[enc] Support training continuation with sklearn. (#11605)

Author: trivialfis

python-package/xgboost/_data_utils.py

@@ -397,7 +397,7 @@ def array_interface_dict(data: np.ndarray) -> ArrayInf:
     return cast(ArrayInf, ainf)
 
 
-def pd_cats_inf(  # pylint: disable=too-many-locals
+def pd_cat_inf(  # pylint: disable=too-many-locals
     cats: DfCatAccessor, codes: "pd.Series"
 ) -> Tuple[Union[StringArray, ArrayInf], ArrayInf, Tuple]:
     """Get the array interface representation of pandas category accessor."""
@@ -665,12 +665,18 @@ def to_arrow(self) -> ArrowCatList:
             )
         return self._arrow_arrays
 
+    def empty(self) -> bool:
+        """Returns True if there's no category."""
+        return self._handle.value is None
+
     def get_handle(self) -> int:
         """Internal method for retrieving the handle."""
         assert self._handle.value
         return self._handle.value
 
     def __del__(self) -> None:
+        if self._handle.value is None:
+            return
         self._free()
 
 
@@ -718,7 +724,7 @@ class TransformedDf(ABC):
 
     def __init__(self, ref_categories: Optional[Categories], aitfs: AifType) -> None:
         self.ref_categories = ref_categories
-        if ref_categories is not None:
+        if ref_categories is not None and ref_categories.get_handle() is not None:
             aif = ref_categories.get_handle()
             self.ref_aif: Optional[int] = aif
         else:

python-package/xgboost/compat.py

@@ -5,11 +5,15 @@
 import logging
 import sys
 import types
-from typing import Any, Sequence, cast
+from typing import TYPE_CHECKING, Any, Sequence, TypeGuard, cast
 
 import numpy as np
 
-from ._typing import _T
+from ._typing import _T, DataType
+
+if TYPE_CHECKING:
+    import pandas as pd
+    import pyarrow as pa
 
 assert sys.version_info[0] == 3, "Python 2 is no longer supported."
 
@@ -31,17 +35,6 @@ def lazy_isinstance(instance: Any, module: str, name: str) -> bool:
     return is_same_module and has_same_name
 
 
-# pandas
-try:
-    from pandas import DataFrame, Series
-
-    PANDAS_INSTALLED = True
-except ImportError:
-    DataFrame = object
-    Series = object
-    PANDAS_INSTALLED = False
-
-
 # sklearn
 try:
     from sklearn import __version__ as _sklearn_version
@@ -139,6 +132,14 @@ def import_pyarrow() -> types.ModuleType:
     return pa
 
 
+@functools.cache
+def import_pandas() -> types.ModuleType:
+    """Import pandas with memory cache."""
+    import pandas as pd
+
+    return pd
+
+
 @functools.cache
 def import_polars() -> types.ModuleType:
     """Import polars with memory cache."""
@@ -147,6 +148,14 @@ def import_polars() -> types.ModuleType:
     return pl
 
 
+@functools.cache
+def is_pandas_available() -> bool:
+    """Check the pandas package is available or not."""
+    if importlib.util.find_spec("pandas") is None:
+        return False
+    return True
+
+
 try:
     import scipy.sparse as scipy_sparse
     from scipy.sparse import csr_matrix as scipy_csr
@@ -155,6 +164,84 @@ def import_polars() -> types.ModuleType:
     scipy_csr = object
 
