[tests working now?]

GPy/plotting/__init__.py

@@ -43,20 +43,20 @@ if config.get('plotting', 'library') is not 'none':
     
     from ..models import GPLVM, BayesianGPLVM, bayesian_gplvm_minibatch, SSGPLVM, SSMRD
     GPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
-    GPLVM.plot_latent_scatter = gpy_plot.latent_plots.plot_latent_scatter
+    GPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
-    GPLVM.plot_latent_inducing = gpy_plot.latent_plots.plot_latent_inducing
+    GPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
     GPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
     BayesianGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
-    BayesianGPLVM.plot_latent_scatter = gpy_plot.latent_plots.plot_latent_scatter
+    BayesianGPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
-    BayesianGPLVM.plot_latent_inducing = gpy_plot.latent_plots.plot_latent_inducing
+    BayesianGPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
     BayesianGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
     bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_latent = gpy_plot.latent_plots.plot_latent
-    bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_latent_scatter = gpy_plot.latent_plots.plot_latent_scatter
+    bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
-    bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_latent_inducing = gpy_plot.latent_plots.plot_latent_inducing
+    bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
     bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
     SSGPLVM.plot_latent = gpy_plot.latent_plots.plot_latent
-    SSGPLVM.plot_latent_scatter = gpy_plot.latent_plots.plot_latent_scatter
+    SSGPLVM.plot_scatter = gpy_plot.latent_plots.plot_latent_scatter
-    SSGPLVM.plot_latent_inducing = gpy_plot.latent_plots.plot_latent_inducing
+    SSGPLVM.plot_inducing = gpy_plot.latent_plots.plot_latent_inducing
     SSGPLVM.plot_steepest_gradient_map = gpy_plot.latent_plots.plot_steepest_gradient_map
     
     from ..kern import Kern

GPy/plotting/gpy_plot/latent_plots.py

@@ -89,7 +89,7 @@ def plot_latent_scatter(self, labels=None,
         labels = np.ones(self.num_data)
         legend = False
     else:
-        legend = find_best_layout_for_subplots(len(np.unique(labels)))
+        legend = find_best_layout_for_subplots(len(np.unique(labels)))[1]
     scatters = _plot_latent_scatter(canvas, X, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
     if projection == '3d':
         return pl.show_canvas(canvas, dict(scatter=scatters), legend=legend,
@@ -126,9 +126,9 @@ def plot_latent_inducing(self,
     if 'color' not in kwargs:
         kwargs['color'] = 'white'
     canvas, kwargs = pl.get_new_canvas(projection=projection, **kwargs)
-    X, _, _ = get_x_y_var(self)
+    Z = self.Z.values
-    labels = np.ones(self.num_data)
+    labels = np.array(['inducing'] * Z.shape[0])
-    scatters = _plot_latent_scatter(canvas, X, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
+    scatters = _plot_latent_scatter(canvas, Z, sig_dims, labels, marker, num_samples, projection=projection, **kwargs)
     if projection == '3d':
         return pl.show_canvas(canvas, dict(scatter=scatters), legend=legend,
                               xlabel='latent dimension %i' % input_1, 

GPy/plotting/gpy_plot/plot_util.py

@@ -138,7 +138,6 @@ def scatter_label_generator(labels, X, visible_dims, marker=None):
     for lab in labels:
         if not lab in ulabels:
             ulabels.append(lab)
-
     if marker is not None:
         marker = itertools.cycle(list(marker))    
     else:
@@ -156,16 +155,17 @@ def scatter_label_generator(labels, X, visible_dims, marker=None):
             input_2 = input_3 = None
 
     for ul in ulabels:
-        if type(ul) is np.string_:
+        from numbers import Number
-            this_label = ul
+        if isinstance(ul, str):
-        elif type(ul) is np.int64:
-            this_label = 'class %i' % ul
-        else:
             try:
                 this_label = unicode(ul)
             except NameError:
                 #python3
                 this_label = ul
+        elif isinstance(ul, Number):
+            this_label = 'class {!s}'.format(ul)
+        else:
+            this_label = ul
         
         if marker is not None:
             m = next(marker)

GPy/plotting/matplot_dep/plot_definitions.py

@@ -76,7 +76,13 @@ class MatplotlibPlots(AbstractPlottingLibrary):
             legend_ontop(ax, ncol=legend, fontdict=fontdict)
         if zlim is not None:
             ax.set_zlim(zlim)
-        #ax.figure.show()
+        ax.figure.canvas.draw()
+        ax.figure.show()
+        #try:
+        #    ax.figure.tight_layout()
+        #except:
+        #    # couldnt do tight layout, python 2.7 on MacOSX
+        #    pass
         ax.figure.canvas.draw()
         return plots
     

