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Complete cudf support. (#4850)

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* Handles missing value.
* Accept all floating point and integer types.
* Move to cudf 9.0 API.
* Remove requirement on `null_count`.
* Arbitrary column types support.
This commit is contained in:
Jiaming Yuan
2019-09-16 23:52:00 -04:00
committed by GitHub
parent 125bcec62e
commit 5374f52531
17 changed files with 702 additions and 339 deletions
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56
python-package/xgboost/core.py
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@@ -7,7 +7,6 @@ import collections
# pylint: disable=no-name-in-module,import-error
from collections.abc import Mapping # Python 3
# pylint: enable=no-name-in-module,import-error
import math
import ctypes
import os
import re
@@ -235,28 +234,13 @@ def _extract_interface_from_cudf(df, is_info):
'columnar format. For other libraries please ' +
'refer to specific API.')
def get_interface(obj):
return obj.mem.__cuda_array_interface__
array_interfaces = []
for col in df.columns:
data = df[col].data
array_interfaces.append(get_interface(data))
validity_masks = []
for col in df.columns:
if df[col].has_null_mask:
mask_interface = get_interface(df[col].nullmask)
mask_interface['null_count'] = df[col].null_count
validity_masks.append(mask_interface)
else:
validity_masks.append(False)
for i in range(len(df.columns)):
col_interface = array_interfaces[i]
mask_interface = validity_masks[i]
if mask_interface is not False:
col_interface['mask'] = mask_interface
data = df[col]
interface = data.__cuda_array_interface__
if data.has_null_mask:
interface['mask'] = interface['mask'].__cuda_array_interface__
array_interfaces.append(interface)
if is_info:
array_interfaces = array_interfaces[0]
@@ -369,22 +353,6 @@ def _maybe_dt_array(array):
return array
def _check_data(data, missing):
'''The missing value applies only to np.ndarray.'''
is_invalid = (not isinstance(data, np.ndarray)) and (missing is not None)
is_invalid = is_invalid and not math.isnan(missing)
if is_invalid:
raise ValueError(
'missing value only applies to dense input, ' +
'e.g. `numpy.ndarray`.' +
' For a possibly sparse data type: ' + str(type(data)) +
' please remove missing values or set it to nan.' +
' Current missing value is set to: ' + str(missing))
if isinstance(data, list):
warnings.warn('Initializing DMatrix from List is deprecated.',
DeprecationWarning)
class DMatrix(object):
"""Data Matrix used in XGBoost.
@@ -443,7 +411,8 @@ class DMatrix(object):
self._feature_types = feature_types
return
_check_data(data, missing)
if isinstance(data, list):
raise TypeError('Input data can not be a list.')
data, feature_names, feature_types = _maybe_pandas_data(data,
feature_names,
@@ -472,7 +441,7 @@ class DMatrix(object):
elif isinstance(data, DataTable):
self._init_from_dt(data, nthread)
elif _use_columnar_initializer(data):
self._init_from_columnar(data)
self._init_from_columnar(data, missing)
else:
try:
csr = scipy.sparse.csr_matrix(data)
@@ -599,15 +568,18 @@ class DMatrix(object):
nthread))
self.handle = handle
def _init_from_columnar(self, df):
def _init_from_columnar(self, df, missing):
'''Initialize DMatrix from columnar memory format.
'''
interfaces = _extract_interface_from_cudf(df, False)
handle = ctypes.c_void_p()
has_missing = missing is not None
missing = missing if has_missing else np.nan
_check_call(
_LIB.XGDMatrixCreateFromArrayInterfaces(interfaces,
ctypes.byref(handle)))
_LIB.XGDMatrixCreateFromArrayInterfaces(
interfaces, ctypes.c_int32(has_missing),
ctypes.c_float(missing), ctypes.byref(handle)))
self.handle = handle
def __del__(self):