[python-package] deprecate support for H2O 'datatable' (#6670)

Co-authored-by: Nikita Titov <[email protected]>
microsoft · Oct 15, 2024 · 668bf5d · 668bf5d
1 parent 5b67546
commit 668bf5d
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Showing 3 changed files with 26 additions and 10 deletions.
diff --git a/docs/Python-Intro.rst b/docs/Python-Intro.rst
@@ -35,7 +35,7 @@ The LightGBM Python module can load data from:
 
 -  LibSVM (zero-based) / TSV / CSV format text file
 
--  NumPy 2D array(s), pandas DataFrame, H2O DataTable's Frame, SciPy sparse matrix
+-  NumPy 2D array(s), pandas DataFrame, H2O DataTable's Frame (deprecated), SciPy sparse matrix
 
 -  LightGBM binary file
 

diff --git a/python-package/lightgbm/basic.py b/python-package/lightgbm/basic.py
@@ -559,6 +559,15 @@ class LGBMDeprecationWarning(FutureWarning):
     pass
 
 
+def _emit_datatable_deprecation_warning() -> None:
+    msg = (
+        "Support for 'datatable' in LightGBM is deprecated, and will be removed in a future release. "
+        "To avoid this warning, convert 'datatable' inputs to a supported format "
+        "(for example, use the 'to_numpy()' method)."
+    )
+    warnings.warn(msg, category=LGBMDeprecationWarning, stacklevel=2)
+
+
 class _ConfigAliases:
     # lazy evaluation to allow import without dynamic library, e.g., for docs generation
     aliases = None
@@ -1086,7 +1095,7 @@ def predict(
 
         Parameters
         ----------
-        data : str, pathlib.Path, numpy array, pandas DataFrame, pyarrow Table, H2O DataTable's Frame or scipy.sparse
+        data : str, pathlib.Path, numpy array, pandas DataFrame, pyarrow Table, H2O DataTable's Frame (deprecated) or scipy.sparse
             Data source for prediction.
             If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM).
         start_iteration : int, optional (default=0)
@@ -1200,6 +1209,7 @@ def predict(
                 predict_type=predict_type,
             )
         elif isinstance(data, dt_DataTable):
+            _emit_datatable_deprecation_warning()
             preds, nrow = self.__pred_for_np2d(
                 mat=data.to_numpy(),
                 start_iteration=start_iteration,
@@ -1766,7 +1776,7 @@ def __init__(
 
         Parameters
         ----------
-        data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse, Sequence, list of Sequence, list of numpy array or pyarrow Table
+        data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame (deprecated), scipy.sparse, Sequence, list of Sequence, list of numpy array or pyarrow Table
             Data source of Dataset.
             If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file.
         label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None)
@@ -2172,6 +2182,7 @@ def _lazy_init(
         elif isinstance(data, Sequence):
             self.__init_from_seqs([data], ref_dataset)
         elif isinstance(data, dt_DataTable):
+            _emit_datatable_deprecation_warning()
             self.__init_from_np2d(data.to_numpy(), params_str, ref_dataset)
         else:
             try:
@@ -2598,7 +2609,7 @@ def create_valid(
 
         Parameters
         ----------
-        data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse, Sequence, list of Sequence or list of numpy array
+        data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame (deprecated), scipy.sparse, Sequence, list of Sequence or list of numpy array
             Data source of Dataset.
             If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file.
         label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None)
@@ -3255,7 +3266,7 @@ def get_data(self) -> Optional[_LGBM_TrainDataType]:
 
