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Unverified Commit b8cc8738 authored by James Lamb's avatar James Lamb Committed by GitHub
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[python-package] make Booster and Dataset 'handle' attributes private (fixes #5313) (#5947)

parent 9edea60e
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Showing with 129 additions and 128 deletions
python-package/lightgbm/basic.py
View file @ b8cc8738
......@@ -852,7 +852,7 @@ class _InnerPredictor:
pred_parameter: dict or None, optional (default=None)
Other parameters for the prediction.
"""
self.handle = ctypes.c_void_p()
self._handle = ctypes.c_void_p()
self.__is_manage_handle = True
if model_file is not None:
"""Prediction task"""
......@@ -860,20 +860,20 @@ class _InnerPredictor:
_safe_call(_LIB.LGBM_BoosterCreateFromModelfile(
_c_str(str(model_file)),
ctypes.byref(out_num_iterations),
ctypes.byref(self.handle)))
ctypes.byref(self._handle)))
out_num_class = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetNumClasses(
self.handle,
self._handle,
ctypes.byref(out_num_class)))
self.num_class = out_num_class.value
self.num_total_iteration = out_num_iterations.value
self.pandas_categorical = _load_pandas_categorical(file_name=model_file)
elif booster_handle is not None:
self.__is_manage_handle = False
self.handle = booster_handle
self._handle = booster_handle
out_num_class = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetNumClasses(
self.handle,
self._handle,
ctypes.byref(out_num_class)))
self.num_class = out_num_class.value
self.num_total_iteration = self.current_iteration()
......@@ -887,13 +887,14 @@ class _InnerPredictor:
def __del__(self) -> None:
try:
if self.__is_manage_handle:
_safe_call(_LIB.LGBM_BoosterFree(self.handle))
_safe_call(_LIB.LGBM_BoosterFree(self._handle))
except AttributeError:
pass
def __getstate__(self) -> Dict[str, Any]:
this = self.__dict__.copy()
this.pop('handle', None)
this.pop('_handle', None)
return this
def predict(
......@@ -945,7 +946,7 @@ class _InnerPredictor:
ptr_names[:] = [x.encode('utf-8') for x in data_names]
_safe_call(
_LIB.LGBM_BoosterValidateFeatureNames(
self.handle,
self._handle,
ptr_names,
ctypes.c_int(len(data_names)),
)
......@@ -963,7 +964,7 @@ class _InnerPredictor:
if isinstance(data, (str, Path)):
with _TempFile() as f:
_safe_call(_LIB.LGBM_BoosterPredictForFile(
self.handle,
self._handle,
_c_str(str(data)),
ctypes.c_int(int_data_has_header),
ctypes.c_int(predict_type),
......@@ -1049,7 +1050,7 @@ class _InnerPredictor:
'and then concatenate predictions for them')
n_preds = ctypes.c_int64(0)
_safe_call(_LIB.LGBM_BoosterCalcNumPredict(
self.handle,
self._handle,
ctypes.c_int(nrow),
ctypes.c_int(predict_type),
ctypes.c_int(start_iteration),
......@@ -1082,7 +1083,7 @@ class _InnerPredictor:
raise ValueError("Wrong length of pre-allocated predict array")
out_num_preds = ctypes.c_int64(0)
_safe_call(_LIB.LGBM_BoosterPredictForMat(
self.handle,
self._handle,
ptr_data,
ctypes.c_int(type_ptr_data),
ctypes.c_int32(mat.shape[0]),
......@@ -1219,7 +1220,7 @@ class _InnerPredictor:
csr_indices = csr.indices.astype(np.int32, copy=False)
_safe_call(_LIB.LGBM_BoosterPredictForCSR(
self.handle,
self._handle,
ptr_indptr,
ctypes.c_int(type_ptr_indptr),
csr_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
......@@ -1262,7 +1263,7 @@ class _InnerPredictor:
out_ptr_data = ctypes.POINTER(ctypes.c_double)()
out_shape = np.empty(2, dtype=np.int64)
_safe_call(_LIB.LGBM_BoosterPredictSparseOutput(
self.handle,
self._handle,
ptr_indptr,
ctypes.c_int(type_ptr_indptr),
csr_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
......@@ -1359,7 +1360,7 @@ class _InnerPredictor:
out_ptr_data = ctypes.POINTER(ctypes.c_double)()
out_shape = np.empty(2, dtype=np.int64)
_safe_call(_LIB.LGBM_BoosterPredictSparseOutput(
self.handle,
self._handle,
ptr_indptr,
ctypes.c_int(type_ptr_indptr),
csc_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
......@@ -1429,7 +1430,7 @@ class _InnerPredictor:
csc_indices = csc.indices.astype(np.int32, copy=False)
_safe_call(_LIB.LGBM_BoosterPredictForCSC(
self.handle,
self._handle,
ptr_indptr,
ctypes.c_int(type_ptr_indptr),
csc_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
......@@ -1458,7 +1459,7 @@ class _InnerPredictor:
"""
out_cur_iter = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetCurrentIteration(
self.handle,
self._handle,
ctypes.byref(out_cur_iter)))
return out_cur_iter.value
......@@ -1518,7 +1519,7 @@ class Dataset:
free_raw_data : bool, optional (default=True)
If True, raw data is freed after constructing inner Dataset.
