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Support pandas 2.1.0. (#9557)

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This commit is contained in:
Jiaming Yuan
2023-09-11 17:44:51 +08:00
committed by GitHub
parent 66a0832778
commit 9027686cac
2 changed files with 47 additions and 27 deletions
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66
python-package/xgboost/data.py
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@@ -317,7 +317,6 @@ def pandas_feature_info(
) -> Tuple[Optional[FeatureNames], Optional[FeatureTypes]]:
"""Handle feature info for pandas dataframe."""
import pandas as pd
from pandas.api.types import is_categorical_dtype, is_sparse
# handle feature names
if feature_names is None and meta is None:
@@ -332,10 +331,10 @@ def pandas_feature_info(
if feature_types is None and meta is None:
feature_types = []
for dtype in data.dtypes:
if is_sparse(dtype):
if is_pd_sparse_dtype(dtype):
feature_types.append(_pandas_dtype_mapper[dtype.subtype.name])
elif (
is_categorical_dtype(dtype) or is_pa_ext_categorical_dtype(dtype)
is_pd_cat_dtype(dtype) or is_pa_ext_categorical_dtype(dtype)
) and enable_categorical:
feature_types.append(CAT_T)
else:
@@ -345,18 +344,13 @@ def pandas_feature_info(
def is_nullable_dtype(dtype: PandasDType) -> bool:
"""Whether dtype is a pandas nullable type."""
from pandas.api.types import (
is_bool_dtype,
is_categorical_dtype,
is_float_dtype,
is_integer_dtype,
)
from pandas.api.types import is_bool_dtype, is_float_dtype, is_integer_dtype
is_int = is_integer_dtype(dtype) and dtype.name in pandas_nullable_mapper
# np.bool has alias `bool`, while pd.BooleanDtype has `boolean`.
is_bool = is_bool_dtype(dtype) and dtype.name == "boolean"
is_float = is_float_dtype(dtype) and dtype.name in pandas_nullable_mapper
return is_int or is_bool or is_float or is_categorical_dtype(dtype)
return is_int or is_bool or is_float or is_pd_cat_dtype(dtype)
def is_pa_ext_dtype(dtype: Any) -> bool:
@@ -371,17 +365,48 @@ def is_pa_ext_categorical_dtype(dtype: Any) -> bool:
)
def is_pd_cat_dtype(dtype: PandasDType) -> bool:
"""Wrapper for testing pandas category type."""
import pandas as pd
if hasattr(pd.util, "version") and hasattr(pd.util.version, "Version"):
Version = pd.util.version.Version
if Version(pd.__version__) >= Version("2.1.0"):
from pandas import CategoricalDtype
return isinstance(dtype, CategoricalDtype)
from pandas.api.types import is_categorical_dtype
return is_categorical_dtype(dtype)
def is_pd_sparse_dtype(dtype: PandasDType) -> bool:
"""Wrapper for testing pandas sparse type."""
import pandas as pd
if hasattr(pd.util, "version") and hasattr(pd.util.version, "Version"):
Version = pd.util.version.Version
if Version(pd.__version__) >= Version("2.1.0"):
from pandas import SparseDtype
return isinstance(dtype, SparseDtype)
from pandas.api.types import is_sparse
return is_sparse(dtype)
def pandas_cat_null(data: DataFrame) -> DataFrame:
"""Handle categorical dtype and nullable extension types from pandas."""
import pandas as pd
from pandas.api.types import is_categorical_dtype
# handle category codes and nullable.
cat_columns = []
nul_columns = []
# avoid an unnecessary conversion if possible
for col, dtype in zip(data.columns, data.dtypes):
if is_categorical_dtype(dtype):
if is_pd_cat_dtype(dtype):
cat_columns.append(col)
elif is_pa_ext_categorical_dtype(dtype):
raise ValueError(
@@ -398,7 +423,7 @@ def pandas_cat_null(data: DataFrame) -> DataFrame:
transformed = data
def cat_codes(ser: pd.Series) -> pd.Series:
if is_categorical_dtype(ser.dtype):
if is_pd_cat_dtype(ser.dtype):
return ser.cat.codes
assert is_pa_ext_categorical_dtype(ser.dtype)
# Not yet supported, the index is not ordered for some reason. Alternately:
@@ -454,14 +479,12 @@ def _transform_pandas_df(
meta: Optional[str] = None,
meta_type: Optional[NumpyDType] = None,
) -> Tuple[np.ndarray, Optional[FeatureNames], Optional[FeatureTypes]]:
from pandas.api.types import is_categorical_dtype, is_sparse
pyarrow_extension = False
for dtype in data.dtypes:
if not (
(dtype.name in _pandas_dtype_mapper)
or is_sparse(dtype)
or (is_categorical_dtype(dtype) and enable_categorical)
or is_pd_sparse_dtype(dtype)
or (is_pd_cat_dtype(dtype) and enable_categorical)
or is_pa_ext_dtype(dtype)
):
_invalid_dataframe_dtype(data)
@@ -515,9 +538,8 @@ def _meta_from_pandas_series(
) -> None:
"""Help transform pandas series for meta data like labels"""
data = data.values.astype("float")
from pandas.api.types import is_sparse
if is_sparse(data):
if is_pd_sparse_dtype(getattr(data, "dtype", data)):
data = data.to_dense() # type: ignore
assert len(data.shape) == 1 or data.shape[1] == 0 or data.shape[1] == 1
_meta_from_numpy(data, name, dtype, handle)
@@ -539,13 +561,11 @@ def _from_pandas_series(
feature_names: Optional[FeatureNames],
feature_types: Optional[FeatureTypes],
) -> DispatchedDataBackendReturnType:
from pandas.api.types import is_categorical_dtype
if (data.dtype.name not in _pandas_dtype_mapper) and not (
is_categorical_dtype(data.dtype) and enable_categorical
is_pd_cat_dtype(data.dtype) and enable_categorical
):
_invalid_dataframe_dtype(data)
if enable_categorical and is_categorical_dtype(data.dtype):
if enable_categorical and is_pd_cat_dtype(data.dtype):
data = data.cat.codes
return _from_numpy_array(
data.values.reshape(data.shape[0], 1).astype("float"),