Added plotly for metrics

multiview_platform/mono_multi_view_classifiers/result_analysis.py

@@ -77,7 +77,7 @@ def plot_results_noise(directory, noise_results, metric_to_plot, name, width=0.1
 
 def plot_metric_scores(train_scores, test_scores, names, nb_results, metric_name,
                        file_name,
-                       tag="", train_STDs=None, test_STDs=None):
+                       tag="", train_STDs=None, test_STDs=None, use_plotly=True):
     r"""Used to plot and save the score barplot for a specific metric.
 
     Parameters
@@ -145,10 +145,28 @@ def plot_metric_scores(train_scores, test_scores, names, nb_results, metric_name
             test_STDs.reshape((train_scores.shape[0], 1))), axis=1)),
             columns=names, index=["Train", "Train STD", "Test", "Test STD"])
     dataframe.to_csv(file_name + ".csv")
+    if use_plotly:
+        fig = plotly.graph_objs.Figure()
+        fig.add_trace(plotly.graph_objs.Bar(
+            name='Train',
+            x=names, y=train_scores,
+            error_y=dict(type='data', array=train_STDs),
+            marker_color="lightgrey",
+        ))
+        fig.add_trace(plotly.graph_objs.Bar(
+            name='Test',
+            x=names, y=test_scores,
+            error_y=dict(type='data', array=test_STDs),
+            marker_color="black",
+        ))
+
+        fig.update_layout(title=metric_name + "\n" + tag + " scores for each classifier")
+        plotly.offline.plot(fig, filename=file_name + ".html", auto_open=False)
+        del fig
 
 
 def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
-            fileName, minSize=10, labels=None,
+            file_name, minSize=10, labels=None,
             width_denominator=2.0, height_denominator=20.0, stats_iter=1,
             use_plotly=True, example_ids=None):
     r"""Used to generate a 2D plot of the errors.
@@ -166,7 +184,7 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
         The number of examples.
     nbCopies : int
         The number of times the data is copied (classifier wise) in order for the figure to be more readable
-    fileName : str
+    file_name : str
         The name of the file in which the figure will be saved ("error_analysis_2D.png" will be added at the end)
     minSize : int, optinal, default: 10
         The minimum width and height of the figure.
@@ -191,7 +209,7 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
     cbar = fig.colorbar(cax, ticks=[-100 * stats_iter / 2, 0, stats_iter])
     cbar.ax.set_yticklabels(['Unseen', 'Always Wrong', 'Always Right'])
 
-    fig.savefig(fileName + "error_analysis_2D.png", bbox_inches="tight", transparent=True)
+    fig.savefig(file_name + "error_analysis_2D.png", bbox_inches="tight", transparent=True)
     plt.close()
     ### The following part is used to generate an interactive graph.
     if use_plotly:
@@ -215,7 +233,7 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
             fig.update_xaxes(showticklabels=False, row=row_index+1, col=1)
 
         fig.update_xaxes(showticklabels=True, row=len(label_index_list), col=1)
-        plotly.offline.plot(fig, filename=fileName + "error_analysis_2D.html", auto_open=False)
+        plotly.offline.plot(fig, filename=file_name + "error_analysis_2D.html", auto_open=False)
         del fig