[inducing] 3d added

GPy/installation.cfg

@@ -1,5 +1,5 @@
 # This is the local installation configuration file for GPy
 
 [plotting]
-#library = plotly
+library = plotly
-library = matplotlib
+#library = matplotlib

GPy/plotting/__init__.py

@@ -22,9 +22,10 @@ def change_plotting_library(lib):
             current_lib[0] = None
         #===========================================================================
     except (ImportError, NameError):
-        import warnings
-        warnings.warn(ImportWarning("{} not available, install newest version of {} for plotting".format(lib, lib)))
         config.set('plotting', 'library', 'none')
+        raise
+        import warnings
+        #warnings.warn(ImportWarning("{} not available, install newest version of {} for plotting".format(lib, lib)))
         
 from ..util.config import config
 lib = config.get('plotting', 'library')

GPy/plotting/abstract_plotting_library.py

@@ -277,4 +277,4 @@ class AbstractPlottingLibrary(object):
                         
         the kwargs are plotting library specific kwargs!
         """
-        raise NotImplementedError("Implement all plot functions in AbstractPlottingLibrary in order to use your own plotting library")
+        print("fill_gradient not implemented in this backend.")

GPy/plotting/gpy_plot/data_plots.py

@@ -159,7 +159,7 @@ def _plot_data_error(self, canvas, which_data_rows='all',
 
     return plots
 
-def plot_inducing(self, visible_dims=None, projection='2d', label=None, **plot_kwargs):
+def plot_inducing(self, visible_dims=None, projection='2d', label='inducing', **plot_kwargs):
     """
     Plot the inducing inputs of a sparse gp model
 
@@ -168,7 +168,7 @@ def plot_inducing(self, visible_dims=None, projection='2d', label=None, **plot_k
     """
     canvas, kwargs = pl().new_canvas(projection=projection, **plot_kwargs)
     plots = _plot_inducing(self, canvas, visible_dims, projection, label, **kwargs)
-    return pl().add_to_canvas(canvas, plots)
+    return pl().add_to_canvas(canvas, plots, legend=label is not None)
 
 def _plot_inducing(self, canvas, visible_dims, projection, label, **plot_kwargs):
     if visible_dims is None:
@@ -182,15 +182,15 @@ def _plot_inducing(self, canvas, visible_dims, projection, label, **plot_kwargs)
     #one dimensional plotting
     if len(free_dims) == 1:
         update_not_existing_kwargs(plot_kwargs, pl().defaults.inducing_1d)  # @UndefinedVariable
-        plots['inducing'] = pl().plot_axis_lines(canvas, Z[:, free_dims], **plot_kwargs)
+        plots['inducing'] = pl().plot_axis_lines(canvas, Z[:, free_dims], label=label, **plot_kwargs)
     #2D plotting
     elif len(free_dims) == 2 and projection == '3d':
         update_not_existing_kwargs(plot_kwargs, pl().defaults.inducing_3d)  # @UndefinedVariable
-        plots['inducing'] = pl().plot_axis_lines(canvas, Z[:, free_dims], **plot_kwargs)
+        plots['inducing'] = pl().plot_axis_lines(canvas, Z[:, free_dims], label=label, **plot_kwargs)
     elif len(free_dims) == 2:
         update_not_existing_kwargs(plot_kwargs, pl().defaults.inducing_2d)  # @UndefinedVariable
         plots['inducing'] = pl().scatter(canvas, Z[:, free_dims[0]], Z[:, free_dims[1]],
-                                       **plot_kwargs)
+                                       label=label, **plot_kwargs)
     elif len(free_dims) == 0:
         pass #Nothing to plot!
     else:

GPy/plotting/gpy_plot/gp_plots.py

@@ -46,9 +46,12 @@ def plot_mean(self, plot_limits=None, fixed_inputs=None,
     """
     Plot the mean of the GP.
 
+    You can deactivate the legend for this one plot by supplying None to label. 
+
     Give the Y_metadata in the predict_kw if you need it.
 
