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<li><a class="reference internal" href="#">Utilities for Developers</a><ul>
<li><a class="reference internal" href="#validation-tools">Validation Tools</a></li>
<li><a class="reference internal" href="#efficient-linear-algebra-array-operations">Efficient Linear Algebra & Array Operations</a></li>
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<div class="section" id="utilities-for-developers">
<span id="developers-utils"></span><h1>Utilities for Developers<a class="headerlink" href="#utilities-for-developers" title="Permalink to this headline">¶</a></h1>
<p>Scikit-learn contains a number of utilities to help with development. These are
located in <a class="reference internal" href="../modules/classes.html#module-sklearn.utils" title="sklearn.utils"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.utils</span></tt></a>, and include tools in a number of categories.
All the following functions and classes are in the module <a class="reference internal" href="../modules/classes.html#module-sklearn.utils" title="sklearn.utils"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.utils</span></tt></a>.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">These utilities are meant to be used internally within the scikit-learn
package. They are not guaranteed to be stable between versions of
scikit-learn. Backports, in particular, will be removed as the scikit-learn
dependencies evolve.</p>
</div>
<div class="section" id="validation-tools">
<h2>Validation Tools<a class="headerlink" href="#validation-tools" title="Permalink to this headline">¶</a></h2>
<p>These are tools used to check and validate input. When you write a function
which accepts arrays, matrices, or sparse matrices as arguments, the following
should be used when applicable.</p>
<ul class="simple">
<li><tt class="xref py py-func docutils literal"><span class="pre">assert_all_finite</span></tt>: Throw an error if array contains NaNs or Infs.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">safe_asarray</span></tt>: Convert input to array or sparse matrix. Equivalent
to <tt class="docutils literal"><span class="pre">np.asarray</span></tt>, but sparse matrices are passed through.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">as_float_array</span></tt>: convert input to an array of floats. If a sparse
matrix is passed, a sparse matrix will be returned.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">array2d</span></tt>: equivalent to <tt class="docutils literal"><span class="pre">np.atleast_2d</span></tt>, but the <tt class="docutils literal"><span class="pre">order</span></tt> and
<tt class="docutils literal"><span class="pre">dtype</span></tt> of the input are maintained.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">atleast2d_or_csr</span></tt>: equivalent to <tt class="docutils literal"><span class="pre">array2d</span></tt>, but if a sparse matrix
is passed, will convert to csr format. Also calls <tt class="docutils literal"><span class="pre">assert_all_finite</span></tt>.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">check_arrays</span></tt>: check that all input arrays have consistent first
dimensions. This will work for an arbitrary number of arrays.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">warn_if_not_float</span></tt>: Warn if input is not a floating-point value.
the input <tt class="docutils literal"><span class="pre">X</span></tt> is assumed to have <tt class="docutils literal"><span class="pre">X.dtype</span></tt>.</li>
</ul>
<p>If your code relies on a random number generator, it should never use
functions like <tt class="docutils literal"><span class="pre">numpy.random.random</span></tt> or <tt class="docutils literal"><span class="pre">numpy.random.normal</span></tt>. This
approach can lead to repeatability issues in unit tests. Instead, a
<tt class="docutils literal"><span class="pre">numpy.random.RandomState</span></tt> object should be used, which is built from
a <tt class="docutils literal"><span class="pre">random_state</span></tt> argument passed to the class or function. The function
<a class="reference internal" href="../modules/generated/sklearn.utils.check_random_state.html#sklearn.utils.check_random_state" title="sklearn.utils.check_random_state"><tt class="xref py py-func docutils literal"><span class="pre">check_random_state</span></tt></a>, below, can then be used to create a random
number generator object.</p>
<ul class="simple">
