ignore unknown parameters when loading from model file

src/boosting/gbdt.h

@@ -179,15 +179,20 @@ class GBDT : public GBDTBase {
       const auto pair = Common::Split(line.c_str(), ":");
       if (pair[1] == " ]")
         continue;
+      const auto param = pair[0].substr(1);
+      const auto value_str = pair[1].substr(1, pair[1].size() - 2);
+      auto iter = param_types.find(param);
+      if (iter == param_types.end()) {
+        Log::Warning("Type for param: %s not found. This doesn't affect inference.", param.c_str());
+        continue;
+      }
+      std::string param_type = iter->second;
       if (first) {
         first = false;
         str_buf << "\"";
       } else {
         str_buf << ",\"";
       }
-      const auto param = pair[0].substr(1);
-      const auto value_str = pair[1].substr(1, pair[1].size() - 2);
-      const auto param_type = param_types.at(param);
       str_buf << param << "\": ";
       if (param_type == "string") {
         str_buf << "\"" << value_str << "\"";

tests/python_package_test/test_engine.py

@@ -1470,7 +1470,7 @@ def test_feature_name_with_non_ascii():
     assert feature_names == gbm2.feature_name()
 
 
-def test_parameters_are_loaded_from_model_file(tmp_path):
+def test_parameters_are_loaded_from_model_file(tmp_path, capsys):
     X = np.hstack([np.random.rand(100, 1), np.random.randint(0, 5, (100, 2))])
     y = np.random.rand(100)
     ds = lgb.Dataset(X, y)
@@ -1487,8 +1487,18 @@ def test_parameters_are_loaded_from_model_file(tmp_path):
         'num_threads': 1,
     }
     model_file = tmp_path / 'model.txt'
-    lgb.train(params, ds, num_boost_round=1, categorical_feature=[1, 2]).save_model(model_file)
+    orig_bst = lgb.train(params, ds, num_boost_round=1, categorical_feature=[1, 2])
+    orig_bst.save_model(model_file)
+    with model_file.open('rt') as f:
+        model_contents = f.readlines()
+    params_start = model_contents.index('parameters:\n')
+    model_contents.insert(params_start + 1, '[max_conflict_rate: 0]\n')
+    with model_file.open('wt') as f:
+        f.writelines(model_contents)
     bst = lgb.Booster(model_file=model_file)
+    expected_msg = "[LightGBM] [Warning] Type for param: 'max_conflict_rate' not found. This doesn't affect inference."
+    stdout = capsys.readouterr().out
+    assert expected_msg in stdout
     set_params = {k: bst.params[k] for k in params.keys()}
     assert set_params == params
     assert bst.params['categorical_feature'] == [1, 2]
@@ -1498,6 +1508,11 @@ def test_parameters_are_loaded_from_model_file(tmp_path):
         bst2 = lgb.Booster(params={'num_leaves': 7}, model_file=model_file)
     assert bst.params == bst2.params
 
+    # check inference isn't affected by unknown parameter
+    orig_preds = orig_bst.predict(X)
+    preds = bst.predict(X)
+    np.testing.assert_allclose(preds, orig_preds)
+
 
 def test_save_load_copy_pickle():
     def train_and_predict(init_model=None, return_model=False):