Merge branch 'master' into async-slack-tests

Author: alwx

changelog/4458.misc.rst

@@ -0,0 +1,2 @@
+Rename ``kwargs`` to ``additional_arguments`` in ``rasa.train`` and ``rasa.core.train`` to make the name of the
+argument less confusing.

rasa/cli/test.py

@@ -85,7 +85,7 @@ def test_core(args: argparse.Namespace) -> None:
                 stories=stories,
                 endpoints=endpoints,
                 output=output,
-                kwargs=vars(args),
+                additional_arguments=vars(args),
             )
 
     else:

rasa/cli/train.py

@@ -73,7 +73,7 @@ def train(args: argparse.Namespace) -> Optional[Text]:
         force_training=args.force,
         fixed_model_name=args.fixed_model_name,
         persist_nlu_training_data=args.persist_nlu_data,
-        kwargs=extract_additional_arguments(args),
+        additional_arguments=extract_additional_arguments(args),
     )
 
 
@@ -92,7 +92,7 @@ def train_core(
     story_file = get_validated_path(
         args.stories, "stories", DEFAULT_DATA_PATH, none_is_valid=True
     )
-    kwargs = extract_additional_arguments(args)
+    additional_arguments = extract_additional_arguments(args)
 
     # Policies might be a list for the compare training. Do normal training
     # if only list item was passed.
@@ -109,12 +109,14 @@ def train_core(
             output=output,
             train_path=train_path,
             fixed_model_name=args.fixed_model_name,
-            kwargs=kwargs,
+            additional_arguments=additional_arguments,
         )
     else:
         from rasa.core.train import do_compare_training
 
-        loop.run_until_complete(do_compare_training(args, story_file, kwargs))
+        loop.run_until_complete(
+            do_compare_training(args, story_file, additional_arguments)
+        )
 
 
 def train_nlu(

rasa/core/train.py

@@ -29,7 +29,7 @@ async def train(
     dump_stories: bool = False,
     policy_config: Optional[Union[Text, Dict]] = None,
     exclusion_percentage: int = None,
-    kwargs: Optional[Dict] = None,
+    additional_arguments: Optional[Dict] = None,
 ):
     from rasa.core.agent import Agent
     from rasa.core import config, utils
@@ -38,8 +38,8 @@ async def train(
     if not endpoints:
         endpoints = AvailableEndpoints()
 
-    if not kwargs:
-        kwargs = {}
+    if not additional_arguments:
+        additional_arguments = {}
 
     policies = config.load(policy_config)
 
@@ -51,8 +51,8 @@ async def train(
         policies=policies,
     )
 
-    data_load_args, kwargs = utils.extract_args(
-        kwargs,
+    data_load_args, additional_arguments = utils.extract_args(
+        additional_arguments,
         {
             "use_story_concatenation",
             "unique_last_num_states",
@@ -64,7 +64,7 @@ async def train(
     training_data = await agent.load_data(
         training_resource, exclusion_percentage=exclusion_percentage, **data_load_args
     )
-    agent.train(training_data, **kwargs)
+    agent.train(training_data, **additional_arguments)
     agent.persist(output_path, dump_stories)
 
     return agent
@@ -78,7 +78,7 @@ async def train_comparison_models(
     policy_configs: Optional[List] = None,
     runs: int = 1,
     dump_stories: bool = False,
-    kwargs: Optional[Dict] = None,
+    additional_arguments: Optional[Dict] = None,
 ):
     """Train multiple models for comparison of policies"""
     from rasa import model
@@ -114,7 +114,7 @@ async def train_comparison_models(
                             train_path,
                             policy_config=policy_config,
                             exclusion_percentage=percentage,
-                            kwargs=kwargs,
+                            additional_arguments=additional_arguments,
                             dump_stories=dump_stories,
                         ),
                         model.model_fingerprint(file_importer),
@@ -148,14 +148,14 @@ async def do_compare_training(
 ):
     _, no_stories = await asyncio.gather(
         train_comparison_models(
-            story_file,
-            args.domain,
-            args.out,
-            args.percentages,
-            args.config,
-            args.runs,
-            args.dump_stories,
-            additional_arguments,
+            story_file=story_file,
+            domain=args.domain,
+            output_path=args.out,
+            exclusion_percentages=args.percentages,
+            policy_configs=args.config,
+            runs=args.runs,
+            dump_stories=args.dump_stories,
+            additional_arguments=additional_arguments,
         ),
         get_no_of_stories(args.stories, args.domain),
     )

rasa/test.py

@@ -40,21 +40,21 @@ def test(
     nlu_data: Text,
     endpoints: Optional[Text] = None,
     output: Text = DEFAULT_RESULTS_PATH,
-    kwargs: Optional[Dict] = None,
+    additional_arguments: Optional[Dict] = None,
 ):
-    if kwargs is None:
-        kwargs = {}
+    if additional_arguments is None:
+        additional_arguments = {}
 
