73 lines
2.8 KiB
PHP
73 lines
2.8 KiB
PHP
..
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For doctests:
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>>> import numpy as np
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>>> import os
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.. _mldata:
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Downloading datasets from the mldata.org repository
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===================================================
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`mldata.org <http://mldata.org>`_ is a public repository for machine learning
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data, supported by the `PASCAL network <http://www.pascal-network.org>`_ .
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The ``sklearn.datasets`` package is able to directly download data
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sets from the repository using the function ``fetch_mldata(dataname)``.
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For example, to download the MNIST digit recognition database::
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>>> from sklearn.datasets import fetch_mldata
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>>> mnist = fetch_mldata('MNIST original', data_home=custom_data_home)
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The MNIST database contains a total of 70000 examples of handwritten digits
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of size 28x28 pixels, labeled from 0 to 9::
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>>> mnist.data.shape
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(70000, 784)
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>>> mnist.target.shape
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(70000,)
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>>> np.unique(mnist.target)
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array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
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After the first download, the dataset is cached locally in the path
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specified by the ``data_home`` keyword argument, which defaults to
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``~/scikit_learn_data/``::
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>>> os.listdir(os.path.join(custom_data_home, 'mldata'))
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['mnist-original.mat']
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Data sets in `mldata.org <http://mldata.org>`_ do not adhere to a strict
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naming or formatting convention. ``fetch_mldata`` is able to make sense
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of the most common cases, but allows to tailor the defaults to individual
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datasets:
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* The data arrays in `mldata.org <http://mldata.org>`_ are most often
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shaped as ``(n_features, n_samples)``. This is the opposite of the
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``scikit-learn`` convention, so ``fetch_mldata`` transposes the matrix
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by default. The ``transpose_data`` keyword controls this behavior::
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>>> iris = fetch_mldata('iris', data_home=custom_data_home)
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>>> iris.data.shape
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(150, 4)
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>>> iris = fetch_mldata('iris', transpose_data=False,
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... data_home=custom_data_home)
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>>> iris.data.shape
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(4, 150)
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* For datasets with multiple columns, ``fetch_mldata`` tries to identify
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the target and data columns and rename them to ``target`` and ``data``.
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This is done by looking for arrays named ``label`` and ``data`` in the
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dataset, and failing that by choosing the first array to be ``target``
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and the second to be ``data``. This behavior can be changed with the
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``target_name`` and ``data_name`` keywords, setting them to a specific
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name or index number (the name and order of the columns in the datasets
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can be found at its `mldata.org <http://mldata.org>`_ under the tab "Data"::
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>>> iris2 = fetch_mldata('datasets-UCI iris', target_name=1, data_name=0,
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... data_home=custom_data_home)
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>>> iris3 = fetch_mldata('datasets-UCI iris', target_name='class',
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... data_name='double0', data_home=custom_data_home)
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