"""
self.handle: Optional[_DatasetHandle] = None
self._handle: Optional[_DatasetHandle] = None
self.data = data
self.label = label
self.reference = reference
......@@ -1594,11 +1595,11 @@ class Dataset:
self : Dataset
Constructed Dataset object.
"""
self.handle = ctypes.c_void_p()
self._handle = ctypes.c_void_p()
_safe_call(_LIB.LGBM_DatasetCreateByReference(
ref_dataset,
ctypes.c_int64(total_nrow),
ctypes.byref(self.handle),
ctypes.byref(self._handle),
))
return self
......@@ -1649,7 +1650,7 @@ class Dataset:
num_per_col = np.array([len(d) for d in sample_indices], dtype=np.int32)
num_per_col_ptr, _, _ = _c_int_array(num_per_col)
self.handle = ctypes.c_void_p()
self._handle = ctypes.c_void_p()
params_str = _param_dict_to_str(self.get_params())
_safe_call(_LIB.LGBM_DatasetCreateFromSampledColumn(
ctypes.cast(sample_col_ptr, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))),
......@@ -1660,7 +1661,7 @@ class Dataset:
ctypes.c_int32(total_nrow),
ctypes.c_int64(total_nrow),
_c_str(params_str),
ctypes.byref(self.handle),
ctypes.byref(self._handle),
))
return self
......@@ -1682,7 +1683,7 @@ class Dataset:
data_ptr, data_type, _ = _c_float_array(data)
_safe_call(_LIB.LGBM_DatasetPushRows(
self.handle,
self._handle,
data_ptr,
data_type,
ctypes.c_int32(nrow),
......@@ -1728,9 +1729,9 @@ class Dataset:
return {}
def _free_handle(self) -> "Dataset":
if self.handle is not None:
_safe_call(_LIB.LGBM_DatasetFree(self.handle))
self.handle = None
if self._handle is not None:
_safe_call(_LIB.LGBM_DatasetFree(self._handle))
self._handle = None
self._need_slice = True
if self.used_indices is not None:
self.data = None
......@@ -1791,7 +1792,7 @@ class Dataset:
params: Optional[Dict[str, Any]]
) -> "Dataset":
if data is None:
self.handle = None
self._handle = None
return self
if reference is not None:
self.pandas_categorical = reference.pandas_categorical
......@@ -1835,17 +1836,17 @@ class Dataset:
# process for reference dataset
ref_dataset = None
if isinstance(reference, Dataset):
ref_dataset = reference.construct().handle
ref_dataset = reference.construct()._handle
elif reference is not None:
raise TypeError('Reference dataset should be None or dataset instance')
# start construct data
if isinstance(data, (str, Path)):
self.handle = ctypes.c_void_p()
self._handle = ctypes.c_void_p()
_safe_call(_LIB.LGBM_DatasetCreateFromFile(
_c_str(str(data)),
_c_str(params_str),
ref_dataset,
ctypes.byref(self.handle)))
ctypes.byref(self._handle)))
elif isinstance(data, scipy.sparse.csr_matrix):
self.__init_from_csr(data, params_str, ref_dataset)
elif isinstance(data, scipy.sparse.csc_matrix):
......@@ -1978,7 +1979,7 @@ class Dataset:
if len(mat.shape) != 2:
raise ValueError('Input numpy.ndarray must be 2 dimensional')
self.handle = ctypes.c_void_p()
self._handle = ctypes.c_void_p()
if mat.dtype == np.float32 or mat.dtype == np.float64:
data = np.array(mat.reshape(mat.size), dtype=mat.dtype, copy=False)
else: # change non-float data to float data, need to copy
......@@ -1993,7 +1994,7 @@ class Dataset:
ctypes.c_int(_C_API_IS_ROW_MAJOR),
_c_str(params_str),
ref_dataset,
ctypes.byref(self.handle)))
ctypes.byref(self._handle)))
return self
def __init_from_list_np2d(
......@@ -2035,7 +2036,7 @@ class Dataset:
type_ptr_data = chunk_type_ptr_data
holders.append(holder)
self.handle = ctypes.c_void_p()
self._handle = ctypes.c_void_p()
_safe_call(_LIB.LGBM_DatasetCreateFromMats(
ctypes.c_int32(len(mats)),
ctypes.cast(ptr_data, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))),
......@@ -2045,7 +2046,7 @@ class Dataset:
ctypes.c_int(_C_API_IS_ROW_MAJOR),
_c_str(params_str),
ref_dataset,
ctypes.byref(self.handle)))
ctypes.byref(self._handle)))
return self
def __init_from_csr(
......@@ -2057,7 +2058,7 @@ class Dataset:
"""Initialize data from a CSR matrix."""
