feat(performance): add display option for charts

aucmedi/evaluation/performance.py

@@ -33,6 +33,7 @@
 def evaluate_performance(preds,
                          labels,
                          out_path,
+                         show=False,
                          class_names=None,
                          multi_label=False,
                          metrics_threshold=0.5,
@@ -99,6 +100,7 @@ def evaluate_performance(preds,
         labels (numpy.ndarray):         Classification list with One-Hot Encoding. Provided by
                                         [input_interface][aucmedi.data_processing.io_data.input_interface].
         out_path (str):                 Path to directory in which plotted figures are stored.
+        show (bool):                    Option, whether to also display the generated charts.
         class_names (list of str):      List of names for corresponding classes. Used for evaluation. Provided by
                                         [input_interface][aucmedi.data_processing.io_data.input_interface].
                                         If not provided (`None` provided), class indices will be used.
@@ -135,19 +137,19 @@ def evaluate_performance(preds,
 
     # Store metrics to CSV
     if store_csv:
-        evalby_csv(metrics, out_path, class_names, suffix)
+        evalby_csv(metrics, out_path, class_names, suffix=suffix)
 
     # Generate bar plot
     if plot_barplot:
-        evalby_barplot(metrics, out_path, class_names, suffix)
+        evalby_barplot(metrics, out_path, class_names, show=show, suffix=suffix)
 
     # Generate confusion matrix plot
     if plot_confusion_matrix and not multi_label:
-        evalby_confusion_matrix(cm, out_path, class_names, suffix)
+        evalby_confusion_matrix(cm, out_path, class_names, show=show, suffix=suffix)
 
     # Generate ROC curve
     if plot_roc_curve:
-        evalby_rocplot(fpr_list, tpr_list, out_path, class_names, suffix)
+        evalby_rocplot(fpr_list, tpr_list, out_path, class_names, show=show, suffix=suffix)
 
     # Return metrics
     return metrics
@@ -156,7 +158,9 @@ def evaluate_performance(preds,
 #      Evaluation Performance - Confusion Matrix      #
 #-----------------------------------------------------#
 def evalby_confusion_matrix(confusion_matrix, out_path, class_names,
+                            show=False,
                             suffix=None):
+
     # Convert confusion matrix to a Pandas dataframe
     rawcm = pd.DataFrame(confusion_matrix)
     # Tidy dataframe
@@ -172,7 +176,7 @@ def evalby_confusion_matrix(confusion_matrix, out_path, class_names,
     dt = dt.melt(id_vars=["index"], var_name="gt", value_name="score")
     dt.rename(columns={"index": "pd"}, inplace=True)
 
-    # Plot confusion matrix
+    # Generate confusion matrix
     fig = (ggplot(dt, aes("pd", "gt", fill="score"))
                   + geom_tile()
                   + geom_text(aes("pd", "gt", label="score"), color="black")
@@ -191,15 +195,18 @@ def evalby_confusion_matrix(confusion_matrix, out_path, class_names,
     filename += ".png"
     fig.save(filename=filename, path=out_path, width=10, height=9, dpi=200)
 
+    # Plot figure
+    if show : print(fig)
+
 #-----------------------------------------------------#
 #          Evaluation Performance - Barplots          #
 #-----------------------------------------------------#
-def evalby_barplot(metrics, out_path, class_names, suffix=None):
+def evalby_barplot(metrics, out_path, class_names, show=False, suffix=None):
     # Remove confusion matrix from metric dataframe
     df_metrics = metrics[~metrics["metric"].isin(["TN", "FN", "FP", "TP"])]
     df_metrics["class"] = pd.Categorical(df_metrics["class"])
 
-    # Plot metric results
+    # Generate metric results
     fig = (ggplot(df_metrics, aes("class", "score", fill="class"))
               + geom_col(stat='identity', width=0.6, color="black",
                          position = position_dodge(width=0.6))
@@ -218,10 +225,13 @@ def evalby_barplot(metrics, out_path, class_names, suffix=None):
     filename += ".png"
     fig.save(filename=filename, path=out_path, width=12, height=9, dpi=200)
 
+    # Plot figure
+    if show : print(fig)
+
 #-----------------------------------------------------#
 #          Evaluation Performance - ROC plot          #
 #-----------------------------------------------------#
-def evalby_rocplot(fpr_list, tpr_list, out_path, class_names, suffix=None):
+def evalby_rocplot(fpr_list, tpr_list, out_path, class_names, show=False, suffix=None):
     # Initialize result dataframe
     df_roc = pd.DataFrame(data=[fpr_list, tpr_list], dtype=np.float64)
     # Preprocess dataframe
@@ -240,7 +250,7 @@ def evalby_rocplot(fpr_list, tpr_list, out_path, class_names, suffix=None):
     df_roc["FPR"] = df_roc["FPR"].astype(float)
     df_roc["TPR"] = df_roc["TPR"].astype(float)
 
-    # Plot roc results
+    # Generate roc results
     fig = (ggplot(df_roc, aes("FPR", "TPR", color="class"))
                + geom_line(size=1.0)
                + geom_abline(intercept=0, slope=1, color="black",
@@ -259,6 +269,9 @@ def evalby_rocplot(fpr_list, tpr_list, out_path, class_names, suffix=None):
     filename += ".png"
     fig.save(filename=filename, path=out_path, width=10, height=9, dpi=200)
 
+    # Plot figure
+    if show : print(fig)
+
 #-----------------------------------------------------#
 #          Evaluation Performance - CSV file          #
 #-----------------------------------------------------#
@@ -271,3 +284,4 @@ def evalby_csv(metrics, out_path, class_names, suffix=None):
 
     # Store file to disk
     metrics.to_csv(path_csv, index=False)
+