[plotting] magnification plot added

GPy/models/gplvm.py

@@ -6,7 +6,6 @@ import numpy as np
 from .. import kern
 from ..core import GP, Param
 from ..likelihoods import Gaussian
-from .. import util
 
 
 class GPLVM(GP):
@@ -43,17 +42,3 @@ class GPLVM(GP):
     def parameters_changed(self):
         super(GPLVM, self).parameters_changed()
         self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dK'], self.X, None)
-
-    def plot_latent(self, labels=None, which_indices=None,
-                resolution=50, ax=None, marker='o', s=40,
-                fignum=None, legend=True,
-                plot_limits=None,
-                aspect='auto', updates=False, **kwargs):
-        import sys
-        assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
-        from ..plotting.matplot_dep import dim_reduction_plots
-
-        return dim_reduction_plots.plot_latent(self, labels, which_indices,
-                resolution, ax, marker, s,
-                fignum, False, legend,
-                plot_limits, aspect, updates, **kwargs)

GPy/plotting/__init__.py

@@ -36,7 +36,7 @@ if config.get('plotting', 'library') is not 'none':
     GP.plot_samples = gpy_plot.gp_plots.plot_samples
     GP.plot = gpy_plot.gp_plots.plot
     GP.plot_f = gpy_plot.gp_plots.plot_f
-    #GP.plot_magnificaion = gpy_plot.latent_plots.plot_magnification
+    GP.plot_magnificaion = gpy_plot.latent_plots.plot_magnification
     
     from ..core import SparseGP
     SparseGP.plot_inducing = gpy_plot.data_plots.plot_inducing
@@ -50,7 +50,8 @@ if config.get('plotting', 'library') is not 'none':
     
     # Variational plot!
 
-    from . import matplot_dep
+    
+    #from . import matplot_dep
     # Still to convert to new style:
     #GP.plot = matplot_dep.models_plots.plot_fit
     #GP.plot_f = matplot_dep.models_plots.plot_fit_f

GPy/plotting/abstract_plotting_library.py

@@ -57,7 +57,7 @@ class AbstractPlottingLibrary(object):
         return self.__defaults
         #===============================================================================
     
-    def get_new_canvas(self, plot_3d=False, xlabel=None, ylabel=None, zlabel=None, title=None, legend=True, **kwargs):
+    def get_new_canvas(self, xlabel=None, ylabel=None, zlabel=None, title=None, projection='2d', legend=True, **kwargs):
         """
         Return a canvas, kwargupdate for your plotting library. 
         
@@ -174,9 +174,16 @@ class AbstractPlottingLibrary(object):
         """
         raise NotImplementedError("Implement all plot functions in AbstractPlottingLibrary in order to use your own plotting library")
 
-    def imshow(self, canvas, X, label=None, color=None, **kwargs):
+    def imshow(self, canvas, X, extent=None, label=None, plot_function=None, vmin=None, vmax=None, **kwargs):
         """
-        Show the image stored in X on the canvas/
+        Show the image stored in X on the canvas.
+        
+        if X is a function, create an imshow controller to stream 
+        the image. There is an imshow controller written for 
+        mmatplotlib, which updates the imshow on changes in axis.
+        
+        Just ignore the plot_function, if you do not have the option
+        to have interactive changes.
         
         the kwargs are plotting library specific kwargs!
         """

GPy/plotting/gpy_plot/latent_plots.py

@@ -32,6 +32,7 @@ from . import pl
 from .plot_util import get_x_y_var, get_free_dims, get_which_data_ycols,\
     get_which_data_rows, update_not_existing_kwargs, helper_predict_with_model,\
     helper_for_plot_data
+import itertools
 
 def plot_prediction_fit(self, plot_limits=None,
         which_data_rows='all', which_data_ycols='all', 
@@ -102,6 +103,164 @@ def _plot_prediction_fit(self, canvas, plot_limits=None,
         raise NotImplementedError("Cannot plot in more then one dimension.")
     return plots
     