if len(csr.indices) != len(csr.data):
raise ValueError(f'Length mismatch: {len(csr.indices)} vs {len(csr.data)}')
self.handle = ctypes.c_void_p()
self._handle = ctypes.c_void_p()
ptr_indptr, type_ptr_indptr, __ = _c_int_array(csr.indptr)
ptr_data, type_ptr_data, _ = _c_float_array(csr.data)
......@@ -2076,7 +2077,7 @@ class Dataset:
ctypes.c_int64(csr.shape[1]),
_c_str(params_str),
ref_dataset,
ctypes.byref(self.handle)))
ctypes.byref(self._handle)))
return self
def __init_from_csc(
......@@ -2088,7 +2089,7 @@ class Dataset:
"""Initialize data from a CSC matrix."""
if len(csc.indices) != len(csc.data):
raise ValueError(f'Length mismatch: {len(csc.indices)} vs {len(csc.data)}')
self.handle = ctypes.c_void_p()
self._handle = ctypes.c_void_p()
ptr_indptr, type_ptr_indptr, __ = _c_int_array(csc.indptr)
ptr_data, type_ptr_data, _ = _c_float_array(csc.data)
......@@ -2107,7 +2108,7 @@ class Dataset:
ctypes.c_int64(csc.shape[0]),
_c_str(params_str),
ref_dataset,
ctypes.byref(self.handle)))
ctypes.byref(self._handle)))
return self
@staticmethod
......@@ -2156,7 +2157,7 @@ class Dataset:
self : Dataset
Constructed Dataset object.
"""
if self.handle is None:
if self._handle is None:
if self.reference is not None:
reference_params = self.reference.get_params()
params = self.get_params()
......@@ -2182,14 +2183,14 @@ class Dataset:
group_info = np.array(self.reference.group).astype(np.int32, copy=False)
_, self.group = np.unique(np.repeat(range(len(group_info)), repeats=group_info)[self.used_indices],
return_counts=True)
self.handle = ctypes.c_void_p()
self._handle = ctypes.c_void_p()
params_str = _param_dict_to_str(self.params)
_safe_call(_LIB.LGBM_DatasetGetSubset(
self.reference.construct().handle,
self.reference.construct()._handle,
used_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
ctypes.c_int32(used_indices.shape[0]),
_c_str(params_str),
ctypes.byref(self.handle)))
ctypes.byref(self._handle)))
if not self.free_raw_data:
self.get_data()
if self.group is not None:
......@@ -2305,7 +2306,7 @@ class Dataset:
Returns self.
"""
_safe_call(_LIB.LGBM_DatasetSaveBinary(
self.construct().handle,
self.construct()._handle,
_c_str(str(filename))))
return self
......@@ -2321,7 +2322,7 @@ class Dataset:
self._params_back_up = deepcopy(self.params)
self.params.update(params)
if self.handle is None:
if self._handle is None:
update()
elif params is not None:
ret = _LIB.LGBM_DatasetUpdateParamChecking(
......@@ -2337,7 +2338,7 @@ class Dataset:
return self
def _reverse_update_params(self) -> "Dataset":
if self.handle is None:
if self._handle is None:
self.params = deepcopy(self._params_back_up)
self._params_back_up = None
return self
......@@ -2361,12 +2362,12 @@ class Dataset:
self : Dataset
Dataset with set property.
"""
if self.handle is None:
if self._handle is None:
raise Exception(f"Cannot set {field_name} before construct dataset")
if data is None:
# set to None
_safe_call(_LIB.LGBM_DatasetSetField(
self.handle,
self._handle,
_c_str(field_name),
None,
ctypes.c_int(0),
......@@ -2399,7 +2400,7 @@ class Dataset:
if type_data != _FIELD_TYPE_MAPPER[field_name]:
raise TypeError("Input type error for set_field")
_safe_call(_LIB.LGBM_DatasetSetField(
self.handle,
self._handle,
_c_str(field_name),
ptr_data,
ctypes.c_int(len(data)),
......@@ -2420,13 +2421,13 @@ class Dataset:
info : numpy array or None
A numpy array with information from the Dataset.
