[python-package] fix access to Dataset metadata in scikit-learn custom metrics...

[python-package] fix access to Dataset metadata in scikit-learn custom metrics and objectives (#6108)

python-package/lightgbm/basic.py

@@ -434,7 +434,7 @@ def _data_to_2d_numpy(
                     "It should be list of lists, numpy 2-D array or pandas DataFrame")
 
 
-def _cfloat32_array_to_numpy(cptr: "ctypes._Pointer", length: int) -> np.ndarray:
+def _cfloat32_array_to_numpy(*, cptr: "ctypes._Pointer", length: int) -> np.ndarray:
     """Convert a ctypes float pointer array to a numpy array."""
     if isinstance(cptr, ctypes.POINTER(ctypes.c_float)):
         return np.ctypeslib.as_array(cptr, shape=(length,)).copy()
@@ -442,7 +442,7 @@ def _cfloat32_array_to_numpy(cptr: "ctypes._Pointer", length: int) -> np.ndarray
         raise RuntimeError('Expected float pointer')
 
 
-def _cfloat64_array_to_numpy(cptr: "ctypes._Pointer", length: int) -> np.ndarray:
+def _cfloat64_array_to_numpy(*, cptr: "ctypes._Pointer", length: int) -> np.ndarray:
     """Convert a ctypes double pointer array to a numpy array."""
     if isinstance(cptr, ctypes.POINTER(ctypes.c_double)):
         return np.ctypeslib.as_array(cptr, shape=(length,)).copy()
@@ -450,7 +450,7 @@ def _cfloat64_array_to_numpy(cptr: "ctypes._Pointer", length: int) -> np.ndarray
         raise RuntimeError('Expected double pointer')
 
 
-def _cint32_array_to_numpy(cptr: "ctypes._Pointer", length: int) -> np.ndarray:
+def _cint32_array_to_numpy(*, cptr: "ctypes._Pointer", length: int) -> np.ndarray:
     """Convert a ctypes int pointer array to a numpy array."""
     if isinstance(cptr, ctypes.POINTER(ctypes.c_int32)):
         return np.ctypeslib.as_array(cptr, shape=(length,)).copy()
@@ -458,7 +458,7 @@ def _cint32_array_to_numpy(cptr: "ctypes._Pointer", length: int) -> np.ndarray:
         raise RuntimeError('Expected int32 pointer')
 
 
-def _cint64_array_to_numpy(cptr: "ctypes._Pointer", length: int) -> np.ndarray:
+def _cint64_array_to_numpy(*, cptr: "ctypes._Pointer", length: int) -> np.ndarray:
     """Convert a ctypes int pointer array to a numpy array."""
     if isinstance(cptr, ctypes.POINTER(ctypes.c_int64)):
         return np.ctypeslib.as_array(cptr, shape=(length,)).copy()
@@ -1295,18 +1295,18 @@ class _InnerPredictor:
         data_indices_len = out_shape[0]
         indptr_len = out_shape[1]
         if indptr_type == _C_API_DTYPE_INT32:
-            out_indptr = _cint32_array_to_numpy(out_ptr_indptr, indptr_len)
+            out_indptr = _cint32_array_to_numpy(cptr=out_ptr_indptr, length=indptr_len)
         elif indptr_type == _C_API_DTYPE_INT64:
-            out_indptr = _cint64_array_to_numpy(out_ptr_indptr, indptr_len)
+            out_indptr = _cint64_array_to_numpy(cptr=out_ptr_indptr, length=indptr_len)
         else:
             raise TypeError("Expected int32 or int64 type for indptr")
         if data_type == _C_API_DTYPE_FLOAT32:
-            out_data = _cfloat32_array_to_numpy(out_ptr_data, data_indices_len)
+            out_data = _cfloat32_array_to_numpy(cptr=out_ptr_data, length=data_indices_len)
         elif data_type == _C_API_DTYPE_FLOAT64:
-            out_data = _cfloat64_array_to_numpy(out_ptr_data, data_indices_len)
+            out_data = _cfloat64_array_to_numpy(cptr=out_ptr_data, length=data_indices_len)
         else:
             raise TypeError("Expected float32 or float64 type for data")
-        out_indices = _cint32_array_to_numpy(out_ptr_indices, data_indices_len)
+        out_indices = _cint32_array_to_numpy(cptr=out_ptr_indices, length=data_indices_len)
         # break up indptr based on number of rows (note more than one matrix in multiclass case)
         per_class_indptr_shape = cs.indptr.shape[0]
         # for CSC there is extra column added
@@ -2609,6 +2609,12 @@ class Dataset:
     def get_field(self, field_name: str) -> Optional[np.ndarray]:
         """Get property from the Dataset.
 
