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<section id="generated-datasets">
<span id="sample-generators"></span><h1><span class="section-number">7.3. </span>Generated datasets<a class="headerlink" href="#generated-datasets" title="Permalink to this headline">¶</a></h1>
<p>In addition, scikit-learn includes various random sample generators that
can be used to build artificial datasets of controlled size and complexity.</p>
<section id="generators-for-classification-and-clustering">
<h2><span class="section-number">7.3.1. </span>Generators for classification and clustering<a class="headerlink" href="#generators-for-classification-and-clustering" title="Permalink to this headline">¶</a></h2>
<p>These generators produce a matrix of features and corresponding discrete
targets.</p>
<section id="single-label">
<h3><span class="section-number">7.3.1.1. </span>Single label<a class="headerlink" href="#single-label" title="Permalink to this headline">¶</a></h3>
<p>Both <a class="reference internal" href="../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_blobs</span></code></a> and <a class="reference internal" href="../modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_classification</span></code></a> create multiclass
datasets by allocating each class one or more normally-distributed clusters of
points. <a class="reference internal" href="../modules/generated/sklearn.datasets.make_blobs.html#sklearn.datasets.make_blobs" title="sklearn.datasets.make_blobs"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_blobs</span></code></a> provides greater control regarding the centers and
standard deviations of each cluster, and is used to demonstrate clustering.
<a class="reference internal" href="../modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_classification</span></code></a> specializes in introducing noise by way of:
correlated, redundant and uninformative features; multiple Gaussian clusters
per class; and linear transformations of the feature space.</p>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_gaussian_quantiles.html#sklearn.datasets.make_gaussian_quantiles" title="sklearn.datasets.make_gaussian_quantiles"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_gaussian_quantiles</span></code></a> divides a single Gaussian cluster into
near-equal-size classes separated by concentric hyperspheres.
<a class="reference internal" href="../modules/generated/sklearn.datasets.make_hastie_10_2.html#sklearn.datasets.make_hastie_10_2" title="sklearn.datasets.make_hastie_10_2"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_hastie_10_2</span></code></a> generates a similar binary, 10-dimensional problem.</p>
<a class="reference external image-reference" href="../auto_examples/datasets/plot_random_dataset.html"><img alt="../_images/sphx_glr_plot_random_dataset_001.png" class="align-center" src="../_images/sphx_glr_plot_random_dataset_001.png" style="width: 400.0px; height: 400.0px;" /></a>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles" title="sklearn.datasets.make_circles"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_circles</span></code></a> and <a class="reference internal" href="../modules/generated/sklearn.datasets.make_moons.html#sklearn.datasets.make_moons" title="sklearn.datasets.make_moons"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_moons</span></code></a> generate 2d binary classification
datasets that are challenging to certain algorithms (e.g. centroid-based
clustering or linear classification), including optional Gaussian noise.
They are useful for visualization. <a class="reference internal" href="../modules/generated/sklearn.datasets.make_circles.html#sklearn.datasets.make_circles" title="sklearn.datasets.make_circles"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_circles</span></code></a> produces Gaussian data
with a spherical decision boundary for binary classification, while
<a class="reference internal" href="../modules/generated/sklearn.datasets.make_moons.html#sklearn.datasets.make_moons" title="sklearn.datasets.make_moons"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_moons</span></code></a> produces two interleaving half circles.</p>
</section>
<section id="multilabel">
<h3><span class="section-number">7.3.1.2. </span>Multilabel<a class="headerlink" href="#multilabel" title="Permalink to this headline">¶</a></h3>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_multilabel_classification.html#sklearn.datasets.make_multilabel_classification" title="sklearn.datasets.make_multilabel_classification"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_multilabel_classification</span></code></a> generates random samples with multiple
labels, reflecting a bag of words drawn from a mixture of topics. The number of
topics for each document is drawn from a Poisson distribution, and the topics
themselves are drawn from a fixed random distribution. Similarly, the number of
words is drawn from Poisson, with words drawn from a multinomial, where each
topic defines a probability distribution over words. Simplifications with
respect to true bag-of-words mixtures include:</p>
<ul class="simple">
<li><p>Per-topic word distributions are independently drawn, where in reality all
would be affected by a sparse base distribution, and would be correlated.</p></li>
<li><p>For a document generated from multiple topics, all topics are weighted
equally in generating its bag of words.</p></li>
<li><p>Documents without labels words at random, rather than from a base
distribution.</p></li>
</ul>