<li><a class="reference internal" href="../modules/generated/sklearn.utils.check_random_state.html#sklearn.utils.check_random_state" title="sklearn.utils.check_random_state"><tt class="xref py py-func docutils literal"><span class="pre">check_random_state</span></tt></a>: create a <tt class="docutils literal"><span class="pre">np.random.RandomState</span></tt> object from
a parameter <tt class="docutils literal"><span class="pre">random_state</span></tt>.<ul>
<li>If <tt class="docutils literal"><span class="pre">random_state</span></tt> is <tt class="docutils literal"><span class="pre">None</span></tt> or <tt class="docutils literal"><span class="pre">np.random</span></tt>, then a
randomly-initialized <tt class="docutils literal"><span class="pre">RandomState</span></tt> object is returned.</li>
<li>If <tt class="docutils literal"><span class="pre">random_state</span></tt> is an integer, then it is used to seed a new
<tt class="docutils literal"><span class="pre">RandomState</span></tt> object.</li>
<li>If <tt class="docutils literal"><span class="pre">random_state</span></tt> is a <tt class="docutils literal"><span class="pre">RandomState</span></tt> object, then it is passed through.</li>
</ul>
</li>
</ul>
<p>For example:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_random_state</span>
<span class="gp">>>> </span><span class="n">random_state</span> <span class="o">=</span> <span class="mi">0</span>
<span class="gp">>>> </span><span class="n">random_state</span> <span class="o">=</span> <span class="n">check_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">random_state</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
<span class="go">array([ 0.5488135 , 0.71518937, 0.60276338, 0.54488318])</span>
</pre></div>
</div>
</div>
<div class="section" id="efficient-linear-algebra-array-operations">
<h2>Efficient Linear Algebra & Array Operations<a class="headerlink" href="#efficient-linear-algebra-array-operations" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><tt class="xref py py-func docutils literal"><span class="pre">extmath.randomized_range_finder</span></tt>: construct an orthonormal matrix
whose range approximates the range of the input. This is used in
<tt class="xref py py-func docutils literal"><span class="pre">extmath.randomized_svd</span></tt>, below.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">extmath.randomized_svd</span></tt>: compute the k-truncated randomized SVD.
This algorithm finds the exact truncated singular values decomposition
using randomization to speed up the computations. It is particularly
fast on large matrices on which you wish to extract only a small
number of components.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">arrayfuncs.cholesky_delete</span></tt>:
(used in <tt class="xref py py-func docutils literal"><span class="pre">sklearn.linear_model.least_angle.lars_path</span></tt>) Remove an
item from a cholesky factorization.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">arrayfuncs.min_pos</span></tt>: (used in <tt class="docutils literal"><span class="pre">sklearn.linear_model.least_angle</span></tt>)
Find the minimum of the positive values within an array.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">extmath.norm</span></tt>: computes Euclidean (L2) vector norm
by directly calling the BLAS
<tt class="docutils literal"><span class="pre">nrm2</span></tt> function. This is more stable than <tt class="docutils literal"><span class="pre">scipy.linalg.norm</span></tt>. See
<a class="reference external" href="http://fseoane.net/blog/2011/computing-the-vector-norm/">Fabian’s blog post</a> for a discussion.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">extmath.fast_logdet</span></tt>: efficiently compute the log of the determinant
of a matrix.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">extmath.density</span></tt>: efficiently compute the density of a sparse vector</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">extmath.safe_sparse_dot</span></tt>: dot product which will correctly handle
<tt class="docutils literal"><span class="pre">scipy.sparse</span></tt> inputs. If the inputs are dense, it is equivalent to
<tt class="docutils literal"><span class="pre">numpy.dot</span></tt>.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">extmath.logsumexp</span></tt>: compute the sum of X assuming X is in the log
domain. This is equivalent to calling <tt class="docutils literal"><span class="pre">np.log(np.sum(np.exp(X)))</span></tt>, but is
robust to overflow/underflow errors. Note that there is similar
functionality in <tt class="docutils literal"><span class="pre">np.logaddexp.reduce</span></tt>, but because of the pairwise nature
of this routine, it is slower for large arrays.
