Downloading datasets from the mldata.org repository

mldata.org is a public repository for machine learning data, supported by the PASCAL network .

The sklearn.datasets package is able to directly download data sets from the repository using the function fetch_mldata(dataname).

For example, to download the MNIST digit recognition database:

>>> from sklearn.datasets import fetch_mldata
>>> mnist = fetch_mldata('MNIST original', data_home=custom_data_home)

The MNIST database contains a total of 70000 examples of handwritten digits of size 28x28 pixels, labeled from 0 to 9:

>>> mnist.data.shape
(70000, 784)
>>> mnist.target.shape
(70000,)
>>> np.unique(mnist.target)
array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])

After the first download, the dataset is cached locally in the path specified by the data_home keyword argument, which defaults to ~/scikit_learn_data/:

>>> os.listdir(os.path.join(custom_data_home, 'mldata'))
['mnist-original.mat']

Data sets in mldata.org do not adhere to a strict naming or formatting convention. fetch_mldata is able to make sense of the most common cases, but allows to tailor the defaults to individual datasets:

• The data arrays in mldata.org are most often shaped as (n_features, n_samples). This is the opposite of the scikit-learn convention, so fetch_mldata transposes the matrix by default. The transpose_data keyword controls this behavior:

  >>> iris = fetch_mldata('iris', data_home=custom_data_home)
  >>> iris.data.shape
  (150, 4)
  >>> iris = fetch_mldata('iris', transpose_data=False,
  ...                     data_home=custom_data_home)
  >>> iris.data.shape
  (4, 150)
  

• For datasets with multiple columns, fetch_mldata tries to identify the target and data columns and rename them to target and data. This is done by looking for arrays named label and data in the dataset, and failing that by choosing the first array to be target and the second to be data. This behavior can be changed with the target_name and data_name keywords, setting them to a specific name or index number (the name and order of the columns in the datasets can be found at its mldata.org under the tab “Data”:

  >>> iris2 = fetch_mldata('datasets-UCI iris', target_name=1, data_name=0,
  ...                      data_home=custom_data_home)
  >>> iris3 = fetch_mldata('datasets-UCI iris', target_name='class',
  ...                      data_name='double0', data_home=custom_data_home)