add tests and allow schemad data

splink/internals/testing.py

@@ -1,6 +1,8 @@
 from __future__ import annotations
 
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Union
+
+import pyarrow as pa
 
 from splink.internals.comparison_creator import ComparisonCreator
 from splink.internals.comparison_level_creator import ComparisonLevelCreator
@@ -12,7 +14,7 @@
 
 def is_in_level(
     comparison_level: ComparisonLevelCreator,
-    literal_values: Dict[str, Any] | List[Dict[str, Any]],
+    literal_values: Union[Dict[str, Any], List[Dict[str, Any]], pa.Table],
     db_api: DatabaseAPISubClass,
 ) -> bool | List[bool]:
     sqlglot_dialect = db_api.sql_dialect.sqlglot_name
@@ -21,8 +23,11 @@ def is_in_level(
         sql_cond = "TRUE"
 
     table_name = f"__splink__temp_table_{ascii_uid(8)}"
-    literal_values_list = ensure_is_list(literal_values)
-    db_api._table_registration(literal_values_list, table_name)
+    if isinstance(literal_values, pa.Table):
+        db_api._table_registration(literal_values, table_name)
+    else:
+        literal_values_list = ensure_is_list(literal_values)
+        db_api._table_registration(literal_values_list, table_name)
 
     sql_to_evaluate = f"SELECT {sql_cond} as result FROM {table_name}"
 
@@ -38,7 +43,7 @@ def is_in_level(
 
 def comparison_vector_value(
     comparison: ComparisonCreator,
-    literal_values: Dict[str, Any] | List[Dict[str, Any]],
+    literal_values: Union[Dict[str, Any], List[Dict[str, Any]], pa.Table],
     db_api: DatabaseAPISubClass,
 ) -> Dict[str, Any] | List[Dict[str, Any]]:
     sqlglot_dialect = db_api.sql_dialect.sqlglot_name
@@ -58,8 +63,11 @@ def comparison_vector_value(
     case_statement = comparison_internal._case_statement
 
     table_name = f"__splink__temp_table_{ascii_uid(8)}"
-    literal_values_list = ensure_is_list(literal_values)
-    db_api._table_registration(literal_values_list, table_name)
+    if isinstance(literal_values, pa.Table):
+        db_api._table_registration(literal_values, table_name)
+    else:
+        literal_values_list = ensure_is_list(literal_values)
+        db_api._table_registration(literal_values_list, table_name)
 
     sql_to_evaluate = f"SELECT {case_statement}  FROM {table_name}"
 
@@ -87,4 +95,4 @@ def comparison_vector_value(
         for row in result_dicts
     ]
 
-    return output if isinstance(literal_values, list) else output[0]
+    return output if isinstance(literal_values, (list, pa.Table)) else output[0]

tests/literal_utils.py

@@ -1,5 +1,7 @@
 from typing import Any, Dict, List
 
+import pyarrow as pa
+
 from splink import DuckDBAPI
 from splink.internals.testing import comparison_vector_value, is_in_level
 
@@ -8,13 +10,16 @@
 
 def run_is_in_level_tests(test_cases: List[Dict[str, Any]], db_api: Any) -> None:
     for case in test_cases:
-        inputs = []
-        expected = []
-
-        for input_data in case["inputs"]:
-            input_dict = {k: v for k, v in input_data.items() if k != "expected"}
-            inputs.append(input_dict)
-            expected.append(input_data["expected"])
+        if isinstance(case["inputs"], pa.Table):
+            inputs = case["inputs"]
+            expected = inputs["expected"].to_pylist()
+        else:
+            inputs = []
+            expected = []
+            for input_data in case["inputs"]:
+                input_dict = {k: v for k, v in input_data.items() if k != "expected"}
+                inputs.append(input_dict)
+                expected.append(input_data["expected"])
 
         results = is_in_level(case["level"], inputs, db_api)
         assert (
@@ -26,19 +31,23 @@ def run_comparison_vector_value_tests(
     test_cases: List[Dict[str, Any]], db_api: Any
 ) -> None:
     for case in test_cases:
-        inputs = []
-        expected_values = []
-        expected_labels = []
-
-        for input_data in case["inputs"]:
-            input_dict = {
-                k: v
-                for k, v in input_data.items()
-                if k not in ["expected_value", "expected_label"]
-            }
-            inputs.append(input_dict)
-            expected_values.append(input_data["expected_value"])
-            expected_labels.append(input_data["expected_label"])
+        if isinstance(case["inputs"], pa.Table):
+            inputs = case["inputs"]
+            expected_values = inputs["expected_value"].to_pylist()
+            expected_labels = inputs["expected_label"].to_pylist()
+        else:
+            inputs = []
+            expected_values = []
+            expected_labels = []
+            for input_data in case["inputs"]:
+                input_dict = {
+                    k: v
+                    for k, v in input_data.items()
+                    if k not in ["expected_value", "expected_label"]
+                }
+                inputs.append(input_dict)
+                expected_values.append(input_data["expected_value"])
+                expected_labels.append(input_data["expected_label"])
 