 
+
     :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 fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input dimension i should be set to value v.
@@ -112,8 +115,11 @@ def plot_confidence(self, lower=2.5, upper=97.5, plot_limits=None, fixed_inputs=
     E.g. the 95% confidence interval is $2.5, 97.5$.
     Note: Only implemented for one dimension!
 
+    You can deactivate the legend for this one plot by supplying None to label. 
+
     Give the Y_metadata in the predict_kw if you need it.
 
+
     :param float lower: the lower percentile to plot
     :param float upper: the upper percentile to plot
     :param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
@@ -134,7 +140,7 @@ def plot_confidence(self, lower=2.5, upper=97.5, plot_limits=None, fixed_inputs=
                                                  (lower, upper),
                                                  ycols, predict_kw)
     plots = _plot_confidence(self, canvas, helper_data, helper_prediction, label, **kwargs)
-    return pl().add_to_canvas(canvas, plots)
+    return pl().add_to_canvas(canvas, plots, legend=label is not None)
 
 def _plot_confidence(self, canvas, helper_data, helper_prediction, label, **kwargs):
     _, _, _, _, free_dims, Xgrid, _, _, _, _, _ = helper_data
@@ -162,9 +168,12 @@ def plot_samples(self, plot_limits=None, fixed_inputs=None,
     """
     Plot the mean of the GP.
 
+    You can deactivate the legend for this one plot by supplying None to label. 
+
     Give the Y_metadata in the predict_kw if you need it.
 
 
+
     :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 fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input dimension i should be set to value v.
@@ -221,8 +230,12 @@ def plot_density(self, plot_limits=None, fixed_inputs=None,
     E.g. the 95% confidence interval is $2.5, 97.5$.
     Note: Only implemented for one dimension!
 
+    You can deactivate the legend for this one plot by supplying None to label. 
+
     Give the Y_metadata in the predict_kw if you need it.
 
+
+
     :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 fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input dimension i should be set to value v.
@@ -271,10 +284,13 @@ def plot(self, plot_limits=None, fixed_inputs=None,
               plot_data=True, plot_inducing=True, plot_density=False,
               predict_kw=None, projection='2d', legend=False, **kwargs):
     """
-    Convinience function for plotting the fit of a GP.
+    Convenience function for plotting the fit of a GP.
+
+    You can deactivate the legend for this one plot by supplying None to label. 
 
     Give the Y_metadata in the predict_kw if you need it.
 
+
     If you want fine graned control use the specific plotting functions supplied in the model.
 
     :param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
@@ -298,6 +314,8 @@ def plot(self, plot_limits=None, fixed_inputs=None,
     :param bool plot_inducing: plot inducing inputs?
     :param bool plot_density: plot density instead of the confidence interval?
     :param dict predict_kw: the keyword arguments for the prediction. If you want to plot a specific kernel give dict(kern=<specific kernel>) in here
+    :param {2d|3d} projection: plot in 2d or 3d?
+    :param bool legend: convenience, whether to put a legend on the plot or not.
     """
     canvas, _ = pl().new_canvas(projection=projection, **kwargs)
     helper_data = helper_for_plot_data(self, plot_limits, visible_dims, fixed_inputs, resolution)
@@ -341,8 +359,11 @@ def plot_f(self, plot_limits=None, fixed_inputs=None,
 
     If you want fine graned control use the specific plotting functions supplied in the model.
 
+    You can deactivate the legend for this one plot by supplying None to label. 
+
     Give the Y_metadata in the predict_kw if you need it.
 
+
     :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 fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input dimension i should be set to value v.

GPy/plotting/plotly_dep/defaults.py

@@ -44,17 +44,17 @@ it gives back an empty default, when defaults are not defined.
 