+        Can only be run on a constructed Dataset.
+
+        Unlike ``get_group()``, ``get_init_score()``, ``get_label()``, ``get_position()``, and ``get_weight()``,
+        this method ignores any raw data passed into ``lgb.Dataset()`` on the Python side, and will only read
+        data from the constructed C++ ``Dataset`` object.
+
         Parameters
         ----------
         field_name : str
@@ -2635,11 +2641,20 @@ class Dataset:
         if tmp_out_len.value == 0:
             return None
         if out_type.value == _C_API_DTYPE_INT32:
-            arr = _cint32_array_to_numpy(ctypes.cast(ret, ctypes.POINTER(ctypes.c_int32)), tmp_out_len.value)
+            arr = _cint32_array_to_numpy(
+                cptr=ctypes.cast(ret, ctypes.POINTER(ctypes.c_int32)),
+                length=tmp_out_len.value
+            )
         elif out_type.value == _C_API_DTYPE_FLOAT32:
-            arr = _cfloat32_array_to_numpy(ctypes.cast(ret, ctypes.POINTER(ctypes.c_float)), tmp_out_len.value)
+            arr = _cfloat32_array_to_numpy(
+                cptr=ctypes.cast(ret, ctypes.POINTER(ctypes.c_float)),
+                length=tmp_out_len.value
+            )
         elif out_type.value == _C_API_DTYPE_FLOAT64:
-            arr = _cfloat64_array_to_numpy(ctypes.cast(ret, ctypes.POINTER(ctypes.c_double)), tmp_out_len.value)
+            arr = _cfloat64_array_to_numpy(
+                cptr=ctypes.cast(ret, ctypes.POINTER(ctypes.c_double)),
+                length=tmp_out_len.value
+            )
         else:
             raise TypeError("Unknown type")
         if field_name == 'init_score':
@@ -2878,6 +2893,10 @@ class Dataset:
         if self._handle is not None and group is not None:
             group = _list_to_1d_numpy(group, dtype=np.int32, name='group')
             self.set_field('group', group)
+            # original values can be modified at cpp side
+            constructed_group = self.get_field('group')
+            if constructed_group is not None:
+                self.group = np.diff(constructed_group)
         return self
 
     def set_position(
@@ -2941,37 +2960,40 @@ class Dataset:
                 ptr_string_buffers))
         return [string_buffers[i].value.decode('utf-8') for i in range(num_feature)]
 
-    def get_label(self) -> Optional[np.ndarray]:
+    def get_label(self) -> Optional[_LGBM_LabelType]:
         """Get the label of the Dataset.
 
         Returns
         -------
-        label : numpy array or None
+        label : list, numpy 1-D array, pandas Series / one-column DataFrame or None
             The label information from the Dataset.
+            For a constructed ``Dataset``, this will only return a numpy array.
         """
         if self.label is None:
             self.label = self.get_field('label')
         return self.label
 
-    def get_weight(self) -> Optional[np.ndarray]:
+    def get_weight(self) -> Optional[_LGBM_WeightType]:
         """Get the weight of the Dataset.
 
