Rename EntityExtractorMixin

changelog/10225.improvement.md

@@ -0,0 +1 @@
+Rename `EntityExtractorMixin` to `EntityExtractor` due to consistency, as it was discussed in issue 10225.

rasa/core/policies/ted_policy.py

@@ -15,7 +15,7 @@
 from rasa.engine.storage.storage import ModelStorage
 from rasa.exceptions import ModelNotFound
 from rasa.nlu.constants import TOKENS_NAMES
-from rasa.nlu.extractors.extractor import EntityTagSpec, EntityExtractorMixin
+from rasa.nlu.extractors.extractor import EntityTagSpec, EntityExtractor
 import rasa.core.actions.action
 from rasa.core.featurizers.precomputation import MessageContainerForCoreFeaturization
 from rasa.core.featurizers.tracker_featurizers import TrackerFeaturizer
@@ -900,7 +900,7 @@ def _create_optional_event_for_entities(
         else:
             parsed_message = Message(data={TEXT: text})
         tokens = parsed_message.get(TOKENS_NAMES[TEXT])
-        entities = EntityExtractorMixin.convert_predictions_into_entities(
+        entities = EntityExtractor.convert_predictions_into_entities(
             text,
             tokens,
             predicted_tags,

rasa/nlu/classifiers/diet_classifier.py

@@ -17,7 +17,7 @@
 from rasa.engine.recipes.default_recipe import DefaultV1Recipe
 from rasa.engine.storage.resource import Resource
 from rasa.engine.storage.storage import ModelStorage
-from rasa.nlu.extractors.extractor import EntityExtractorMixin
+from rasa.nlu.extractors.extractor import EntityExtractor
 from rasa.nlu.classifiers.classifier import IntentClassifier
 import rasa.shared.utils.io
 import rasa.utils.io as io_utils
@@ -127,7 +127,7 @@
     ],
     is_trainable=True,
 )
-class DIETClassifier(GraphComponent, IntentClassifier, EntityExtractorMixin):
+class DIETClassifier(GraphComponent, IntentClassifier, EntityExtractor):
     """A multi-task model for intent classification and entity extraction.
 
     DIET is Dual Intent and Entity Transformer.

rasa/nlu/classifiers/regex_message_handler.py

@@ -6,7 +6,7 @@
 from rasa.engine.recipes.default_recipe import DefaultV1Recipe
 from rasa.engine.storage.resource import Resource
 from rasa.engine.storage.storage import ModelStorage
-from rasa.nlu.extractors.extractor import EntityExtractorMixin
+from rasa.nlu.extractors.extractor import EntityExtractor
 from rasa.shared.core.domain import Domain
 from rasa.shared.core.training_data.story_reader.yaml_story_reader import (
     YAMLStoryReader,
@@ -19,7 +19,7 @@
 @DefaultV1Recipe.register(
     DefaultV1Recipe.ComponentType.INTENT_CLASSIFIER, is_trainable=False
 )
-class RegexMessageHandler(GraphComponent, EntityExtractorMixin):
+class RegexMessageHandler(GraphComponent, EntityExtractor):
     """Handles hardcoded NLU predictions from messages starting with a `/`."""
 
     @classmethod

rasa/nlu/extractors/crf_entity_extractor.py

@@ -17,7 +17,7 @@
 from rasa.engine.storage.storage import ModelStorage
 from rasa.nlu.test import determine_token_labels
 from rasa.nlu.tokenizers.spacy_tokenizer import POS_TAG_KEY
-from rasa.nlu.extractors.extractor import EntityExtractorMixin
+from rasa.nlu.extractors.extractor import EntityExtractor
 from rasa.nlu.tokenizers.tokenizer import Token, Tokenizer
 from rasa.shared.nlu.training_data.training_data import TrainingData
 from rasa.shared.nlu.training_data.message import Message
@@ -85,7 +85,7 @@ class CRFEntityExtractorOptions(str, Enum):
 @DefaultV1Recipe.register(
     DefaultV1Recipe.ComponentType.ENTITY_EXTRACTOR, is_trainable=True
 )
-class CRFEntityExtractor(GraphComponent, EntityExtractorMixin):
+class CRFEntityExtractor(GraphComponent, EntityExtractor):
     """Implements conditional random fields (CRF) to do named entity recognition."""
 