 # Data plots:
 data_1d = dict(marker_kwargs=dict(), marker='x', color='black')
-data_2d = dict(marker='o', cmap='Hot', marker_kwargs=dict(opacity=1., size='10', line=Line(width=.5, color='black')))
+data_2d = dict(marker='o', cmap='Hot', marker_kwargs=dict(opacity=1., size='5', line=Line(width=.5, color='black')))
 inducing_1d = dict(color=Tango.colorsHex['darkRed'])
-inducing_2d = dict(marker_kwargs=dict(size='8', opacity=.7, line=Line(width=.5, color='black')), opacity=.7, color='white', marker='star-triangle-up')
+inducing_2d = dict(marker_kwargs=dict(size='5', opacity=.7, line=Line(width=.5, color='black')), opacity=.7, color='white', marker='star-triangle-up')
-inducing_3d = dict(marker_kwargs=dict(size='8', opacity=.7, line=Line(width=.5, color='black')), opacity=.7, color='white', marker='star-triangle-up')
+inducing_3d = dict(marker_kwargs=dict(symbol='diamond', size='5', opacity=.7, line=Line(width=.1, color='black')), color='#F5F5F5')
 xerrorbar = dict(color='black', error_kwargs=dict(thickness=.5), opacity=.5)
 yerrorbar = dict(color=Tango.colorsHex['darkRed'], error_kwargs=dict(thickness=.5), opacity=.5)
 #
 # # GP plots:
 meanplot_1d = dict(color=Tango.colorsHex['mediumBlue'], line_kwargs=dict(width=2))
 meanplot_2d = dict(colorscale='Hot')
-meanplot_3d = dict(colorscale='Hot', opacity=.8)
+meanplot_3d = dict(colorscale='Hot', opacity=.9)
 samples_1d = dict(color=Tango.colorsHex['mediumBlue'], line_kwargs=dict(width=.3))
 samples_3d = dict(cmap='Hot', opacity=.5)
 confidence_interval = dict(mode='lines', line_kwargs=dict(color=Tango.colorsHex['darkBlue'], width=.4),

GPy/plotting/plotly_dep/plot_definitions.py

@@ -35,7 +35,7 @@ from plotly import tools
 from plotly import plotly as py
 from plotly.graph_objs import Scatter, Scatter3d, Line,\
     Marker, ErrorX, ErrorY, Bar, Heatmap, Trace,\
-    Annotations, Annotation, Contour, Contours, Font, Surface
+    Annotations, Annotation, Contour, Font, Surface
 from plotly.exceptions import PlotlyDictKeyError
 
 SYMBOL_MAP = {
@@ -78,6 +78,9 @@ class PlotlyPlots(AbstractPlottingLibrary):
             figure.layout.font = Font(family="Raleway, sans-serif")
         else:
             return canvas, kwargs
+        if projection == '3d':
+            figure.layout.legend.x=.5
+            figure.layout.legend.bgcolor='#DCDCDC'
         return (figure, row, col), kwargs
 
     def add_to_canvas(self, canvas, traces, legend=False, **kwargs):
@@ -129,9 +132,15 @@ class PlotlyPlots(AbstractPlottingLibrary):
         except:
             #not matplotlib marker
             pass
+        marker_kwargs.setdefault('symbol', marker)
         if Z is not None:
-            return Scatter3d(x=X, y=Y, z=Z, mode='markers', showlegend=label is not None, marker=Marker(color=color, symbol=marker, colorscale=cmap, **marker_kwargs or {}), name=label, **kwargs)
+            return Scatter3d(x=X, y=Y, z=Z, mode='markers', 
-        return Scatter(x=X, y=Y, mode='markers', showlegend=label is not None, marker=Marker(color=color, symbol=marker, colorscale=cmap, **marker_kwargs or {}), name=label, **kwargs)
+                             showlegend=label is not None, 
+                             marker=Marker(color=color, colorscale=cmap, **marker_kwargs or {}), 
+                             name=label, **kwargs)
+        return Scatter(x=X, y=Y, mode='markers', showlegend=label is not None, 
+                       marker=Marker(color=color, colorscale=cmap, **marker_kwargs or {}), 
+                       name=label, **kwargs)
 
     def plot(self, ax, X, Y, Z=None, color=None, label=None, line_kwargs=None, **kwargs):
         if 'mode' not in kwargs:
@@ -140,14 +149,14 @@ class PlotlyPlots(AbstractPlottingLibrary):
             return Scatter3d(x=X, y=Y, z=Z, showlegend=label is not None, line=Line(color=color, **line_kwargs or {}), name=label, **kwargs)
         return Scatter(x=X, y=Y, showlegend=label is not None, line=Line(color=color, **line_kwargs or {}), name=label, **kwargs)
 
