问题
This question is similar to what asked here and here. Unfortunately, in my case the suggested solution didn't fix the problem.
I need to work with the MNIST dataset but I can't fetch it, even if I specify the address of the scikit_learn_data/mldata/ folder (see below). How can I fix this?
In case it might help, I'm using Anaconda.
Code:
from sklearn.datasets.mldata import fetch_mldata
dataset = fetch_mldata('mnist-original', data_home='/Users/michelangelo/scikit_learn_data/mldata/')
mnist = fetch_mldata('MNIST original')
Error:
---------------------------------------------------------------------------
IOError Traceback (most recent call last)
<ipython-input-5-dc4d45bc928e> in <module>()
----> 1 mnist = fetch_mldata('MNIST original')
/Users/michelangelo/anaconda2/lib/python2.7/site-packages/sklearn/datasets/mldata.pyc in fetch_mldata(dataname, target_name, data_name, transpose_data, data_home)
168 # load dataset matlab file
169 with open(filename, 'rb') as matlab_file:
--> 170 matlab_dict = io.loadmat(matlab_file, struct_as_record=True)
171
172 # -- extract data from matlab_dict
/Users/michelangelo/anaconda2/lib/python2.7/site-packages/scipy/io/matlab/mio.pyc in loadmat(file_name, mdict, appendmat, **kwargs)
134 variable_names = kwargs.pop('variable_names', None)
135 MR = mat_reader_factory(file_name, appendmat, **kwargs)
--> 136 matfile_dict = MR.get_variables(variable_names)
137 if mdict is not None:
138 mdict.update(matfile_dict)
/Users/michelangelo/anaconda2/lib/python2.7/site-packages/scipy/io/matlab/mio5.pyc in get_variables(self, variable_names)
290 continue
291 try:
--> 292 res = self.read_var_array(hdr, process)
293 except MatReadError as err:
294 warnings.warn(
/Users/michelangelo/anaconda2/lib/python2.7/site-packages/scipy/io/matlab/mio5.pyc in read_var_array(self, header, process)
250 `process`.
251 '''
--> 252 return self._matrix_reader.array_from_header(header, process)
253
254 def get_variables(self, variable_names=None):
mio5_utils.pyx in scipy.io.matlab.mio5_utils.VarReader5.array_from_header()
mio5_utils.pyx in scipy.io.matlab.mio5_utils.VarReader5.array_from_header()
mio5_utils.pyx in scipy.io.matlab.mio5_utils.VarReader5.read_real_complex()
mio5_utils.pyx in scipy.io.matlab.mio5_utils.VarReader5.read_numeric()
mio5_utils.pyx in scipy.io.matlab.mio5_utils.VarReader5.read_element()
streams.pyx in scipy.io.matlab.streams.FileStream.read_string()
IOError: could not read bytes
回答1:
I just faced the same issue and it took me some time to find the problem. One reason is, data can be corrupted during the first download. Remove the cached data. Find the scikit data home dir as follows:
from sklearn.datasets.base import get_data_home
print (get_data_home())
Clean the directory and redownload the dataset. This solution works for me. For reference: https://github.com/ageron/handson-ml/issues/143
This is also related with the following question: How to use datasets.fetch_mldata() in sklearn?
回答2:
A quick update for the question here:
mldata.org seems to still be down. Then scikit-learn will remove fetch_mldata.
Solution for the moment: Since using the lines above will create a empty folder a the place of data_home, find the copy of the data here: https://github.com/amplab/datascience-sp14/blob/master/lab7/mldata/mnist-original.mat and download it. Then place it the ~/sklearn_data/mldata/ which is empty.
It worked for me.
回答3:
Instead of :
from sklearn.datasets.mldata import fetch_mldata
use:
from sklearn.datasets import fetch_mldata
And then:
mnist = fetch_mldata('MNIST original')
X = mnist.data.astype('float64')
y = mnist.target
Please see this example:
- http://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py
回答4:
For people having the same issue: it was a connection problem. If you get a similar error, check that you have the entire mnist-original.mat file, as suggested by @vivek-kumar. Current file size: 55.4 MB.
回答5:
Unfortunately fetch_mldata() has been replaced in the latest version of sklearn as fetch_openml().
So, instead of using :
from sklearn.datasets import fetch_mldata()
mnist = fetch_mldata('MNIST original')
You must use :
from sklearn.datasets import fetch_openml()
mnist = fetch_openml('mnist_784')
x = mnist.data
y = mnist.target
shape of x will be = (70000,784)
shape of y will be = (70000,)
回答6:
In the latest sklearn version (0.21) use this:
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_digits
digits = load_digits()
X = digits.data
y = digits.target
回答7:
Try this one, this will work.
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original')
来源：https://stackoverflow.com/questions/47324921/cant-load-mnist-original-dataset-using-sklearn