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validate training data only when training
RasaHQ · Jan 31, 2019 · 1b44a05 · 1b44a05
1 parent 8e16ad1
commit 1b44a05
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Showing 3 changed files with 4 additions and 7 deletions.
diff --git a/rasa_nlu/evaluate.py b/rasa_nlu/evaluate.py
@@ -714,8 +714,7 @@ def run_evaluation(data_path, model,
     else:
         interpreter = Interpreter.load(model, component_builder)
     test_data = training_data.load_data(data_path,
-                                        interpreter.model_metadata.language,
-                                        validate=False)
+                                        interpreter.model_metadata.language)
     extractors = get_entity_extractors(interpreter)
     entity_predictions, tokens = get_entity_predictions(interpreter,
                                                         test_data)

diff --git a/rasa_nlu/model.py b/rasa_nlu/model.py
@@ -163,6 +163,8 @@ def train(self, data: TrainingData, **kwargs: Any)-> 'Interpreter':
 
         self.training_data = data
 
+        self.training_data.validate()
+
         context = kwargs
 
         for component in self.pipeline:

diff --git a/rasa_nlu/training_data/loading.py b/rasa_nlu/training_data/loading.py
@@ -41,8 +41,7 @@
 
 
 def load_data(resource_name: Text,
-              language: Optional[Text] = 'en',
-              validate: bool = True) -> 'TrainingData':
+              language: Optional[Text] = 'en') -> 'TrainingData':
     """Load training data from disk.
 
     Merges them if loaded from disk and multiple files are found."""
@@ -58,8 +57,6 @@ def load_data(resource_name: Text,
     else:
         training_data = data_sets[0].merge(*data_sets[1:])
 
-    if validate:
-        training_data.validate()
     return training_data
 
 
@@ -75,7 +72,6 @@ def load_data_from_endpoint(data_endpoint: EndpointConfig,
         temp_data_file = utils.create_temporary_file(response.content,
                                                      mode="w+b")
         training_data = _load(temp_data_file, language)
-        training_data.validate()
 
         return training_data
     except Exception as e: