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<section id="random-projection">
<span id="id1"></span><h1><span class="section-number">6.6. </span>Random Projection<a class="headerlink" href="#random-projection" title="Link to this heading">#</a></h1>
<p>The <a class="reference internal" href="../api/sklearn.random_projection.html#module-sklearn.random_projection" title="sklearn.random_projection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.random_projection</span></code></a> module implements a simple and
computationally efficient way to reduce the dimensionality of the data by
trading a controlled amount of accuracy (as additional variance) for faster
processing times and smaller model sizes. This module implements two types of
unstructured random matrix:
<a class="reference internal" href="#gaussian-random-matrix"><span class="std std-ref">Gaussian random matrix</span></a> and
<a class="reference internal" href="#sparse-random-matrix"><span class="std std-ref">sparse random matrix</span></a>.</p>
<p>The dimensions and distribution of random projections matrices are
controlled so as to preserve the pairwise distances between any two
samples of the dataset. Thus random projection is a suitable approximation
technique for distance based method.</p>
<p class="rubric">References</p>
<ul class="simple">
<li><p>Sanjoy Dasgupta. 2000.
<a class="reference external" href="https://cseweb.ucsd.edu/~dasgupta/papers/randomf.pdf">Experiments with random projection.</a>
In Proceedings of the Sixteenth conference on Uncertainty in artificial
intelligence (UAI’00), Craig Boutilier and Moisés Goldszmidt (Eds.). Morgan
Kaufmann Publishers Inc., San Francisco, CA, USA, 143-151.</p></li>
<li><p>Ella Bingham and Heikki Mannila. 2001.
<a class="reference external" href="https://citeseerx.ist.psu.edu/doc_view/pid/aed77346f737b0ed5890b61ad02e5eb4ab2f3dc6">Random projection in dimensionality reduction: applications to image and text data.</a>
In Proceedings of the seventh ACM SIGKDD international conference on
Knowledge discovery and data mining (KDD ‘01). ACM, New York, NY, USA,
245-250.</p></li>
</ul>
<section id="the-johnson-lindenstrauss-lemma">
<span id="johnson-lindenstrauss"></span><h2><span class="section-number">6.6.1. </span>The Johnson-Lindenstrauss lemma<a class="headerlink" href="#the-johnson-lindenstrauss-lemma" title="Link to this heading">#</a></h2>
<p>The main theoretical result behind the efficiency of random projection is the
<a class="reference external" href="https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma">Johnson-Lindenstrauss lemma (quoting Wikipedia)</a>:</p>
<blockquote>
<div><p>In mathematics, the Johnson-Lindenstrauss lemma is a result
concerning low-distortion embeddings of points from high-dimensional
into low-dimensional Euclidean space. The lemma states that a small set
of points in a high-dimensional space can be embedded into a space of
much lower dimension in such a way that distances between the points are
nearly preserved. The map used for the embedding is at least Lipschitz,
and can even be taken to be an orthogonal projection.</p>
</div></blockquote>
<p>Knowing only the number of samples, the
<a class="reference internal" href="generated/sklearn.random_projection.johnson_lindenstrauss_min_dim.html#sklearn.random_projection.johnson_lindenstrauss_min_dim" title="sklearn.random_projection.johnson_lindenstrauss_min_dim"><code class="xref py py-func docutils literal notranslate"><span class="pre">johnson_lindenstrauss_min_dim</span></code></a> estimates
conservatively the minimal size of the random subspace to guarantee a
bounded distortion introduced by the random projection:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.random_projection</span> <span class="kn">import</span> <span class="n">johnson_lindenstrauss_min_dim</span>
<span class="gp">>>> </span><span class="n">johnson_lindenstrauss_min_dim</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mf">1e6</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="go">663</span>
<span class="gp">>>> </span><span class="n">johnson_lindenstrauss_min_dim</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mf">1e6</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">])</span>
<span class="go">array([ 663, 11841, 1112658])</span>
<span class="gp">>>> </span><span class="n">johnson_lindenstrauss_min_dim</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="p">[</span><span class="mf">1e4</span><span class="p">,</span> <span class="mf">1e5</span><span class="p">,</span> <span class="mf">1e6</span><span class="p">],</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="go">array([ 7894, 9868, 11841])</span>
</pre></div>
</div>
<figure class="align-center">
<a class="reference external image-reference" href="../auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html"><img alt="../_images/sphx_glr_plot_johnson_lindenstrauss_bound_001.png" src="../_images/sphx_glr_plot_johnson_lindenstrauss_bound_001.png" style="width: 480.0px; height: 360.0px;" />
</a>
</figure>
<figure class="align-center">
<a class="reference external image-reference" href="../auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html"><img alt="../_images/sphx_glr_plot_johnson_lindenstrauss_bound_002.png" src="../_images/sphx_glr_plot_johnson_lindenstrauss_bound_002.png" style="width: 480.0px; height: 360.0px;" />
</a>
</figure>
<p class="rubric">Examples</p>
<ul class="simple">
<li><p>See <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>
for a theoretical explication on the Johnson-Lindenstrauss lemma and an
empirical validation using sparse random matrices.</p></li>
</ul>
<p class="rubric">References</p>
<ul class="simple">
<li><p>Sanjoy Dasgupta and Anupam Gupta, 1999.
