extract_pandas_traintime_categories: return [] if pandas_categorical …

…is null in model file (#14)

* extract_pandas_traintime_categories: return empty list if pandas_categorical is null in model file

* Test: Prediction for df with empty categoricals

Co-authored-by: chenglin <chenglin.wang@amh-group.com>
Co-authored-by: Simon Boehm <simon@siboehm.com>

lleaves/data_processing.py

@@ -145,7 +145,10 @@ def extract_pandas_traintime_categories(file_path):
         if not last_line.startswith(pandas_key):
             last_line = lines[-2].decode().strip()
         if last_line.startswith(pandas_key):
-            return json.loads(last_line[len(pandas_key) :])
+            pandas_categorical = json.loads(last_line[len(pandas_key) :])
+            if pandas_categorical is None:
+                pandas_categorical = []
+            return pandas_categorical
     raise ValueError("Ill formatted model file!")
 
 

tests/test_dataprocessing.py

@@ -28,7 +28,7 @@ def test_parsing_pandas(tmp_path):
         file.writelines(lines)
 
     pandas_categorical = extract_pandas_traintime_categories(model_file)
-    assert pandas_categorical is None
+    assert pandas_categorical == []
     pandas_categorical = extract_pandas_traintime_categories(mod_model_file)
     assert pandas_categorical == [
         ["a", "b", "c"],
@@ -106,3 +106,16 @@ def test_sliced_arrays():
         llvm_model.predict(sliced, n_jobs=4), lgbm_model.predict(sliced), decimal=13
     )
     return
+
+
+def test_pd_empty_categories():
+    # this model has `pandas_categorical:null`
+    llvm_model = Model(model_file="tests/models/tiniest_single_tree/model.txt")
+    llvm_model.compile()
+    lgbm_model = Booster(model_file="tests/models/tiniest_single_tree/model.txt")
+    df = pd.DataFrame(
+        {str(i): list(range(10)) for i in range(llvm_model.num_feature())}
+    )
+    np.testing.assert_almost_equal(
+        llvm_model.predict(df), lgbm_model.predict(df), decimal=13
+    )