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Take datatable as row major input. (#8472)

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* Take datatable as row major input.

Try to avoid a transform with dense table.
This commit is contained in:
Jiaming Yuan 2022-11-24 09:20:13 +08:00 committed by GitHub
parent 284dcf8d22
commit e07245f110
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 84 additions and 92 deletions
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49
python-package/xgboost/data.py
View File

@ -44,8 +44,9 @@ _matrix_meta = {"base_margin", "label"}
def _warn_unused_missing(data: DataType, missing: Optional[FloatCompatible]) -> None:
if (missing is not None) and (not np.isnan(missing)):
warnings.warn(
'`missing` is not used for current input data type:' +
str(type(data)), UserWarning)
"`missing` is not used for current input data type:" + str(type(data)),
UserWarning,
)
def _check_complex(data: DataType) -> None:
@ -459,12 +460,9 @@ def _from_pandas_series(
def _is_dt_df(data: DataType) -> bool:
return lazy_isinstance(data, 'datatable', 'Frame') or \
lazy_isinstance(data, 'datatable', 'DataTable')
_dt_type_mapper = {'bool': 'bool', 'int': 'int', 'real': 'float'}
_dt_type_mapper2 = {'bool': 'i', 'int': 'int', 'real': 'float'}
return lazy_isinstance(data, "datatable", "Frame") or lazy_isinstance(
data, "datatable", "DataTable"
)
def _transform_dt_df(
@ -475,8 +473,10 @@ def _transform_dt_df(
meta_type: Optional[NumpyDType] = None,
) -> Tuple[np.ndarray, Optional[FeatureNames], Optional[FeatureTypes]]:
"""Validate feature names and types if data table"""
_dt_type_mapper = {"bool": "bool", "int": "int", "real": "float"}
_dt_type_mapper2 = {"bool": "i", "int": "int", "real": "float"}
if meta and data.shape[1] > 1:
raise ValueError('DataTable for meta info cannot have multiple columns')
raise ValueError("DataTable for meta info cannot have multiple columns")
if meta:
meta_type = "float" if meta_type is None else meta_type
# below requires new dt version
@ -485,23 +485,23 @@ def _transform_dt_df(
return data, None, None
data_types_names = tuple(lt.name for lt in data.ltypes)
bad_fields = [data.names[i]
bad_fields = [
data.names[i]
for i, type_name in enumerate(data_types_names)
if type_name not in _dt_type_mapper]
if type_name not in _dt_type_mapper
]
if bad_fields:
msg = """DataFrame.types for data must be int, float or bool.
Did not expect the data types in fields """
raise ValueError(msg + ', '.join(bad_fields))
raise ValueError(msg + ", ".join(bad_fields))
if feature_names is None and meta is None:
feature_names = data.names
# always return stypes for dt ingestion
if feature_types is not None:
raise ValueError(
'DataTable has own feature types, cannot pass them in.')
feature_types = np.vectorize(_dt_type_mapper2.get)(
data_types_names).tolist()
raise ValueError("DataTable has own feature types, cannot pass them in.")
feature_types = np.vectorize(_dt_type_mapper2.get)(data_types_names).tolist()
return data, feature_names, feature_types
@ -517,7 +517,8 @@ def _from_dt_df(
if enable_categorical:
raise ValueError("categorical data in datatable is not supported yet.")
data, feature_names, feature_types = _transform_dt_df(
data, feature_names, feature_types, None, None)
data, feature_names, feature_types, None, None
)
ptrs = (ctypes.c_void_p * data.ncols)()
if hasattr(data, "internal") and hasattr(data.internal, "column"):
@ -531,6 +532,7 @@ def _from_dt_df(
from datatable.internal import (
frame_column_data_r, # pylint: disable=no-name-in-module
)
for icol in range(data.ncols):
ptrs[icol] = frame_column_data_r(data, icol)
@ -538,16 +540,21 @@ def _from_dt_df(
feature_type_strings = (ctypes.c_char_p * data.ncols)()
for icol in range(data.ncols):
feature_type_strings[icol] = ctypes.c_char_p(
data.stypes[icol].name.encode('utf-8'))
data.stypes[icol].name.encode("utf-8")
)
_warn_unused_missing(data, missing)
handle = ctypes.c_void_p()
_check_call(_LIB.XGDMatrixCreateFromDT(
ptrs, feature_type_strings,
_check_call(
_LIB.XGDMatrixCreateFromDT(
ptrs,
feature_type_strings,
c_bst_ulong(data.shape[0]),
c_bst_ulong(data.shape[1]),
ctypes.byref(handle),
ctypes.c_int(nthread)))
ctypes.c_int(nthread),
)
)
return handle, feature_names, feature_types