<a class="reference external image-reference" href="../auto_examples/datasets/plot_random_multilabel_dataset.html"><img alt="../_images/sphx_glr_plot_random_multilabel_dataset_001.png" class="align-center" src="../_images/sphx_glr_plot_random_multilabel_dataset_001.png" style="width: 400.0px; height: 200.0px;" /></a>
</section>
<section id="biclustering">
<h3><span class="section-number">7.3.1.3. </span>Biclustering<a class="headerlink" href="#biclustering" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_biclusters.html#sklearn.datasets.make_biclusters" title="sklearn.datasets.make_biclusters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_biclusters</span></code></a>(shape, n_clusters, *[, ...])</p></td>
<td><p>Generate a constant block diagonal structure array for biclustering.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_checkerboard.html#sklearn.datasets.make_checkerboard" title="sklearn.datasets.make_checkerboard"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_checkerboard</span></code></a>(shape, n_clusters, *[, ...])</p></td>
<td><p>Generate an array with block checkerboard structure for biclustering.</p></td>
</tr>
</tbody>
</table>
</section>
</section>
<section id="generators-for-regression">
<h2><span class="section-number">7.3.2. </span>Generators for regression<a class="headerlink" href="#generators-for-regression" title="Permalink to this headline">¶</a></h2>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_regression.html#sklearn.datasets.make_regression" title="sklearn.datasets.make_regression"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_regression</span></code></a> produces regression targets as an optionally-sparse
random linear combination of random features, with noise. Its informative
features may be uncorrelated, or low rank (few features account for most of the
variance).</p>
<p>Other regression generators generate functions deterministically from
randomized features. <a class="reference internal" href="../modules/generated/sklearn.datasets.make_sparse_uncorrelated.html#sklearn.datasets.make_sparse_uncorrelated" title="sklearn.datasets.make_sparse_uncorrelated"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_sparse_uncorrelated</span></code></a> produces a target as a
linear combination of four features with fixed coefficients.
Others encode explicitly non-linear relations:
<a class="reference internal" href="../modules/generated/sklearn.datasets.make_friedman1.html#sklearn.datasets.make_friedman1" title="sklearn.datasets.make_friedman1"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_friedman1</span></code></a> is related by polynomial and sine transforms;
<a class="reference internal" href="../modules/generated/sklearn.datasets.make_friedman2.html#sklearn.datasets.make_friedman2" title="sklearn.datasets.make_friedman2"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_friedman2</span></code></a> includes feature multiplication and reciprocation; and
<a class="reference internal" href="../modules/generated/sklearn.datasets.make_friedman3.html#sklearn.datasets.make_friedman3" title="sklearn.datasets.make_friedman3"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_friedman3</span></code></a> is similar with an arctan transformation on the target.</p>
</section>
<section id="generators-for-manifold-learning">
<h2><span class="section-number">7.3.3. </span>Generators for manifold learning<a class="headerlink" href="#generators-for-manifold-learning" title="Permalink to this headline">¶</a></h2>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_s_curve.html#sklearn.datasets.make_s_curve" title="sklearn.datasets.make_s_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_s_curve</span></code></a>([n_samples, noise, random_state])</p></td>
<td><p>Generate an S curve dataset.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_swiss_roll.html#sklearn.datasets.make_swiss_roll" title="sklearn.datasets.make_swiss_roll"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_swiss_roll</span></code></a>([n_samples, noise, ...])</p></td>
<td><p>Generate a swiss roll dataset.</p></td>
</tr>
</tbody>
</table>
</section>
<section id="generators-for-decomposition">
<h2><span class="section-number">7.3.4. </span>Generators for decomposition<a class="headerlink" href="#generators-for-decomposition" title="Permalink to this headline">¶</a></h2>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_low_rank_matrix.html#sklearn.datasets.make_low_rank_matrix" title="sklearn.datasets.make_low_rank_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_low_rank_matrix</span></code></a>([n_samples, ...])</p></td>
<td><p>Generate a mostly low rank matrix with bell-shaped singular values.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_sparse_coded_signal.html#sklearn.datasets.make_sparse_coded_signal" title="sklearn.datasets.make_sparse_coded_signal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_sparse_coded_signal</span></code></a>(n_samples, *, ...)</p></td>
<td><p>Generate a signal as a sparse combination of dictionary elements.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_spd_matrix.html#sklearn.datasets.make_spd_matrix" title="sklearn.datasets.make_spd_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_spd_matrix</span></code></a>(n_dim, *[, random_state])</p></td>
<td><p>Generate a random symmetric, positive-definite matrix.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.make_sparse_spd_matrix.html#sklearn.datasets.make_sparse_spd_matrix" title="sklearn.datasets.make_sparse_spd_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_sparse_spd_matrix</span></code></a>([dim, alpha, ...])</p></td>
<td><p>Generate a sparse symmetric definite positive matrix.</p></td>
</tr>
</tbody>
</table>
</section>
</section>
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