<a class="reference external" href="https://citeseerx.ist.psu.edu/doc_view/pid/95cd464d27c25c9c8690b378b894d337cdf021f9">An elementary proof of the Johnson-Lindenstrauss Lemma.</a></p></li>
</ul>
</section>
<section id="gaussian-random-projection">
<span id="gaussian-random-matrix"></span><h2><span class="section-number">6.6.2. </span>Gaussian random projection<a class="headerlink" href="#gaussian-random-projection" title="Link to this heading">#</a></h2>
<p>The <a class="reference internal" href="generated/sklearn.random_projection.GaussianRandomProjection.html#sklearn.random_projection.GaussianRandomProjection" title="sklearn.random_projection.GaussianRandomProjection"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianRandomProjection</span></code></a> reduces the
dimensionality by projecting the original input space on a randomly generated
matrix where components are drawn from the following distribution
<span class="math notranslate nohighlight">\(N(0, \frac{1}{n_{components}})\)</span>.</p>
<p>Here a small excerpt which illustrates how to use the Gaussian random
projection transformer:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">random_projection</span>
<span class="gp">>>> </span><span class="n">X</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">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">transformer</span> <span class="o">=</span> <span class="n">random_projection</span><span class="o">.</span><span class="n">GaussianRandomProjection</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">(100, 3947)</span>
</pre></div>
</div>
</section>
<section id="sparse-random-projection">
<span id="sparse-random-matrix"></span><h2><span class="section-number">6.6.3. </span>Sparse random projection<a class="headerlink" href="#sparse-random-projection" title="Link to this heading">#</a></h2>
<p>The <a class="reference internal" href="generated/sklearn.random_projection.SparseRandomProjection.html#sklearn.random_projection.SparseRandomProjection" title="sklearn.random_projection.SparseRandomProjection"><code class="xref py py-class docutils literal notranslate"><span class="pre">SparseRandomProjection</span></code></a> reduces the
dimensionality by projecting the original input space using a sparse
random matrix.</p>
<p>Sparse random matrices are an alternative to dense Gaussian 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 define <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 elements of the random matrix
are drawn from</p>
<div class="math notranslate nohighlight">
\[\begin{split}\left\{
\begin{array}{c c l}
-\sqrt{\frac{s}{n_{\text{components}}}} & & 1 / 2s\\
0 &\text{with probability} & 1 - 1 / s \\
+\sqrt{\frac{s}{n_{\text{components}}}} & & 1 / 2s\\
\end{array}
\right.\end{split}\]</div>
<p>where <span class="math notranslate nohighlight">\(n_{\text{components}}\)</span> is the size of the projected subspace.
By default the density of non zero elements is set to the minimum density as
recommended by Ping Li et al.: <span class="math notranslate nohighlight">\(1 / \sqrt{n_{\text{features}}}\)</span>.</p>
<p>Here a small excerpt which illustrates how to use the sparse random
projection transformer:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">random_projection</span>
<span class="gp">>>> </span><span class="n">X</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">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">transformer</span> <span class="o">=</span> <span class="n">random_projection</span><span class="o">.</span><span class="n">SparseRandomProjection</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">(100, 3947)</span>
</pre></div>
</div>
<p class="rubric">References</p>
<ul class="simple">
<li><p>D. Achlioptas. 2003.
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0022000003000254">Database-friendly random projections: Johnson-Lindenstrauss with binary
coins</a>.
Journal of Computer and System Sciences 66 (2003) 671-687.</p></li>
<li><p>Ping Li, Trevor J. Hastie, and Kenneth W. Church. 2006.
<a class="reference external" href="https://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf">Very sparse random projections.</a>
In Proceedings of the 12th ACM SIGKDD international conference on
Knowledge discovery and data mining (KDD ‘06). ACM, New York, NY, USA, 287-296.</p></li>
</ul>
</section>
<section id="inverse-transform">
<span id="random-projection-inverse-transform"></span><h2><span class="section-number">6.6.4. </span>Inverse Transform<a class="headerlink" href="#inverse-transform" title="Link to this heading">#</a></h2>
<p>The random projection transformers have <code class="docutils literal notranslate"><span class="pre">compute_inverse_components</span></code> parameter. When
set to True, after creating the random <code class="docutils literal notranslate"><span class="pre">components_</span></code> matrix during fitting,
the transformer computes the pseudo-inverse of this matrix and stores it as
<code class="docutils literal notranslate"><span class="pre">inverse_components_</span></code>. The <code class="docutils literal notranslate"><span class="pre">inverse_components_</span></code> matrix has shape
<span class="math notranslate nohighlight">\(n_{features} \times n_{components}\)</span>, and it is always a dense matrix,
regardless of whether the components matrix is sparse or dense. So depending on
the number of features and components, it may use a lot of memory.</p>
<p>When the <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code> method is called, it computes the product of the
input <code class="docutils literal notranslate"><span class="pre">X</span></code> and the transpose of the inverse components. If the inverse components have
been computed during fit, they are reused at each call to <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code>.
Otherwise they are recomputed each time, which can be costly. The result is always
dense, even if <code class="docutils literal notranslate"><span class="pre">X</span></code> is sparse.</p>
<p>Here a small code example which illustrates how to use the inverse transform
feature:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.random_projection</span> <span class="kn">import</span> <span class="n">SparseRandomProjection</span>
<span class="gp">>>> </span><span class="n">X</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">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">10000</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="gp">... </span> <span class="n">compute_inverse_components</span><span class="o">=</span><span class="kc">True</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">...</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">(100, 3947)</span>
<span class="gp">>>> </span><span class="n">X_new_inversed</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">X_new</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X_new_inversed</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(100, 10000)</span>
<span class="gp">>>> </span><span class="n">X_new_again</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_new_inversed</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">X_new</span><span class="p">,</span> <span class="n">X_new_again</span><span class="p">)</span>
<span class="go">True</span>
</pre></div>
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