潜在空間の描画をpcolormeshからimshowに変更しオプションにinterpolationを追加

libs/models/unsupervised_kernel_regression.py

@@ -176,7 +176,7 @@ def inverse_transform(self, Znew):
 
     def visualize(self, n_grid_points=30, cmap=None, label_data=None, label_feature=None,
                   title_latent_space=None, title_feature_bars=None, is_show_all_label_data=False,
-                  fig=None, fig_size=None, ax_latent_space=None, ax_feature_bars=None):
+                  interpolation=None, fig=None, fig_size=None, ax_latent_space=None, ax_feature_bars=None):
         """Visualize fit model interactively.
         The dataset can be visualized in an exploratory way using the latent variables and the mapping estimated by UKR.
         When an arbitrary coordinate on the latent space is specified, the corresponding feature is displayed as a bar.
@@ -199,6 +199,8 @@ def visualize(self, n_grid_points=30, cmap=None, label_data=None, label_feature=
         :param is_show_all_label_data: bool, optional, default = False
             When True the labels of the data is always shown.
             When False the label is only shown when the cursor overlaps the corresponding latent variable.
+        :param interpolation: str, optional, default = None
+            Interpolation method by imshow.
         :param fig: matplotlib.figure.Figure, default = True
             The figure to visualize.
             It is assigned only when you want to specify a figure to visualize.
@@ -218,7 +220,7 @@ def visualize(self, n_grid_points=30, cmap=None, label_data=None, label_feature=
 
         self._initialize_to_visualize(n_grid_points, cmap, label_data, label_feature,
                                       title_latent_space, title_feature_bars, is_show_all_label_data,
-                                      fig, fig_size, ax_latent_space, ax_feature_bars)
+                                      interpolation, fig, fig_size, ax_latent_space, ax_feature_bars)
 
         self._draw_latent_space()
         self._draw_feature_bars()
@@ -259,7 +261,7 @@ def __mouse_over_fig(self, event):
 
     def _initialize_to_visualize(self, n_grid_points, cmap, label_data, label_feature,
                                  title_latent_space, title_feature_bars, is_show_all_label_data,
-                                 fig, fig_size, ax_latent_space, ax_feature_bars):
+                                 interpolation, fig, fig_size, ax_latent_space, ax_feature_bars):
         # invalid check
         if self.n_components != 2:
             raise ValueError('Now support only n_components = 2')
@@ -328,6 +330,7 @@ def _initialize_to_visualize(self, n_grid_points, cmap, label_data, label_featur
             self.ax_feature_bars = ax_feature_bars
 
         self.cmap = cmap
+        self.interpolation = interpolation
         self.click_point_latent_space = None  # index of the clicked representative point
         self.clicked_mapping = self.X.mean(axis=0)
         self.is_initial_view = True
@@ -388,10 +391,30 @@ def _draw_latent_space(self):
             # To draw by pcolormesh and contour, reshape arrays like grid
             grid_values_to_draw_3d = self.__unflatten_grid_array(self.grid_values_to_draw)
             grid_points_3d = self.__unflatten_grid_array(self.grid_points)
-            self.ax_latent_space.pcolormesh(grid_points_3d[:, :, 0],
-                                            grid_points_3d[:, :, 1],
-                                            grid_values_to_draw_3d,
-                                            cmap=self.cmap)
+            # set coordinate of axis
+            any_index = 0
+            if grid_points_3d[any_index, 0, 0] < grid_points_3d[any_index, -1, 0]:
+                coordinate_ax_left = grid_points_3d[any_index, 0, 0]
+                coordinate_ax_right = grid_points_3d[any_index, -1, 0]
+            else:
+                coordinate_ax_left = grid_points_3d[any_index, -1, 0]
+                coordinate_ax_right = grid_points_3d[any_index, 0, 0]
+                grid_values_to_draw_3d = np.flip(grid_values_to_draw_3d, axis=1).copy()
+
+            if grid_points_3d[-1, any_index, 1] < grid_points_3d[0, any_index, 1]:
+                coordinate_ax_bottom = grid_points_3d[-1, any_index, 1]
+                coordinate_ax_top = grid_points_3d[0, any_index, 1]
+            else:
+                coordinate_ax_bottom = grid_points_3d[0, any_index, 1]
+                coordinate_ax_top = grid_points_3d[-1, any_index, 1]
+                grid_values_to_draw_3d = np.flip(grid_values_to_draw_3d, axis=0).copy()
+            self.ax_latent_space.imshow(grid_values_to_draw_3d,
+                                        extent=[coordinate_ax_left,
+                                                coordinate_ax_right,
+                                                coordinate_ax_bottom,
+                                                coordinate_ax_top],
+                                        interpolation=self.interpolation,
+                                        cmap=self.cmap)
             ctr = self.ax_latent_space.contour(grid_points_3d[:, :, 0],
                                                grid_points_3d[:, :, 1],
                                                grid_values_to_draw_3d, 6, colors='k')