Issue warnings when altering a virtual dataset

conx/dataset.py

@@ -1800,6 +1800,13 @@ def __len__(self):
         else:
             return size
 
+    def reshape(self, *args, **kwargs):
+        print("WARNING: applying to virtual dataset, this cache only!",
+              file=sys.stderr)
+        super().reshape(*args, **kwargs)
+
+    ## FIXME: any other virtual functions that should get a datavector warning?
+
 class VirtualDataset(Dataset):
     """
     Create a virtual dataset. VirtualData set takes:
@@ -2075,6 +2082,23 @@ def get_validation_generator(self, batch_size):
             ## split can be 1.0 (use all), or > 0 and < 1
             return DatasetGenerator(self, validation_set=True)
 
+    def set_targets_from_labels(self, *args, **kwargs):
+        print("WARNING: applying to virtual dataset, this cache only!",
+              file=sys.stderr)
+        super().set_targets_from_labels(*args, **kwargs)
+
+    def set_targets_from_inputs(self, *args, **kwargs):
+        print("WARNING: applying to virtual dataset, this cache only!",
+              file=sys.stderr)
+        super().set_targets_from_inputs(*args, **kwargs)
+
+    def set_inputs_from_targets(self, *args, **kwargs):
+        print("WARNING: applying to virtual dataset, this cache only!",
+              file=sys.stderr)
+        super().set_inputs_from_targets(*args, **kwargs)
+
+    ## FIXME: any other virtual functions that should get a dataset warning?
+
 class DatasetGenerator(keras.utils.Sequence):
     """
     DatasetGenerator takes a VirtualDataset and can be trained

conx/network.py

@@ -883,7 +883,7 @@ def evaluate(self, batch_size=None, show=False, show_inputs=True, show_targets=T
 
         If show is True, then it will show details for each
         training/test pair, the amount of detail then determined by
-        show_inputs, and show_outputs.
+        show_inputs, and show_targets.
 
         If force is True, then it will show all patterns, even if there are many.
 
@@ -1055,7 +1055,7 @@ def evaluate_and_label(self, batch_size=32, tolerance=None):
                 categories[label] = []
             categories[label].append(self.dataset.inputs[i])
         return sorted(categories.items())
-    
+
     def compute_correct(self, outputs, targets, tolerance=None):
         """
         Both are np.arrays. Return [True, ...].
@@ -4093,11 +4093,11 @@ def pf(self, vector, max_line_width=79, **opts):
             formatter={'float_kind': precision.format,
                        'int_kind': precision.format},
             separator=", ",
-            max_line_width=max_line_width).replace("\n", "")[:max_line_width]
-        if len(retval) == max_line_width:
-            return retval + "..."
-        else:
+            max_line_width=max_line_width).replace("\n", "")
+        if len(retval) <= max_line_width:
             return retval
+        else:
+            return retval[:max_line_width - 3] + "..."
 
     def set_weights(self, weights, layer_name=None):
         """