Add the infer_cardinality method.

src/sssom/constants.py

@@ -89,6 +89,7 @@
 MAPPING_SET_SOURCE = "mapping_set_source"
 MAPPING_SOURCE = "mapping_source"
 MAPPING_CARDINALITY = "mapping_cardinality"
+CARDINALITY_SCOPE = "cardinality_scope"
 MAPPING_TOOL = "mapping_tool"
 MAPPING_TOOL_VERSION = "mapping_tool_version"
 MAPPING_DATE = "mapping_date"
@@ -109,6 +110,10 @@
 SUBJECT_SOURCE_ID = "subject_source_id"
 OBJECT_SOURCE_ID = "object_source_id"
 
+# Special value for "unmapped" entities
+# see <https://mapping-commons.github.io/sssom/spec-model/#representing-unmapped-entities>
+NO_TERM_FOUND = "sssom:NoTermFound"
+
 # PREDICATES
 OWL_EQUIVALENT_CLASS = "owl:equivalentClass"
 OWL_EQUIVALENT_PROPERTY = "owl:equivalentProperty"

src/sssom/util.py

@@ -23,12 +23,15 @@
 from sssom_schema import MappingSet, slots
 
 from .constants import (
+    CARDINALITY_SCOPE,
     COLUMN_INVERT_DICTIONARY,
     COMMENT,
     CONFIDENCE,
+    MAPPING_CARDINALITY,
     MAPPING_JUSTIFICATION,
     MAPPING_SET_ID,
     MAPPING_SET_SOURCE,
+    NO_TERM_FOUND,
     OBJECT_CATEGORY,
     OBJECT_ID,
     OBJECT_LABEL,
@@ -393,6 +396,97 @@ def condense(self) -> List[str]:
         self.df.drop(columns=condensed, inplace=True)
         return condensed
 
+    def infer_cardinality(self, scope: List[str] = None) -> None:
+        """Infer cardinality values in the set.
+
+        This method will automatically fill the `mapping_cardinality` slot for
+        all records in the set, overwriting any pre-existing values.
+
+        See <https://mapping-commons.github.io/sssom/spec-model/#mapping-cardinality-and-cardinality-scope>
+        for more information about cardinality computation,
+        <https://mapping-commons.github.io/sssom/spec-model/#literal-mappings>
+        for how to deal with literal mapping records, and
+        <https://mapping-commons.github.io/sssom/spec-model/#representing-unmapped-entities>
+        for how to deal with mapping records involving `sssom:NoTermFound`.
+
+        :param scope: A list of slot names that defines the subset of the
+                      records in which cardinality will be computed. For
+                      example, with a scope of `['predicate_id']`, for any
+                      given record the cardinality will be computed relatively
+                      to the subset of records that have the same predicate.
+                      The default is an empty list, meaning that cardinality is
+                      computed relatively to the entire set of records.
+        """
+        if scope is None:
+            scope = []
+        subjects_by_object: dict[str, set[str]] = {}  # Unique subjects for any given object
+        objects_by_subject: dict[str, set[str]] = {}  # Unique objects for any given subject
+
+        # Helper function to transform a row into a string that represents
+        # a subject (or object) in a given scope; `side` is either `subject`
+        # or `object`.
+        def _to_string(row, side):
+            # We prepend a one-letter code (`L` or `E`) to the actual subject
+            # or object so that literal and non-literal mapping records are
+            # always distinguishable and can be counted separately.
+            if row.get(f"{side}_type") == "rdfs literal":
+                s = "L\0" + row.get(f"{side}_label", "")
+            else:
+                s = "E\0" + row.get(f"{side}_id", "")
+            for slot in scope:
+                s += "\0" + row.get(slot, "")
+            return s
+
+        # We iterate over the records a first time to collect the different
+        # objects mapped to each subject and vice versa
+        for _, row in self.df.iterrows():
+            if row.get(SUBJECT_ID) == NO_TERM_FOUND or row.get(OBJECT_ID) == NO_TERM_FOUND:
+                # Mappings to sssom:NoTermFound are ignored for cardinality computations
+                continue
+
+            subj = _to_string(row, "subject")
+            obj = _to_string(row, "object")
+
+            subjects_by_object.setdefault(obj, set()).add(subj)
+            objects_by_subject.setdefault(subj, set()).add(obj)
+
+        # Second iteration to compute the actual cardinality values. Since we
+        # must not modify a row while we are iterating over the dataframe, we
+        # collect the values in a separate array.
+        cards = []
+        for _, row in self.df.iterrows():
+            # Special cases involving sssom:NoTermFound on either side
+            if row.get(SUBJECT_ID) == NO_TERM_FOUND:
+                if row.get(OBJECT_ID) == NO_TERM_FOUND:
+                    cards.append("0:0")
+                else:
+                    cards.append("0:1")
+            elif row.get(OBJECT_ID) == NO_TERM_FOUND:
+                cards.append("1:0")
+            else:
+                # General case
+                n_subjects = len(subjects_by_object[_to_string(row, "object")])
+                n_objects = len(objects_by_subject[_to_string(row, "subject")])
+
+                if n_subjects == 1:
+                    if n_objects == 1:
+                        cards.append("1:1")
+                    else:
+                        cards.append("1:n")
+                else:
+                    if n_objects == 1:
+                        cards.append("n:1")
+                    else:
+                        cards.append("n:n")
+
+        # Add the computed values to the dataframe
+        self.df[MAPPING_CARDINALITY] = cards
+        if len(scope) > 0:
+            self.df[CARDINALITY_SCOPE] = "|".join(scope)
+        else:
+            # No scope, so remove any pre-existing "cardinality_scope" column
+            self.df.drop(columns=CARDINALITY_SCOPE, inplace=True, errors="ignore")
+
 
