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炼丹笔记
作者：Coggle
XGBoost迭代读取数据集
简介
在大规模数据集进行读取进行训练的过程中，迭代读取数据集是一个非常合适的选择，在Pytorch中支持迭代读取的方式。接下来我们将介绍XGBoost的迭代读取的方式。
内存数据读取
class IterLoadForDMatrix(xgb.core.DataIter):
def __init__(self, df=None, features=None, target=None, batch_size=256*1024):
self.features = features
self.target = target
self.df = df
self.batch_size = batch_size
self.batches = int( np.ceil( len(df) / self.batch_size ) )
self.it = 0 # set iterator to 0
super().__init__()
def reset(self):
'''Reset the iterator'''
self.it = 0
def next(self, input_data):
'''Yield next batch of data.'''
if self.it == self.batches:
return 0 # Return 0 when there's no more batch.
a = self.it * self.batch_size
b = min( (self.it 1) * self.batch_size, len(self.df) )
dt = pd.DataFrame(self.df.iloc[a:b])
input_data(data=dt[self.features], label=dt[self.target]) #, weight=dt['weight'])
self.it = 1
return 1
调用方法（此种方式比较适合GPU训练）：
代码语言：javascript复制Xy_train = IterLoadForDMatrix(train.loc[train_idx], FEATURES, 'target')
dtrain = xgb.DeviceQuantileDMatrix(Xy_train, max_bin=256)
参考文档：
https://xgboost.readthedocs.io/en/latest/python/examples/quantile_data_iterator.html
外部数据迭代读取
class Iterator(xgboost.DataIter):
def __init__(self, svm_file_paths: List[str]):
self._file_paths = svm_file_paths
self._it = 0
super().__init__(cache_prefix=os.path.join(".", "cache"))
def next(self, input_data: Callable):
if self._it == len(self._file_paths):
# return 0 to let XGBoost know this is the end of iteration
return 0
X, y = load_svmlight_file(self._file_paths[self._it])
input_data(X, y)
self._it = 1
return 1
def reset(self):
"""Reset the iterator to its beginning"""
self._it = 0
调用方法（此种方式比较适合CPU训练）：
代码语言：javascript复制it = Iterator(["file_0.svm", "file_1.svm", "file_2.svm"])
Xy = xgboost.DMatrix(it)
# Other tree methods including ``hist`` and ``gpu_hist`` also work, but has some caveats
# as noted in following sections.
booster = xgboost.train({"tree_method": "approx"}, Xy)
参考文档：
https://xgboost.readthedocs.io/en/stable/tutorials/external_memory.html
