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Add high level tests for categorical data. (#6179)

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* Fix unique.
This commit is contained in:
Jiaming Yuan
2020-10-09 09:27:23 +08:00
committed by GitHub
parent 6bc9747df5
commit 70ce5216b5
4 changed files with 78 additions and 21 deletions
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7
python-package/xgboost/data.py
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@@ -160,6 +160,7 @@ def _is_pandas_df(data):
return False
return isinstance(data, pd.DataFrame)
def _is_modin_df(data):
try:
import modin.pandas as pd
@@ -188,11 +189,11 @@ def _transform_pandas_df(data, enable_categorical,
feature_names=None, feature_types=None,
meta=None, meta_type=None):
from pandas import MultiIndex, Int64Index
from pandas.api.types import is_sparse, is_categorical
from pandas.api.types import is_sparse, is_categorical_dtype
data_dtypes = data.dtypes
if not all(dtype.name in _pandas_dtype_mapper or is_sparse(dtype) or
(is_categorical(dtype) and enable_categorical)
(is_categorical_dtype(dtype) and enable_categorical)
for dtype in data_dtypes):
bad_fields = [
str(data.columns[i]) for i, dtype in enumerate(data_dtypes)
@@ -220,7 +221,7 @@ def _transform_pandas_df(data, enable_categorical,
if is_sparse(dtype):
feature_types.append(_pandas_dtype_mapper[
dtype.subtype.name])
elif is_categorical(dtype) and enable_categorical:
elif is_categorical_dtype(dtype) and enable_categorical:
feature_types.append('categorical')
else:
feature_types.append(_pandas_dtype_mapper[dtype.name])