我在使用逻辑回归模型来训练一些文本数据。以下是我使用的代码：
from fonduer.learning import LogisticRegressiondisc_model = LogisticRegression()%time disc_model.train((train_cands[0], F_train[0]), train_marginals, n_epochs=50, lr=0.001)当我运行20个文档时，代码没有问题，但当我将文档数量增加到40个时，出现了以下错误：
[INFO] fonduer.learning.disc_learning - Load defalut parameters for Logistic Regression---------------------------------------------------------------------------MemoryError Traceback (most recent call last)<timed eval> in <module>~/.venv/lib/python3.6/site-packages/fonduer/learning/disc_learning.py in train(self, X_train, Y_train, n_epochs, lr, batch_size, rebalance, X_dev, Y_dev, print_freq, dev_ckpt, dev_ckpt_delay, save_dir, seed, host_device) 169 170 _X_train, _Y_train = self._preprocess_data(--> 171 X_train, Y_train, idxs=train_idxs, train=True 172 ) 173 if X_dev is not None:~/.venv/lib/python3.6/site-packages/fonduer/learning/disc_models/logistic_regression.py in _preprocess_data(self, X, Y, idxs, train) 59 C, F = X 60 if issparse(F):---> 61 F = F.todense() 62 63 if idxs is None:~/.venv/lib/python3.6/site-packages/scipy/sparse/base.py in todense(self, order, out) 844 `numpy.matrix` object that shares the same memory. 845 """--> 846 return np.asmatrix(self.toarray(order=order, out=out)) 847 848 def toarray(self, order=None, out=None):~/.venv/lib/python3.6/site-packages/scipy/sparse/compressed.py in toarray(self, order, out) 945 if out is None and order is None: 946 order = self._swap('cf')[0]--> 947 out = self._process_toarray_args(order, out) 948 if not (out.flags.c_contiguous or out.flags.f_contiguous): 949 raise ValueError('Output array must be C or F contiguous')~/.venv/lib/python3.6/site-packages/scipy/sparse/base.py in _process_toarray_args(self, order, out) 1182 return out 1183 else:-> 1184 return np.zeros(self.shape, dtype=self.dtype, order=order) 1185 1186 MemoryError: 回答：
看起来是特征大小对于LogisticRegression来说太大了。我用SparseLogisticRegression替换了LogisticRegression，问题就解决了。
