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<section id="sparserandomprojection">
<h1>SparseRandomProjection<a class="headerlink" href="#sparserandomprojection" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.random_projection.</span></span><span class="sig-name descname"><span class="pre">SparseRandomProjection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</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">density</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dense_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_inverse_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</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">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/random_projection.py#L615"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection" title="Link to this definition">#</a></dt>
<dd><p>Reduce dimensionality through sparse random projection.</p>
<p>Sparse random matrix is an alternative to dense random
projection matrix that guarantees similar embedding quality while being
much more memory efficient and allowing faster computation of the
projected data.</p>
<p>If we note <code class="docutils literal notranslate"><span class="pre">s</span> <span class="pre">=</span> <span class="pre">1</span> <span class="pre">/</span> <span class="pre">density</span></code> the components of the random matrix are
drawn from:</p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>-sqrt(s) / sqrt(n_components) with probability 1 / 2s
0 with probability 1 - 1 / s
+sqrt(s) / sqrt(n_components) with probability 1 / 2s
</pre></div>
</div>
<p>Read more in the <a class="reference internal" href="../random_projection.html#sparse-random-matrix"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.13.</span></p>
</div>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>n_components</strong><span class="classifier">int or ‘auto’, default=’auto’</span></dt><dd><p>Dimensionality of the target projection space.</p>
<p>n_components can be automatically adjusted according to the
number of samples in the dataset and the bound given by the
Johnson-Lindenstrauss lemma. In that case the quality of the
embedding is controlled by the <code class="docutils literal notranslate"><span class="pre">eps</span></code> parameter.</p>
<p>It should be noted that Johnson-Lindenstrauss lemma can yield
very conservative estimated of the required number of components
as it makes no assumption on the structure of the dataset.</p>
</dd>
<dt><strong>density</strong><span class="classifier">float or ‘auto’, default=’auto’</span></dt><dd><p>Ratio in the range (0, 1] of non-zero component in the random
projection matrix.</p>
<p>If density = ‘auto’, the value is set to the minimum density
as recommended by Ping Li et al.: 1 / sqrt(n_features).</p>
<p>Use density = 1 / 3.0 if you want to reproduce the results from
Achlioptas, 2001.</p>
</dd>
<dt><strong>eps</strong><span class="classifier">float, default=0.1</span></dt><dd><p>Parameter to control the quality of the embedding according to
the Johnson-Lindenstrauss lemma when n_components is set to
‘auto’. This value should be strictly positive.</p>
<p>Smaller values lead to better embedding and higher number of
dimensions (n_components) in the target projection space.</p>
</dd>
<dt><strong>dense_output</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, ensure that the output of the random projection is a
dense numpy array even if the input and random projection matrix
are both sparse. In practice, if the number of components is
small the number of zero components in the projected data will
be very small and it will be more CPU and memory efficient to
use a dense representation.</p>
<p>If False, the projected data uses a sparse representation if
the input is sparse.</p>
</dd>
<dt><strong>compute_inverse_components</strong><span class="classifier">bool, default=False</span></dt><dd><p>Learn the inverse transform by computing the pseudo-inverse of the
components during fit. Note that the pseudo-inverse is always a dense
array, even if the training data was sparse. This means that it might be
necessary to call <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> on a small batch of samples at a
time to avoid exhausting the available memory on the host. Moreover,
computing the pseudo-inverse does not scale well to large matrices.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Controls the pseudo random number generator used to generate the
projection matrix at fit time.