         results = comparison_vector_value(case["comparison"], inputs, db_api)
 

tests/test_comparison_level_lib.py

@@ -363,3 +363,97 @@ def test_absolute_difference(test_helpers, dialect):
     ]
 
     run_comparison_vector_value_tests(test_cases, db_api)
+
+
+@mark_with_dialects_excluding()
+def test_cosine_similarity_level(test_helpers, dialect):
+    import pyarrow as pa
+
+    helper = test_helpers[dialect]
+    db_api = helper.extra_linker_args()["db_api"]
+
+    EMBEDDING_DIMENSION = 4
+
+    cosine_similarity_comparison_using_levels = cl.CustomComparison(
+        comparison_description="text_vector",
+        comparison_levels=[
+            cll.NullLevel("text_vector"),
+            cll.CosineSimilarityLevel("text_vector", 0.9),  # 3
+            cll.CosineSimilarityLevel("text_vector", 0.7),  # 2
+            cll.CosineSimilarityLevel("text_vector", 0.5),  # 1
+            cll.ElseLevel(),
+        ],
+    )
+
+    input_dicts = [
+        {
+            "text_vector_l": [0.5205, 0.4616, 0.3333, 0.2087],
+            "text_vector_r": [0.4137, 0.5439, 0.0737, 0.2041],
+            "expected_value": 3,
+            "expected_label": "Cosine similarity of text_vector >= 0.9",
+        },  # Cosine similarity: 0.9312
+        {
+            "text_vector_l": [0.7026, 0.8887, 0.1711, 0.0525],
+            "text_vector_r": [0.4549, 0.4891, 0.1555, 0.6263],
+            "expected_value": 2,
+            "expected_label": "Cosine similarity of text_vector >= 0.7",
+        },  # Cosine similarity: 0.7639
+        {
+            "text_vector_l": [0.8713, 0.3416, 0.4024, 0.1350],
+            "text_vector_r": [0.2104, 0.5763, 0.0442, 0.0872],
+            "expected_value": 1,
+            "expected_label": "Cosine similarity of text_vector >= 0.5",
+        },  # Cosine similarity: 0.6418
+        {
+            "text_vector_l": [0.99, 0.00, 0.99, 0.00],
+            "text_vector_r": [0.00, 0.99, 0.00, 0.99],
+            "expected_value": 0,
+            "expected_label": "All other comparisons",
+        },
+        {
+            "text_vector_l": None,
+            "text_vector_r": [0.99, 0.99, 0.99, 0.99],
+            "expected_value": -1,
+            "expected_label": "text_vector is NULL",
+        },
+    ]
+
+    # Convert input_dicts to a pyarrow Table
+    inputs_pa = pa.Table.from_pydict(
+        {
+            "text_vector_l": [d["text_vector_l"] for d in input_dicts],
+            "text_vector_r": [d["text_vector_r"] for d in input_dicts],
+            "expected_value": [d["expected_value"] for d in input_dicts],
+            "expected_label": [d["expected_label"] for d in input_dicts],
+        },
+        schema=pa.schema(
+            [
+                ("text_vector_l", pa.list_(pa.float32(), EMBEDDING_DIMENSION)),
+                ("text_vector_r", pa.list_(pa.float32(), EMBEDDING_DIMENSION)),
+                ("expected_value", pa.int16()),
+                ("expected_label", pa.string()),
+            ]
+        ),
+    )
+
+    test_cases = [
+        {
+            "comparison": cosine_similarity_comparison_using_levels,
+            "inputs": inputs_pa,
+        },
+    ]
+
+    run_comparison_vector_value_tests(test_cases, db_api)
+
+    cosine_similarity_comparison = cl.CosineSimilarityAtThresholds(
+        "text_vector", [0.9, 0.7, 0.5]
+    )
+
+    test_cases = [
+        {
+            "comparison": cosine_similarity_comparison,
+            "inputs": inputs_pa,
+        },
+    ]
+
+    run_comparison_vector_value_tests(test_cases, db_api)