Validate features for inplace predict. (#8359)
This commit is contained in:
Jiaming Yuan
GitHub
@@ -406,10 +406,7 @@ def c_array(
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def _prediction_output(
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shape: CNumericPtr,
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dims: c_bst_ulong,
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predts: CFloatPtr,
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is_cuda: bool
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shape: CNumericPtr, dims: c_bst_ulong, predts: CFloatPtr, is_cuda: bool
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) -> NumpyOrCupy:
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arr_shape = ctypes2numpy(shape, dims.value, np.uint64)
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length = int(np.prod(arr_shape))
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@@ -1555,7 +1552,7 @@ class Booster:
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ctypes.byref(self.handle)))
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for d in cache:
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# Validate feature only after the feature names are saved into booster.
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self._validate_features(d)
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self._validate_dmatrix_features(d)
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if isinstance(model_file, Booster):
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assert self.handle is not None
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@@ -1914,7 +1911,7 @@ class Booster:
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"""
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if not isinstance(dtrain, DMatrix):
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raise TypeError(f"invalid training matrix: {type(dtrain).__name__}")
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self._validate_features(dtrain)
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self._validate_dmatrix_features(dtrain)
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if fobj is None:
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_check_call(_LIB.XGBoosterUpdateOneIter(self.handle,
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@@ -1946,7 +1943,7 @@ class Booster:
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)
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if not isinstance(dtrain, DMatrix):
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raise TypeError(f"invalid training matrix: {type(dtrain).__name__}")
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self._validate_features(dtrain)
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self._validate_dmatrix_features(dtrain)
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_check_call(_LIB.XGBoosterBoostOneIter(self.handle, dtrain.handle,
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c_array(ctypes.c_float, grad),
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@@ -1982,7 +1979,7 @@ class Booster:
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raise TypeError(f"expected DMatrix, got {type(d[0]).__name__}")
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if not isinstance(d[1], str):
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raise TypeError(f"expected string, got {type(d[1]).__name__}")
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self._validate_features(d[0])
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self._validate_dmatrix_features(d[0])
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dmats = c_array(ctypes.c_void_p, [d[0].handle for d in evals])
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evnames = c_array(ctypes.c_char_p, [c_str(d[1]) for d in evals])
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@@ -2033,7 +2030,7 @@ class Booster:
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result: str
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Evaluation result string.
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"""
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self._validate_features(data)
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self._validate_dmatrix_features(data)
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return self.eval_set([(data, name)], iteration)
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# pylint: disable=too-many-function-args
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@@ -2136,7 +2133,7 @@ class Booster:
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if not isinstance(data, DMatrix):
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raise TypeError('Expecting data to be a DMatrix object, got: ', type(data))
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if validate_features:
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self._validate_features(data)
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self._validate_dmatrix_features(data)
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iteration_range = _convert_ntree_limit(self, ntree_limit, iteration_range)
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args = {
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"type": 0,
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@@ -2184,8 +2181,8 @@ class Booster:
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base_margin: Any = None,
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strict_shape: bool = False
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) -> NumpyOrCupy:
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"""Run prediction in-place, Unlike :py:meth:`predict` method, inplace prediction does not
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cache the prediction result.
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"""Run prediction in-place, Unlike :py:meth:`predict` method, inplace prediction
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does not cache the prediction result.
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Calling only ``inplace_predict`` in multiple threads is safe and lock
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free. But the safety does not hold when used in conjunction with other
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@@ -2273,18 +2270,22 @@ class Booster:
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)
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from .data import (
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_is_pandas_df,
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_transform_pandas_df,
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_array_interface,
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_is_cudf_df,
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_is_cupy_array,
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_array_interface,
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_is_pandas_df,
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_transform_pandas_df,
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)
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enable_categorical = _has_categorical(self, data)
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if _is_pandas_df(data):
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data, _, _ = _transform_pandas_df(data, enable_categorical)
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data, fns, _ = _transform_pandas_df(data, enable_categorical)
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if validate_features:
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self._validate_features(fns)
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if isinstance(data, np.ndarray):
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from .data import _ensure_np_dtype
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data, _ = _ensure_np_dtype(data, data.dtype)
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_check_call(
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_LIB.XGBoosterPredictFromDense(
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@@ -2334,10 +2335,13 @@ class Booster:
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return _prediction_output(shape, dims, preds, True)
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if _is_cudf_df(data):
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from .data import _cudf_array_interfaces, _transform_cudf_df
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data, cat_codes, _, _ = _transform_cudf_df(
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data, cat_codes, fns, _ = _transform_cudf_df(
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data, None, None, enable_categorical
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)
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interfaces_str = _cudf_array_interfaces(data, cat_codes)
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if validate_features:
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self._validate_features(fns)
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_check_call(
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_LIB.XGBoosterPredictFromCudaColumnar(
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self.handle,
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@@ -2723,40 +2727,55 @@ class Booster:
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# pylint: disable=no-member
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return df.sort(['Tree', 'Node']).reset_index(drop=True)
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def _validate_features(self, data: DMatrix) -> None:
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"""
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Validate Booster and data's feature_names are identical.
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Set feature_names and feature_types from DMatrix
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"""
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def _validate_dmatrix_features(self, data: DMatrix) -> None:
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if data.num_row() == 0:
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return
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fn = data.feature_names
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ft = data.feature_types
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# Be consistent with versions before 1.7, "validate" actually modifies the
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# booster.
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if self.feature_names is None:
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self.feature_names = data.feature_names
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self.feature_types = data.feature_types
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if data.feature_names is None and self.feature_names is not None:
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raise ValueError(
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"training data did not have the following fields: " +
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", ".join(self.feature_names)
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)
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# Booster can't accept data with different feature names
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if self.feature_names != data.feature_names:
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dat_missing = set(cast(FeatureNames, self.feature_names)) - \
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set(cast(FeatureNames, data.feature_names))
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my_missing = set(cast(FeatureNames, data.feature_names)) - \
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set(cast(FeatureNames, self.feature_names))
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self.feature_names = fn
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if self.feature_types is None:
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self.feature_types = ft
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msg = 'feature_names mismatch: {0} {1}'
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self._validate_features(fn)
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def _validate_features(self, feature_names: Optional[FeatureNames]) -> None:
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if self.feature_names is None:
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return
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if feature_names is None and self.feature_names is not None:
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raise ValueError(
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"training data did not have the following fields: "
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+ ", ".join(self.feature_names)
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)
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if self.feature_names != feature_names:
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dat_missing = set(cast(FeatureNames, self.feature_names)) - set(
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cast(FeatureNames, feature_names)
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)
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my_missing = set(cast(FeatureNames, feature_names)) - set(
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cast(FeatureNames, self.feature_names)
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)
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msg = "feature_names mismatch: {0} {1}"
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if dat_missing:
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msg += ('\nexpected ' + ', '.join(
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str(s) for s in dat_missing) + ' in input data')
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msg += (
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"\nexpected "
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+ ", ".join(str(s) for s in dat_missing)
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+ " in input data"
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)
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if my_missing:
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msg += ('\ntraining data did not have the following fields: ' +
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', '.join(str(s) for s in my_missing))
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msg += (
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"\ntraining data did not have the following fields: "
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+ ", ".join(str(s) for s in my_missing)
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)
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raise ValueError(msg.format(self.feature_names, data.feature_names))
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raise ValueError(msg.format(self.feature_names, feature_names))
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def get_split_value_histogram(
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self,
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