     CONFIG_FEATURES = "features"

rasa/nlu/extractors/duckling_entity_extractor.py

@@ -13,7 +13,7 @@
 from rasa.engine.storage.storage import ModelStorage
 from rasa.shared.constants import DOCS_URL_COMPONENTS
 from rasa.shared.nlu.constants import ENTITIES, TEXT
-from rasa.nlu.extractors.extractor import EntityExtractorMixin
+from rasa.nlu.extractors.extractor import EntityExtractor
 from rasa.shared.nlu.training_data.message import Message
 import rasa.shared.utils.io
 
@@ -58,7 +58,7 @@ def convert_duckling_format_to_rasa(
 @DefaultV1Recipe.register(
     DefaultV1Recipe.ComponentType.ENTITY_EXTRACTOR, is_trainable=False
 )
-class DucklingEntityExtractor(GraphComponent, EntityExtractorMixin):
+class DucklingEntityExtractor(GraphComponent, EntityExtractor):
     """Searches for structured entities, e.g. dates, using a duckling server."""
 
     @staticmethod

rasa/nlu/extractors/entity_synonyms.py

@@ -10,7 +10,7 @@
 from rasa.shared.nlu.training_data.training_data import TrainingData
 from rasa.shared.nlu.training_data.message import Message
 from rasa.nlu.utils import write_json_to_file
-from rasa.nlu.extractors.extractor import EntityExtractorMixin
+from rasa.nlu.extractors.extractor import EntityExtractor
 import rasa.utils.io
 import rasa.shared.utils.io
 from rasa.engine.storage.resource import Resource
@@ -22,7 +22,7 @@
 @DefaultV1Recipe.register(
     DefaultV1Recipe.ComponentType.ENTITY_EXTRACTOR, is_trainable=True
 )
-class EntitySynonymMapper(GraphComponent, EntityExtractorMixin):
+class EntitySynonymMapper(GraphComponent, EntityExtractor):
     """Maps entities to their synonyms if they appear in the training data."""
 
     SYNONYM_FILENAME = "synonyms.json"

rasa/nlu/extractors/extractor.py

@@ -39,7 +39,7 @@ class EntityTagSpec(NamedTuple):
     num_tags: int
 
 
-class EntityExtractorMixin(abc.ABC):
+class EntityExtractor(abc.ABC):
     """Provides functionality for components that do entity extraction.
 
     Inheriting from this class will add utility functions for entity extraction.
@@ -182,7 +182,7 @@ def convert_predictions_into_entities(
         last_token_end = -1
 
         for idx, token in enumerate(tokens):
-            current_entity_tag = EntityExtractorMixin.get_tag_for(
+            current_entity_tag = EntityExtractor.get_tag_for(
                 tags, ENTITY_ATTRIBUTE_TYPE, idx
             )
 
@@ -191,11 +191,11 @@ def convert_predictions_into_entities(
                 last_token_end = token.end
                 continue
 
-            current_group_tag = EntityExtractorMixin.get_tag_for(
+            current_group_tag = EntityExtractor.get_tag_for(
                 tags, ENTITY_ATTRIBUTE_GROUP, idx
             )
             current_group_tag = bilou_utils.tag_without_prefix(current_group_tag)
-            current_role_tag = EntityExtractorMixin.get_tag_for(
+            current_role_tag = EntityExtractor.get_tag_for(
                 tags, ENTITY_ATTRIBUTE_ROLE, idx
             )
             current_role_tag = bilou_utils.tag_without_prefix(current_role_tag)
@@ -237,7 +237,7 @@ def convert_predictions_into_entities(
 
             if new_tag_found:
                 # new entity found
-                entity = EntityExtractorMixin._create_new_entity(
+                entity = EntityExtractor._create_new_entity(
                     list(tags.keys()),
                     current_entity_tag,
                     current_group_tag,
@@ -247,7 +247,7 @@ def convert_predictions_into_entities(
                     confidences,
                 )
                 entities.append(entity)
-            elif EntityExtractorMixin._check_is_single_entity(
+            elif EntityExtractor._check_is_single_entity(
                 text, token, last_token_end, split_entities_config, current_entity_tag
             ):
                 # current token has the same entity tag as the token before and
@@ -256,7 +256,7 @@ def convert_predictions_into_entities(
                 # and a whitespace.
                 entities[-1][ENTITY_ATTRIBUTE_END] = token.end
                 if confidences is not None:
-                    EntityExtractorMixin._update_confidence_values(
+                    EntityExtractor._update_confidence_values(
                         entities, confidences, idx
                     )
 