 
+def _is_polars_lazyframe(data: DataType) -> bool:
+    return lazy_isinstance(data, "polars.lazyframe.frame", "LazyFrame")
+
+
+def _is_polars_series(data: DataType) -> bool:
+    return lazy_isinstance(data, "polars.series.series", "Series")
+
+
+def _is_polars(data: DataType) -> bool:
+    lf = _is_polars_lazyframe(data)
+    df = lazy_isinstance(data, "polars.dataframe.frame", "DataFrame")
+    return lf or df
+
+
+def _is_arrow(data: DataType) -> TypeGuard["pa.Table"]:
+    return lazy_isinstance(data, "pyarrow.lib", "Table")
+
+
+def _is_cudf_df(data: DataType) -> bool:
+    return lazy_isinstance(data, "cudf.core.dataframe", "DataFrame")
+
+
+def _is_cudf_ser(data: DataType) -> bool:
+    return lazy_isinstance(data, "cudf.core.series", "Series")
+
+
+def _is_cudf_pandas(data: DataType) -> bool:
+    """Must go before both pandas and cudf checks."""
+    return (_is_pandas_df(data) or _is_pandas_series(data)) and lazy_isinstance(
+        type(data), "cudf.pandas.fast_slow_proxy", "_FastSlowProxyMeta"
+    )
+
+
+def _is_pandas_df(data: DataType) -> TypeGuard["pd.DataFrame"]:
+    return lazy_isinstance(data, "pandas.core.frame", "DataFrame")
+
+
+def _is_pandas_series(data: DataType) -> TypeGuard["pd.Series"]:
+    return lazy_isinstance(data, "pandas.core.series", "Series")
+
+
+def _is_modin_df(data: DataType) -> bool:
+    return lazy_isinstance(data, "modin.pandas.dataframe", "DataFrame")
+
+
+def _is_modin_series(data: DataType) -> bool:
+    return lazy_isinstance(data, "modin.pandas.series", "Series")
+
+
+def is_dataframe(data: DataType) -> bool:
+    """Whether the input is a dataframe. Currently supported dataframes:
+
+    - pandas
+    - cudf
+    - cudf.pandas
+    - polars
+    - pyarrow
+    - modin
+
+
+    """
+    return any(
+        p(data)
+        for p in (
+            _is_polars,
+            _is_polars_series,
+            _is_arrow,
+            _is_cudf_df,
+            _is_cudf_ser,
+            _is_cudf_pandas,
+            _is_pandas_df,
+            _is_pandas_series,
+            _is_modin_df,
+            _is_modin_series,
+        )
+    )
+
+
 def concat(value: Sequence[_T]) -> _T:  # pylint: disable=too-many-return-statements
     """Concatenate row-wise."""
     if isinstance(value[0], np.ndarray):
@@ -167,7 +254,7 @@ def concat(value: Sequence[_T]) -> _T:  # pylint: disable=too-many-return-statem
     if scipy_sparse and isinstance(value[0], scipy_sparse.spmatrix):
         # other sparse format will be converted to CSR.
         return scipy_sparse.vstack(value, format="csr")
-    if PANDAS_INSTALLED and isinstance(value[0], (DataFrame, Series)):
+    if _is_pandas_df(value[0]) or _is_pandas_series(value[0]):
         from pandas import concat as pd_concat
 
         return pd_concat(value, axis=0)

python-package/xgboost/core.py

@@ -1,6 +1,7 @@
 # pylint: disable=too-many-arguments, too-many-branches, invalid-name
 # pylint: disable=too-many-lines, too-many-locals
 """Core XGBoost Library."""
+
 import copy
 import ctypes
 import json
@@ -69,15 +70,17 @@
     c_bst_ulong,
 )
 from .compat import (
-    PANDAS_INSTALLED,
-    DataFrame,
     import_polars,
     import_pyarrow,
+    is_pandas_available,
     is_pyarrow_available,
     py_str,
 )
 from .libpath import find_lib_path, is_sphinx_build
 
+if TYPE_CHECKING:
+    from pandas import DataFrame as PdDataFrame
+
 
 class XGBoostError(ValueError):
     """Error thrown by xgboost trainer."""
@@ -782,7 +785,7 @@ def _get_categories(
     cfn: Callable[[ctypes.c_char_p], int],
     feature_names: FeatureNames,
     n_features: int,
-) -> Optional[ArrowCatList]:
+) -> ArrowCatList:
     if not is_pyarrow_available():
         raise ImportError(
             "`pyarrow` is required for exporting categories to arrow arrays."
@@ -797,7 +800,9 @@ def _get_categories(
 
     ret = ctypes.c_char_p()
     _check_call(cfn(ret))
-    assert ret.value is not None
+    if ret.value is None:
+        results = [(feature_names[i], None) for i in range(n_features)]
+        return results
 
     retstr = ret.value.decode()  # pylint: disable=no-member
     jcats = json.loads(retstr)
@@ -3201,7 +3206,8 @@ def get_score(
         """Get feature importance of each feature.
         For tree model Importance type can be defined as:
 
