test: update retrieval test case

tests/test_retrieve.py

@@ -1,17 +1,48 @@
+import pandas as pd
+
 import cbrkit
 
+query_name = 42
+casebase_file = "data/cars-1k.csv"
+
 
-def test_retrieve():
-    casebase: cbrkit.model.Casebase[dict[str, str]] = cbrkit.load_path(
-        "data/cars-1k.csv"
+def test_retrieve_default():
+    casebase: cbrkit.model.Casebase[dict[str, str]] = cbrkit.load_path(casebase_file)
+    query = casebase[query_name]
+
+    result = cbrkit.retrieve(
+        casebase,
+        query=query,
+        similarity_func="datatypes",
+        casebase_limit=5,
     )
-    query_name = "42"
+
+    assert len(casebase) == 999  # csv contains header
+    assert len(result.ranking) == len(casebase)
+    assert len(result.casebase) == 5
+    assert result.ranking[0] == query_name
+
+
+# TODO: Create some taxonomy similarity measure
+custom_sim_func = cbrkit.case_sim.factories.by_attributes(
+    {
+        "manufacturer": cbrkit.data_sim.strings.levenshtein(),
+        "miles": cbrkit.data_sim.numeric.linear(max=1000000),
+    },
+    aggregate=cbrkit.case_sim.aggregate(),
+)
+
+
+# TODO: Pandas dataframe is indexed by int, but should use strings instead!
+def test_retrieve_custom():
+    df = pd.read_csv(casebase_file)
+    casebase = cbrkit.load_dataframe(df)
     query = casebase[query_name]
 
     result = cbrkit.retrieve(
         casebase,
         query=query,
-        similarity_func="equality",
+        similarity_func=custom_sim_func,
         casebase_limit=5,
     )