-    def plot_axis_lines(self, ax, X, Z=None, color=Tango.colorsHex['mediumBlue'], label=None, marker_kwargs=None, **kwargs):
+    def plot_axis_lines(self, ax, X, color=Tango.colorsHex['mediumBlue'], label=None, marker_kwargs=None, **kwargs):
         if X.shape[1] == 1:
             annotations = Annotations()
             for n, row in enumerate(X):
                 annotations.append(
                     Annotation(
                         text='',
-                        x=row[0], y=0,
+                        x=row[n], y=0,
                         yref='paper',
                         ax=0, ay=20,
                         arrowhead=2,
@@ -156,9 +165,16 @@ class PlotlyPlots(AbstractPlottingLibrary):
                         arrowcolor=color,
                         showarrow=True))
             return annotations
-        #if Z is not None:
+        elif X.shape[1] == 2:
-        #    return Scatter3d(x=X[:,0], y=X[:,1], z=0, zref='paper', showlegend=label is not None, mode='markers', marker=Marker(color=color, symbol='diamond-tall', **marker_kwargs or {}), name=label, **kwargs)
+            marker_kwargs.setdefault('symbol', 'diamond')
-        #return Scatter(x=X, y=0, mode='markers', showlegend=label is not None, marker=Marker(yref='paper', color=color, symbol='diamond-tall', **marker_kwargs or {}), name=label, **kwargs)
+            opacity = kwargs.pop('opacity', .8)
+            return Scatter3d(x=X[:, 0], y=X[:, 1], z=np.zeros(X.shape[0]), 
+                             mode='markers',
+                             projection=dict(z=dict(show=True, opacity=opacity)), 
+                             marker=Marker(color=color, **marker_kwargs or {}), 
+                             opacity=0,
+                             name=label,
+                             showlegend=label is not None, **kwargs)
 
     def barplot(self, canvas, x, height, width=0.8, bottom=0, color=Tango.colorsHex['mediumBlue'], label=None, **kwargs):
         figure, _, _ = canvas
@@ -247,7 +263,7 @@ class PlotlyPlots(AbstractPlottingLibrary):
                        name=label, **kwargs)
 
     def surface(self, ax, X, Y, Z, color=None, label=None, **kwargs):
-        return Surface(x=X, y=Y, z=Z, name=label, **kwargs)
+        return Surface(x=X, y=Y, z=Z, name=label, showlegend=label is not None, **kwargs)
 
     def fill_between(self, ax, X, lower, upper, color=Tango.colorsHex['mediumBlue'], label=None, line_kwargs=None, **kwargs):
         if not 'line' in kwargs:
@@ -257,9 +273,9 @@ class PlotlyPlots(AbstractPlottingLibrary):
         if color.startswith('#'):
             fcolor = 'rgba ({c[0]}, {c[1]}, {c[2]}, {alpha})'.format(c=Tango.hex2rgb(color), alpha=kwargs.get('opacity', 1.0))
         else: fcolor = color
-        u = Scatter(x=X, y=upper, fillcolor=fcolor, showlegend=label is not None, name=label, fill='tonexty', **kwargs)
+        u = Scatter(x=X, y=upper, fillcolor=fcolor, showlegend=label is not None, name=label, fill='tonextx', legendgroup='density', **kwargs)
-        fcolor = '{}, {alpha})'.format(','.join(fcolor.split(',')[:-1]), alpha=0.0)
+        #fcolor = '{}, {alpha})'.format(','.join(fcolor.split(',')[:-1]), alpha=0.0)
-        l = Scatter(x=X, y=lower, fillcolor=fcolor, showlegend=False, fill='tonexty', name=label, **kwargs)
+        l = Scatter(x=X, y=lower, fillcolor=fcolor, showlegend=False, name=label, legendgroup='density', **kwargs)
         return l, u
 
     def fill_gradient(self, canvas, X, percentiles, color=Tango.colorsHex['mediumBlue'], label=None, **kwargs):