-    test_core(model, stories, endpoints, output, kwargs)
-    test_nlu(model, nlu_data, output, kwargs)
+    test_core(model, stories, endpoints, output, additional_arguments)
+    test_nlu(model, nlu_data, output, additional_arguments)
 
 
 def test_core(
     model: Optional[Text] = None,
     stories: Optional[Text] = None,
     endpoints: Optional[Text] = None,
     output: Text = DEFAULT_RESULTS_PATH,
-    kwargs: Optional[Dict] = None,
+    additional_arguments: Optional[Dict] = None,
 ):
     import rasa.core.test
     import rasa.core.utils as core_utils
@@ -64,8 +64,8 @@ def test_core(
 
     _endpoints = core_utils.AvailableEndpoints.read_endpoints(endpoints)
 
-    if kwargs is None:
-        kwargs = {}
+    if additional_arguments is None:
+        additional_arguments = {}
 
     if output:
         io_utils.create_directory(output)
@@ -87,7 +87,7 @@ def test_core(
             "Rasa model and provide it via the '--model' argument."
         )
 
-    use_e2e = kwargs["e2e"] if "e2e" in kwargs else False
+    use_e2e = additional_arguments.get("e2e", False)
 
     _interpreter = RegexInterpreter()
     if use_e2e:
@@ -101,7 +101,9 @@ def test_core(
 
     _agent = Agent.load(unpacked_model, interpreter=_interpreter)
 
-    kwargs = utils.minimal_kwargs(kwargs, rasa.core.test, ["stories", "agent"])
+    kwargs = utils.minimal_kwargs(
+        additional_arguments, rasa.core.test, ["stories", "agent"]
+    )
 
     loop = asyncio.get_event_loop()
     loop.run_until_complete(
@@ -113,7 +115,7 @@ def test_nlu(
     model: Optional[Text],
     nlu_data: Optional[Text],
     output_directory: Text = DEFAULT_RESULTS_PATH,
-    kwargs: Optional[Dict] = None,
+    additional_arguments: Optional[Dict] = None,
 ):
     from rasa.nlu.test import run_evaluation
     from rasa.model import get_model
@@ -132,7 +134,9 @@ def test_nlu(
     nlu_model = os.path.join(unpacked_model, "nlu")
 
     if os.path.exists(nlu_model):
-        kwargs = utils.minimal_kwargs(kwargs, run_evaluation, ["data_path", "model"])
+        kwargs = utils.minimal_kwargs(
+            additional_arguments, run_evaluation, ["data_path", "model"]
+        )
         run_evaluation(nlu_data, nlu_model, output_directory=output_directory, **kwargs)
     else:
         print_error(
@@ -186,7 +190,10 @@ def compare_nlu_models(
 
 
 def perform_nlu_cross_validation(
-    config: Text, nlu: Text, output: Text, kwargs: Optional[Dict[Text, Any]]
+    config: Text,
+    nlu: Text,
+    output: Text,
+    additional_arguments: Optional[Dict[Text, Any]],
 ):
     import rasa.nlu.config
     from rasa.nlu.test import (
@@ -196,12 +203,12 @@ def perform_nlu_cross_validation(
         return_entity_results,
     )
 
-    kwargs = kwargs or {}
-    folds = int(kwargs.get("folds", 3))
+    additional_arguments = additional_arguments or {}
+    folds = int(additional_arguments.get("folds", 3))
     nlu_config = rasa.nlu.config.load(config)
     data = rasa.nlu.training_data.load_data(nlu)
     data = drop_intents_below_freq(data, cutoff=folds)
-    kwargs = utils.minimal_kwargs(kwargs, cross_validate)
+    kwargs = utils.minimal_kwargs(additional_arguments, cross_validate)
     results, entity_results, response_selection_results = cross_validate(
         data, folds, nlu_config, output, **kwargs
     )

rasa/train.py

@@ -28,7 +28,7 @@ def train(
     force_training: bool = False,
     fixed_model_name: Optional[Text] = None,
     persist_nlu_training_data: bool = False,
-    kwargs: Optional[Dict] = None,
+    additional_arguments: Optional[Dict] = None,
     loop: Optional[asyncio.AbstractEventLoop] = None,
 ) -> Optional[Text]:
     if loop is None:
@@ -43,7 +43,7 @@ def train(
             force_training=force_training,
             fixed_model_name=fixed_model_name,
             persist_nlu_training_data=persist_nlu_training_data,
-            kwargs=kwargs,
+            additional_arguments=additional_arguments,
         )
     )
 
@@ -56,7 +56,7 @@ async def train_async(
     force_training: bool = False,
     fixed_model_name: Optional[Text] = None,
     persist_nlu_training_data: bool = False,
-    kwargs: Optional[Dict] = None,
+    additional_arguments: Optional[Dict] = None,
 ) -> Optional[Text]:
     """Trains a Rasa model (Core and NLU).
 