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>
</dl>
</dd>
<dt class="field-even">Attributes<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>n_components_</strong><span class="classifier">int</span></dt><dd><p>Concrete number of components computed when n_components=”auto”.</p>
</dd>
<dt><strong>components_</strong><span class="classifier">sparse matrix of shape (n_components, n_features)</span></dt><dd><p>Random matrix used for the projection. Sparse matrix will be of CSR
format.</p>
</dd>
<dt><strong>inverse_components_</strong><span class="classifier">ndarray of shape (n_features, n_components)</span></dt><dd><p>Pseudo-inverse of the components, only computed if
<code class="docutils literal notranslate"><span class="pre">compute_inverse_components</span></code> is True.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.1.</span></p>
</div>
</dd>
<dt><strong>density_</strong><span class="classifier">float in range 0.0 - 1.0</span></dt><dd><p>Concrete density computed from when density = “auto”.</p>
</dd>
<dt><strong>n_features_in_</strong><span class="classifier">int</span></dt><dd><p>Number of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 0.24.</span></p>
</div>
</dd>
<dt><strong>feature_names_in_</strong><span class="classifier">ndarray of shape (<code class="docutils literal notranslate"><span class="pre">n_features_in_</span></code>,)</span></dt><dd><p>Names of features seen during <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a>. Defined only when <code class="docutils literal notranslate"><span class="pre">X</span></code>
has feature names that are all strings.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.0.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.random_projection.GaussianRandomProjection.html#sklearn.random_projection.GaussianRandomProjection" title="sklearn.random_projection.GaussianRandomProjection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GaussianRandomProjection</span></code></a></dt><dd><p>Reduce dimensionality through Gaussian random projection.</p>
</dd>
</dl>
</div>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="r0fecf191e4b8-1" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
<p>Ping Li, T. Hastie and K. W. Church, 2006,
“Very Sparse Random Projections”.
<a class="reference external" href="https://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf">https://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf</a></p>
</div>
<div class="citation" id="r0fecf191e4b8-2" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
<p>D. Achlioptas, 2001, “Database-friendly random projections”,
<a class="reference external" href="https://cgi.di.uoa.gr/~optas/papers/jl.pdf">https://cgi.di.uoa.gr/~optas/papers/jl.pdf</a></p>
</div>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.random_projection</span><span class="w"> </span><span class="kn">import</span> <span class="n">SparseRandomProjection</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">3000</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">transformer</span> <span class="o">=</span> <span class="n">SparseRandomProjection</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="n">rng</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_new</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_new</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(25, 2759)</span>
<span class="gp">>>> </span><span class="c1"># very few components are non-zero</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">transformer</span><span class="o">.</span><span class="n">components_</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span>
<span class="go">np.float64(0.0182...)</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</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/b54e4deea/sklearn/random_projection.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.fit" title="Link to this definition">#</a></dt>
<dd><p>Generate a sparse random projection matrix.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Training set: only the shape is used to find optimal random
matrix dimensions based on the theory referenced in the
afore mentioned papers.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>BaseRandomProjection class instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.fit_transform">
<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</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="o"><span class="pre">**</span></span><span class="n"><span class="pre">fit_params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/base.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.fit_transform" title="Link to this definition">#</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> with optional parameters <code class="docutils literal notranslate"><span class="pre">fit_params</span></code>
and returns a transformed version of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Input samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs), default=None</span></dt><dd><p>Target values (None for unsupervised transformations).</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Additional fit parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">ndarray array of shape (n_samples, n_features_new)</span></dt><dd><p>Transformed array.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.get_feature_names_out">
<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</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/b54e4deea/sklearn/base.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.get_feature_names_out" title="Link to this definition">#</a></dt>
<dd><p>Get output feature names for transformation.</p>
<p>The feature names out will prefixed by the lowercased class name. For
example, if the transformer outputs 3 features, then the feature names
out are: <code class="docutils literal notranslate"><span class="pre">["class_name0",</span> <span class="pre">"class_name1",</span> <span class="pre">"class_name2"]</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>input_features</strong><span class="classifier">array-like of str or None, default=None</span></dt><dd><p>Only used to validate feature names with the names seen in <code class="docutils literal notranslate"><span class="pre">fit</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>feature_names_out</strong><span class="classifier">ndarray of str objects</span></dt><dd><p>Transformed feature names.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.get_metadata_routing">
<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/utils/_metadata_requests.py#L1500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.get_metadata_routing" title="Link to this definition">#</a></dt>
<dd><p>Get metadata routing of this object.</p>
<p>Please check <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">User Guide</span></a> on how the routing
mechanism works.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>routing</strong><span class="classifier">MetadataRequest</span></dt><dd><p>A <a class="reference internal" href="sklearn.utils.metadata_routing.MetadataRequest.html#sklearn.utils.metadata_routing.MetadataRequest" title="sklearn.utils.metadata_routing.MetadataRequest"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code></a> encapsulating
