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Validate features for inplace predict. (#8359)

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Jiaming Yuan 2022-10-19 23:05:36 +08:00 committed by GitHub
parent 52977f0cdf
commit c884b9e888
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2 changed files with 78 additions and 52 deletions
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101
python-package/xgboost/core.py
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@ -406,10 +406,7 @@ def c_array(
def _prediction_output( def _prediction_output(
shape: CNumericPtr, shape: CNumericPtr, dims: c_bst_ulong, predts: CFloatPtr, is_cuda: bool
dims: c_bst_ulong,
predts: CFloatPtr,
is_cuda: bool
) -> NumpyOrCupy: ) -> NumpyOrCupy:
arr_shape = ctypes2numpy(shape, dims.value, np.uint64) arr_shape = ctypes2numpy(shape, dims.value, np.uint64)
length = int(np.prod(arr_shape)) length = int(np.prod(arr_shape))
@ -1555,7 +1552,7 @@ class Booster:
ctypes.byref(self.handle))) ctypes.byref(self.handle)))
for d in cache: for d in cache:
# Validate feature only after the feature names are saved into booster. # Validate feature only after the feature names are saved into booster.
self._validate_features(d) self._validate_dmatrix_features(d)
if isinstance(model_file, Booster): if isinstance(model_file, Booster):
assert self.handle is not None assert self.handle is not None
@ -1914,7 +1911,7 @@ class Booster:
""" """
if not isinstance(dtrain, DMatrix): if not isinstance(dtrain, DMatrix):
raise TypeError(f"invalid training matrix: {type(dtrain).__name__}") raise TypeError(f"invalid training matrix: {type(dtrain).__name__}")
self._validate_features(dtrain) self._validate_dmatrix_features(dtrain)
if fobj is None: if fobj is None:
_check_call(_LIB.XGBoosterUpdateOneIter(self.handle, _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,
@ -1946,7 +1943,7 @@ class Booster:
) )
if not isinstance(dtrain, DMatrix): if not isinstance(dtrain, DMatrix):
raise TypeError(f"invalid training matrix: {type(dtrain).__name__}") raise TypeError(f"invalid training matrix: {type(dtrain).__name__}")
self._validate_features(dtrain) self._validate_dmatrix_features(dtrain)
_check_call(_LIB.XGBoosterBoostOneIter(self.handle, dtrain.handle, _check_call(_LIB.XGBoosterBoostOneIter(self.handle, dtrain.handle,
c_array(ctypes.c_float, grad), c_array(ctypes.c_float, grad),
@ -1982,7 +1979,7 @@ class Booster:
raise TypeError(f"expected DMatrix, got {type(d[0]).__name__}") raise TypeError(f"expected DMatrix, got {type(d[0]).__name__}")
if not isinstance(d[1], str): if not isinstance(d[1], str):
raise TypeError(f"expected string, got {type(d[1]).__name__}") raise TypeError(f"expected string, got {type(d[1]).__name__}")
self._validate_features(d[0]) self._validate_dmatrix_features(d[0])
dmats = c_array(ctypes.c_void_p, [d[0].handle for d in evals]) dmats = c_array(ctypes.c_void_p, [d[0].handle for d in evals])
evnames = c_array(ctypes.c_char_p, [c_str(d[1]) for d in evals]) evnames = c_array(ctypes.c_char_p, [c_str(d[1]) for d in evals])
@ -2033,7 +2030,7 @@ class Booster:
result: str result: str
Evaluation result string. Evaluation result string.