@@ -265,7 +265,7 @@ def convert_predictions_into_entities(
                 # tokens are separated by at least 2 symbols (e.g. multiple spaces,
                 # a comma and a space, etc.) and also shouldn't be represented as a
                 # single entity
-                entity = EntityExtractorMixin._create_new_entity(
+                entity = EntityExtractor._create_new_entity(
                     list(tags.keys()),
                     current_entity_tag,
                     current_group_tag,

rasa/nlu/extractors/mitie_entity_extractor.py

@@ -19,7 +19,7 @@
     ENTITIES,
 )
 from rasa.nlu.utils.mitie_utils import MitieModel, MitieNLP
-from rasa.nlu.extractors.extractor import EntityExtractorMixin
+from rasa.nlu.extractors.extractor import EntityExtractor
 from rasa.shared.nlu.training_data.training_data import TrainingData
 from rasa.shared.nlu.training_data.message import Message
 import rasa.shared.utils.io
@@ -36,7 +36,7 @@
     is_trainable=True,
     model_from="MitieNLP",
 )
-class MitieEntityExtractor(GraphComponent, EntityExtractorMixin):
+class MitieEntityExtractor(GraphComponent, EntityExtractor):
     """A Mitie Entity Extractor (which is a thin wrapper around `Dlib-ml`)."""
 
     MITIE_RESOURCE_FILE = "mitie_ner.dat"

rasa/nlu/extractors/regex_entity_extractor.py

@@ -19,15 +19,15 @@
     TEXT,
     ENTITY_ATTRIBUTE_TYPE,
 )
-from rasa.nlu.extractors.extractor import EntityExtractorMixin
+from rasa.nlu.extractors.extractor import EntityExtractor
 
 logger = logging.getLogger(__name__)
 
 
 @DefaultV1Recipe.register(
     DefaultV1Recipe.ComponentType.ENTITY_EXTRACTOR, is_trainable=True
 )
-class RegexEntityExtractor(GraphComponent, EntityExtractorMixin):
+class RegexEntityExtractor(GraphComponent, EntityExtractor):
     """Extracts entities via lookup tables and regexes defined in the training data."""
 
     REGEX_FILE_NAME = "regex.json"

rasa/nlu/extractors/spacy_entity_extractor.py

@@ -7,7 +7,7 @@
 from rasa.engine.storage.storage import ModelStorage
 from rasa.shared.nlu.constants import ENTITIES, TEXT
 from rasa.nlu.utils.spacy_utils import SpacyModel, SpacyNLP
-from rasa.nlu.extractors.extractor import EntityExtractorMixin
+from rasa.nlu.extractors.extractor import EntityExtractor
 from rasa.shared.nlu.training_data.message import Message
 
 if typing.TYPE_CHECKING:
@@ -19,7 +19,7 @@
     is_trainable=False,
     model_from="SpacyNLP",
 )
-class SpacyEntityExtractor(GraphComponent, EntityExtractorMixin):
+class SpacyEntityExtractor(GraphComponent, EntityExtractor):
     """Entity extractor which uses SpaCy."""
 
     @classmethod

tests/nlu/extractors/test_extractor.py

@@ -5,7 +5,7 @@
 
 from rasa.shared.nlu.constants import TEXT, SPLIT_ENTITIES_BY_COMMA
 from rasa.shared.nlu.training_data.message import Message
-from rasa.nlu.extractors.extractor import EntityExtractorMixin
+from rasa.nlu.extractors.extractor import EntityExtractor
 from rasa.nlu.tokenizers.whitespace_tokenizer import WhitespaceTokenizer
 from rasa.shared.nlu.training_data.formats.rasa_yaml import RasaYAMLReader
 
@@ -224,7 +224,7 @@ def test_convert_tags_to_entities(
     expected_entities: List[Dict[Text, Any]],
     whitespace_tokenizer: WhitespaceTokenizer,
 ):
-    extractor = EntityExtractorMixin()
+    extractor = EntityExtractor()
 
     message = Message(data={TEXT: text})
     tokens = whitespace_tokenizer.tokenize(message, TEXT)
@@ -399,7 +399,7 @@ def test_split_entities_by_comma(
     expected_entities: List[Dict[Text, Any]],
     whitespace_tokenizer: WhitespaceTokenizer,
 ):
-    extractor = EntityExtractorMixin()
+    extractor = EntityExtractor()
 
     message = Message(data={TEXT: text})
     tokens = whitespace_tokenizer.tokenize(message, TEXT)
@@ -473,7 +473,7 @@ def test_check_correct_entity_annotations(
     whitespace_tokenizer.process_training_data(training_data)
 
     with pytest.warns(UserWarning) as record:
-        EntityExtractorMixin.check_correct_entity_annotations(training_data)
+        EntityExtractor.check_correct_entity_annotations(training_data)
 
     assert len(record) == warnings
     assert all(