         Returns
         -------
-        data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse, Sequence, list of Sequence or list of numpy array or None
+        data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame (deprecated), scipy.sparse, Sequence, list of Sequence or list of numpy array or None
             Raw data used in the Dataset construction.
         """
         if self._handle is None:
@@ -3268,6 +3279,7 @@ def get_data(self) -> Optional[_LGBM_TrainDataType]:
                 elif isinstance(self.data, pd_DataFrame):
                     self.data = self.data.iloc[self.used_indices].copy()
                 elif isinstance(self.data, dt_DataTable):
+                    _emit_datatable_deprecation_warning()
                     self.data = self.data[self.used_indices, :]
                 elif isinstance(self.data, Sequence):
                     self.data = self.data[self.used_indices]
@@ -3456,6 +3468,7 @@ def add_features_from(self, other: "Dataset") -> "Dataset":
                 elif isinstance(other.data, pd_DataFrame):
                     self.data = np.hstack((self.data, other.data.values))
                 elif isinstance(other.data, dt_DataTable):
+                    _emit_datatable_deprecation_warning()
                     self.data = np.hstack((self.data, other.data.to_numpy()))
                 else:
                     self.data = None
@@ -3466,6 +3479,7 @@ def add_features_from(self, other: "Dataset") -> "Dataset":
                 elif isinstance(other.data, pd_DataFrame):
                     self.data = scipy.sparse.hstack((self.data, other.data.values), format=sparse_format)
                 elif isinstance(other.data, dt_DataTable):
+                    _emit_datatable_deprecation_warning()
                     self.data = scipy.sparse.hstack((self.data, other.data.to_numpy()), format=sparse_format)
                 else:
                     self.data = None
@@ -3483,10 +3497,12 @@ def add_features_from(self, other: "Dataset") -> "Dataset":
                 elif isinstance(other.data, pd_DataFrame):
                     self.data = concat((self.data, other.data), axis=1, ignore_index=True)
                 elif isinstance(other.data, dt_DataTable):
+                    _emit_datatable_deprecation_warning()
                     self.data = concat((self.data, pd_DataFrame(other.data.to_numpy())), axis=1, ignore_index=True)
                 else:
                     self.data = None
             elif isinstance(self.data, dt_DataTable):
+                _emit_datatable_deprecation_warning()
                 if isinstance(other.data, np.ndarray):
                     self.data = dt_DataTable(np.hstack((self.data.to_numpy(), other.data)))
                 elif isinstance(other.data, scipy.sparse.spmatrix):
@@ -4688,7 +4704,7 @@ def predict(
 
         Parameters
         ----------
-        data : str, pathlib.Path, numpy array, pandas DataFrame, pyarrow Table, H2O DataTable's Frame or scipy.sparse
+        data : str, pathlib.Path, numpy array, pandas DataFrame, pyarrow Table, H2O DataTable's Frame (deprecated) or scipy.sparse
             Data source for prediction.
             If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM).
         start_iteration : int, optional (default=0)
@@ -4769,7 +4785,7 @@ def refit(
 
         Parameters
         ----------
-        data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse, Sequence, list of Sequence or list of numpy array
+        data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame (deprecated), scipy.sparse, Sequence, list of Sequence or list of numpy array
             Data source for refit.
             If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM).
         label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array or pyarrow ChunkedArray

diff --git a/python-package/lightgbm/sklearn.py b/python-package/lightgbm/sklearn.py
@@ -1043,7 +1043,7 @@ def fit(
 
     fit.__doc__ = (
         _lgbmmodel_doc_fit.format(
-            X_shape="numpy array, pandas DataFrame, H2O DataTable's Frame , scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]",
+            X_shape="numpy array, pandas DataFrame, H2O DataTable's Frame (deprecated), scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]",
             y_shape="numpy array, pandas DataFrame, pandas Series, list of int or float of shape = [n_samples]",
             sample_weight_shape="numpy array, pandas Series, list of int or float of shape = [n_samples] or None, optional (default=None)",
             init_score_shape="numpy array, pandas DataFrame, pandas Series, list of int or float of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task) or shape = [n_samples, n_classes] (for multi-class task) or None, optional (default=None)",
@@ -1120,7 +1120,7 @@ def predict(
 
     predict.__doc__ = _lgbmmodel_doc_predict.format(
         description="Return the predicted value for each sample.",
-        X_shape="numpy array, pandas DataFrame, H2O DataTable's Frame , scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]",
+        X_shape="numpy array, pandas DataFrame, H2O DataTable's Frame (deprecated), scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]",
         output_name="predicted_result",
         predicted_result_shape="array-like of shape = [n_samples] or shape = [n_samples, n_classes]",
         X_leaves_shape="array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]",
@@ -1509,7 +1509,7 @@ def predict_proba(
 
     predict_proba.__doc__ = _lgbmmodel_doc_predict.format(
         description="Return the predicted probability for each class for each sample.",
-        X_shape="numpy array, pandas DataFrame, H2O DataTable's Frame , scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]",
+        X_shape="numpy array, pandas DataFrame, H2O DataTable's Frame (deprecated), scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]",
         output_name="predicted_probability",
         predicted_result_shape="array-like of shape = [n_samples] or shape = [n_samples, n_classes]",
         X_leaves_shape="array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]",