         Returns
         -------
-        weight : numpy array or None
+        weight : list, numpy 1-D array, pandas Series or None
             Weight for each data point from the Dataset. Weights should be non-negative.
+            For a constructed ``Dataset``, this will only return ``None`` or a numpy array.
         """
         if self.weight is None:
             self.weight = self.get_field('weight')
         return self.weight
 
-    def get_init_score(self) -> Optional[np.ndarray]:
+    def get_init_score(self) -> Optional[_LGBM_InitScoreType]:
         """Get the initial score of the Dataset.
 
         Returns
         -------
-        init_score : numpy array or None
+        init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None
             Init score of Booster.
+            For a constructed ``Dataset``, this will only return ``None`` or a numpy array.
         """
         if self.init_score is None:
             self.init_score = self.get_field('init_score')
@@ -3009,17 +3031,18 @@ class Dataset:
                                 "set free_raw_data=False when construct Dataset to avoid this.")
         return self.data
 
-    def get_group(self) -> Optional[np.ndarray]:
+    def get_group(self) -> Optional[_LGBM_GroupType]:
         """Get the group of the Dataset.
 
         Returns
         -------
-        group : numpy array or None
+        group : list, numpy 1-D array, pandas Series or None
             Group/query data.
             Only used in the learning-to-rank task.
             sum(group) = n_samples.
             For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,
             where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
+            For a constructed ``Dataset``, this will only return ``None`` or a numpy array.
         """
         if self.group is None:
             self.group = self.get_field('group')
@@ -3028,13 +3051,14 @@ class Dataset:
                 self.group = np.diff(self.group)
         return self.group
 
-    def get_position(self) -> Optional[np.ndarray]:
+    def get_position(self) -> Optional[_LGBM_PositionType]:
         """Get the position of the Dataset.
 
         Returns
         -------
-        position : numpy 1-D array or None
+        position : numpy 1-D array, pandas Series or None
             Position of items used in unbiased learning-to-rank task.
+            For a constructed ``Dataset``, this will only return ``None`` or a numpy array.
         """
         if self.position is None:
             self.position = self.get_field('position')

python-package/lightgbm/sklearn.py

@@ -86,6 +86,36 @@ _LGBM_ScikitEvalMetricType = Union[
 _LGBM_ScikitValidSet = Tuple[_LGBM_ScikitMatrixLike, _LGBM_LabelType]
 
 
+def _get_group_from_constructed_dataset(dataset: Dataset) -> Optional[np.ndarray]:
+    group = dataset.get_group()
+    error_msg = (
+        "Estimators in lightgbm.sklearn should only retrieve query groups from a constructed Dataset. "
+        "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/microsoft/LightGBM/issues."
+    )
+    assert (group is None or isinstance(group, np.ndarray)), error_msg
+    return group
+
+
+def _get_label_from_constructed_dataset(dataset: Dataset) -> np.ndarray:
+    label = dataset.get_label()
+    error_msg = (
+        "Estimators in lightgbm.sklearn should only retrieve labels from a constructed Dataset. "
+        "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/microsoft/LightGBM/issues."
+    )
+    assert isinstance(label, np.ndarray), error_msg
+    return label
+
+
+def _get_weight_from_constructed_dataset(dataset: Dataset) -> Optional[np.ndarray]:
+    weight = dataset.get_weight()
+    error_msg = (
+        "Estimators in lightgbm.sklearn should only retrieve weights from a constructed Dataset. "
+        "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/microsoft/LightGBM/issues."
+    )
+    assert (weight is None or isinstance(weight, np.ndarray)), error_msg
+    return weight
+
+
 class _ObjectiveFunctionWrapper:
     """Proxy class for objective function."""
 