GPy/plotting/matplot_dep/util.py

@@ -35,7 +35,7 @@ def legend_ontop(ax, mode='expand', ncol=3, fontdict=None):
     from mpl_toolkits.axes_grid1 import make_axes_locatable
     handles, labels = ax.get_legend_handles_labels()
     divider = make_axes_locatable(ax)
-    cax = divider.append_axes("top", "5%", pad="1%")
+    cax = divider.append_axes("top", "5%", pad=0)
     lgd = cax.legend(handles, labels, bbox_to_anchor=(0., 0., 1., 1.), loc=3,
             ncol=ncol, mode=mode, borderaxespad=0., prop=fontdict or {})
     cax.set_axis_off()

GPy/testing/plotting_tests.py

@@ -74,7 +74,7 @@ def _image_comparison(baseline_images, extensions=['pdf','svg','ong'], tol=10):
             fig.axes[0].set_axis_off()
             fig.set_frameon(False)
             fig.canvas.draw()
-            fig.savefig(os.path.join(result_dir, "{}.{}".format(base, ext)))
+            fig.savefig(os.path.join(result_dir, "{}.{}".format(base, ext)), transparent=True, edgecolor='none', facecolor='none')
     for num, base in zip(plt.get_fignums(), baseline_images):
         for ext in extensions:
             #plt.close(num)
@@ -145,9 +145,9 @@ def test_classification():
     Y = f+np.random.normal(0, .1, f.shape)
     m = GPy.models.GPClassification(X, Y>Y.mean())
     m.optimize()
-    fig, ax = plt.subplots()
+    _, ax = plt.subplots()
     m.plot(plot_raw=False, apply_link=False, ax=ax)
-    fig, ax = plt.subplots()
+    _, ax = plt.subplots()
     m.plot(plot_raw=True, apply_link=False, ax=ax)
     m.plot(plot_raw=True, apply_link=True)
     for do_test in _image_comparison(baseline_images=['gp_class_{}'.format(sub) for sub in ["likelihood", "raw", 'raw_link']], extensions=extensions):
@@ -182,7 +182,7 @@ def test_gplvm():
     #m.optimize(messages=0)
     labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
     m.plot_latent()
-    m.plot_latent_scatter(projection='3d', labels=labels)
+    m.plot_scatter(projection='3d', labels=labels)
     m.plot_magnification(labels=labels)
     m.plot_steepest_gradient_map(resolution=7)
     for do_test in _image_comparison(baseline_images=['gplvm_{}'.format(sub) for sub in ["latent", "latent_3d", "magnification", 'gradient']], extensions=extensions):
@@ -202,9 +202,9 @@ def test_bayesian_gplvm():
     m.likelihood.variance = .1
     #m.optimize(messages=0)
     labels = np.random.multinomial(1, np.random.dirichlet([.3333333, .3333333, .3333333]), size=(m.Y.shape[0])).nonzero()[1]
-    m.plot_latent_inducing(projection='2d')
+    m.plot_inducing(projection='2d')
-    m.plot_latent_inducing(projection='3d')
+    m.plot_inducing(projection='3d')
-    m.plot_latent_scatter(projection='3d')
+    m.plot_scatter(projection='3d')
     m.plot_magnification(labels=labels)
     m.plot_steepest_gradient_map(resolution=7)
     for do_test in _image_comparison(baseline_images=['bayesian_gplvm_{}'.format(sub) for sub in ["inducing", "inducing_3d", "latent_3d", "magnification", 'gradient']], extensions=extensions):

GPy/util/datasets.py

@@ -94,10 +94,13 @@ def prompt_user(prompt):
 
 def data_available(dataset_name=None):
     """Check if the data set is available on the local machine already."""
-    from itertools import izip_longest
+    try:
+        from itertools import izip_longest
+    except ImportError:
+        from itertools import zip_longest as izip_longest
     dr = data_resources[dataset_name]
     zip_urls = (dr['files'], )
-    if dr.has_key('save_names'): zip_urls += (dr['save_names'], )
+    if 'save_names' in dr: zip_urls += (dr['save_names'], )
     else: zip_urls += ([],)
 
     for file_list, save_list in izip_longest(*zip_urls, fillvalue=[]):

travis_tests.py

@@ -38,5 +38,5 @@ matplotlib.rcParams[u'figure.figsize'] = (4,3)
 matplotlib.rcParams[u'text.usetex'] = False
 
 import nose 
-nose.main('GPy', defaultTest='GPy/testing')  
+nose.main('GPy', defaultTest='GPy/testing/plotting_tests.py')