Question

我建立了一个神经网络，它可以处理大约300,000行的小型数据集，其中包含2个类别变量和1个自变量，但是当我将其增加到650万行时遇到了内存错误。因此，我决定修改代码，并且距离越来越近，但是现在我遇到了适合错误的问题。我有2个类别变量，其中一列是1和0（可疑或不可疑的因变量。从数据集开始看起来像这样：

DBF2
   ParentProcess                   ChildProcess               Suspicious
0  C:\Program Files (x86)\Wireless AutoSwitch\wrl...    ...            0
1  C:\Program Files (x86)\Wireless AutoSwitch\wrl...    ...            0
2  C:\Windows\System32\svchost.exe                      ...            1
3  C:\Program Files (x86)\Wireless AutoSwitch\wrl...    ...            0
4  C:\Program Files (x86)\Wireless AutoSwitch\wrl...    ...            0
5  C:\Program Files (x86)\Wireless AutoSwitch\wrl...    ...            0

我的代码后面有错误：

import pandas as pd
import numpy as np
import hashlib
import matplotlib.pyplot as plt
import timeit

X = DBF2.iloc[:, 0:2].values
y = DBF2.iloc[:, 2].values#.ravel()

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 0] = labelencoder_X_1.fit_transform(X[:, 0])
labelencoder_X_2 = LabelEncoder()
X[:, 1] = labelencoder_X_2.fit_transform(X[:, 1])

onehotencoder = OneHotEncoder(categorical_features = [0,1])
X = onehotencoder.fit_transform(X)

index_to_drop = [0, 2039]
to_keep = list(set(xrange(X.shape[1]))-set(index_to_drop))
X = X[:,to_keep]

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)

#ERROR
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/dist-packages/sklearn/base.py", line 517, in fit_transform
    return self.fit(X, **fit_params).transform(X)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/preprocessing/data.py", line 590, in fit
    return self.partial_fit(X, y)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/preprocessing/data.py", line 621, in partial_fit
    "Cannot center sparse matrices: pass `with_mean=False` "
ValueError: Cannot center sparse matrices: pass `with_mean=False` instead. See docstring for motivation and alternatives.

X_test = sc.transform(X_test)

#ERROR
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/dist-packages/sklearn/preprocessing/data.py", line 677, in transform
    check_is_fitted(self, 'scale_')
  File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/validation.py", line 768, in check_is_fitted
    raise NotFittedError(msg % {'name': type(estimator).__name__})
sklearn.exceptions.NotFittedError: This StandardScaler instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.

如果有帮助，我可以打印X_train和y_train：

X_train
<5621203x7043 sparse matrix of type '<type 'numpy.float64'>'
with 11242334 stored elements in Compressed Sparse Row format>

y_train
array([0, 0, 0, ..., 0, 0, 0])

Answer 1

X_train是一个稀疏矩阵，非常适合在您使用大型数据集（如您的情况）的情况下使用。问题是，如documentation所述：

with_mean：布尔值，默认为True

如果为True，则在缩放之前将数据居中。这不起作用（并且将   引发异常）尝试使用稀疏矩阵时，因为   将它们居中需要构建一个常用的密集矩阵   情况可能太大而无法容纳在内存中。

您可以尝试传递with_mean=False：

sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)

由于sc仍然是未经修改的StandardScaler对象，因此以下行失败。

X_test = sc.transform(X_test)

要能够使用transform方法，首先必须将StandardScaler拟合到数据集。如果您打算将StandardScaler放在训练集上，并使用它来将训练集和测试集转换为相同的空间，则可以按照以下步骤进行操作：

sc = StandardScaler(with_mean=False)
X_train_sc = sc.fit(X_train)
X_train = X_train_sc.transform(X_train)
X_test = X_train_sc.transform(X_test)

Python-遇到x_test y_test适合错误

1 个答案: