Added boxplot over all cross-validation results to classification evaluation.

ANN_Training.py

@@ -3,7 +3,7 @@
 @author: Laura C. Kühle, Soraya Terrab (sorayaterrab)
 
 TODO: Give option to compare multiple models
-TODO: Add more evaluation measures (AUROC, ROC, F1, training accuracy, boxplot over CVF, etc.)
+TODO: Add more evaluation measures (AUROC, ROC, F1, training accuracy, boxplot over CVF, etc.) -> Done
 TODO: Add log to pipeline
 TODO: Remove object set-up
 TODO: Optimize Snakefile-vs-config relation
@@ -23,7 +23,7 @@ from sklearn.model_selection import KFold
 from sklearn.metrics import accuracy_score, precision_recall_fscore_support, precision_score, roc_auc_score, roc_curve
 
 import ANN_Model
-from Plotting import plot_classification_accuracy
+from Plotting import plot_classification_accuracy, plot_boxplot
 
 
 class ModelTrainer(object):
@@ -121,6 +121,7 @@ class ModelTrainer(object):
 
         # print(classification_stats)
         # print(np.array(classification_stats).mean(axis=0))
+        plot_boxplot([self._model_name], *np.array(classification_stats).transpose())
         classification_stats = np.array(classification_stats).mean(axis=0)
 
         plot_classification_accuracy([self._model_name], *classification_stats)

Plotting.py

@@ -259,7 +259,7 @@ def plot_classification_accuracy(xlabels, precision, recall, accuracy, fscore, a
     fscore = [fscore]
     auroc = [auroc]
     pos = np.arange(len(xlabels))
-    width = 0.3
+    width = 1/(3*len(xlabels))
     fig = plt.figure('classification_accuracy')
     ax = fig.add_axes([0.15, 0.1, 0.75, 0.8])
     ax.bar(pos - 2*width, fscore, width, label='F-Score')
@@ -270,8 +270,45 @@ def plot_classification_accuracy(xlabels, precision, recall, accuracy, fscore, a
     ax.set_xticks(pos)
     ax.set_xticklabels(xlabels)
     ax.set_ylabel('Classification (%)')
-    ax.set_ylim(bottom=0.6)
+    ax.set_ylim(bottom=-0.02)
     ax.set_ylim(top=1.02)
     ax.set_title('Non-Normalized Test Data')
     ax.legend(loc='upper right')
     # fig.tight_layout()
+
+
+def plot_boxplot(xlabels, precision, recall, accuracy, fscore, auroc):
+    precision = [precision]
+    recall = [recall]
+    accuracy = [accuracy]
+    fscore = [fscore]
+    auroc = [auroc]
+    fig = plt.figure('boxplot_accuracy')
+    pos = np.arange(len(xlabels))
+    width = 1/(5*len(xlabels))
+    ax = fig.add_axes([0.15, 0.1, 0.75, 0.8])
+    boxplots = []
+    boxplots.append(ax.boxplot(fscore, positions=pos - 3*width, widths=width, meanline=True,
+                               showmeans=True, patch_artist=True))
+    boxplots.append(ax.boxplot(precision, positions=pos - 1.5*width, widths=width, meanline=True,
+                               showmeans=True, patch_artist=True))
+    boxplots.append(ax.boxplot(recall, positions=pos, widths=width, meanline=True, showmeans=True,
+                               patch_artist=True))
+    boxplots.append(ax.boxplot(accuracy, positions=pos + 1.5*width, widths=width, meanline=True,
+                               showmeans=True, patch_artist=True))
+    boxplots.append(ax.boxplot(auroc, positions=pos + 3*width, widths=width, meanline=True,
+                               showmeans=True, patch_artist=True))
+    count = 0
+    colors = ['red', 'yellow', 'blue', 'tan', 'green']
+    for bp in boxplots:
+        for patch in bp['boxes']:
+            patch.set(facecolor=colors[count])
+        count +=1
+    ax.set_xticks(pos)
+    ax.set_xticklabels(xlabels)
+    ax.set_ylim(bottom=-0.02)
+    ax.set_ylim(top=1.02)
+    ax.set_ylabel('Classification (%)')
+    ax.set_title('Non-Normalized Test Data')
+    ax.legend([bp["boxes"][0] for bp in boxplots],
+              ['F-Score', 'Precision', 'Recall', 'Accuracy', 'AUROC'], loc='upper right')