@@ -69,7 +69,7 @@ async def train_async(
         fixed_model_name: Name of model to be stored.
         persist_nlu_training_data: `True` if the NLU training data should be persisted
                                    with the model.
-        kwargs: Additional training parameters.
+        additional_arguments: Additional training parameters.
 
     Returns:
         Path of the trained model archive.
@@ -94,7 +94,7 @@ async def train_async(
             force_training,
             fixed_model_name,
             persist_nlu_training_data,
-            kwargs,
+            additional_arguments,
         )
 
 
@@ -118,7 +118,7 @@ async def _train_async_internal(
     force_training: bool,
     fixed_model_name: Optional[Text],
     persist_nlu_training_data: bool,
-    kwargs: Optional[Dict],
+    additional_arguments: Optional[Dict],
 ) -> Optional[Text]:
     """Trains a Rasa model (Core and NLU). Use only from `train_async`.
 
@@ -130,7 +130,7 @@ async def _train_async_internal(
         persist_nlu_training_data: `True` if the NLU training data should be persisted
                                    with the model.
         fixed_model_name: Name of model to be stored.
-        kwargs: Additional training parameters.
+        additional_arguments: Additional training parameters.
 
     Returns:
         Path of the trained model archive.
@@ -162,7 +162,7 @@ async def _train_async_internal(
             file_importer,
             output=output_path,
             fixed_model_name=fixed_model_name,
-            kwargs=kwargs,
+            additional_arguments=additional_arguments,
         )
 
     new_fingerprint = await model.model_fingerprint(file_importer)
@@ -181,7 +181,7 @@ async def _train_async_internal(
             fingerprint_comparison_result=fingerprint_comparison,
             fixed_model_name=fixed_model_name,
             persist_nlu_training_data=persist_nlu_training_data,
-            kwargs=kwargs,
+            additional_arguments=additional_arguments,
         )
 
         return model.package_model(
@@ -205,7 +205,7 @@ async def _do_training(
     fingerprint_comparison_result: Optional[FingerprintComparisonResult] = None,
     fixed_model_name: Optional[Text] = None,
     persist_nlu_training_data: bool = False,
-    kwargs: Optional[Dict] = None,
+    additional_arguments: Optional[Dict] = None,
 ):
     if not fingerprint_comparison_result:
         fingerprint_comparison_result = FingerprintComparisonResult()
@@ -216,7 +216,7 @@ async def _do_training(
             output=output_path,
             train_path=train_path,
             fixed_model_name=fixed_model_name,
-            kwargs=kwargs,
+            additional_arguments=additional_arguments,
         )
     elif fingerprint_comparison_result.should_retrain_nlg():
         print_color(
@@ -254,7 +254,7 @@ def train_core(
     output: Text,
     train_path: Optional[Text] = None,
     fixed_model_name: Optional[Text] = None,
-    kwargs: Optional[Dict] = None,
+    additional_arguments: Optional[Dict] = None,
 ) -> Optional[Text]:
     loop = asyncio.get_event_loop()
     return loop.run_until_complete(
@@ -265,7 +265,7 @@ def train_core(
             output=output,
             train_path=train_path,
             fixed_model_name=fixed_model_name,
-            kwargs=kwargs,
+            additional_arguments=additional_arguments,
         )
     )
 
@@ -277,7 +277,7 @@ async def train_core_async(
     output: Text,
     train_path: Optional[Text] = None,
     fixed_model_name: Optional[Text] = None,
-    kwargs: Optional[Dict] = None,
+    additional_arguments: Optional[Dict] = None,
 ) -> Optional[Text]:
     """Trains a Core model.
 
@@ -290,7 +290,7 @@ async def train_core_async(
             directory, otherwise in the provided directory.
         fixed_model_name: Name of model to be stored.
         uncompress: If `True` the model will not be compressed.
-        kwargs: Additional training parameters.
+        additional_arguments: Additional training parameters.
 
     Returns:
         If `train_path` is given it returns the path to the model archive,
@@ -321,7 +321,7 @@ async def train_core_async(
         output=output,
         train_path=train_path,
         fixed_model_name=fixed_model_name,
-        kwargs=kwargs,
+        additional_arguments=additional_arguments,
     )
 
 
@@ -330,7 +330,7 @@ async def _train_core_with_validated_data(
     output: Text,
     train_path: Optional[Text] = None,
     fixed_model_name: Optional[Text] = None,
-    kwargs: Optional[Dict] = None,
+    additional_arguments: Optional[Dict] = None,
 ) -> Optional[Text]:
     """Train Core with validated training and config data."""
 
@@ -354,7 +354,7 @@ async def _train_core_with_validated_data(
             training_resource=file_importer,
             output_path=os.path.join(_train_path, DEFAULT_CORE_SUBDIRECTORY_NAME),
             policy_config=config,
-            kwargs=kwargs,
+            additional_arguments=additional_arguments,
         )
         print_color("Core model training completed.", color=bcolors.OKBLUE)