"""
if self.handle is None:
if self._handle is None:
raise Exception(f"Cannot get {field_name} before construct Dataset")
tmp_out_len = ctypes.c_int(0)
out_type = ctypes.c_int(0)
ret = ctypes.POINTER(ctypes.c_void_p)()
_safe_call(_LIB.LGBM_DatasetGetField(
self.handle,
self._handle,
_c_str(field_name),
ctypes.byref(tmp_out_len),
ctypes.byref(ret),
......@@ -2498,7 +2499,7 @@ class Dataset:
elif isinstance(predictor, _InnerPredictor) and isinstance(self._predictor, _InnerPredictor):
if (predictor == self._predictor) and (predictor.current_iteration() == self._predictor.current_iteration()):
return self
if self.handle is None:
if self._handle is None:
self._predictor = predictor
elif self.data is not None:
self._predictor = predictor
......@@ -2560,12 +2561,12 @@ class Dataset:
"""
if feature_name != 'auto':
self.feature_name = feature_name
if self.handle is not None and feature_name is not None and feature_name != 'auto':
if self._handle is not None and feature_name is not None and feature_name != 'auto':
if len(feature_name) != self.num_feature():
raise ValueError(f"Length of feature_name({len(feature_name)}) and num_feature({self.num_feature()}) don't match")
c_feature_name = [_c_str(name) for name in feature_name]
_safe_call(_LIB.LGBM_DatasetSetFeatureNames(
self.handle,
self._handle,
_c_array(ctypes.c_char_p, c_feature_name),
ctypes.c_int(len(feature_name))))
return self
......@@ -2584,7 +2585,7 @@ class Dataset:
Dataset with set label.
"""
self.label = label
if self.handle is not None:
if self._handle is not None:
if isinstance(label, pd_DataFrame):
if len(label.columns) > 1:
raise ValueError('DataFrame for label cannot have multiple columns')
......@@ -2625,7 +2626,7 @@ class Dataset:
if weight is not None and np.all(weight == 1):
weight = None
self.weight = weight
if self.handle is not None and weight is not None:
if self._handle is not None and weight is not None:
weight = _list_to_1d_numpy(weight, dtype=np.float32, name='weight')
self.set_field('weight', weight)
self.weight = self.get_field('weight') # original values can be modified at cpp side
......@@ -2648,7 +2649,7 @@ class Dataset:
Dataset with set init score.
"""
self.init_score = init_score
if self.handle is not None and init_score is not None:
if self._handle is not None and init_score is not None:
self.set_field('init_score', init_score)
self.init_score = self.get_field('init_score') # original values can be modified at cpp side
return self
......@@ -2674,7 +2675,7 @@ class Dataset:
Dataset with set group.
"""
self.group = group
if self.handle is not None and group is not None:
if self._handle is not None and group is not None:
group = _list_to_1d_numpy(group, dtype=np.int32, name='group')
self.set_field('group', group)
return self
......@@ -2687,7 +2688,7 @@ class Dataset:
feature_names : list of str
The names of columns (features) in the Dataset.
"""
if self.handle is None:
if self._handle is None:
raise LightGBMError("Cannot get feature_name before construct dataset")
num_feature = self.num_feature()
tmp_out_len = ctypes.c_int(0)
......@@ -2696,7 +2697,7 @@ class Dataset:
string_buffers = [ctypes.create_string_buffer(reserved_string_buffer_size) for _ in range(num_feature)]
ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers))
_safe_call(_LIB.LGBM_DatasetGetFeatureNames(
self.handle,
self._handle,
ctypes.c_int(num_feature),
ctypes.byref(tmp_out_len),
ctypes.c_size_t(reserved_string_buffer_size),
......@@ -2710,7 +2711,7 @@ class Dataset:
string_buffers = [ctypes.create_string_buffer(actual_string_buffer_size) for _ in range(num_feature)]
ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers))
_safe_call(_LIB.LGBM_DatasetGetFeatureNames(
self.handle,
self._handle,
ctypes.c_int(num_feature),
ctypes.byref(tmp_out_len),
ctypes.c_size_t(actual_string_buffer_size),
......@@ -2762,7 +2763,7 @@ class Dataset:
data : str, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse, Sequence, list of Sequence or list of numpy array or None
Raw data used in the Dataset construction.
