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..
For doctests:
>>> import numpy as np
>>> import os
.. _mldata:
Downloading datasets from the mldata.org repository
===================================================
`mldata.org <http://mldata.org>`_ is a public repository for machine learning
data, supported by the `PASCAL network <http://www.pascal-network.org>`_ .
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 <http://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 <http://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 <http://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)
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