more options for overlay functions - multiple projects in browsing script

browse.py

@@ -10,3 +10,10 @@ logging.basicConfig()
 logging.getLogger("KerasROOTClassification").setLevel(logging.INFO)
 
 c = ClassificationProject(sys.argv[1])
+
+cs = []
+cs.append(c)
+
+if len(sys.argv) > 2:
+    for project_name in sys.argv[2:]:
+        cs.append(ClassificationProject(project_name))

compare.py

@@ -37,7 +37,13 @@ def overlay_ROC(filename, *projects):
     plt.savefig(filename)
     plt.clf()
 
-def overlay_loss(filename, *projects):
+def overlay_loss(filename, *projects, **kwargs):
+
+    xlim = kwargs.pop("xlim", None)
+    ylim = kwargs.pop("ylim", None)
+    log = kwargs.pop("log", False)
+    if kwargs:
+        raise KeyError("Unknown kwargs: {}".format(kwargs))
 
     logger.info("Overlay loss curves for {}".format([p.name for p in projects]))
 
@@ -45,11 +51,18 @@ def overlay_loss(filename, *projects):
     colors = prop_cycle.by_key()['color']
 
     for p,color in zip(projects,colors):
-        plt.semilogy(p.history.history['loss'], linestyle='--', label="Training Loss "+p.name, color=color)
-        plt.semilogy(p.history.history['val_loss'], label="Validation Loss "+p.name, color=color)
-        
+        hist_dict = p.csv_hist
+        plt.plot(hist_dict['loss'], linestyle='--', label="Training Loss "+p.name, color=color)
+        plt.plot(hist_dict['val_loss'], label="Validation Loss "+p.name, color=color)
+
     plt.ylabel('loss')
     plt.xlabel('epoch')
+    if log:
+        plt.yscale("log")
+    if xlim is not None:
+        plt.xlim(*xlim)
+    if ylim is not None:
+        plt.ylim(*ylim)
     plt.legend(loc='upper right')
     plt.savefig(filename)
     plt.clf()

toolkit.py

@@ -980,7 +980,7 @@ class ClassificationProject(object):
             hist_dict[hist_key] = [float(line[hist_key_index]) for line in history_list[1:]]
         return hist_dict
 
-    def plot_loss(self, all_trainings=False, log=False, ylim=None):
+    def plot_loss(self, all_trainings=False, log=False, ylim=None, xlim=None):
         """
         Plot the value of the loss function for each epoch
 
@@ -1004,6 +1004,8 @@ class ClassificationProject(object):
         plt.legend(['train','test'], loc='upper left')
         if log:
             plt.yscale("log")
+        if xlim is not None:
+            plt.xlim(*xlim)
         if ylim is not None:
             plt.ylim(*ylim)
         plt.savefig(os.path.join(self.project_dir, "losses.pdf"))