[ci] prevent categorical.model and model.txt files being left beh…

…ind after testing (#6590)

tests/c_api_test/test_.py

@@ -175,11 +175,12 @@ def test_dataset():
     free_dataset(train)
 
 
-def test_booster():
+def test_booster(tmp_path):
     binary_example_dir = Path(__file__).absolute().parents[2] / "examples" / "binary_classification"
     train = load_from_mat(binary_example_dir / "binary.train", None)
     test = load_from_mat(binary_example_dir / "binary.test", train)
     booster = ctypes.c_void_p()
+    model_path = tmp_path / "model.txt"
     LIB.LGBM_BoosterCreate(train, c_str("app=binary metric=auc num_leaves=31 verbose=0"), ctypes.byref(booster))
     LIB.LGBM_BoosterAddValidData(booster, test)
     is_finished = ctypes.c_int(0)
@@ -192,13 +193,13 @@ def test_booster():
         )
         if i % 10 == 0:
             print(f"{i} iteration test AUC {result[0]:.6f}")
-    LIB.LGBM_BoosterSaveModel(booster, ctypes.c_int(0), ctypes.c_int(-1), ctypes.c_int(0), c_str("model.txt"))
+    LIB.LGBM_BoosterSaveModel(booster, ctypes.c_int(0), ctypes.c_int(-1), ctypes.c_int(0), c_str(str(model_path)))
     LIB.LGBM_BoosterFree(booster)
     free_dataset(train)
     free_dataset(test)
     booster2 = ctypes.c_void_p()
     num_total_model = ctypes.c_int(0)
-    LIB.LGBM_BoosterCreateFromModelfile(c_str("model.txt"), ctypes.byref(num_total_model), ctypes.byref(booster2))
+    LIB.LGBM_BoosterCreateFromModelfile(c_str(str(model_path)), ctypes.byref(num_total_model), ctypes.byref(booster2))
     data = np.loadtxt(str(binary_example_dir / "binary.test"), dtype=np.float64)
     mat = data[:, 1:]
     preb = np.empty(mat.shape[0], dtype=np.float64)

tests/python_package_test/test_engine.py

@@ -1112,15 +1112,15 @@ def _early_stop_after_seventh_iteration(env):
     assert bst.current_iteration() == 7
 
 
-def test_continue_train():
+def test_continue_train(tmp_path):
     X, y = make_synthetic_regression()
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
     params = {"objective": "regression", "metric": "l1", "verbose": -1}
     lgb_train = lgb.Dataset(X_train, y_train, free_raw_data=False)
     lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, free_raw_data=False)
     init_gbm = lgb.train(params, lgb_train, num_boost_round=20)
-    model_name = "model.txt"
-    init_gbm.save_model(model_name)
+    model_path = tmp_path / "model.txt"
+    init_gbm.save_model(model_path)
     evals_result = {}
     gbm = lgb.train(
         params,
@@ -1130,7 +1130,7 @@ def test_continue_train():
         # test custom eval metrics
         feval=(lambda p, d: ("custom_mae", mean_absolute_error(p, d.get_label()), False)),
         callbacks=[lgb.record_evaluation(evals_result)],
-        init_model="model.txt",
+        init_model=model_path,
     )
     ret = mean_absolute_error(y_test, gbm.predict(X_test))
     assert ret < 13.6
@@ -1713,7 +1713,7 @@ def test_all_expected_params_are_written_out_to_model_text(tmp_path):
 
 # why fixed seed?
 # sometimes there is no difference how cols are treated (cat or not cat)
-def test_pandas_categorical(rng_fixed_seed):
+def test_pandas_categorical(rng_fixed_seed, tmp_path):
     pd = pytest.importorskip("pandas")
     X = pd.DataFrame(
         {
@@ -1756,8 +1756,9 @@ def test_pandas_categorical(rng_fixed_seed):
     gbm3 = lgb.train(params, lgb_train, num_boost_round=10, categorical_feature=["A", "B", "C", "D"])
     pred3 = gbm3.predict(X_test)
     assert lgb_train.categorical_feature == ["A", "B", "C", "D"]
-    gbm3.save_model("categorical.model")
-    gbm4 = lgb.Booster(model_file="categorical.model")
+    categorical_model_path = tmp_path / "categorical.model"
+    gbm3.save_model(categorical_model_path)
+    gbm4 = lgb.Booster(model_file=categorical_model_path)
     pred4 = gbm4.predict(X_test)
     model_str = gbm4.model_to_string()
     gbm4.model_from_string(model_str)

tests/python_package_test/test_sklearn.py

@@ -558,7 +558,7 @@ def test_feature_importances_type():
 
 # why fixed seed?
 # sometimes there is no difference how cols are treated (cat or not cat)
-def test_pandas_categorical(rng_fixed_seed):
+def test_pandas_categorical(rng_fixed_seed, tmp_path):
     pd = pytest.importorskip("pandas")
     X = pd.DataFrame(
         {
@@ -593,8 +593,9 @@ def test_pandas_categorical(rng_fixed_seed):
     pred2 = gbm2.predict(X_test, raw_score=True)
     gbm3 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=["A", "B", "C", "D"])
     pred3 = gbm3.predict(X_test, raw_score=True)
-    gbm3.booster_.save_model("categorical.model")
-    gbm4 = lgb.Booster(model_file="categorical.model")
+    categorical_model_path = tmp_path / "categorical.model"
+    gbm3.booster_.save_model(categorical_model_path)
+    gbm4 = lgb.Booster(model_file=categorical_model_path)
     pred4 = gbm4.predict(X_test)
     gbm5 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=["A", "B", "C", "D", "E"])
     pred5 = gbm5.predict(X_test, raw_score=True)