"""
if self.handle is None:
if self._handle is None:
raise Exception("Cannot get data before construct Dataset")
if self._need_slice and self.used_indices is not None and self.reference is not None:
self.data = self.reference.data
......@@ -2813,9 +2814,9 @@ class Dataset:
number_of_rows : int
The number of rows in the Dataset.
"""
if self.handle is not None:
if self._handle is not None:
ret = ctypes.c_int(0)
_safe_call(_LIB.LGBM_DatasetGetNumData(self.handle,
_safe_call(_LIB.LGBM_DatasetGetNumData(self._handle,
ctypes.byref(ret)))
return ret.value
else:
......@@ -2829,9 +2830,9 @@ class Dataset:
number_of_columns : int
The number of columns (features) in the Dataset.
"""
if self.handle is not None:
if self._handle is not None:
ret = ctypes.c_int(0)
_safe_call(_LIB.LGBM_DatasetGetNumFeature(self.handle,
_safe_call(_LIB.LGBM_DatasetGetNumFeature(self._handle,
ctypes.byref(ret)))
return ret.value
else:
......@@ -2850,13 +2851,13 @@ class Dataset:
number_of_bins : int
The number of constructed bins for the feature in the Dataset.
"""
if self.handle is not None:
if self._handle is not None:
if isinstance(feature, str):
feature_index = self.feature_name.index(feature)
else:
feature_index = feature
ret = ctypes.c_int(0)
_safe_call(_LIB.LGBM_DatasetGetFeatureNumBin(self.handle,
_safe_call(_LIB.LGBM_DatasetGetFeatureNumBin(self._handle,
ctypes.c_int(feature_index),
ctypes.byref(ret)))
return ret.value
......@@ -2908,9 +2909,9 @@ class Dataset:
self : Dataset
Dataset with the new features added.
"""
if self.handle is None or other.handle is None:
if self._handle is None or other._handle is None:
raise ValueError('Both source and target Datasets must be constructed before adding features')
_safe_call(_LIB.LGBM_DatasetAddFeaturesFrom(self.handle, other.handle))
_safe_call(_LIB.LGBM_DatasetAddFeaturesFrom(self._handle, other._handle))
was_none = self.data is None
old_self_data_type = type(self.data).__name__
if other.data is None:
......@@ -2998,7 +2999,7 @@ class Dataset:
Returns self.
"""
_safe_call(_LIB.LGBM_DatasetDumpText(
self.construct().handle,
self.construct()._handle,
_c_str(str(filename))))
return self
......@@ -3042,7 +3043,7 @@ class Booster:
model_str : str or None, optional (default=None)
Model will be loaded from this string.
"""
self.handle = None
self._handle = None
self._network = False
self.__need_reload_eval_info = True
self._train_data_name = "training"
......@@ -3093,11 +3094,11 @@ class Booster:
# copy the parameters from train_set
params.update(train_set.get_params())
params_str = _param_dict_to_str(params)
self.handle = ctypes.c_void_p()
self._handle = ctypes.c_void_p()
_safe_call(_LIB.LGBM_BoosterCreate(
train_set.handle,
train_set._handle,
_c_str(params_str),
ctypes.byref(self.handle)))
ctypes.byref(self._handle)))
# save reference to data
self.train_set = train_set
self.valid_sets: List[Dataset] = []
......@@ -3106,11 +3107,11 @@ class Booster:
self.__init_predictor = train_set._predictor
if self.__init_predictor is not None:
_safe_call(_LIB.LGBM_BoosterMerge(
self.handle,
self.__init_predictor.handle))
self._handle,
self.__init_predictor._handle))
out_num_class = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetNumClasses(
self.handle,
self._handle,
ctypes.byref(out_num_class)))
self.__num_class = out_num_class.value
# buffer for inner predict
......@@ -3122,14 +3123,14 @@ class Booster:
elif model_file is not None:
# Prediction task
out_num_iterations = ctypes.c_int(0)
self.handle = ctypes.c_void_p()
self._handle = ctypes.c_void_p()
_safe_call(_LIB.LGBM_BoosterCreateFromModelfile(
_c_str(str(model_file)),
ctypes.byref(out_num_iterations),
ctypes.byref(self.handle)))
ctypes.byref(self._handle)))
out_num_class = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetNumClasses(
self.handle,
self._handle,
ctypes.byref(out_num_class)))
self.__num_class = out_num_class.value
self.pandas_categorical = _load_pandas_categorical(file_name=model_file)
......@@ -3150,8 +3151,8 @@ class Booster:
except AttributeError:
pass
try:
if self.handle is not None:
_safe_call(_LIB.LGBM_BoosterFree(self.handle))
if self._handle is not None:
_safe_call(_LIB.LGBM_BoosterFree(self._handle))
except AttributeError:
pass
......@@ -3165,15 +3166,15 @@ class Booster:
def __getstate__(self) -> Dict[str, Any]:
this = self.__dict__.copy()
handle = this['handle']
handle = this['_handle']
this.pop('train_set', None)
this.pop('valid_sets', None)
if handle is not None:
