python - 如何在 sklearn 中使用 datasets.fetch_mldata()？

Question

我正在尝试为简短的机器学习算法运行以下代码：

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 读取 mldata.org 上的数据集“MNIST Original”。这会导致以下错误（有更多代码行，但我在此特定行出现错误）：

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将不胜感激。

TIA。

score 31 · Accepted Answer

从 0.20 版开始，sklearn弃用 fetch_mldata了函数并fetch_openml改为添加。

使用以下代码下载MNIST 数据集：

from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784')

不过格式有一些变化。例如，mnist['target']是一个字符串类别标签数组（不像以前那样浮动）。

score 10 · Accepted Answer

看起来缓存的数据已损坏。尝试删除它们并重新下载（需要一点时间）。如果没有另外指定，“MINST original”的数据应该在

~/scikit_learn_data/mldata/mnist-original.mat

score 5 · Accepted Answer

我从这个链接下载了数据集

https://github.com/amplab/datascience-sp14/blob/master/lab7/mldata/mnist-original.mat

然后我输入了这些行

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

我希望你能明白，它对我很有效

score 1 · Accepted Answer

以下是一些示例代码，如何让 MNIST 数据准备好用于 sklearn：

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

score 1 · Accepted Answer

当我使用较差的 WiFi 时，我遇到了同样的问题，并在不同的时间发现了不同的 mnist-original.mat 文件大小。我切换到局域网，它工作正常。可能是网络的问题。

score 0 · Accepted Answer

试试这样：

dataDict = fetch_mldata('MNIST original')

这对我有用。由于您使用了from ... import ...语法，因此您不应该datasets在使用时预先添加

score 0 · Accepted Answer

我过去也有这个问题。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，需要一段时间才能将它们全部下载。我不知道并打断了这个过程。

数据集已损坏，这就是错误发生的原因。

score 0 · Accepted Answer

如果你没有给data_home，程序看看 ${yourprojectpath}/mldata/minist-original.mat 你可以下载程序并将文件放在正确的路径

score 0 · Accepted Answer

我还收到了 fetch_mldata() “IOError: could not read bytes”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/')

...请务必将“data_home”更改为您的首选位置（目录）。

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'

score 0 · Accepted Answer

除了@szymon 提到的内容之外，您还可以使用以下方法加载数据集：

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",
}

score -1 · Accepted Answer

-1

That's 'MNIST original'. With a lowercase on "o".

于 2013-10-23T00:01:17.820 回答

python - 如何在 sklearn 中使用 datasets.fetch_mldata()？

11 回答 11

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