Fix doc gen (apache#12)

* fix warining

* fix test

* fix

* fix

* fix print

docs/tutorials/gluon/fit_api_tutorial.md

@@ -137,8 +137,12 @@ est = estimator.Estimator(net=resnet_18_v1,
                           trainer=trainer, 
                           context=ctx)
 
-# Magic line
-est.fit(train_data=train_data_loader,
+# ignore warnings for nightly test on CI only
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    # Magic line
+    est.fit(train_data=train_data_loader,
         epochs=num_epochs)
 ```
 
@@ -224,11 +228,15 @@ checkpoint_handler = CheckpointHandler(model_dir='./',
                                        save_best=True)  # Save the best model in terms of
 # Let's instantiate another handler which we defined above 
 loss_record_handler = LossRecordHandler()
-# Magic line
-est.fit(train_data=train_data_loader,
-        val_data=val_data_loader,
-        epochs=num_epochs,
-        event_handlers=[checkpoint_handler, loss_record_handler]) # Add the event handlers
+# ignore warnings for nightly test on CI only
+import warnings
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    # Magic line
+    est.fit(train_data=train_data_loader,
+            val_data=val_data_loader,
+            epochs=num_epochs,
+            event_handlers=[checkpoint_handler, loss_record_handler]) # Add the event handlers
 ```
 
     Training begin: using optimizer SGD with current learning rate 0.0400 <!--notebook-skip-line-->

python/mxnet/gluon/contrib/estimator/estimator.py

@@ -334,28 +334,36 @@ def fit(self, train_data,
     def _prepare_default_handlers(self, val_data, event_handlers):
         event_handlers = event_handlers or []
         default_handlers = []
-        train_metrics, val_metrics = self.prepare_loss_and_metrics()
+        self.prepare_loss_and_metrics()
 
         # no need to add to default handler check as StoppingHandler does not use metrics
         event_handlers.append(StoppingHandler(self.max_epoch, self.max_batch))
+        default_handlers.append("StoppingHandler")
 
         if not any(isinstance(handler, MetricHandler) for handler in event_handlers):
-            event_handlers.append(MetricHandler(train_metrics=train_metrics))
+            event_handlers.append(MetricHandler(train_metrics=self.train_metrics))
             default_handlers.append("MetricHandler")
 
-        if val_data and not any(isinstance(handler, ValidationHandler) for handler in event_handlers):
-            event_handlers.append(ValidationHandler(val_data=val_data, eval_fn=self.evaluate,
-                                                    val_metrics=val_metrics))
-            default_handlers.append("ValidationHandler")
+        if not any(isinstance(handler, ValidationHandler) for handler in event_handlers):
+            # no validation handler
+            if val_data:
+                # add default validation handler if validation data found
+                event_handlers.append(ValidationHandler(val_data=val_data, eval_fn=self.evaluate,
+                                                        val_metrics=self.val_metrics))
+                default_handlers.append("ValidationHandler")
+                val_metrics = self.val_metrics
+            else:
+                # set validation metrics to None if no validation data and no validation handler
+                val_metrics = []
 
         if not any(isinstance(handler, LoggingHandler) for handler in event_handlers):
-            event_handlers.append(LoggingHandler(train_metrics=train_metrics,
+            event_handlers.append(LoggingHandler(train_metrics=self.train_metrics,
                                                  val_metrics=val_metrics))
             default_handlers.append("LoggingHandler")
 
         # if there is a mix of user defined event handlers and default event handlers
         # they should have the same set of loss and metrics
-        if default_handlers:
+        if default_handlers and len(event_handlers) > len(default_handlers):
             msg = "You are training with the following default event handlers: %s. " \
                   "They use loss and metrics from estimator.prepare_loss_and_metrics(). " \
                   "Please use the same set of metrics for all your other handlers." % \
@@ -374,7 +382,7 @@ def _prepare_default_handlers(self, val_data, event_handlers):
             # remove None metric references
             references = set([ref for ref in references if ref])
             for metric in references:
-                if metric not in train_metrics + val_metrics:
+                if metric not in self.train_metrics + self.val_metrics:
                     msg = "We have added following default handlers for you: %s and used " \
                           "estimator.prepare_loss_and_metrics() to pass metrics to " \
                           "those handlers. Please use the same set of metrics " \

python/mxnet/gluon/contrib/estimator/event_handler.py

@@ -29,7 +29,8 @@
 from ....metric import EvalMetric
 from ....metric import Loss as metric_loss
 
-__all__ = ['StoppingHandler', 'MetricHandler', 'ValidationHandler',
+__all__ = ['TrainBegin', 'TrainEnd', 'EpochBegin', 'EpochEnd','BatchBegin', 'BatchEnd',
+           'StoppingHandler', 'MetricHandler', 'ValidationHandler',
            'LoggingHandler', 'CheckpointHandler', 'EarlyStoppingHandler']
 
 class TrainBegin(object):
@@ -513,8 +514,8 @@ def _save_symbol(self, estimator):
             sym = estimator.net._cached_graph[1]
             sym.save(symbol_file)
         else:
-            self.logger.info("Model architecture(symbol file) is not saved, please use HybridBlock"
-                             "to construct your model, can call net.hybridize() before passing to"
+            self.logger.info("Model architecture(symbol file) is not saved, please use HybridBlock "
+                             "to construct your model, can call net.hybridize() before passing to "
                              "Estimator in order to save model architecture as %s.", symbol_file)
 
     def _save_params_and_trainer(self, estimator, file_prefix):

tests/python/unittest/test_gluon_estimator.py

@@ -19,11 +19,13 @@
 
 import sys
 import unittest
+import warnings
 
 import mxnet as mx
 from mxnet import gluon
 from mxnet.gluon import nn
 from mxnet.gluon.contrib.estimator import *
+from mxnet.gluon.contrib.estimator.event_handler import *
 from nose.tools import assert_raises