[mrd] plotting, init, inference etc.

GPy/core/gp.py

@@ -180,40 +180,80 @@ class GP(Model):
 
         return Ysim
 
-    def plot_f(self, *args, **kwargs):
+    def plot_f(self, plot_limits=None, which_data_rows='all',
+        which_data_ycols='all', fixed_inputs=[],
+        levels=20, samples=0, fignum=None, ax=None, resolution=None,
+        plot_raw=True,
+        linecol=None,fillcol=None, Y_metadata=None, data_symbol='kx'):
         """
-
-        Plot the GP's view of the world, where the data is normalized and
-        before applying a likelihood.
-
-        This is a convenience function: arguments are passed to
-        GPy.plotting.matplot_dep.models_plots.plot_f_fit
-
+        Plot the GP's view of the world, where the data is normalized and before applying a likelihood.
+        This is a call to plot with plot_raw=True.
+        Data will not be plotted in this, as the GP's view of the world
+        may live in another space, or units then the data.
         """
         assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
         from ..plotting.matplot_dep import models_plots
-        return models_plots.plot_fit_f(self,*args,**kwargs)
+        kw = {}
+        if linecol is not None:
+            kw['linecol'] = linecol
+        if fillcol is not None:
+            kw['fillcol'] = fillcol
+        return models_plots.plot_fit(self, plot_limits, which_data_rows,
+                                     which_data_ycols, fixed_inputs, 
+                                     levels, samples, fignum, ax, resolution, 
+                                     plot_raw=plot_raw, Y_metadata=Y_metadata, 
+                                     data_symbol=data_symbol, **kw)
 
-    def plot(self, *args, **kwargs):
+    def plot(self, plot_limits=None, which_data_rows='all',
+        which_data_ycols='all', fixed_inputs=[],
+        levels=20, samples=0, fignum=None, ax=None, resolution=None,
+        plot_raw=False,
+        linecol=None,fillcol=None, Y_metadata=None, data_symbol='kx'):
         """
         Plot the posterior of the GP.
-          - In one dimension, the function is plotted with a shaded region
-            identifying two standard deviations.
-          - In two dimsensions, a contour-plot shows the mean predicted
-            function
-          - In higher dimensions, use fixed_inputs to plot the GP  with some of
-            the inputs fixed.
+          - In one dimension, the function is plotted with a shaded region identifying two standard deviations.
+          - In two dimsensions, a contour-plot shows the mean predicted function
+          - In higher dimensions, use fixed_inputs to plot the GP  with some of the inputs fixed.
 
         Can plot only part of the data and part of the posterior functions
-        using which_data_rows which_data_ycols and which_parts
-
-        This is a convenience function: arguments are passed to
-        GPy.plotting.matplot_dep.models_plots.plot_fit
+        using which_data_rowsm which_data_ycols.
 
+        :param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
+        :type plot_limits: np.array
+        :param which_data_rows: which of the training data to plot (default all)
+        :type which_data_rows: 'all' or a slice object to slice model.X, model.Y
+        :param which_data_ycols: when the data has several columns (independant outputs), only plot these
+        :type which_data_rows: 'all' or a list of integers
+        :param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
+        :type fixed_inputs: a list of tuples
+        :param resolution: the number of intervals to sample the GP on. Defaults to 200 in 1D and 50 (a 50x50 grid) in 2D
+        :type resolution: int
+        :param levels: number of levels to plot in a contour plot.
+        :type levels: int
+        :param samples: the number of a posteriori samples to plot
+        :type samples: int
+        :param fignum: figure to plot on.
+        :type fignum: figure number
+        :param ax: axes to plot on.
+        :type ax: axes handle
+        :type output: integer (first output is 0)
+        :param linecol: color of line to plot [Tango.colorsHex['darkBlue']]
+        :type linecol:
+        :param fillcol: color of fill [Tango.colorsHex['lightBlue']]
+        :param levels: for 2D plotting, the number of contour levels to use is ax is None, create a new figure
         """
         assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
         from ..plotting.matplot_dep import models_plots
-        return models_plots.plot_fit(self,*args,**kwargs)
+        kw = {}
+        if linecol is not None:
+            kw['linecol'] = linecol
+        if fillcol is not None:
+            kw['fillcol'] = fillcol
+        return models_plots.plot_fit(self, plot_limits, which_data_rows,
+                                     which_data_ycols, fixed_inputs, 
+                                     levels, samples, fignum, ax, resolution, 
+                                     plot_raw=plot_raw, Y_metadata=Y_metadata, 
+                                     data_symbol=data_symbol, **kw)
 
     def input_sensitivity(self):
         """

GPy/core/parameterization/parameterized.py

@@ -272,8 +272,11 @@ class Parameterized(Parameterizable):
     def __setattr__(self, name, val):
         # override the default behaviour, if setting a param, so broadcasting can by used
         if hasattr(self, "parameters"):
-            pnames = self.parameter_names(False, adjust_for_printing=True, recursive=False)
-            if name in pnames: self.parameters[pnames.index(name)][:] = val; return
+            try:
+                pnames = self.parameter_names(False, adjust_for_printing=True, recursive=False)
+                if name in pnames: self.parameters[pnames.index(name)][:] = val; return
+            except AttributeError:
+                pass
         object.__setattr__(self, name, val);
 
     #===========================================================================
@@ -281,11 +284,15 @@ class Parameterized(Parameterizable):
     #===========================================================================
     def __setstate__(self, state):
         super(Parameterized, self).__setstate__(state)
-        self._connect_parameters()
-        self._connect_fixes()
-        self._notify_parent_change()
+        try:
+            self._connect_parameters()
+            self._connect_fixes()
+            self._notify_parent_change()
+    
+            self.parameters_changed()
+        except Exception as e:
+            print "WARNING: caught exception {!s}, trying to continue".format(e)
 
-        self.parameters_changed()
     def copy(self):
         c = super(Parameterized, self).copy()
         c._connect_parameters()

GPy/core/sparse_gp.py

@@ -66,7 +66,11 @@ class SparseGP(GP):
             #gradients wrt Z
             self.Z.gradient = self.kern.gradients_X(dL_dKmm, self.Z)
             self.Z.gradient += self.kern.gradients_Z_expectations(
-                               self.grad_dict['dL_dpsi0'], self.grad_dict['dL_dpsi1'], self.grad_dict['dL_dpsi2'], Z=self.Z, variational_posterior=self.X)
+                               self.grad_dict['dL_dpsi0'], 
+                               self.grad_dict['dL_dpsi1'], 
+                               self.grad_dict['dL_dpsi2'], 
+                               Z=self.Z, 
+                               variational_posterior=self.X)
         else:
             #gradients wrt kernel
             self.kern.update_gradients_diag(self.grad_dict['dL_dKdiag'], self.X)