this["handle"] = self.model_to_string(num_iteration=-1)
this["_handle"] = self.model_to_string(num_iteration=-1)
return this
def __setstate__(self, state: Dict[str, Any]) -> None:
model_str = state.get('handle', None)
model_str = state.get('_handle', state.get('handle', None))
if model_str is not None:
handle = ctypes.c_void_p()
out_num_iterations = ctypes.c_int(0)
......@@ -3181,7 +3182,7 @@ class Booster:
_c_str(model_str),
ctypes.byref(out_num_iterations),
ctypes.byref(handle)))
state['handle'] = handle
state['_handle'] = handle
self.__dict__.update(state)
def _get_loaded_param(self) -> Dict[str, Any]:
......@@ -3190,7 +3191,7 @@ class Booster:
string_buffer = ctypes.create_string_buffer(buffer_len)
ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
_safe_call(_LIB.LGBM_BoosterGetLoadedParam(
self.handle,
self._handle,
ctypes.c_int64(buffer_len),
ctypes.byref(tmp_out_len),
ptr_string_buffer))
......@@ -3200,7 +3201,7 @@ class Booster:
string_buffer = ctypes.create_string_buffer(actual_len)
ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
_safe_call(_LIB.LGBM_BoosterGetLoadedParam(
self.handle,
self._handle,
ctypes.c_int64(actual_len),
ctypes.byref(tmp_out_len),
ptr_string_buffer))
......@@ -3460,8 +3461,8 @@ class Booster:
raise LightGBMError("Add validation data failed, "
"you should use same predictor for these data")
_safe_call(_LIB.LGBM_BoosterAddValidData(
self.handle,
data.construct().handle))
self._handle,
data.construct()._handle))
self.valid_sets.append(data)
self.name_valid_sets.append(name)
self.__num_dataset += 1
......@@ -3485,7 +3486,7 @@ class Booster:
params_str = _param_dict_to_str(params)
if params_str:
_safe_call(_LIB.LGBM_BoosterResetParameter(
self.handle,
self._handle,
_c_str(params_str)))
self.params.update(params)
return self
......@@ -3542,8 +3543,8 @@ class Booster:
"you should use same predictor for these data")
self.train_set = train_set
_safe_call(_LIB.LGBM_BoosterResetTrainingData(
self.handle,
self.train_set.construct().handle))
self._handle,
self.train_set.construct()._handle))
self.__inner_predict_buffer[0] = None
self.train_set_version = self.train_set.version
is_finished = ctypes.c_int(0)
......@@ -3551,7 +3552,7 @@ class Booster:
if self.__set_objective_to_none:
raise LightGBMError('Cannot update due to null objective function.')
_safe_call(_LIB.LGBM_BoosterUpdateOneIter(
self.handle,
self._handle,
ctypes.byref(is_finished)))
self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)]
return is_finished.value == 1
......@@ -3607,7 +3608,7 @@ class Booster:
)
is_finished = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterUpdateOneIterCustom(
self.handle,
self._handle,
grad.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
hess.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
ctypes.byref(is_finished)))
......@@ -3623,7 +3624,7 @@ class Booster:
Booster with rolled back one iteration.
"""
_safe_call(_LIB.LGBM_BoosterRollbackOneIter(
self.handle))
self._handle))
self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)]
return self
......@@ -3637,7 +3638,7 @@ class Booster:
"""
out_cur_iter = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetCurrentIteration(
self.handle,
self._handle,
ctypes.byref(out_cur_iter)))
return out_cur_iter.value
......@@ -3651,7 +3652,7 @@ class Booster:
"""
model_per_iter = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterNumModelPerIteration(
self.handle,
self._handle,
ctypes.byref(model_per_iter)))
return model_per_iter.value
......@@ -3665,7 +3666,7 @@ class Booster:
"""
num_trees = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterNumberOfTotalModel(
self.handle,
self._handle,
ctypes.byref(num_trees)))
return num_trees.value
......@@ -3679,7 +3680,7 @@ class Booster:
"""
ret = ctypes.c_double(0)
_safe_call(_LIB.LGBM_BoosterGetUpperBoundValue(
self.handle,
self._handle,
ctypes.byref(ret)))
return ret.value
......@@ -3693,7 +3694,7 @@ class Booster:
"""
ret = ctypes.c_double(0)
_safe_call(_LIB.LGBM_BoosterGetLowerBoundValue(
self.handle,
self._handle,
ctypes.byref(ret)))
return ret.value
......@@ -3854,7 +3855,7 @@ class Booster:
num_iteration = self.best_iteration
importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type]
_safe_call(_LIB.LGBM_BoosterSaveModel(
self.handle,
self._handle,
ctypes.c_int(start_iteration),
ctypes.c_int(num_iteration),
ctypes.c_int(importance_type_int),
......@@ -3883,7 +3884,7 @@ class Booster:
Booster with shuffled models.