routing information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/base.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.get_params" title="Link to this definition">#</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.inverse_transform">
<span class="sig-name descname"><span class="pre">inverse_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/random_projection.py#L434"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.inverse_transform" title="Link to this definition">#</a></dt>
<dd><p>Project data back to its original space.</p>
<p>Returns an array X_original whose transform would be X. Note that even
if X is sparse, X_original is dense: this may use a lot of RAM.</p>
<p>If <code class="docutils literal notranslate"><span class="pre">compute_inverse_components</span></code> is False, the inverse of the components is
computed during each call to <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> which can be costly.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_components)</span></dt><dd><p>Data to be transformed back.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_original</strong><span class="classifier">ndarray of shape (n_samples, n_features)</span></dt><dd><p>Reconstructed data.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.set_output">
<span class="sig-name descname"><span class="pre">set_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</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/b54e4deea/sklearn/utils/_set_output.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.set_output" title="Link to this definition">#</a></dt>
<dd><p>Set output container.</p>
<p>See <a class="reference internal" href="../../auto_examples/miscellaneous/plot_set_output.html#sphx-glr-auto-examples-miscellaneous-plot-set-output-py"><span class="std std-ref">Introducing the set_output API</span></a>
for an example on how to use the API.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>transform</strong><span class="classifier">{“default”, “pandas”, “polars”}, default=None</span></dt><dd><p>Configure output of <code class="docutils literal notranslate"><span class="pre">transform</span></code> and <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code>.</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">"default"</span></code>: Default output format of a transformer</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"pandas"</span></code>: DataFrame output</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"polars"</span></code>: Polars output</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: Transform configuration is unchanged</p></li>
</ul>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.4: </span><code class="docutils literal notranslate"><span class="pre">"polars"</span></code> option was added.</p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/base.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.set_params" title="Link to this definition">#</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
possible to update each component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.random_projection.SparseRandomProjection.transform">
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/random_projection.py#L801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.random_projection.SparseRandomProjection.transform" title="Link to this definition">#</a></dt>
<dd><p>Project the data by using matrix product with the random matrix.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The input data to project into a smaller dimensional space.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">{ndarray, sparse matrix} of shape (n_samples, n_components)</span></dt><dd><p>Projected array. It is a sparse matrix only when the input is sparse and
<code class="docutils literal notranslate"><span class="pre">dense_output</span> <span class="pre">=</span> <span class="pre">False</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<section id="gallery-examples">
<h2>Gallery examples<a class="headerlink" href="#gallery-examples" title="Link to this heading">#</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="We illustrate various embedding techniques on the digits dataset."><img alt="" src="../../_images/sphx_glr_plot_lle_digits_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-py"><span class="std std-ref">Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…</span></a></p>
<div class="sphx-glr-thumbnail-title">Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...</div>
</div><div class="sphx-glr-thumbcontainer" tooltip=" The `Johnson-Lindenstrauss lemma`_ states that any high dimensional dataset can be randomly projected into a lower dimensional Euclidean space while controlling the distortion in the pairwise distances."><img alt="" src="../../_images/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-miscellaneous-plot-johnson-lindenstrauss-bound-py"><span class="std std-ref">The Johnson-Lindenstrauss bound for embedding with random projections</span></a></p>
<div class="sphx-glr-thumbnail-title">The Johnson-Lindenstrauss bound for embedding with random projections</div>
</div></div></section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.random_projection.SparseRandomProjection"><code class="docutils literal notranslate"><span class="pre">SparseRandomProjection</span></code></a><ul class="nav section-nav flex-column visible">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.random_projection.SparseRandomProjection.fit"><code class="docutils literal notranslate"><span class="pre">fit</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.random_projection.SparseRandomProjection.fit_transform"><code class="docutils literal notranslate"><span class="pre">fit_transform</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.random_projection.SparseRandomProjection.get_feature_names_out"><code class="docutils literal notranslate"><span class="pre">get_feature_names_out</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.random_projection.SparseRandomProjection.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.random_projection.SparseRandomProjection.get_params"><code class="docutils literal notranslate"><span class="pre">get_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.random_projection.SparseRandomProjection.inverse_transform"><code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.random_projection.SparseRandomProjection.set_output"><code class="docutils literal notranslate"><span class="pre">set_output</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.random_projection.SparseRandomProjection.set_params"><code class="docutils literal notranslate"><span class="pre">set_params</span></code></a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sklearn.random_projection.SparseRandomProjection.transform"><code class="docutils literal notranslate"><span class="pre">transform</span></code></a></li>
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