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<section id="load-files">
<h1>load_files<a class="headerlink" href="#load-files" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.load_files">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_files</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">container_path</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">categories</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_content</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decode_error</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'strict'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allowed_extensions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/34db65a3a/sklearn/datasets/_base.py#L128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_files" title="Link 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="simple">
<dt>container_folder/</dt><dd><dl class="simple">
<dt>category_1_folder/</dt><dd><p>file_1.txt
file_2.txt
…
file_42.txt</p>
</dd>
<dt>category_2_folder/</dt><dd><p>file_43.txt
file_44.txt
…</p>
</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 <a class="reference internal" href="../../api/sklearn.feature_extraction.html#module-sklearn.feature_extraction.text" title="sklearn.feature_extraction.text"><code class="xref py py-mod docutils literal notranslate"><span class="pre">text</span></code></a> 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 <a class="reference internal" href="../../api/sklearn.feature_extraction.html#module-sklearn.feature_extraction.text" title="sklearn.feature_extraction.text"><code class="xref py py-mod docutils literal notranslate"><span class="pre">text</span></code></a>.</p>
<p>Similar feature extractors should be built for other kind of unstructured
data input such as images, audio, video, …</p>
<p>If you want files with a specific file extension (e.g. <code class="docutils literal notranslate"><span class="pre">.txt</span></code>) then you
can pass a list of those file extensions to <code class="docutils literal notranslate"><span class="pre">allowed_extensions</span></code>.</p>
<p>Read more in the <a class="reference internal" href="../../datasets.html#datasets"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>container_path</strong><span class="classifier">str</span></dt><dd><p>Path to the main folder holding one subfolder per category.</p>
</dd>
<dt><strong>description</strong><span class="classifier">str, default=None</span></dt><dd><p>A paragraph describing the characteristic of the dataset: its source,
reference, etc.</p>
</dd>
<dt><strong>categories</strong><span class="classifier">list of str, default=None</span></dt><dd><p>If None (default), load all the categories. If not None, list of
category names to load (other categories ignored).</p>
</dd>
<dt><strong>load_content</strong><span class="classifier">bool, default=True</span></dt><dd><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>
</dd>
<dt><strong>shuffle</strong><span class="classifier">bool, default=True</span></dt><dd><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>
</dd>
<dt><strong>encoding</strong><span class="classifier">str, default=None</span></dt><dd><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>
</dd>
<dt><strong>decode_error</strong><span class="classifier">{‘strict’, ‘ignore’, ‘replace’}, default=’strict’</span></dt><dd><p>Instruction on what to do if a byte sequence is given to analyze that
contains characters not of the given <code class="docutils literal notranslate"><span class="pre">encoding</span></code>. Passed as keyword
argument ‘errors’ to bytes.decode.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=0</span></dt><dd><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>
</dd>
<dt><strong>allowed_extensions</strong><span class="classifier">list of str, default=None</span></dt><dd><p>List of desired file extensions to filter the files to be loaded.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>data</strong><span class="classifier"><a class="reference internal" href="sklearn.utils.Bunch.html#sklearn.utils.Bunch" title="sklearn.utils.Bunch"><code class="xref py py-class docutils literal notranslate"><span class="pre">Bunch</span></code></a></span></dt><dd><p>Dictionary-like object, with the following attributes.</p>
<dl class="simple">
<dt>data<span class="classifier">list of str</span></dt><dd><p>Only present when <code class="docutils literal notranslate"><span class="pre">load_content=True</span></code>.
The raw text data to learn.</p>
</dd>
<dt>target<span class="classifier">ndarray</span></dt><dd><p>The target labels (integer index).</p>
</dd>
<dt>target_names<span class="classifier">list</span></dt><dd><p>The names of target classes.</p>
</dd>
<dt>DESCR<span class="classifier">str</span></dt><dd><p>The full description of the dataset.</p>
</dd>
<dt>filenames: ndarray</dt><dd><p>The filenames holding the dataset.</p>
</dd>
</dl>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_files</span>
<span class="gp">>>> </span><span class="n">container_path</span> <span class="o">=</span> <span class="s2">"./"</span>
<span class="gp">>>> </span><span class="n">load_files</span><span class="p">(</span><span class="n">container_path</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
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