Bugfix in SpacyRecognizer (#1221)

presidio-analyzer/presidio_analyzer/predefined_recognizers/spacy_recognizer.py

@@ -103,7 +103,8 @@ def analyze(self, text: str, entities, nlp_artifacts=None):  # noqa D102
         ner_scores = nlp_artifacts.scores
 
         for ner_entity, ner_score in zip(ner_entities, ner_scores):
-            if ner_entity.label_ not in entities:
+            if (ner_entity.label_ not in entities
+                    or ner_entity.label_ not in self.supported_entities):
                 logger.debug(
                     f"Skipping entity {ner_entity.label_} "
                     f"as it is not in the supported entities list"

presidio-analyzer/presidio_analyzer/recognizer_registry.py

@@ -318,3 +318,31 @@ def __instantiate_recognizer(
         if isinstance(inst, PatternRecognizer):
             inst.global_regex_flags = self.global_regex_flags
         return inst
+
+    def _get_supported_languages(self) -> List[str]:
+        languages = []
+        for rec in self.recognizers:
+            languages.append(rec.supported_language)
+
+        return list(set(languages))
+
+    def get_supported_entities(
+        self, languages: Optional[List[str]] = None
+    ) -> List[str]:
+        """
+        Return the supported entities by the set of recognizers loaded.
+
+        :param languages: The languages to get the supported entities for.
+        If languages=None, returns all entities for all languages.
+        """
+        if not languages:
+            languages = self._get_supported_languages()
+
+        supported_entities = []
+        for language in languages:
+            recognizers = self.get_recognizers(language=language, all_fields=True)
+
+            for recognizer in recognizers:
+                supported_entities.extend(recognizer.get_supported_entities())
+
+        return list(set(supported_entities))

presidio-analyzer/tests/test_recognizer_registry.py

@@ -8,7 +8,9 @@
     PatternRecognizer,
     EntityRecognizer,
     Pattern,
+    AnalyzerEngine
 )
+from presidio_analyzer.predefined_recognizers import SpacyRecognizer
 
 
 @pytest.fixture(scope="module")
@@ -213,3 +215,23 @@ def test_predefined_pattern_recognizers_have_the_right_regex_flags():
     for rec in registry.recognizers:
         if isinstance(rec, PatternRecognizer):
             assert rec.global_regex_flags == re.DOTALL
+
+
+def test_recognizer_removed_and_returned_entities_are_correct():
+    registry = RecognizerRegistry()
+    registry.load_predefined_recognizers()
+    registry.remove_recognizer("SpacyRecognizer")
+    sr = SpacyRecognizer(supported_entities=["DATE_TIME", "NRP"])
+    registry.add_recognizer(sr)
+
+    supported_entities = registry.get_supported_entities(languages=["en"])
+
+    assert "DATE_TIME" in supported_entities
+    assert "PERSON" not in supported_entities
+
+    analyzer = AnalyzerEngine(
+        registry=registry,
+        supported_languages='en'
+    )
+
+    analyzer.analyze("My name is David", language="en")

presidio-analyzer/tests/test_spacy_recognizer.py

@@ -1,5 +1,9 @@
+
 import pytest
+from spacy.tokens import Span, Doc
+from spacy.util import get_lang_class
 
+from presidio_analyzer.nlp_engine import NlpArtifacts, SpacyNlpEngine
 from presidio_analyzer.predefined_recognizers import SpacyRecognizer
 from tests import assert_result_within_score_range
 
@@ -14,6 +18,24 @@ def nlp_recognizer(nlp_recognizers):
     return nlp_recognizers["spacy"]
 
 
+@pytest.fixture(scope="module")
+def mock_nlp_artifacts():
+    en_vocab=get_lang_class("en")().vocab
+    doc = Doc(en_vocab, words=["My", "name", "is", "Mitchell"])
+    doc.ents = [Span(doc, 2, 3, label="PERSON")]
+
+    nlp_artifacts = NlpArtifacts(
+            entities=doc.ents,
+            tokens=doc,
+            tokens_indices=[token.idx for token in doc],
+            lemmas=[token.lemma_ for token in doc],
+            nlp_engine=None,
+            language="en",
+            scores=[0.9 for _ in doc.ents],
+        )
+    return nlp_artifacts
+
+
 def prepare_and_analyze(nlp, recognizer, text, ents):
     nlp_artifacts = nlp.process_text(text, "en")
     results = recognizer.analyze(text, ents, nlp_artifacts)
@@ -84,3 +106,12 @@ def test_analyze_no_nlp_artifacts():
     spacy_recognizer = SpacyRecognizer()
     res = spacy_recognizer.analyze(text="text", nlp_artifacts=None, entities=["PERSON"])
     assert len(res) == 0
+
+
+def test_entity_not_returned_if_not_in_supported_entities(mock_nlp_artifacts):
+    spacy_recognizer = SpacyRecognizer(supported_entities=["NRP"])
+
+    res = spacy_recognizer.analyze(
+        text="text", nlp_artifacts=mock_nlp_artifacts, entities=["DATE_TIME"]
+    )
+    assert len(res) == 0