@@ -151,17 +181,22 @@ class _ObjectiveFunctionWrapper:
             The value of the second order derivative (Hessian) of the loss
             with respect to the elements of preds for each sample point.
         """
-        labels = dataset.get_label()
+        labels = _get_label_from_constructed_dataset(dataset)
         argc = len(signature(self.func).parameters)
         if argc == 2:
             grad, hess = self.func(labels, preds)  # type: ignore[call-arg]
-        elif argc == 3:
-            grad, hess = self.func(labels, preds, dataset.get_weight())  # type: ignore[call-arg]
-        elif argc == 4:
-            grad, hess = self.func(labels, preds, dataset.get_weight(), dataset.get_group())  # type: ignore [call-arg]
-        else:
-            raise TypeError(f"Self-defined objective function should have 2, 3 or 4 arguments, got {argc}")
-        return grad, hess
+            return grad, hess
+
+        weight = _get_weight_from_constructed_dataset(dataset)
+        if argc == 3:
+            grad, hess = self.func(labels, preds, weight)  # type: ignore[call-arg]
+            return grad, hess
+
+        if argc == 4:
+            group = _get_group_from_constructed_dataset(dataset)
+            return self.func(labels, preds, weight, group)  # type: ignore[call-arg]
+
+        raise TypeError(f"Self-defined objective function should have 2, 3 or 4 arguments, got {argc}")
 
 
 class _EvalFunctionWrapper:
@@ -229,16 +264,20 @@ class _EvalFunctionWrapper:
         is_higher_better : bool
             Is eval result higher better, e.g. AUC is ``is_higher_better``.
         """
-        labels = dataset.get_label()
+        labels = _get_label_from_constructed_dataset(dataset)
         argc = len(signature(self.func).parameters)
         if argc == 2:
             return self.func(labels, preds)  # type: ignore[call-arg]
-        elif argc == 3:
-            return self.func(labels, preds, dataset.get_weight())  # type: ignore[call-arg]
-        elif argc == 4:
-            return self.func(labels, preds, dataset.get_weight(), dataset.get_group())  # type: ignore[call-arg]
-        else:
-            raise TypeError(f"Self-defined eval function should have 2, 3 or 4 arguments, got {argc}")
+
+        weight = _get_weight_from_constructed_dataset(dataset)
+        if argc == 3:
+            return self.func(labels, preds, weight)  # type: ignore[call-arg]
+
+        if argc == 4:
+            group = _get_group_from_constructed_dataset(dataset)
+            return self.func(labels, preds, weight, group)  # type: ignore[call-arg]
+
+        raise TypeError(f"Self-defined eval function should have 2, 3 or 4 arguments, got {argc}")
 
 
 # documentation templates for LGBMModel methods are shared between the classes in

tests/python_package_test/test_basic.py

@@ -15,7 +15,7 @@ from sklearn.model_selection import train_test_split
 import lightgbm as lgb
 from lightgbm.compat import PANDAS_INSTALLED, pd_DataFrame, pd_Series
 
-from .utils import dummy_obj, load_breast_cancer, mse_obj
+from .utils import dummy_obj, load_breast_cancer, mse_obj, np_assert_array_equal
 
 
 def test_basic(tmp_path):
@@ -499,6 +499,94 @@ def test_consistent_state_for_dataset_fields():
     check_asserts(lgb_data)
 
