Added a wrapper in the EMBEDR_sweep class to enable use of the EMBEDR…

… class plotting routines on embeddings within the sweep.
ejohnson643 · Nov 6, 2021 · 170875c · 170875c
1 parent 31ebd8a
commit 170875c
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Showing 4 changed files with 107 additions and 2 deletions.
diff --git a/EMBEDR/embedr.py b/EMBEDR/embedr.py
@@ -1986,6 +1986,19 @@ def plot(self,
                                   cite_EMBEDR=cite_EMBEDR,
                                   **plot_kwds)
 
+        elif plot_type.lower() in ['perplexity']:
+
+            from EMBEDR.plots.embedr_scatterplots import Scatterplot
+
+            plotObj = Scatterplot(plot_Y,
+                                  self.perplexity,
+                                  fig=fig,
+                                  axis=axis,
+                                  cbar_ax=cbar_ax,
+                                  show_cbar=show_cbar,
+                                  cite_EMBEDR=cite_EMBEDR,
+                                  **plot_kwds)
+
         elif (metadata is not None) and (plot_type in metadata):
 
             if is_categorical:
@@ -2641,5 +2654,86 @@ def sweep_lineplot(self, sweep_type='pvalues', metadata=None, labels=None,
                                         xticklabels=xticklabels,
                                         **kwargs)
 
+    def plot_embedding(self,
+                       param_2_plot='optimal',
+                       plot_type='pvalue',
+                       metadata=None,
+                       is_categorical=False,
+                       plot_data=True,
+                       embed_2_show=0,
+                       fig=None,
+                       axis=None,
+                       cbar_ax=None,
+                       show_cbar=True,
+                       cite_EMBEDR=True,
+                       **plot_kwds):
+
+        if param_2_plot == 'optimal':
+            try:
+                return self.opt_obj.plot(plot_type=plot_type,
+                                         metadata=metadata,
+                                         is_categorical=is_categorical,
+                                         plot_data=plot_data,
+                                         embed_2_show=embed_2_show,
+                                         fig=fig,
+                                         axis=axis,
+                                         cbar_ax=cbar_ax,
+                                         show_cbar=show_cbar,
+                                         cite_EMBEDR=cite_EMBEDR,
+                                         **plot_kwds)
+            except AttributeError:
+                err_str  = f"Could not plot 'optimal' embedding as it does not"
+                err_str += f" exist.  (Run EMBEDR_sweep.fit_samplewise_optimal"
+                err_str += f" or set `param_2_plot` to a value that was"
+                err_str += f" swept by the hyperparameter sweep in `fit`."
+                raise ValueError(err_str)
+
+        perp = knn = None
+        if self.sweep_type == 'perplexity':
+            perp = param_2_plot
+        else:
+            knn = param_2_plot
+
+        tmpEmb = EMBEDR(perplexity=perp,
+                        n_neighbors=knn,
+                        kNN_metric=self.kNN_metric,
+                        kNN_alg=self.kNN_alg,
+                        kNN_params=self.kNN_params,
+                        aff_type=self.aff_type,
+                        aff_params=self.aff_params,
+                        n_components=self.n_components,
+                        DRA=self.DRA,
+                        DRA_params=self.DRA_params,
+                        EES_type=self.EES_type,
+                        EES_params=self.EES_params,
+                        pVal_type=self.pVal_type,
+                        n_data_embed=self.n_data_embed,
+                        n_null_embed=self.n_null_embed,
+                        n_jobs=self.n_jobs,
+                        random_state=self.rs,
+                        verbose=0,
+                        do_cache=self.do_cache,
+                        project_name=self.project_name,
+                        project_dir=self.project_dir)
+
+        tmpEmb.data_Y = self.embeddings[param_2_plot]
+        tmpEmb.data_EES = self.data_EES[param_2_plot]
+        tmpEmb.null_EES = self.null_EES[param_2_plot]
+        tmpEmb.pValues = self.pValues[param_2_plot]
+
+        return tmpEmb.plot(plot_type=plot_type,
+                           metadata=metadata,
+                           is_categorical=is_categorical,
+                           plot_data=plot_data,
+                           embed_2_show=embed_2_show,
+                           fig=fig,
+                           axis=axis,
+                           cbar_ax=cbar_ax,
+                           show_cbar=show_cbar,
+                           cite_EMBEDR=cite_EMBEDR,
+                           **plot_kwds)
+
+
+
 
 
diff --git a/EMBEDR/plots/embedr_scatterplots.py b/EMBEDR/plots/embedr_scatterplots.py
@@ -261,6 +261,8 @@ def __init__(self, Y, label, metadata, **kwargs):
         if self.cmap is None:
             self.cmap = 'husl'
 
+        print(self.category_kwds)
+
         out = putl.process_categorical_label(metadata,
                                              label,
                                              cmap=self.cmap,
@@ -291,21 +293,25 @@ def _plot(self, **kwargs):
                 color = self.label_cmap[self._l2i_map[label]]
 
                 if self.show_legend:
-                    title = self.long_labels[lNo]
+                    title = self.long_labels[self._l2i_map[label]]
                     if "Multipotent" in title:
                         title = " ".join(title.split(" Multipotent "))
                 else:
                     title = None
 
+                zorder = None
+
             else:
                 color = self.bkgd_label_kwds['color']
                 title = None
+                zorder = -1
 
             self.axis.scatter(*self.Y[good_idx, :2].T,
                               color=color,
                               s=self.sct_s[good_idx],
                               alpha=self.sct_a,
                               label=title,
+                              zorder=zorder,
                               **self.sct_kwds)
 
         if self.show_legend:

diff --git a/EMBEDR/plots/sweep_lineplots.py b/EMBEDR/plots/sweep_lineplots.py
@@ -161,6 +161,9 @@ def sweep_lineplot_byCat(hyperparam_array,
 
     n_labels = len(label_counts)
 
+    if labels_2_show == 'all':
+        labels_2_show = label_counts.index.values
+
     if n_rows is None:
         n_rows = int(np.ceil(n_labels / n_cols))
 

diff --git a/EMBEDR/plotting_utility.py b/EMBEDR/plotting_utility.py
@@ -624,7 +624,9 @@ def process_categorical_label(metadata, label, cmap='colorblind',
     unique_labels = label_counts.index.values
 
     if alphabetical_sort:
-        unique_labels = np.sort(unique_labels)
+        str_labels = unique_labels.astype(str)
+        unique_labels = unique_labels[np.argsort(str_labels)]
+    label_counts = label_counts.reindex(unique_labels)
 
     ## Make some nice long labels.
     long_labels = np.asarray([f"{ll} (N = {label_counts.loc[ll]:d})"