[python-package] Load parameters from model string (#6852)

* [python-package] Test serialization and deserialization from in-memory string

Test case for #6851

* [python-package] Fill in `params` when loading from in-memory string

This fixes #6851 by using the same workaround as when loading the model
from a file.

* test_basic: use rng instead of legacy numpy RandomState

* test_basic: remove debug prints leftovers
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* test_basic: add boolean, array of float and array of integers to testcase
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* test_basic: make a cheaper model (2 rounds with 7 leaves each)
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* test_basic: bugfix typos

* python_package_test: move string load test from basic to engine

* test_engine: catch params ignored warnings

* test_engine: be explicit about parameters assertion

* test_engine: shush linter complaint

* test_basic: delete empty line
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* test_engine: even cheaper model with less features
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* test_engine: delete redundant assert
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* test_engine: run pre-commit and take it's word for it

* test_engine: be explicit in an E712 compliant way

* test_engine.py: pass different value as argument to make sure it is ignored

---------
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

python-package/lightgbm/basic.py

@@ -3715,6 +3715,9 @@ class Booster:
             params = self._get_loaded_param()
         elif model_str is not None:
             self.model_from_string(model_str)
+            if params:
+                _log_warning("Ignoring params argument, using parameters from model string.")
+            params = self._get_loaded_param()
         else:
             raise TypeError(
                 "Need at least one training dataset or model file or model string to create Booster instance"

tests/python_package_test/test_engine.py

@@ -1498,7 +1498,7 @@ def test_parameters_are_loaded_from_model_file(tmp_path, capsys, rng):
     assert bst.params["categorical_feature"] == [1, 2]
 
     # check that passing parameters to the constructor raises warning and ignores them
-    with pytest.warns(UserWarning, match="Ignoring params argument"):
+    with pytest.warns(UserWarning, match="Ignoring params argument, using parameters from model file."):
         bst2 = lgb.Booster(params={"num_leaves": 7}, model_file=model_file)
     assert bst.params == bst2.params
 
@@ -1508,6 +1508,50 @@ def test_parameters_are_loaded_from_model_file(tmp_path, capsys, rng):
     np.testing.assert_allclose(preds, orig_preds)
 
 
+def test_string_serialized_params_retrieval(rng):
+    # Random train data
+    train_x = rng.random((500, 3))
+    train_y = rng.integers(0, 1, 500)
+    train_data = lgb.Dataset(train_x, train_y)
+
+    # Parameters
+    params = {
+        "boosting": "gbdt",
+        "deterministic": True,
+        "feature_contri": [0.5] * train_x.shape[1],
+        "interaction_constraints": [[0, 1], [0]],
+        "objective": "binary",
+        "metric": ["auc"],
+        "num_leaves": 7,
+        "learning_rate": 0.05,
+        "feature_fraction": 0.9,
+        "bagging_fraction": 0.8,
+        "bagging_freq": 5,
+        "verbosity": -100,
+    }
+
+    # train a model and serialize it to a string in memory
+    model = lgb.train(params, train_data, num_boost_round=2)
+    model_serialized = model.model_to_string()
+
+    # load a new model with the string
+    with pytest.warns(UserWarning, match="Ignoring params argument, using parameters from model string."):
+        new_model = lgb.Booster(params={"num_leaves": 32}, model_str=model_serialized)
+
+    assert new_model.params["boosting"] == "gbdt"
+    assert new_model.params["deterministic"] is True
+    assert new_model.params["feature_contri"] == [0.5] * train_x.shape[1]
+    assert new_model.params["interaction_constraints"] == [[0, 1], [0]]
+    assert new_model.params["objective"] == "binary"
+    assert new_model.params["metric"] == ["auc"]
+    assert new_model.params["num_leaves"] == 7
+    assert new_model.params["learning_rate"] == 0.05
+    assert new_model.params["feature_fraction"] == 0.9
+    assert new_model.params["bagging_fraction"] == 0.8
+    assert new_model.params["bagging_freq"] == 5
+    assert new_model.params["verbosity"] == -100
+
+
 def test_save_load_copy_pickle(tmp_path):
     def train_and_predict(init_model=None, return_model=False):
         X, y = make_synthetic_regression()