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config_files/config_test.yml

@@ -23,7 +23,7 @@ Classification:
   nb_class: 2
   classes:
   type: ["monoview"]
-  algos_monoview: ["all", ]
+  algos_monoview: ["adaboost",]
   algos_multiview: ["weighted_linear_early_fusion"]
   stats_iter: 2
   metrics: ["accuracy_score", "f1_score"]

docs/source/tutorials/example4.rst

@@ -118,7 +118,7 @@ In order to be able to analyze the results with more clarity, one can add the ex
 
 Let's suppose that the objects we are trying to classify between 'Animal' and 'Object' are all bears, cars, planes, and birds. And that one has a ``.csv`` file with an ID for each of them (:python:`"bear_112", "plane_452", "bird_785", "car_369", ...` for example)
 
-Then as long as the IDs order conresspond to the example order in the lines of the previous matrices, to add the IDs in the hdf5 file, just add :
+Then as long as the IDs order corresponds to the example order in the lines of the previous matrices, to add the IDs in the hdf5 file, just add :
 
 .. code-block:: python
 

multiview_platform/mono_multi_view_classifiers/result_analysis.py

@@ -136,7 +136,7 @@ def plot_metric_scores(train_scores, test_scores, names, nb_results, metric_name
 
 
 def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
-            fileName, minSize=10,
+            fileName, 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.
@@ -174,8 +174,8 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
                      aspect='auto')
     plt.title('Errors depending on the classifier')
     ticks = np.arange(0, nbClassifiers, 1)
-    labels = classifiers_names
-    plt.xticks(ticks, labels, rotation="vertical")
+    tick_labels = classifiers_names
+    plt.xticks(ticks, tick_labels, rotation="vertical")
     cbar = fig.colorbar(cax, ticks=[-100 * stats_iter / 2, 0, stats_iter])
     cbar.ax.set_yticklabels(['Unseen', 'Always Wrong', 'Always Right'])
 
@@ -183,22 +183,27 @@ def plot_2d(data, classifiers_names, nbClassifiers, nbExamples,
     plt.close()
     ### The following part is used to generate an interactive graph.
     if use_plotly:
-        hover_text = [["Failed "+ str(stats_iter-data[i,j])+" time(s)"
+        label_index_list = [np.where(labels==i)[0] for i in np.unique(labels)]
+        print(label_index_list)
+        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.graph_objs.Figure(data=plotly.graph_objs.Heatmap(
+        fig = plotly.subplots.make_subplots(rows=len(label_index_list), cols=1)
+        for row_index, label_index in enumerate(label_index_list):
+            fig.add_trace(plotly.graph_objs.Heatmap(
                 x=list(classifiers_names),
-            y=[_ for _ in example_ids],
-            z=data,
+                y=[example_ids[label_ind] for label_ind in label_index],
+                z=data[label_index, :],
                 text=hover_text,
                 hoverinfo=["y", "x", "text"],
                 colorscale="Greys",
                 colorbar=dict(tickvals=[0, stats_iter],
                               ticktext=["Always Wrong", "Always Right"]),
-            reversescale=True))
-        fig.update_layout(
-            xaxis={"showgrid": False, "showticklabels": False, "ticks": ''},
-            yaxis={"showgrid": False, "showticklabels": False, "ticks": ''})
+                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)
+
+        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)
         del fig
 
@@ -537,7 +542,7 @@ def gen_error_data(example_errors):
     return nb_classifiers, nb_examples, classifiers_names, data_2d, error_on_examples
 
 
-def publishExampleErrors(example_errors, directory, databaseName, labels_names, example_ids):
+def publishExampleErrors(example_errors, directory, databaseName, labels_names, example_ids, labels):
     logging.debug("Start:\t Biclass Label analysis figure generation")
 
     base_file_name = directory + time.strftime(
@@ -552,7 +557,7 @@ def publishExampleErrors(example_errors, directory, databaseName, labels_names,
                delimiter=",")
 
     plot_2d(data_2d, classifiers_names, nb_classifiers, nb_examples,
-            base_file_name, example_ids=example_ids)
+            base_file_name, example_ids=example_ids, labels=labels)
 
     plot_errors_bar(error_on_examples, nb_classifiers, nb_examples,
                     base_file_name)
@@ -689,7 +694,7 @@ def analyze_biclass(results, benchmark_argument_dictionaries, stats_iter, metric
         results = publishMetricsGraphs(metrics_scores, directory, database_name,
                              labels_names)
         publishExampleErrors(example_errors, directory, database_name,
-                             labels_names, example_ids)
+                             labels_names, example_ids, arguments["labels"])
         publish_feature_importances(feature_importances, directory, database_name, labels_names)
         if not str(classifierPositive) + str(classifierNegative) in biclass_results:
             biclass_results[str(classifierPositive) + str(classifierNegative)] = {}