Plotting functions modified

GPy/models/bayesian_gplvm.py

@@ -3,14 +3,12 @@
 
 import numpy as np
 import itertools
-from matplotlib import pyplot
 from gplvm import GPLVM
 from .. import kern
 from ..core import SparseGP
 from ..likelihoods import Gaussian
 from ..inference.optimization import SCG
-from ..util import plot_latent, linalg
-from ..util.plot_latent import most_significant_input_dimensions
+from ..util import linalg
 from ..core.parameterization.variational import Normal
 
 class BayesianGPLVM(SparseGP, GPLVM):
@@ -75,11 +73,11 @@ class BayesianGPLVM(SparseGP, GPLVM):
 #         """
 #         Horizontally stacks the parameters in order to present them to the optimizer.
 #         The resulting 1-input_dim array has this structure:
-# 
+#
 #         ===============================================================
 #         |       mu       |        S        |    Z    | theta |  beta  |
 #         ===============================================================
-# 
+#
 #         """
 #         x = np.hstack((self.X.flatten(), self.X_variance.flatten(), SparseGP._get_params(self)))
 #         return x
@@ -131,7 +129,13 @@ class BayesianGPLVM(SparseGP, GPLVM):
 #         return np.hstack((self.dbound_dmuS.flatten(), self.dbound_dZtheta))
 
     def plot_latent(self, plot_inducing=True, *args, **kwargs):
-        return plot_latent.plot_latent(self, plot_inducing=plot_inducing, *args, **kwargs)
+        """
+        See GPy.plotting.matplot_dep.dim_reduction_plots.plot_latent
+        """
+        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, plot_inducing=plot_inducing, *args, **kwargs)
 
     def do_test_latents(self, Y):
         """
@@ -190,65 +194,14 @@ class BayesianGPLVM(SparseGP, GPLVM):
             dK_dX[:, i] = self.kern.dK_dX(ones, Xnew, self.Z[i:i + 1, :]).sum(-1)
         return np.dot(dK_dX, self.Cpsi1Vf)
 
-    def plot_steepest_gradient_map(self, fignum=None, ax=None, which_indices=None, labels=None, data_labels=None, data_marker='o', data_s=40, resolution=20, aspect='auto', updates=False, ** kwargs):
-        input_1, input_2 = significant_dims = most_significant_input_dimensions(self, which_indices)
+    def plot_steepest_gradient_map(self, *args, ** kwargs):
+        """
+        See GPy.plotting.matplot_dep.dim_reduction_plots.plot_steepest_gradient_map
+        """
+        assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
+        from ..plotting.matplot_dep import dim_reduction_plots
 
-        X = np.zeros((resolution ** 2, self.input_dim))
-        indices = np.r_[:X.shape[0]]
-        if labels is None:
-            labels = range(self.output_dim)
-
-        def plot_function(x):
-            X[:, significant_dims] = x
-            dmu_dX = self.dmu_dXnew(X)
-            argmax = np.argmax(dmu_dX, 1)
-            return dmu_dX[indices, argmax], np.array(labels)[argmax]
-
-        if ax is None:
-            fig = pyplot.figure(num=fignum)
-            ax = fig.add_subplot(111)
-
-        if data_labels is None:
-            data_labels = np.ones(self.num_data)
-        ulabels = []
-        for lab in data_labels:
-            if not lab in ulabels:
-                ulabels.append(lab)
-        marker = itertools.cycle(list(data_marker))
-        from GPy.util import Tango
-        for i, ul in enumerate(ulabels):
-            if type(ul) is np.string_:
-                this_label = ul
-            elif type(ul) is np.int64:
-                this_label = 'class %i' % ul
-            else:
-                this_label = 'class %i' % i
-            m = marker.next()
-            index = np.nonzero(data_labels == ul)[0]
-            x = self.X[index, input_1]
-            y = self.X[index, input_2]
-            ax.scatter(x, y, marker=m, s=data_s, color=Tango.nextMedium(), label=this_label)
-
-        ax.set_xlabel('latent dimension %i' % input_1)
-        ax.set_ylabel('latent dimension %i' % input_2)
-
-        from matplotlib.cm import get_cmap
-        from GPy.util.latent_space_visualizations.controllers.imshow_controller import ImAnnotateController
-        controller = ImAnnotateController(ax,
-                                      plot_function,
-                                      tuple(self.X.min(0)[:, significant_dims]) + tuple(self.X.max(0)[:, significant_dims]),
-                                      resolution=resolution,
-                                      aspect=aspect,
-                                      cmap=get_cmap('jet'),
-                                      **kwargs)
-        ax.legend()
-        ax.figure.tight_layout()
-        if updates:
-            pyplot.show()
-            clear = raw_input('Enter to continue')
-            if clear.lower() in 'yes' or clear == '':
-                controller.deactivate()
-        return controller.view
+        return dim_reduction_plots.plot_steepest_gradient_map(model,*args,**kwargs)
 
 def latent_cost_and_grad(mu_S, kern, Z, dL_dpsi0, dL_dpsi1, dL_dpsi2):
     """
@@ -304,5 +257,3 @@ def latent_grad(mu_S, kern, Z, dL_dpsi0, dL_dpsi1, dL_dpsi2):
     dlnS = S * (S0 + S1 + S2 - 0.5) + .5
 
     return -np.hstack((dmu.flatten(), dlnS.flatten()))
-
-