-        * 'weight': the number of times a feature is used to split the data across all trees.
+        * 'weight': the number of times a feature is used to split the data across all
+           trees.
         * 'gain': the average gain across all splits the feature is used in.
         * 'cover': the average coverage across all splits the feature is used in.
         * 'total_gain': the total gain across all splits the feature is used in.
@@ -3261,7 +3267,7 @@ def get_score(
         return results
 
     # pylint: disable=too-many-statements
-    def trees_to_dataframe(self, fmap: PathLike = "") -> DataFrame:
+    def trees_to_dataframe(self, fmap: PathLike = "") -> "PdDataFrame":
         """Parse a boosted tree model text dump into a pandas DataFrame structure.
 
         This feature is only defined when the decision tree model is chosen as base
@@ -3274,8 +3280,10 @@ def trees_to_dataframe(self, fmap: PathLike = "") -> DataFrame:
            The name of feature map file.
         """
         # pylint: disable=too-many-locals
+        from pandas import DataFrame
+
         fmap = os.fspath(os.path.expanduser(fmap))
-        if not PANDAS_INSTALLED:
+        if not is_pandas_available():
             raise ImportError(
                 (
                     "pandas must be available to use this method."
@@ -3426,7 +3434,7 @@ def get_split_value_histogram(
         fmap: PathLike = "",
         bins: Optional[int] = None,
         as_pandas: bool = True,
-    ) -> Union[np.ndarray, DataFrame]:
+    ) -> Union[np.ndarray, "PdDataFrame"]:
         """Get split value histogram of a feature
 
         Parameters
@@ -3482,9 +3490,11 @@ def get_split_value_histogram(
                     "Split value historgam doesn't support categorical split."
                 )
 
-        if as_pandas and PANDAS_INSTALLED:
+        if as_pandas and is_pandas_available():
+            from pandas import DataFrame
+
             return DataFrame(nph_stacked, columns=["SplitValue", "Count"])
-        if as_pandas and not PANDAS_INSTALLED:
+        if as_pandas and not is_pandas_available():
             warnings.warn(
                 "Returning histogram as ndarray"
                 " (as_pandas == True, but pandas is not installed).",

python-package/xgboost/dask/__init__.py

@@ -94,7 +94,7 @@
 from ..collective import Config as CollConfig
 from ..collective import _Args as CollArgs
 from ..collective import _ArgVals as CollArgsVals
-from ..compat import DataFrame, lazy_isinstance
+from ..compat import _is_cudf_df
 from ..core import (
     Booster,
     DMatrix,
@@ -942,7 +942,7 @@ def _maybe_dataframe(
         # In older versions of dask, the partition is actually a numpy array when input
         # is dataframe.
         index = getattr(data, "index", None)
-        if lazy_isinstance(data, "cudf.core.dataframe", "DataFrame"):
+        if _is_cudf_df(data):
             import cudf
 
             if prediction.size == 0:
@@ -952,10 +952,14 @@ def _maybe_dataframe(
                 prediction, columns=columns, dtype=numpy.float32, index=index
             )
         else:
+            import pandas as pd
+
             if prediction.size == 0:
-                return DataFrame({}, columns=columns, dtype=numpy.float32, index=index)
+                return pd.DataFrame(
+                    {}, columns=columns, dtype=numpy.float32, index=index
+                )
 
-            prediction = DataFrame(
+            prediction = pd.DataFrame(
                 prediction, columns=columns, dtype=numpy.float32, index=index
             )
     return prediction