Removed subplots and tried fbeta

multiview-machine-learning-omis.iml

 <?xml version="1.0" encoding="UTF-8"?>
 <module type="WEB_MODULE" version="4">
-  <component name="NewModuleRootManager" inherit-compiler-output="true">
-    <exclude-output />
+  <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
     <orderEntry type="inheritedJdk" />
     <orderEntry type="sourceFolder" forTests="false" />
+    <orderEntry type="module" module-name="multiviewmetriclearning" />
+    <orderEntry type="module" module-name="multiview_generator" />
+    <orderEntry type="module" module-name="short_projects" />
+    <orderEntry type="library" name="R User Library" level="project" />
+    <orderEntry type="library" name="R Skeletons" level="application" />
+    <orderEntry type="module" module-name="Datasets" />
   </component>
 </module>
\ No newline at end of file

multiview_platform/mono_multi_view_classifiers/metrics/fbeta_score.py

@@ -18,7 +18,7 @@ def score(y_true, y_pred, multiclass=False, **kwargs):
     try:
         beta = kwargs["1"]
     except Exception:
-        beta = 1.0
+        beta = 10.0
     try:
         labels = kwargs["2"]
     except Exception:

multiview_platform/mono_multi_view_classifiers/result_analysis.py

@@ -217,27 +217,27 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
     plt.close()
     ### The following part is used to generate an interactive graph.
     if use_plotly:
-        label_index_list = [np.arange(len(labels))] #[np.where(labels==i)[0] for i in np.unique(labels)]
-        hover_text = [[example_ids[i] + " failed "+ str(stats_iter-data[i,j])+" time(s)"
-                       for j in range(data.shape[1])]
-                      for i in range(data.shape[0]) ]
-        fig = plotly.subplots.make_subplots(rows=len(label_index_list), cols=1)
-        for row_index, label_index in enumerate(label_index_list):
+        label_index_list = np.concatenate([np.where(labels==i)[0] for i in np.unique(labels)]) #[np.where(labels==i)[0] for i in np.unique(labels)]
+        hover_text = [[example_ids[example_index] + " failed "+ str(stats_iter-data[example_index,classifier_index])+" time(s), labelled "+str(example_index)
+                       for classifier_index in range(data.shape[1])]
+                      for example_index in range(data.shape[0]) ]
+        fig = plotly.graph_objs.Figure()
+        # for row_index, label_index in enumerate(label_index_list):
         fig.add_trace(plotly.graph_objs.Heatmap(
             x=list(classifiers_names),
-                y=[example_ids[label_ind] for label_ind in label_index],
-                z=data[label_index, :],
-                text=hover_text,
+            y=[example_ids[label_ind] for label_ind in label_index_list],
+            z=data[label_index_list, :],
+            text=[hover_text[label_ind] for label_ind in label_index_list],
             hoverinfo=["y", "x", "text"],
             colorscale="Greys",
             colorbar=dict(tickvals=[0, stats_iter],
                           ticktext=["Always Wrong", "Always Right"]),
-                reversescale=True), row=row_index+1, col=1)
-            fig.update_yaxes(title_text="Label "+str(row_index), showticklabels=False, ticks='', row=row_index+1, col=1)
-            fig.update_xaxes(showticklabels=False, row=row_index+1, col=1)
+            reversescale=True),)
+        fig.update_yaxes(title_text="Examples", showticklabels=False, ticks='')
+        fig.update_xaxes(showticklabels=False,)
         fig.update_layout(paper_bgcolor = 'rgba(0,0,0,0)',
                           plot_bgcolor = 'rgba(0,0,0,0)')
-        fig.update_xaxes(showticklabels=True, row=len(label_index_list), col=1)
+        fig.update_xaxes(showticklabels=True, )
         plotly.offline.plot(fig, filename=file_name + "error_analysis_2D.html", auto_open=False)
         del fig