"""
_safe_call(_LIB.LGBM_BoosterShuffleModels(
self.handle,
self._handle,
ctypes.c_int(start_iteration),
ctypes.c_int(end_iteration)))
return self
......@@ -3901,18 +3902,18 @@ class Booster:
self : Booster
Loaded Booster object.
"""
if self.handle is not None:
_safe_call(_LIB.LGBM_BoosterFree(self.handle))
if self._handle is not None:
_safe_call(_LIB.LGBM_BoosterFree(self._handle))
self._free_buffer()
self.handle = ctypes.c_void_p()
self._handle = ctypes.c_void_p()
out_num_iterations = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterLoadModelFromString(
_c_str(model_str),
ctypes.byref(out_num_iterations),
ctypes.byref(self.handle)))
ctypes.byref(self._handle)))
out_num_class = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetNumClasses(
self.handle,
self._handle,
ctypes.byref(out_num_class)))
self.__num_class = out_num_class.value
self.pandas_categorical = _load_pandas_categorical(model_str=model_str)
......@@ -3952,7 +3953,7 @@ class Booster:
string_buffer = ctypes.create_string_buffer(buffer_len)
ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
_safe_call(_LIB.LGBM_BoosterSaveModelToString(
self.handle,
self._handle,
ctypes.c_int(start_iteration),
ctypes.c_int(num_iteration),
ctypes.c_int(importance_type_int),
......@@ -3965,7 +3966,7 @@ class Booster:
string_buffer = ctypes.create_string_buffer(actual_len)
ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
_safe_call(_LIB.LGBM_BoosterSaveModelToString(
self.handle,
self._handle,
ctypes.c_int(start_iteration),
ctypes.c_int(num_iteration),
ctypes.c_int(importance_type_int),
......@@ -4020,7 +4021,7 @@ class Booster:
string_buffer = ctypes.create_string_buffer(buffer_len)
ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
_safe_call(_LIB.LGBM_BoosterDumpModel(
self.handle,
self._handle,
ctypes.c_int(start_iteration),
ctypes.c_int(num_iteration),
ctypes.c_int(importance_type_int),
......@@ -4033,7 +4034,7 @@ class Booster:
string_buffer = ctypes.create_string_buffer(actual_len)
ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
_safe_call(_LIB.LGBM_BoosterDumpModel(
self.handle,
self._handle,
ctypes.c_int(start_iteration),
ctypes.c_int(num_iteration),
ctypes.c_int(importance_type_int),
......@@ -4202,7 +4203,7 @@ class Booster:
nrow, ncol = leaf_preds.shape
out_is_linear = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetLinear(
self.handle,
self._handle,
ctypes.byref(out_is_linear)))
new_params = _choose_param_value(
main_param_name="linear_tree",
......@@ -4227,12 +4228,12 @@ class Booster:
new_booster = Booster(new_params, train_set)
# Copy models
_safe_call(_LIB.LGBM_BoosterMerge(
new_booster.handle,
predictor.handle))
new_booster._handle,
predictor._handle))
leaf_preds = leaf_preds.reshape(-1)
ptr_data, _, _ = _c_int_array(leaf_preds)
_safe_call(_LIB.LGBM_BoosterRefit(
new_booster.handle,
new_booster._handle,
ptr_data,
ctypes.c_int32(nrow),
ctypes.c_int32(ncol)))
......@@ -4256,7 +4257,7 @@ class Booster:
"""
ret = ctypes.c_double(0)
_safe_call(_LIB.LGBM_BoosterGetLeafValue(
self.handle,
self._handle,
ctypes.c_int(tree_id),
ctypes.c_int(leaf_id),
ctypes.byref(ret)))
......@@ -4286,7 +4287,7 @@ class Booster:
"""
_safe_call(
_LIB.LGBM_BoosterSetLeafValue(
self.handle,
self._handle,
ctypes.c_int(tree_id),
ctypes.c_int(leaf_id),
ctypes.c_double(value)
......@@ -4299,7 +4300,7 @@ class Booster:
pred_parameter: Dict[str, Any]
) -> _InnerPredictor:
"""Convert to predictor."""