 def _standardize_curie_or_iri(curie_or_iri: str, *, converter: Converter) -> str:
     """Standardize a CURIE or IRI, returning the original if not possible.

tests/data/cardinality-scope.sssom.tsv

@@ -0,0 +1,14 @@
+#curie_map:
+#  COMENT: https://example.com/entities/
+#  NETENT: https://example.net/entities/
+#  ORGENT: https://example.org/entities/
+#  SRC: https://example.org/sources/
+#mapping_set_id: https://example.org/sets/cardinality-scope-empty
+#license: https://creativecommons.org/licenses/by/4.0/
+subject_id	subject_label	predicate_id	object_id	object_label	mapping_justification	object_source	mapping_cardinality
+ORGENT:0001	alice	skos:closeMatch	COMENT:0011	alpha	semapv:ManualMappingCuration	SRC:com	1:n
+ORGENT:0001	alice	skos:closeMatch	NETENT:0111	alpha	semapv:ManualMappingCuration	SRC:net	1:n
+ORGENT:0002	bob	skos:closeMatch	COMENT:0012	beta	semapv:ManualMappingCuration	SRC:com	1:n
+ORGENT:0002	bob	skos:closeMatch	NETENT:0112	bravo	semapv:ManualMappingCuration	SRC:net	1:n
+ORGENT:0007	gavin	skos:closeMatch	NETENT:0117	golf	semapv:ManualMappingCuration	SRC:net	1:n
+ORGENT:0007	gavin	skos:exactMatch	COMENT:0013	gamma	semapv:ManualMappingCuration	SRC:com	1:n

tests/data/cardinality-with-NoTermFound.sssom.tsv

@@ -0,0 +1,11 @@
+#curie_map:
+#  OBJ: https://example.org/object/
+#  SRC: https://example.org/sources/
+#  SUBJ: https://example.org/subject/
+#mapping_set_id: https://example.org/sets/cardinality-with-unmapped-entities
+#license: https://creativecommons.org/licenses/by/4.0/
+subject_id	predicate_id	object_id	mapping_justification	subject_source	object_source	mapping_cardinality	comment
+SUBJ:0001	skos:exactMatch	sssom:NoTermFound	semapv:ManualMappingCuration	SRC:A	SRC:B	1:0	S1 in vocabulary A has no exact match in vocabulary B
+SUBJ:0001	skos:closeMatch	OBJ:0001	semapv:ManualMappingCuration	SRC:A	SRC:B	1:1	S1 mapped only to O1, O1 mapped only to S1 -- the record involving sssom:NoTermFound does not count, as it is an absence of match rather than an actual mapping
+sssom:NoTermFound	skos:exactMatch	OBJ:0002	semapv:ManualMappingCuration	SRC:C	SRC:D	0:1	O2 in vocabulary D has no exact match in vocabulary C
+sssom:NoTermFound	skos:exactMatch	sssom:NoTermFound	semapv:ManualMappingCuration	SRC:E	SRC:F	0:0	No exact match between any term from vocabulary E and any term for vocabulary F (in other words, the two vocabularies are completely disjoint, at least as far as exact matches are considered)