81
src/data/adapter.h
View File

@ -473,16 +473,7 @@ class CSCAdapter : public detail::SingleBatchDataIter<CSCAdapterBatch> {
};
class DataTableAdapterBatch : public detail::NoMetaInfo {
public:
DataTableAdapterBatch(void** data, const char** feature_stypes,
size_t num_rows, size_t num_features)
: data_(data),
feature_stypes_(feature_stypes),
num_features_(num_features),
num_rows_(num_rows) {}
private:
enum class DTType : uint8_t {
enum class DTType : std::uint8_t {
kFloat32 = 0,
kFloat64 = 1,
kBool8 = 2,
@ -493,7 +484,7 @@ class DataTableAdapterBatch : public detail::NoMetaInfo {
kUnknown = 7
};
DTType DTGetType(std::string type_string) const {
static DTType DTGetType(std::string type_string) {
if (type_string == "float32") {
return DTType::kFloat32;
} else if (type_string == "float64") {
@ -514,8 +505,23 @@ class DataTableAdapterBatch : public detail::NoMetaInfo {
}
}
public:
DataTableAdapterBatch(void const* const* const data, char const* const* feature_stypes,
std::size_t num_rows, std::size_t num_features)
: data_(data), num_rows_(num_rows) {
CHECK(feature_types_.empty());
std::transform(feature_stypes, feature_stypes + num_features,
std::back_inserter(feature_types_),
[](char const* stype) { return DTGetType(stype); });
}
private:
class Line {
float DTGetValue(const void* column, DTType dt_type, size_t ridx) const {
std::size_t row_idx_;
void const* const* const data_;
std::vector<DTType> const& feature_types_;
float DTGetValue(void const* column, DTType dt_type, std::size_t ridx) const {
float missing = std::numeric_limits<float>::quiet_NaN();
switch (dt_type) {
case DTType::kFloat32: {
@ -544,8 +550,7 @@ class DataTableAdapterBatch : public detail::NoMetaInfo {
}
case DTType::kInt64: {
int64_t val = reinterpret_cast<const int64_t*>(column)[ridx];
return val != -9223372036854775807 - 1 ? static_cast<float>(val)
: missing;
return val != -9223372036854775807 - 1 ? static_cast<float>(val) : missing;
}
default: {
LOG(FATAL) << "Unknown data table type.";
@ -555,51 +560,41 @@ class DataTableAdapterBatch : public detail::NoMetaInfo {
}
public:
Line(DTType type, size_t size, size_t column_idx, const void* column)
: type_(type), size_(size), column_idx_(column_idx), column_(column) {}
size_t Size() const { return size_; }
COOTuple GetElement(size_t idx) const {
return COOTuple{idx, column_idx_, DTGetValue(column_, type_, idx)};
Line(std::size_t ridx, void const* const* const data, std::vector<DTType> const& ft)
: row_idx_{ridx}, data_{data}, feature_types_{ft} {}
std::size_t Size() const { return feature_types_.size(); }
COOTuple GetElement(std::size_t idx) const {
return COOTuple{row_idx_, idx, DTGetValue(data_[idx], feature_types_[idx], row_idx_)};
}
private:
DTType type_;
size_t size_;
size_t column_idx_;
const void* column_;
};
public:
size_t Size() const { return num_features_; }
const Line GetLine(size_t idx) const {
return Line(DTGetType(feature_stypes_[idx]), num_rows_, idx, data_[idx]);
}
static constexpr bool kIsRowMajor = false;
size_t Size() const { return num_rows_; }
const Line GetLine(std::size_t ridx) const { return {ridx, data_, feature_types_}; }
static constexpr bool kIsRowMajor = true;
private:
void** data_;
const char** feature_stypes_;
size_t num_features_;
size_t num_rows_;
void const* const* const data_;
std::vector<DTType> feature_types_;
std::size_t num_rows_;
};
class DataTableAdapter
: public detail::SingleBatchDataIter<DataTableAdapterBatch> {
class DataTableAdapter : public detail::SingleBatchDataIter<DataTableAdapterBatch> {
public:
DataTableAdapter(void** data, const char** feature_stypes, size_t num_rows,
size_t num_features)
DataTableAdapter(void** data, const char** feature_stypes, std::size_t num_rows,
std::size_t num_features)
: batch_(data, feature_stypes, num_rows, num_features),
num_rows_(num_rows),
num_columns_(num_features) {}
const DataTableAdapterBatch& Value() const override { return batch_; }
size_t NumRows() const { return num_rows_; }
size_t NumColumns() const { return num_columns_; }
std::size_t NumRows() const { return num_rows_; }
std::size_t NumColumns() const { return num_columns_; }
private:
DataTableAdapterBatch batch_;
size_t num_rows_;
size_t num_columns_;
std::size_t num_rows_;
std::size_t num_columns_;
};
class FileAdapterBatch {