+def plot_magnification(self, labels=None, which_indices=None,
+                resolution=60, legend=True,
+                plot_limits=None,
+                updates=False, 
+                mean=True, covariance=True, 
+                kern=None, marker='<>^vsd', imshow_kwargs=None, **kwargs):
+    """
+    Plot the magnification factor of the GP on the inputs. This is the 
+    density of the GP as a gray scale.
+    
+    :param array-like labels: a label for each data point (row) of the inputs
+    :param (int, int) which_indices: which input dimensions to plot against each other
+    :param int resolution: the resolution at which we predict the magnification factor
+    :param bool legend: whether to plot the legend on the figure
+    :param plot_limits: the plot limits for the plot
+    :type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
+    :param bool updates: if possible, make interactive updates using the specific library you are using
+    :param bool mean: use the mean of the Wishart embedding for the magnification factor
+    :param bool covariance: use the covariance of the Wishart embedding for the magnification factor
+    :param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
+    :param str marker: markers to use - cycle if more labels then markers are given
+    :param imshow_kwargs: the kwargs for the imshow (magnification factor)
+    :param kwargs: the kwargs for the scatter plots
+    """
+    input_1, input_2 = self.get_most_significant_input_dimensions(which_indices)
+
+    #fethch the data points X that we'd like to plot
+    X, _, _ = get_x_y_var(self)
+
+    if plot_limits is None:
+        xmin, ymin = X[:, [input_1, input_2]].min(0)
+        xmax, ymax = X[:, [input_1, input_2]].max(0)
+        x_r, y_r = xmax-xmin, ymax-ymin
+        xmin -= .1*x_r
+        xmax += .1*x_r
+        ymin -= .1*y_r
+        ymax += .1*y_r
+    else:
+        try:
+            xmin, xmax, ymin, ymax = plot_limits
+        except (TypeError, ValueError) as e:
+            try:
+                xmin, xmax = plot_limits
+                ymin, ymax = xmin, xmax
+            except (TypeError, ValueError) as e:
+                raise e.__class__("Wrong plot limits: {} given -> need (xmin, xmax, ymin, ymax)".format(plot_limits))
+    xlim = (xmin, xmax)
+    ylim = (ymin, ymax)
+
+    from .. import Tango
+    Tango.reset()
+    
+    if labels is None:
+        labels = np.ones(self.num_data)
+
+    if X.shape[0] > 1000:
+        print("Warning: subsampling X, as it has more samples then 1000. X.shape={!s}".format(X.shape))
+        subsample = np.random.choice(X.shape[0], size=1000, replace=False)
+        X = X[subsample]
+        labels = labels[subsample]
+        #=======================================================================
+        #     <<<WORK IN PROGRESS>>>
+        #     <<<DO NOT DELETE>>>
+        #     plt.close('all')
+        #     fig, ax = plt.subplots(1,1)
+        #     from GPy.plotting.matplot_dep.dim_reduction_plots import most_significant_input_dimensions
+        #     import matplotlib.patches as mpatches
+        #     i1, i2 = most_significant_input_dimensions(m, None)
+        #     xmin, xmax = 100, -100
+        #     ymin, ymax = 100, -100
+        #     legend_handles = []
+        #
+        #     X = m.X.mean[:, [i1, i2]]
+        #     X = m.X.variance[:, [i1, i2]]
+        #
+        #     xmin = X[:,0].min(); xmax = X[:,0].max()
+        #     ymin = X[:,1].min(); ymax = X[:,1].max()
+        #     range_ = [[xmin, xmax], [ymin, ymax]]
+        #     ul = np.unique(labels)
+        #
+        #     for i, l in enumerate(ul):
+        #         #cdict = dict(red  =[(0., colors[i][0], colors[i][0]), (1., colors[i][0], colors[i][0])],
+        #         #             green=[(0., colors[i][0], colors[i][1]), (1., colors[i][1], colors[i][1])],
+        #         #             blue =[(0., colors[i][0], colors[i][2]), (1., colors[i][2], colors[i][2])],
+        #         #             alpha=[(0., 0., .0), (.5, .5, .5), (1., .5, .5)])
+        #         #cmap = LinearSegmentedColormap('{}'.format(l), cdict)
+        #         cmap = LinearSegmentedColormap.from_list('cmap_{}'.format(str(l)), [colors[i], colors[i]], 255)
+        #         cmap._init()
+        #         #alphas = .5*(1+scipy.special.erf(np.linspace(-2,2, cmap.N+3)))#np.log(np.linspace(np.exp(0), np.exp(1.), cmap.N+3))
+        #         alphas = (scipy.special.erf(np.linspace(0,2.4, cmap.N+3)))#np.log(np.linspace(np.exp(0), np.exp(1.), cmap.N+3))
+        #         cmap._lut[:, -1] = alphas
+        #         print l
+        #         x, y = X[labels==l].T
+        #
+        #         heatmap, xedges, yedges = np.histogram2d(x, y, bins=300, range=range_)
+        #         #heatmap, xedges, yedges = np.histogram2d(x, y, bins=100)
+        #
+        #         im = ax.imshow(heatmap, extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]], cmap=cmap, aspect='auto', interpolation='nearest', label=str(l))
+        #         legend_handles.append(mpatches.Patch(color=colors[i], label=l))
+        #     ax.set_xlim(xmin, xmax)
+        #     ax.set_ylim(ymin, ymax)
+        #     plt.legend(legend_handles, [l.get_label() for l in legend_handles])
+        #     plt.draw()
+        #     plt.show()
+        #=======================================================================
     