tests/data/cardinality-with-literal-mappings.sssom.tsv

@@ -0,0 +1,8 @@
+#curie_map:
+#  OBJ: https://example.org/object/
+#  SUBJ: https://example.org/subject/
+#mapping_set_id: https://example.org/sets/cardinality-with-literal-mappings
+#license: https://creativecommons.org/licenses/by/4.0/
+subject_id	predicate_id	object_id	object_label	mapping_justification	object_type	mapping_cardinality	comment
+SUBJ:0001	skos:exactMatch	OBJ:0001		semapv:LexicalMatching		1:n	S1 mapped to O1 (entity) and O1 (literal)
+SUBJ:0001	skos:exactMatch	OBJ:0001	OBJ:0001	semapv:MappingReview	rdfs literal	1:n	S1 mapped to O1 (entity) and O1 (literal)

tests/data/cardinality.sssom.tsv

@@ -0,0 +1,15 @@
+#curie_map:
+#  OBJ: https://example.org/object/
+#  SUBJ: https://example.org/subject/
+#mapping_set_id: https://example.org/sets/cardinality
+#license: https://creativecommons.org/licenses/by/4.0/
+subject_id	predicate_id	object_id	mapping_justification	mapping_cardinality	comment
+SUBJ:0001	skos:exactMatch	OBJ:0001	semapv:LexicalMatching	1:1	S1 and O1 only mapped to each other
+SUBJ:0001	skos:exactMatch	OBJ:0001	semapv:MappingReview	1:1	S1 and O1 only mapped to each other
+SUBJ:0002	skos:exactMatch	OBJ:0002	semapv:LexicalMatching	1:n	S2 mapped to both O2 and O3, O2 mapped only to S2
+SUBJ:0002	skos:exactMatch	OBJ:0003	semapv:LexicalMatching	1:n	S2 mapped to both O2 and O3, O3 mapped only to S2
+SUBJ:0003	skos:exactMatch	OBJ:0004	semapv:LexicalMatching	n:1	S3 and S4 both mapped to only O4
+SUBJ:0004	skos:exactMatch	OBJ:0004	semapv:LexicalMatching	n:1	S3 and S4 both mapped to only O4
+SUBJ:0005	skos:exactMatch	OBJ:0005	semapv:LexicalMatching	n:n	S5 mapped to O5 and O6, O5 mapped to S5 and S6
+SUBJ:0005	skos:exactMatch	OBJ:0006	semapv:LexicalMatching	1:n	S5 mapped to O5 and O6, O6 mapped only to S5
+SUBJ:0006	skos:exactMatch	OBJ:0005	semapv:LexicalMatching	n:1	S6 mapped only to O5, O5 mapped to both S5 and S6

tests/test_utils.py

@@ -13,6 +13,7 @@
 
 from sssom.constants import (
     CREATOR_ID,
+    MAPPING_CARDINALITY,
     OBJECT_ID,
     OBJECT_LABEL,
     PREDICATE_ID,
@@ -595,3 +596,30 @@ def test_propagation_fill_empty_mode(self) -> None:
         self.assertIn("mapping_tool", propagated_slots)
         self.assertNotIn("mapping_tool", msdf.metadata)
         self.assertEqual(2, len(msdf.df["mapping_tool"].unique()))
+
+    def test_infer_cardinality(self) -> None:
+        """Test cardinality computation."""
+
+        def _check_against_precomputed_values(filename):
+            msdf = parse_sssom_table(f"{data_dir}/{filename}")
+            # Expected values are already contained in the test file
+            expected = list(msdf.df[MAPPING_CARDINALITY].values)
+            msdf.df.drop(columns=MAPPING_CARDINALITY, inplace=True)
+            msdf.infer_cardinality()
+            self.assertEqual(expected, list(msdf.df[MAPPING_CARDINALITY].values))
+
+        _check_against_precomputed_values("cardinality.sssom.tsv")
+        _check_against_precomputed_values("cardinality-with-NoTermFound.sssom.tsv")
+        _check_against_precomputed_values("cardinality-with-literal-mappings.sssom.tsv")
+
+    def test_infer_scoped_cardinality(self) -> None:
+        """Test cardinality computation with scopes."""
+        msdf = parse_sssom_table(f"{data_dir}/cardinality-scope.sssom.tsv")
+
+        msdf.infer_cardinality(["predicate_id"])
+        expected = ["1:n", "1:n", "1:n", "1:n", "1:1", "1:1"]
+        self.assertEqual(expected, list(msdf.df[MAPPING_CARDINALITY].values))
+
+        msdf.infer_cardinality(["object_source"])
+        expected = ["1:1", "1:1", "1:1", "1:1", "1:1", "1:1"]
+        self.assertEqual(expected, list(msdf.df[MAPPING_CARDINALITY].values))