2
tests/ci_build/lint_python.py
View File

@ -144,6 +144,7 @@ def main(args: argparse.Namespace) -> None:
# tests
"tests/python/test_config.py",
"tests/python/test_data_iterator.py",
"tests/python/test_dt.py",
"tests/python/test_quantile_dmatrix.py",
"tests/python-gpu/test_gpu_data_iterator.py",
"tests/test_distributed/test_with_spark/",
@ -194,6 +195,7 @@ def main(args: argparse.Namespace) -> None:
"demo/guide-python/external_memory.py",
"demo/guide-python/cat_in_the_dat.py",
# tests
"tests/python/test_dt.py",
"tests/python/test_data_iterator.py",
"tests/python-gpu/test_gpu_data_iterator.py",
"tests/test_distributed/test_with_spark/test_data.py",

36
tests/python/test_dt.py
View File

@ -2,52 +2,40 @@ import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
try:
import datatable as dt
import pandas as pd
except ImportError:
pass
pytestmark = pytest.mark.skipif(
tm.no_dt()['condition'] or tm.no_pandas()['condition'],
reason=tm.no_dt()['reason'] + ' or ' + tm.no_pandas()['reason'])
dt = pytest.importorskip("datatable")
pd = pytest.importorskip("pandas")
class TestDataTable:
def test_dt(self):
df = pd.DataFrame([[1, 2., True], [2, 3., False]],
columns=['a', 'b', 'c'])
def test_dt(self) -> None:
df = pd.DataFrame([[1, 2.0, True], [2, 3.0, False]], columns=["a", "b", "c"])
dtable = dt.Frame(df)
labels = dt.Frame([1, 2])
dm = xgb.DMatrix(dtable, label=labels)
assert dm.feature_names == ['a', 'b', 'c']
assert dm.feature_types == ['int', 'float', 'i']
assert dm.feature_names == ["a", "b", "c"]
assert dm.feature_types == ["int", "float", "i"]
assert dm.num_row() == 2
assert dm.num_col() == 3
np.testing.assert_array_equal(np.array([1, 2]), dm.get_label())
# overwrite feature_names
dm = xgb.DMatrix(dtable, label=pd.Series([1, 2]),
feature_names=['x', 'y', 'z'])
assert dm.feature_names == ['x', 'y', 'z']
dm = xgb.DMatrix(dtable, label=pd.Series([1, 2]), feature_names=["x", "y", "z"])
assert dm.feature_names == ["x", "y", "z"]
assert dm.num_row() == 2
assert dm.num_col() == 3
# incorrect dtypes
df = pd.DataFrame([[1, 2., 'x'], [2, 3., 'y']],
columns=['a', 'b', 'c'])
df = pd.DataFrame([[1, 2.0, "x"], [2, 3.0, "y"]], columns=["a", "b", "c"])
dtable = dt.Frame(df)
with pytest.raises(ValueError):
xgb.DMatrix(dtable)
df = pd.DataFrame({'A=1': [1, 2, 3], 'A=2': [4, 5, 6]})
df = pd.DataFrame({"A=1": [1, 2, 3], "A=2": [4, 5, 6]})
dtable = dt.Frame(df)
dm = xgb.DMatrix(dtable)
assert dm.feature_names == ['A=1', 'A=2']
assert dm.feature_types == ['int', 'int']
assert dm.feature_names == ["A=1", "A=2"]
assert dm.feature_types == ["int", "int"]
assert dm.num_row() == 3
assert dm.num_col() == 2