[python-package] move validation up earlier in cv() and train() (#5836)

python-package/lightgbm/engine.py

@@ -141,6 +141,20 @@ def train(
     booster : Booster
         The trained Booster model.
     """
+    if not isinstance(train_set, Dataset):
+        raise TypeError(f"train() only accepts Dataset object, train_set has type '{type(train_set).__name__}'.")
+
+    if num_boost_round <= 0:
+        raise ValueError(f"num_boost_round must be greater than 0. Got {num_boost_round}.")
+
+    if isinstance(valid_sets, list):
+        for i, valid_item in enumerate(valid_sets):
+            if not isinstance(valid_item, Dataset):
+                raise TypeError(
+                    "Every item in valid_sets must be a Dataset object. "
+                    f"Item {i} has type '{type(valid_item).__name__}'."
+                )
+
     # create predictor first
     params = copy.deepcopy(params)
     params = _choose_param_value(
@@ -167,17 +181,12 @@ def train(
         params.pop("early_stopping_round")
     first_metric_only = params.get('first_metric_only', False)
 
-    if num_boost_round <= 0:
-        raise ValueError("num_boost_round should be greater than zero.")
     predictor: Optional[_InnerPredictor] = None
     if isinstance(init_model, (str, Path)):
         predictor = _InnerPredictor(model_file=init_model, pred_parameter=params)
     elif isinstance(init_model, Booster):
         predictor = init_model._to_predictor(pred_parameter=dict(init_model.params, **params))
     init_iteration = predictor.num_total_iteration if predictor is not None else 0
-    # check dataset
-    if not isinstance(train_set, Dataset):
-        raise TypeError("Training only accepts Dataset object")
 
     train_set._update_params(params) \
              ._set_predictor(predictor) \
@@ -200,8 +209,6 @@ def train(
                 if valid_names is not None:
                     train_data_name = valid_names[i]
                 continue
-            if not isinstance(valid_data, Dataset):
-                raise TypeError("Training only accepts Dataset object")
             reduced_valid_sets.append(valid_data._update_params(params).set_reference(train_set))
             if valid_names is not None and len(valid_names) > i:
                 name_valid_sets.append(valid_names[i])
@@ -647,7 +654,11 @@ def cv(
         If ``return_cvbooster=True``, also returns trained boosters via ``cvbooster`` key.
     """
     if not isinstance(train_set, Dataset):
-        raise TypeError("Training only accepts Dataset object")
+        raise TypeError(f"cv() only accepts Dataset object, train_set has type '{type(train_set).__name__}'.")
+
+    if num_boost_round <= 0:
+        raise ValueError(f"num_boost_round must be greater than 0. Got {num_boost_round}.")
+
     params = copy.deepcopy(params)
     params = _choose_param_value(
         main_param_name='objective',
@@ -673,8 +684,6 @@ def cv(
         params.pop("early_stopping_round")
     first_metric_only = params.get('first_metric_only', False)
 
-    if num_boost_round <= 0:
-        raise ValueError("num_boost_round should be greater than zero.")
     if isinstance(init_model, (str, Path)):
         predictor = _InnerPredictor(model_file=init_model, pred_parameter=params)
     elif isinstance(init_model, Booster):

tests/python_package_test/test_engine.py

@@ -4017,6 +4017,38 @@ def test_validate_features():
     bst.refit(df2, y, validate_features=False)
 
 
+def test_train_and_cv_raise_informative_error_for_train_set_of_wrong_type():
+    with pytest.raises(TypeError, match=r"train\(\) only accepts Dataset object, train_set has type 'list'\."):
+        lgb.train({}, train_set=[])
+    with pytest.raises(TypeError, match=r"cv\(\) only accepts Dataset object, train_set has type 'list'\."):
+        lgb.cv({}, train_set=[])
+
+
+@pytest.mark.parametrize('num_boost_round', [-7, -1, 0])
+def test_train_and_cv_raise_informative_error_for_impossible_num_boost_round(num_boost_round):
+    X, y = make_synthetic_regression(n_samples=100)
+    error_msg = rf"num_boost_round must be greater than 0\. Got {num_boost_round}\."
+    with pytest.raises(ValueError, match=error_msg):
+        lgb.train({}, train_set=lgb.Dataset(X, y), num_boost_round=num_boost_round)
+    with pytest.raises(ValueError, match=error_msg):
+        lgb.cv({}, train_set=lgb.Dataset(X, y), num_boost_round=num_boost_round)
+
+
+def test_train_raises_informative_error_if_any_valid_sets_are_not_dataset_objects():
+    X, y = make_synthetic_regression(n_samples=100)
+    X_valid = X * 2.0
+    with pytest.raises(TypeError, match=r"Every item in valid_sets must be a Dataset object\. Item 1 has type 'tuple'\."):
+        lgb.train(
+            params={},
+            train_set=lgb.Dataset(X, y),
+            valid_sets=[
+                lgb.Dataset(X_valid, y),
+                ([1.0], [2.0]),
+                [5.6, 5.7, 5.8]
+            ]
+        )
+
+
 def test_train_raises_informative_error_for_params_of_wrong_type():
     X, y = make_synthetic_regression()
     params = {"early_stopping_round": "too-many"}