_gp_regression.py

@@ -43,3 +43,13 @@ class SparseGPRegression(SparseGP):
 
         SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X, X_variance=X_variance)
         self.ensure_default_constraints()
+        pass
+
+    def getstate(self):
+        return SparseGP.getstate(self)
+
+
+    def setstate(self, state):
+        return SparseGP.setstate(self, state)
+
+    pass

GPy/models/sparse_gplvm.py

@@ -28,6 +28,14 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
         SparseGPRegression.__init__(self, X, Y, kernel=kernel, num_inducing=num_inducing)
         self.ensure_default_constraints()
 
+    def getstate(self):
+        return SparseGPRegression.getstate(self)
+
+
+    def setstate(self, state):
+        return SparseGPRegression.setstate(self, state)
+
+
     def _get_param_names(self):
         return (sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
                 + SparseGPRegression._get_param_names(self))

GPy/models/svigp_regression.py

@@ -42,3 +42,11 @@ class SVIGPRegression(SVIGP):
 
         SVIGP.__init__(self, X, likelihood, kernel, Z, q_u=q_u, batchsize=batchsize)
         self.load_batch()
+
+    def getstate(self):
+        return GPBase.getstate(self)
+
+
+    def setstate(self, state):
+        return GPBase.setstate(self, state)
+

GPy/models/warped_gp.py

@@ -28,6 +28,14 @@ class WarpedGP(GP):
         GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
         self._set_params(self._get_params())
 
+    def getstate(self):
+        return GP.getstate(self)
+
+
+    def setstate(self, state):
+        return GP.setstate(self, state)
+
+
     def _scale_data(self, Y):
         self._Ymax = Y.max()
         self._Ymin = Y.min()