""" """
self._validate_features(data) self._validate_dmatrix_features(data)
return self.eval_set([(data, name)], iteration) return self.eval_set([(data, name)], iteration)
# pylint: disable=too-many-function-args # pylint: disable=too-many-function-args
@ -2136,7 +2133,7 @@ class Booster:
if not isinstance(data, DMatrix): if not isinstance(data, DMatrix):
raise TypeError('Expecting data to be a DMatrix object, got: ', type(data)) raise TypeError('Expecting data to be a DMatrix object, got: ', type(data))
if validate_features: if validate_features:
self._validate_features(data) self._validate_dmatrix_features(data)
iteration_range = _convert_ntree_limit(self, ntree_limit, iteration_range) iteration_range = _convert_ntree_limit(self, ntree_limit, iteration_range)
args = { args = {
"type": 0, "type": 0,
@ -2184,8 +2181,8 @@ class Booster:
base_margin: Any = None, base_margin: Any = None,
strict_shape: bool = False strict_shape: bool = False
) -> NumpyOrCupy: ) -> NumpyOrCupy:
"""Run prediction in-place, Unlike :py:meth:`predict` method, inplace prediction does not """Run prediction in-place, Unlike :py:meth:`predict` method, inplace prediction
cache the prediction result. does not cache the prediction result.
Calling only ``inplace_predict`` in multiple threads is safe and lock Calling only ``inplace_predict`` in multiple threads is safe and lock
free. But the safety does not hold when used in conjunction with other free. But the safety does not hold when used in conjunction with other
@ -2273,18 +2270,22 @@ class Booster:
) )
from .data import ( from .data import (
_is_pandas_df, _array_interface,
_transform_pandas_df,
_is_cudf_df, _is_cudf_df,
_is_cupy_array, _is_cupy_array,
_array_interface, _is_pandas_df,
_transform_pandas_df,
) )
enable_categorical = _has_categorical(self, data) enable_categorical = _has_categorical(self, data)
if _is_pandas_df(data): if _is_pandas_df(data):
data, _, _ = _transform_pandas_df(data, enable_categorical) data, fns, _ = _transform_pandas_df(data, enable_categorical)
if validate_features:
self._validate_features(fns)
if isinstance(data, np.ndarray): if isinstance(data, np.ndarray):
from .data import _ensure_np_dtype from .data import _ensure_np_dtype
data, _ = _ensure_np_dtype(data, data.dtype) data, _ = _ensure_np_dtype(data, data.dtype)
_check_call( _check_call(
_LIB.XGBoosterPredictFromDense( _LIB.XGBoosterPredictFromDense(
@ -2334,10 +2335,13 @@ class Booster:
return _prediction_output(shape, dims, preds, True) return _prediction_output(shape, dims, preds, True)
if _is_cudf_df(data): if _is_cudf_df(data):
from .data import _cudf_array_interfaces, _transform_cudf_df from .data import _cudf_array_interfaces, _transform_cudf_df
data, cat_codes, _, _ = _transform_cudf_df(
data, cat_codes, fns, _ = _transform_cudf_df(
data, None, None, enable_categorical data, None, None, enable_categorical
) )
interfaces_str = _cudf_array_interfaces(data, cat_codes) interfaces_str = _cudf_array_interfaces(data, cat_codes)
if validate_features:
self._validate_features(fns)
_check_call( _check_call(
_LIB.XGBoosterPredictFromCudaColumnar( _LIB.XGBoosterPredictFromCudaColumnar(
self.handle, self.handle,
@ -2723,40 +2727,55 @@ class Booster:
# pylint: disable=no-member # pylint: disable=no-member
return df.sort(['Tree', 'Node']).reset_index(drop=True) return df.sort(['Tree', 'Node']).reset_index(drop=True)
def _validate_features(self, data: DMatrix) -> None: def _validate_dmatrix_features(self, data: DMatrix) -> None:
"""
Validate Booster and data's feature_names are identical.
Set feature_names and feature_types from DMatrix
"""
if data.num_row() == 0: if data.num_row() == 0:
return return
fn = data.feature_names
ft = data.feature_types
# Be consistent with versions before 1.7, "validate" actually modifies the
# booster.
if self.feature_names is None: if self.feature_names is None:
self.feature_names = data.feature_names self.feature_names = fn
self.feature_types = data.feature_types if self.feature_types is None:
if data.feature_names is None and self.feature_names is not None: self.feature_types = ft
raise ValueError(
"training data did not have the following fields: " +
", ".join(self.feature_names)
)
# Booster can't accept data with different feature names
if self.feature_names != data.feature_names:
dat_missing = set(cast(FeatureNames, self.feature_names)) - \
set(cast(FeatureNames, data.feature_names))
my_missing = set(cast(FeatureNames, data.feature_names)) - \
set(cast(FeatureNames, self.feature_names))
msg = 'feature_names mismatch: {0} {1}' self._validate_features(fn)
def _validate_features(self, feature_names: Optional[FeatureNames]) -> None:
if self.feature_names is None:
return
if feature_names is None and self.feature_names is not None:
raise ValueError(
"training data did not have the following fields: "
+ ", ".join(self.feature_names)
)
if self.feature_names != feature_names:
dat_missing = set(cast(FeatureNames, self.feature_names)) - set(
cast(FeatureNames, feature_names)
)
my_missing = set(cast(FeatureNames, feature_names)) - set(
cast(FeatureNames, self.feature_names)
)
msg = "feature_names mismatch: {0} {1}"
if dat_missing: if dat_missing:
msg += ('\nexpected ' + ', '.join( msg += (
str(s) for s in dat_missing) + ' in input data') "\nexpected "
+ ", ".join(str(s) for s in dat_missing)
+ " in input data"
)
if my_missing: if my_missing:
msg += ('\ntraining data did not have the following fields: ' + msg += (
', '.join(str(s) for s in my_missing)) "\ntraining data did not have the following fields: "
+ ", ".join(str(s) for s in my_missing)
)
raise ValueError(msg.format(self.feature_names, data.feature_names)) raise ValueError(msg.format(self.feature_names, feature_names))
def get_split_value_histogram( def get_split_value_histogram(
self, self,

29
tests/python/test_with_sklearn.py
View File

@ -2,6 +2,7 @@ import collections
import importlib.util import importlib.util
import json import json
import os import os
import random
import tempfile import tempfile
from typing import Callable, Optional from typing import Callable, Optional
@ -998,34 +999,40 @@ def test_deprecate_position_arg():
def test_pandas_input(): def test_pandas_input():
import pandas as pd import pandas as pd
from sklearn.calibration import CalibratedClassifierCV from sklearn.calibration import CalibratedClassifierCV
rng = np.random.RandomState(1994) rng = np.random.RandomState(1994)
kRows = 100 kRows = 100
kCols = 6 kCols = 6
X = rng.randint(low=0, high=2, size=kRows*kCols) X = rng.randint(low=0, high=2, size=kRows * kCols)
X = X.reshape(kRows, kCols) X = X.reshape(kRows, kCols)
df = pd.DataFrame(X) df = pd.DataFrame(X)
feature_names = [] feature_names = []
for i in range(1, kCols): for i in range(1, kCols):
feature_names += ['k'+str(i)] feature_names += ["k" + str(i)]
df.columns = ['status'] + feature_names df.columns = ["status"] + feature_names
target = df['status'] target = df["status"]
train = df.drop(columns=['status']) train = df.drop(columns=["status"])
model = xgb.XGBClassifier() model = xgb.XGBClassifier()
model.fit(train, target) model.fit(train, target)
np.testing.assert_equal(model.feature_names_in_, np.array(feature_names)) np.testing.assert_equal(model.feature_names_in_, np.array(feature_names))
clf_isotonic = CalibratedClassifierCV(model, columns = list(train.columns)
cv='prefit', method='isotonic') random.shuffle(columns, lambda: 0.1)
df_incorrect = df[columns]
with pytest.raises(ValueError):
model.predict(df_incorrect)
clf_isotonic = CalibratedClassifierCV(model, cv="prefit", method="isotonic")
clf_isotonic.fit(train, target) clf_isotonic.fit(train, target)
assert isinstance(clf_isotonic.calibrated_classifiers_[0].base_estimator, assert isinstance(
xgb.XGBClassifier) clf_isotonic.calibrated_classifiers_[0].base_estimator, xgb.XGBClassifier
np.testing.assert_allclose(np.array(clf_isotonic.classes_), )
np.array([0, 1])) np.testing.assert_allclose(np.array(clf_isotonic.classes_), np.array([0, 1]))
def run_feature_weights(X, y, fw, tree_method, model=xgb.XGBRegressor): def run_feature_weights(X, y, fw, tree_method, model=xgb.XGBRegressor):