     
+    canvas, kwargs = pl.get_new_canvas(xlabel='latent dimension %i' % input_1, ylabel='latent dimension %i' % input_2, **kwargs)
 
+    _, _, _, _, _, Xgrid, _, _, _, _, resolution = helper_for_plot_data(self, ((xmin, ymin), (xmax, ymax)), (input_1, input_2), None, resolution)
+    
+    def plot_function(x):
+        Xtest_full = np.zeros((x.shape[0], X.shape[1]))
+        Xtest_full[:, [input_1, input_2]] = x
+        mf = self.predict_magnification(Xtest_full, kern=kern, mean=mean, covariance=covariance)
+        return mf
+
+    imshow_kwargs = update_not_existing_kwargs(imshow_kwargs, pl.defaults.magnification)
+    Y = plot_function(Xgrid[:, [input_1, input_2]]).reshape(resolution, resolution).T[::-1, :]
+    view = pl.imshow(canvas, Y, 
+                     (xmin, ymin, xmax, ymax), 
+                     None, plot_function, resolution,
+                     vmin=Y.min(), vmax=Y.max(), 
+                     **imshow_kwargs)
+    
+    # make sure labels are in order of input:
+    ulabels = []
+    for lab in labels:
+        if not lab in ulabels:
+            ulabels.append(lab)
+
+    marker = itertools.cycle(list(marker))
+    scatters = []
+
+    for ul in ulabels:
+        if type(ul) is np.string_:
+            this_label = ul
+        elif type(ul) is np.int64:
+            this_label = 'class %i' % ul
+        else:
+            this_label = unicode(ul)
+        m = marker.next()
+
+        index = np.nonzero(labels == ul)[0]
+        if self.input_dim == 1:
+            x = X[index, input_1]
+            y = np.zeros(index.size)
+        else:
+            x = X[index, input_1]
+            y = X[index, input_2]
+        update_not_existing_kwargs(kwargs, pl.defaults.latent_scatter)
+        scatters.append(pl.scatter(canvas, x, y, marker=m, color=Tango.nextMedium(), label=this_label, **kwargs))
+    
+    plots = pl.show_canvas(canvas, dict(scatter=scatters, imshow=view), legend=legend, xlim=xlim, ylim=ylim)
+    if updates:
+        clear = raw_input('yes or enter to deactivate updates - otherwise still do updates - use plots[imshow].deactivate() to clear')
+        if clear.lower() in 'yes' or clear == '':
+            view.deactivate()
+    else:
+        view.deactivate()
+    return plots

GPy/plotting/gpy_plot/plot_util.py

@@ -125,7 +125,10 @@ def update_not_existing_kwargs(to_update, update_from):
     
     This is used for updated kwargs from the default dicts.
     """
-    return to_update.update({k:v for k,v in update_from.items() if k not in to_update})
+    if to_update is None:
+        to_update = {}
+    to_update.update({k:v for k,v in update_from.items() if k not in to_update})
+    return to_update
 
 def get_x_y_var(model):
     """
@@ -208,12 +211,14 @@ def x_frame2D(X,plot_limits=None,resolution=None):
     """
     Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits
     """
-    assert X.shape[1] ==2, "x_frame2D is defined for two-dimensional inputs"
+    assert X.shape[1]==2, "x_frame2D is defined for two-dimensional inputs"
     if plot_limits is None:
-        xmin,xmax = X.min(0),X.max(0)
+        xmin, xmax = X.min(0),X.max(0)
         xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
     elif len(plot_limits)==2:
         xmin, xmax = plot_limits
+    elif len(plot_limits)==4:
+        xmin, xmax = (plot_limits[0], plot_limits[2]), (plot_limits[1], plot_limits[3])
     else:
         raise ValueError("Bad limits for plotting")
 

GPy/plotting/matplot_dep/controllers/imshow_controller.py

@@ -22,11 +22,10 @@ class ImshowController(BufferedAxisChangedController):
         """
         super(ImshowController, self).__init__(ax, plot_function, plot_limits, resolution, update_lim, **kwargs)
 
-    def _init_view(self, canvas, X, xmin, xmax, ymin, ymax, **kwargs):
-        return pl.imshow(canvas, X, extent=(xmin, xmax,
+    def _init_view(self, canvas, X, xmin, xmax, ymin, ymax, vmin=None, vmax=None, **kwargs):
+        return canvas.imshow(X, extent=(xmin, xmax,
                                     ymin, ymax),
-                         vmin=X.min(),
-                         vmax=X.max(),
+                             vmin=vmin, vmax=vmax,
                          **kwargs)
 
     def update_view(self, view, X, xmin, xmax, ymin, ymax):