 
+def test_dataset_construction_overwrites_user_provided_metadata_fields():
+
+    X = np.array([[1.0, 2.0], [3.0, 4.0]])
+
+    position = np.array([0.0, 1.0], dtype=np.float32)
+    if getenv('TASK', '') == 'cuda':
+        position = None
+
+    dtrain = lgb.Dataset(
+        X,
+        params={
+            "min_data_in_bin": 1,
+            "min_data_in_leaf": 1,
+            "verbosity": -1
+        },
+        group=[1, 1],
+        init_score=[0.312, 0.708],
+        label=[1, 2],
+        position=position,
+        weight=[0.5, 1.5],
+    )
+
+    # unconstructed, get_* methods should return whatever was provided
+    assert dtrain.group == [1, 1]
+    assert dtrain.get_group() == [1, 1]
+    assert dtrain.init_score == [0.312, 0.708]
+    assert dtrain.get_init_score() == [0.312, 0.708]
+    assert dtrain.label == [1, 2]
+    assert dtrain.get_label() == [1, 2]
+    if getenv('TASK', '') != 'cuda':
+        np_assert_array_equal(
+            dtrain.position,
+            np.array([0.0, 1.0], dtype=np.float32),
+            strict=True
+        )
+        np_assert_array_equal(
+            dtrain.get_position(),
+            np.array([0.0, 1.0], dtype=np.float32),
+            strict=True
+        )
+    assert dtrain.weight == [0.5, 1.5]
+    assert dtrain.get_weight() == [0.5, 1.5]
+
+    # before construction, get_field() should raise an exception
+    for field_name in ["group", "init_score", "label", "position", "weight"]:
+        with pytest.raises(Exception, match=f"Cannot get {field_name} before construct Dataset"):
+            dtrain.get_field(field_name)
+
+    # constructed, get_* methods should return numpy arrays, even when the provided
+    # input was a list of floats or ints
+    dtrain.construct()
+    expected_group = np.array([1, 1], dtype=np.int32)
+    np_assert_array_equal(dtrain.group, expected_group, strict=True)
+    np_assert_array_equal(dtrain.get_group(), expected_group, strict=True)
+    # get_field("group") returns a numpy array with boundaries, instead of size
+    np_assert_array_equal(
+        dtrain.get_field("group"),
+        np.array([0, 1, 2], dtype=np.int32),
+        strict=True
+    )
+
+    expected_init_score = np.array([0.312, 0.708],)
+    np_assert_array_equal(dtrain.init_score, expected_init_score, strict=True)
+    np_assert_array_equal(dtrain.get_init_score(), expected_init_score, strict=True)
+    np_assert_array_equal(dtrain.get_field("init_score"), expected_init_score, strict=True)
+
+    expected_label = np.array([1, 2], dtype=np.float32)
+    np_assert_array_equal(dtrain.label, expected_label, strict=True)
+    np_assert_array_equal(dtrain.get_label(), expected_label, strict=True)
+    np_assert_array_equal(dtrain.get_field("label"), expected_label, strict=True)
+
+    if getenv('TASK', '') != 'cuda':
+        expected_position = np.array([0.0, 1.0], dtype=np.float32)
+        np_assert_array_equal(dtrain.position, expected_position, strict=True)
+        np_assert_array_equal(dtrain.get_position(), expected_position, strict=True)
+        # NOTE: "position" is converted to int32 on the C++ side
+        np_assert_array_equal(
+            dtrain.get_field("position"),
+            np.array([0.0, 1.0], dtype=np.int32),
+            strict=True
+        )
+
+    expected_weight = np.array([0.5, 1.5], dtype=np.float32)
+    np_assert_array_equal(dtrain.weight, expected_weight, strict=True)
+    np_assert_array_equal(dtrain.get_weight(), expected_weight, strict=True)
+    np_assert_array_equal(dtrain.get_field("weight"), expected_weight, strict=True)
+
+
 def test_choose_param_value():
 
     original_params = {

tests/python_package_test/utils.py

 # coding: utf-8
 import pickle
 from functools import lru_cache
+from inspect import getfullargspec
 
 import cloudpickle
 import joblib
@@ -193,3 +194,22 @@ def pickle_and_unpickle_object(obj, serializer):
             serializer=serializer
         )
     return obj_from_disk  # noqa: RET504
+
+
+# doing this here, at import time, to ensure it only runs once_per import
+# instead of once per assertion
+_numpy_testing_supports_strict_kwarg = (
+    "strict" in getfullargspec(np.testing.assert_array_equal).kwonlyargs
+)
+
+
+def np_assert_array_equal(*args, **kwargs):
+    """
+    np.testing.assert_array_equal() only got the kwarg ``strict`` in June 2022:
+    https://github.com/numpy/numpy/pull/21595
+
+    This function is here for testing on older Python (and therefore ``numpy``)
+    """
+    if not _numpy_testing_supports_strict_kwarg:
+        kwargs.pop("strict")
+    np.testing.assert_array_equal(*args, **kwargs)