我正在尝试运行以下代码以获得简短的机器学习算法：
import re
import argparse
import csv
from collections import Counter
from sklearn import datasets
import sklearn
from sklearn.datasets import fetch_mldata
dataDict = datasets.fetch_mldata('MNIST Original')
在这段代码中，我试图通过sklearn阅读mldata.org上的数据集“MNIST Original”。这会导致以下错误（代码行数较多但我在此特定行收到错误）：
Traceback (most recent call last):
File "C:\Program Files (x86)\JetBrains\PyCharm 2.7.3\helpers\pydev\pydevd.py", line 1481, in <module>
debugger.run(setup['file'], None, None)
File "C:\Program Files (x86)\JetBrains\PyCharm 2.7.3\helpers\pydev\pydevd.py", line 1124, in run
pydev_imports.execfile(file, globals, locals) #execute the script
File "C:/Users/sony/PycharmProjects/Machine_Learning_Homework1/zeroR.py", line 131, in <module>
dataDict = datasets.fetch_mldata('MNIST Original')
File "C:\Anaconda\lib\site-packages\sklearn\datasets\mldata.py", line 157, in fetch_mldata
matlab_dict = io.loadmat(matlab_file, struct_as_record=True)
File "C:\Anaconda\lib\site-packages\scipy\io\matlab\mio.py", line 176, in loadmat
matfile_dict = MR.get_variables(variable_names)
File "C:\Anaconda\lib\site-packages\scipy\io\matlab\mio5.py", line 294, in get_variables
res = self.read_var_array(hdr, process)
File "C:\Anaconda\lib\site-packages\scipy\io\matlab\mio5.py", line 257, in read_var_array
return self._matrix_reader.array_from_header(header, process)
File "mio5_utils.pyx", line 624, in scipy.io.matlab.mio5_utils.VarReader5.array_from_header (scipy\io\matlab\mio5_utils.c:5717)
File "mio5_utils.pyx", line 653, in scipy.io.matlab.mio5_utils.VarReader5.array_from_header (scipy\io\matlab\mio5_utils.c:5147)
File "mio5_utils.pyx", line 721, in scipy.io.matlab.mio5_utils.VarReader5.read_real_complex (scipy\io\matlab\mio5_utils.c:6134)
File "mio5_utils.pyx", line 424, in scipy.io.matlab.mio5_utils.VarReader5.read_numeric (scipy\io\matlab\mio5_utils.c:3704)
File "mio5_utils.pyx", line 360, in scipy.io.matlab.mio5_utils.VarReader5.read_element (scipy\io\matlab\mio5_utils.c:3429)
File "streams.pyx", line 181, in scipy.io.matlab.streams.FileStream.read_string (scipy\io\matlab\streams.c:2711)
IOError: could not read bytes
我曾尝试过在互联网上进行研究，但几乎没有任何帮助。任何与解决此错误相关的专家帮助都将非常感激。
TIA。
答案 0 :(得分：10)
看起来缓存的数据已损坏。尝试删除它们并再次下载（需要一点时间）。如果没有另外指定，'MINST original'的数据应该在
中~/scikit_learn_data/mldata/mnist-original.mat
答案 1 :(得分：7)
从0.20版开始，sklearn deprecates fetch_mldata开始运行，并添加了fetch_openml。
使用以下代码下载MNIST dataset：
from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784')
虽然格式有所更改。例如，mnist['target']是字符串类别标签的数组（不像以前那样浮动）。
答案 2 :(得分：4)
我从此链接下载了数据集
https://github.com/amplab/datascience-sp14/blob/master/lab7/mldata/mnist-original.mat
然后我输入这些行
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original', transpose_data=True, data_home='files')
***路径是（您的工作目录）/files/mldata/mnist-original.mat
我希望你能得到它，对我来说效果很好
答案 3 :(得分：1)
以下是一些示例代码，说明如何准备好用于sklearn的MNIST数据：
def get_data():
"""
Get MNIST data ready to learn with.
Returns
-------
dict
With keys 'train' and 'test'. Both do have the keys 'X' (features)
and'y' (labels)
"""
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original')
x = mnist.data
y = mnist.target
# Scale data to [-1, 1] - This is of mayor importance!!!
x = x/255.0*2 - 1
from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y,
test_size=0.33,
random_state=42)
data = {'train': {'X': x_train,
'y': y_train},
'test': {'X': x_test,
'y': y_test}}
return data
答案 4 :(得分：1)
我遇到了同样的问题，并且在我使用可怜的WiFi时，在不同的时间发现了mnist-original.mat的不同文件大小。我切换到LAN，它工作正常。这可能是网络问题。
答案 5 :(得分：0)
试试这样：
dataDict = fetch_mldata('MNIST original')
这对我有用。由于您使用了from ... import ...语法，因此在使用时不应添加datasets
答案 6 :(得分：0)
我还得到了一个fetch_mldata（）“IOError：无法读取字节”错误。这是解决方案;相关的代码行是
from sklearn.datasets.mldata import fetch_mldata
mnist = fetch_mldata('mnist-original', data_home='/media/Vancouver/apps/mnist_dataset/')
...请务必更改首选位置（目录）的“data_home”。
这是一个脚本：
#!/usr/bin/python
# coding: utf-8
# Source:
# https://stackoverflow.com/questions/19530383/how-to-use-datasets-fetch-mldata-in-sklearn
# ... modified, below, by Victoria
"""
pers. comm. (Jan 27, 2016) from MLdata.org MNIST dataset contactee "Cheng Ong:"
The MNIST data is called 'mnist-original'. The string you pass to sklearn
has to match the name of the URL:
from sklearn.datasets.mldata import fetch_mldata
data = fetch_mldata('mnist-original')
"""
def get_data():
"""
Get MNIST data; returns a dict with keys 'train' and 'test'.
Both have the keys 'X' (features) and 'y' (labels)
"""
from sklearn.datasets.mldata import fetch_mldata
mnist = fetch_mldata('mnist-original', data_home='/media/Vancouver/apps/mnist_dataset/')
x = mnist.data
y = mnist.target
# Scale data to [-1, 1]
x = x/255.0*2 - 1
from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y,
test_size=0.33, random_state=42)
data = {'train': {'X': x_train, 'y': y_train},
'test': {'X': x_test, 'y': y_test}}
return data
data = get_data()
print '\n', data, '\n'
答案 7 :(得分：0)
如果您没有提供data_home，程序会查看$ {yourprojectpath} /mldata/minist-original.mat，您可以下载程序并将文件放入正确的路径
答案 8 :(得分：0)
我过去也遇到过这个问题。这是由于数据集非常大（大约55.4 mb），我运行“fetch_mldata”，但由于互联网连接，它需要一段时间才能全部下载。我不知道并打断这个过程。
数据集已损坏，并说明错误发生的原因。
答案 9 :(得分：0)
除了@szymon提到的内容外，您还可以使用以下方式加载数据集：
from six.moves import urllib
from sklearn.datasets import fetch_mldata
from scipy.io import loadmat
mnist_alternative_url = "https://github.com/amplab/datascience-sp14/raw/master/lab7/mldata/mnist-original.mat"
mnist_path = "./mnist-original.mat"
response = urllib.request.urlopen(mnist_alternative_url)
with open(mnist_path, "wb") as f:
content = response.read()
f.write(content)
mnist_raw = loadmat(mnist_path)
mnist = {
"data": mnist_raw["data"].T,
"target": mnist_raw["label"][0],
"COL_NAMES": ["label", "data"],
"DESCR": "mldata.org dataset: mnist-original",
}
答案 10 :(得分：-1)
那是'MNIST原创'。小写在“o”。