Fix and clarify doc

apache · Nov 14, 2019 · c47217e · c47217e
1 parent 8354b1b
commit c47217e
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Showing 3 changed files with 10 additions and 9 deletions.
diff --git a/python/mxnet/gluon/contrib/estimator/estimator.py b/python/mxnet/gluon/contrib/estimator/estimator.py
@@ -328,7 +328,11 @@ def fit(self, train_data,
             Number of epochs to iterate on the training data.
             You can only specify one and only one type of iteration(epochs or batches).
         event_handlers : EventHandler or list of EventHandler
-            List of :py:class:`EventHandlers` to apply during training.
+            List of :py:class:`EventHandlers` to apply during training. Besides
+            the event handlers specified here, a StoppingHandler,
+            LoggingHandler and MetricHandler will be added by default if not
+            yet specified manually. If validation data is provided, a
+            ValidationHandler is also added if not already specified.
         batches : int, default None
             Number of batches to iterate on the training data.
             You can only specify one and only one type of iteration(epochs or batches).

diff --git a/python/mxnet/gluon/contrib/estimator/event_handler.py b/python/mxnet/gluon/contrib/estimator/event_handler.py
@@ -408,13 +408,13 @@ def __init__(self,
             else:
                 # use greater for accuracy and f1 and less otherwise
                 if 'acc' or 'f1' in self.monitor.get()[0].lower():
-                    warnings.warn("`greater` operator will be used to determine if %s has improved. "
+                    warnings.warn("`greater` operator will be used to determine if {} has improved. "
                                   "Please specify `mode='min'` to use the `less` operator. "
                                   "Specify `mode='max' to disable this warning.`"
                                   .format(self.monitor.get()[0]))
                     self.monitor_op = np.greater
                 else:
-                    warnings.warn("`less` operator will be used to determine if %s has improved. "
+                    warnings.warn("`less` operator will be used to determine if {} has improved. "
                                   "Please specify `mode='max'` to use the `greater` operator. "
                                   "Specify `mode='min' to disable this warning.`"
                                   .format(self.monitor.get()[0]))
@@ -656,13 +656,13 @@ def __init__(self,
             self.monitor_op = np.greater
         else:
             if 'acc' or 'f1' in self.monitor.get()[0].lower():
-                warnings.warn("`greater` operator will be used to determine if %s has improved. "
+                warnings.warn("`greater` operator will be used to determine if {} has improved. "
                               "Please specify `mode='min'` to use the `less` operator. "
                               "Specify `mode='max' to disable this warning.`"
                               .format(self.monitor.get()[0]))
                 self.monitor_op = np.greater
             else:
-                warnings.warn("`less` operator will be used to determine if %s has improved. "
+                warnings.warn("`less` operator will be used to determine if {} has improved. "
                               "Please specify `mode='max'` to use the `greater` operator. "
                               "Specify `mode='min' to disable this warning.`"
                               .format(self.monitor.get()[0]))

diff --git a/tests/python/unittest/test_gluon_estimator.py b/tests/python/unittest/test_gluon_estimator.py
@@ -346,10 +346,7 @@ def test_default_handlers():
     train_metrics = est.train_metrics
     val_metrics = est.val_metrics
     logging = LoggingHandler(train_metrics=train_metrics, val_metrics=val_metrics)
-    with warnings.catch_warnings(record=True) as w:
-        est.fit(train_data=train_data, epochs=num_epochs, event_handlers=[logging])
-        # provide metric handler by default
-        assert 'MetricHandler' in str(w[-1].message)
+    est.fit(train_data=train_data, epochs=num_epochs, event_handlers=[logging])
 
     # handler with all user defined metrics
     # use mix of default and user defined handlers