predictor = _InnerPredictor(booster_handle=self.handle, pred_parameter=pred_parameter)
predictor = _InnerPredictor(booster_handle=self._handle, pred_parameter=pred_parameter)
predictor.pandas_categorical = self.pandas_categorical
return predictor
......@@ -4313,7 +4314,7 @@ class Booster:
"""
out_num_feature = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetNumFeature(
self.handle,
self._handle,
ctypes.byref(out_num_feature)))
return out_num_feature.value
......@@ -4333,7 +4334,7 @@ class Booster:
string_buffers = [ctypes.create_string_buffer(reserved_string_buffer_size) for _ in range(num_feature)]
ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers))
_safe_call(_LIB.LGBM_BoosterGetFeatureNames(
self.handle,
self._handle,
ctypes.c_int(num_feature),
ctypes.byref(tmp_out_len),
ctypes.c_size_t(reserved_string_buffer_size),
......@@ -4347,7 +4348,7 @@ class Booster:
string_buffers = [ctypes.create_string_buffer(actual_string_buffer_size) for _ in range(num_feature)]
ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers))
_safe_call(_LIB.LGBM_BoosterGetFeatureNames(
self.handle,
self._handle,
ctypes.c_int(num_feature),
ctypes.byref(tmp_out_len),
ctypes.c_size_t(actual_string_buffer_size),
......@@ -4383,7 +4384,7 @@ class Booster:
importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type]
result = np.empty(self.num_feature(), dtype=np.float64)
_safe_call(_LIB.LGBM_BoosterFeatureImportance(
self.handle,
self._handle,
ctypes.c_int(iteration),
ctypes.c_int(importance_type_int),
result.ctypes.data_as(ctypes.POINTER(ctypes.c_double))))
......@@ -4481,7 +4482,7 @@ class Booster:
result = np.empty(self.__num_inner_eval, dtype=np.float64)
tmp_out_len = ctypes.c_int(0)
_safe_call(_LIB.LGBM_BoosterGetEval(
self.handle,
self._handle,
ctypes.c_int(data_idx),
ctypes.byref(tmp_out_len),
result.ctypes.data_as(ctypes.POINTER(ctypes.c_double))))
......@@ -4524,7 +4525,7 @@ class Booster:
tmp_out_len = ctypes.c_int64(0)
data_ptr = self.__inner_predict_buffer[data_idx].ctypes.data_as(ctypes.POINTER(ctypes.c_double)) # type: ignore[union-attr]
_safe_call(_LIB.LGBM_BoosterGetPredict(
self.handle,
self._handle,
ctypes.c_int(data_idx),
ctypes.byref(tmp_out_len),
data_ptr))
......@@ -4544,7 +4545,7 @@ class Booster:
out_num_eval = ctypes.c_int(0)
# Get num of inner evals
_safe_call(_LIB.LGBM_BoosterGetEvalCounts(
self.handle,
self._handle,
ctypes.byref(out_num_eval)))
self.__num_inner_eval = out_num_eval.value
if self.__num_inner_eval > 0:
......@@ -4557,7 +4558,7 @@ class Booster:
]
ptr_string_buffers = (ctypes.c_char_p * self.__num_inner_eval)(*map(ctypes.addressof, string_buffers))
_safe_call(_LIB.LGBM_BoosterGetEvalNames(
self.handle,
self._handle,
ctypes.c_int(self.__num_inner_eval),
ctypes.byref(tmp_out_len),
ctypes.c_size_t(reserved_string_buffer_size),
......@@ -4573,7 +4574,7 @@ class Booster:
]
ptr_string_buffers = (ctypes.c_char_p * self.__num_inner_eval)(*map(ctypes.addressof, string_buffers))
_safe_call(_LIB.LGBM_BoosterGetEvalNames(
self.handle,
self._handle,
ctypes.c_int(self.__num_inner_eval),
ctypes.byref(tmp_out_len),
ctypes.c_size_t(actual_string_buffer_size),
......
tests/python_package_test/test_basic.py
View file @ b8cc8738
......@@ -393,11 +393,11 @@ def test_add_features_does_not_fail_if_initial_dataset_has_zero_informative_feat
dataset_b = lgb.Dataset(arr_b).construct()
original_handle = dataset_a.handle.value
original_handle = dataset_a._handle.value
dataset_a.add_features_from(dataset_b)
assert dataset_a.num_feature() == 6
assert dataset_a.num_data() == 100
assert dataset_a.handle.value == original_handle
assert dataset_a._handle.value == original_handle
def test_cegb_affects_behavior(tmp_path):
......
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