GPy/plotting/matplot_dep/defaults.py

@@ -66,3 +66,8 @@ data_y_1d_plot = dict(color='k', linewidth=1.5)
 
 # Kernel plots:
 ard = dict(edgecolor='k', linewidth=1.2)
+
+# Input plots:
+latent = dict(aspect='auto', cmap='Greys', interpolation='bilinear')
+magnification = dict(aspect='auto', cmap='Greys', interpolation='bilinear')
+latent_scatter = dict(s=40, linewidth=.2, edgecolor='k', alpha=.9)

GPy/plotting/matplot_dep/dim_reduction_plots.py

@@ -7,7 +7,7 @@ from ...core.parameterization.variational import VariationalPosterior
 from .base_plots import x_frame2D
 import itertools
 try:
-from GPy.plotting import Tango
+    from GPy.plotting import Tango
     from matplotlib.cm import get_cmap
     from matplotlib import pyplot as pb
     from matplotlib import cm

GPy/plotting/matplot_dep/latent_space_visualizations/__init__.py

@@ -1 +0,0 @@
-from .import controllers

GPy/plotting/matplot_dep/latent_space_visualizations/controllers/__init__.py

@@ -1 +0,0 @@
-from . import axis_event_controller, imshow_controller

GPy/plotting/matplot_dep/plot_definitions.py

@@ -33,13 +33,14 @@ from ..abstract_plotting_library import AbstractPlottingLibrary
 from .. import Tango
 from . import defaults
 from matplotlib.colors import LinearSegmentedColormap
+from .controllers.imshow_controller import ImshowController
 
 class MatplotlibPlots(AbstractPlottingLibrary):
     def __init__(self):
         super(MatplotlibPlots, self).__init__()
         self._defaults = defaults.__dict__
     
-    def get_new_canvas(self, xlabel=None, ylabel=None, zlabel=None, title=None, legend=True, projection='2d', **kwargs):
+    def get_new_canvas(self, xlabel=None, ylabel=None, zlabel=None, title=None, projection='2d', **kwargs):
         if projection == '3d':
             from mpl_toolkits.mplot3d import Axes3D
         elif projection == '2d':
@@ -61,22 +62,23 @@ class MatplotlibPlots(AbstractPlottingLibrary):
         if title is not None: ax.set_title(title)
         return ax, kwargs
     
-    def show_canvas(self, ax, plots, xlim=None, ylim=None, zlim=None, **kwargs):
+    def show_canvas(self, ax, plots, xlim=None, ylim=None, zlim=None, legend=True, **kwargs):
         try:
             ax.autoscale_view()
             ax.set_xlim(xlim)
             ax.set_ylim(ylim)
+            if legend:
+                ax.legend()
             if zlim is not None:
                 ax.set_zlim(zlim)
             ax.figure.canvas.draw()
-            #ax.figure.tight_layout()
         except:
             pass
         return plots
     
-    def scatter(self, ax, X, Y, Z=None, color=Tango.colorsHex['mediumBlue'], label=None, **kwargs):
+    def scatter(self, ax, X, Y, Z=None, color=Tango.colorsHex['mediumBlue'], label=None, marker='o', **kwargs):
         if Z is not None:
-            return ax.scatter(X, Y, c=color, zs=Z, label=label, **kwargs)
+            return ax.scatter(X, Y, c=color, zs=Z, label=label, marker=marker, **kwargs)
         return ax.scatter(X, Y, c=color, label=label, **kwargs)
     
     def plot(self, ax, X, Y, color=None, label=None, **kwargs):
@@ -116,8 +118,11 @@ class MatplotlibPlots(AbstractPlottingLibrary):
             return ax.errorbar(X, Y, Z, yerr=error, ecolor=color, label=label, **kwargs)
         return ax.errorbar(X, Y, yerr=error, ecolor=color, label=label, **kwargs)
     
-    def imshow(self, ax, X, label=None, **kwargs):
-        return ax.imshow(X, label=label, **kwargs)
+    def imshow(self, ax, X, extent=None, label=None, plot_function=None, resolution=None, vmin=None, vmax=None, **kwargs):
+        if plot_function is not None:
+            self.controller = ImshowController(ax, plot_function, extent, resolution=resolution, vmin=vmin, vmax=vmax, **kwargs)
+            return self.controller
+        return ax.imshow(X, label=label, extent=extent, vmin=vmin, vmax=vmax, **kwargs)
     
     def contour(self, ax, X, Y, C, levels=20, label=None, **kwargs):
         return ax.contour(X, Y, C, levels=np.linspace(C.min(), C.max(), levels), label=label, **kwargs)