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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.load_files</a></li>
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<div class="section" id="sklearn-datasets-load-files">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.datasets</span></code></a>.load_files<a class="headerlink" href="#sklearn-datasets-load-files" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.load_files">
<code class="descclassname">sklearn.datasets.</code><code class="descname">load_files</code><span class="sig-paren">(</span><em>container_path</em>, <em>description=None</em>, <em>categories=None</em>, <em>load_content=True</em>, <em>shuffle=True</em>, <em>encoding=None</em>, <em>decode_error='strict'</em>, <em>random_state=0</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/datasets/base.py#L72"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.load_files" title="Permalink to this definition">¶</a></dt>
<dd><p>Load text files with categories as subfolder names.</p>
<p>Individual samples are assumed to be files stored a two levels folder
structure such as the following:</p>
<blockquote>
<div><dl class="docutils">
<dt>container_folder/</dt>
<dd><dl class="first last docutils">
<dt>category_1_folder/</dt>
<dd>file_1.txt
file_2.txt
…
file_42.txt</dd>
<dt>category_2_folder/</dt>
<dd>file_43.txt
file_44.txt
…</dd>
</dl>
</dd>
</dl>
</div></blockquote>
<p>The folder names are used as supervised signal label names. The individual
file names are not important.</p>
<p>This function does not try to extract features into a numpy array or scipy
sparse matrix. In addition, if load_content is false it does not try to
load the files in memory.</p>
<p>To use text files in a scikit-learn classification or clustering algorithm,
you will need to use the <cite>sklearn.feature_extraction.text</cite> module to build
a feature extraction transformer that suits your problem.</p>
<p>If you set load_content=True, you should also specify the encoding of the
text using the ‘encoding’ parameter. For many modern text files, ‘utf-8’
will be the correct encoding. If you leave encoding equal to None, then the
content will be made of bytes instead of Unicode, and you will not be able
to use most functions in <cite>sklearn.feature_extraction.text</cite>.</p>
<p>Similar feature extractors should be built for other kind of unstructured
data input such as images, audio, video, …</p>
<p>Read more in the <a class="reference internal" href="../../datasets/index.html#datasets"><span class="std std-ref">User Guide</span></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>container_path</strong> : string or unicode</p>
<blockquote>
<div><p>Path to the main folder holding one subfolder per category</p>
</div></blockquote>
<p><strong>description</strong> : string or unicode, optional (default=None)</p>
<blockquote>
<div><p>A paragraph describing the characteristic of the dataset: its source,
reference, etc.</p>
</div></blockquote>
<p><strong>categories</strong> : A collection of strings or None, optional (default=None)</p>
<blockquote>
<div><p>If None (default), load all the categories. If not None, list of
category names to load (other categories ignored).</p>
</div></blockquote>
<p><strong>load_content</strong> : boolean, optional (default=True)</p>
<blockquote>
<div><p>Whether to load or not the content of the different files. If true a
‘data’ attribute containing the text information is present in the data
structure returned. If not, a filenames attribute gives the path to the
files.</p>
</div></blockquote>
<p><strong>shuffle</strong> : bool, optional (default=True)</p>
<blockquote>
<div><p>Whether or not to shuffle the data: might be important for models that
make the assumption that the samples are independent and identically
distributed (i.i.d.), such as stochastic gradient descent.</p>
</div></blockquote>
<p><strong>encoding</strong> : string or None (default is None)</p>
<blockquote>
<div><p>If None, do not try to decode the content of the files (e.g. for images
or other non-text content). If not None, encoding to use to decode text
files to Unicode if load_content is True.</p>
</div></blockquote>
<p><strong>decode_error</strong> : {‘strict’, ‘ignore’, ‘replace’}, optional</p>
<blockquote>
<div><p>Instruction on what to do if a byte sequence is given to analyze that
contains characters not of the given <cite>encoding</cite>. Passed as keyword
argument ‘errors’ to bytes.decode.</p>
</div></blockquote>
<p><strong>random_state</strong> : int, RandomState instance or None (default=0)</p>
<blockquote>
<div><p>Determines random number generation for dataset shuffling. Pass an int
for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random-state"><span class="xref std std-term">Glossary</span></a>.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>data</strong> : Bunch</p>
<blockquote class="last">
<div><p>Dictionary-like object, the interesting attributes are: either
data, the raw text data to learn, or ‘filenames’, the files
holding it, ‘target’, the classification labels (integer index),
‘target_names’, the meaning of the labels, and ‘DESCR’, the full
description of the dataset.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
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