Scipy has a similar routine in <tt class="docutils literal"><span class="pre">scipy.misc.logsumexp</span></tt> (In scipy versions
< 0.10, this is found in <tt class="docutils literal"><span class="pre">scipy.maxentropy.logsumexp</span></tt>),
but the scipy version does not accept an <tt class="docutils literal"><span class="pre">axis</span></tt> keyword.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">extmath.weighted_mode</span></tt>: an extension of <tt class="docutils literal"><span class="pre">scipy.stats.mode</span></tt> which
allows each item to have a real-valued weight.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.utils.resample.html#sklearn.utils.resample" title="sklearn.utils.resample"><tt class="xref py py-func docutils literal"><span class="pre">resample</span></tt></a>: Resample arrays or sparse matrices in a consistent way.
used in <a class="reference internal" href="../modules/generated/sklearn.utils.shuffle.html#sklearn.utils.shuffle" title="sklearn.utils.shuffle"><tt class="xref py py-func docutils literal"><span class="pre">shuffle</span></tt></a>, below.</li>
<li><a class="reference internal" href="../modules/generated/sklearn.utils.shuffle.html#sklearn.utils.shuffle" title="sklearn.utils.shuffle"><tt class="xref py py-func docutils literal"><span class="pre">shuffle</span></tt></a>: Shuffle arrays or sparse matrices in a consistent way.
Used in <tt class="docutils literal"><span class="pre">sklearn.cluster.k_means</span></tt>.</li>
</ul>
</div>
<div class="section" id="efficient-random-sampling">
<h2>Efficient Random Sampling<a class="headerlink" href="#efficient-random-sampling" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><tt class="xref py py-func docutils literal"><span class="pre">random.sample_without_replacement</span></tt>: implements efficient algorithms
for sampling <tt class="docutils literal"><span class="pre">n_samples</span></tt> integers from a population of size <tt class="docutils literal"><span class="pre">n_population</span></tt>
without replacement.</li>
</ul>
</div>
<div class="section" id="efficient-routines-for-sparse-matrices">
<h2>Efficient Routines for Sparse Matrices<a class="headerlink" href="#efficient-routines-for-sparse-matrices" title="Permalink to this headline">¶</a></h2>
<p>The <tt class="docutils literal"><span class="pre">sklearn.utils.sparsefuncs</span></tt> cython module hosts compiled extensions to
efficiently process <tt class="docutils literal"><span class="pre">scipy.sparse</span></tt> data.</p>
<ul class="simple">
<li><tt class="xref py py-func docutils literal"><span class="pre">sparsefuncs.mean_variance_axis0</span></tt>: compute the means and
variances along axis 0 of a CSR matrix.
Used for normalizing the tolerance stopping criterion in
<tt class="xref py py-class docutils literal"><span class="pre">sklearn.cluster.k_means_.KMeans</span></tt>.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">sparsefuncs.inplace_csr_row_normalize_l1</span></tt> and
<tt class="xref py py-func docutils literal"><span class="pre">sparsefuncs.inplace_csr_row_normalize_l2</span></tt>: can be used to normalize
individual sparse samples to unit l1 or l2 norm as done in
<a class="reference internal" href="../modules/generated/sklearn.preprocessing.Normalizer.html#sklearn.preprocessing.Normalizer" title="sklearn.preprocessing.Normalizer"><tt class="xref py py-class docutils literal"><span class="pre">sklearn.preprocessing.Normalizer</span></tt></a>.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">sparsefuncs.inplace_csr_column_scale</span></tt>: can be used to multiply the
columns of a CSR matrix by a constant scale (one scale per column).
Used for scaling features to unit standard deviation in
<a class="reference internal" href="../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler"><tt class="xref py py-class docutils literal"><span class="pre">sklearn.preprocessing.StandardScaler</span></tt></a>.</li>
</ul>
</div>
<div class="section" id="graph-routines">
<h2>Graph Routines<a class="headerlink" href="#graph-routines" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><tt class="xref py py-func docutils literal"><span class="pre">graph.single_source_shortest_path_length</span></tt>:
(not currently used in scikit-learn)
Return the shortest path from a single source
to all connected nodes on a graph. Code is adapted from networkx.
If this is ever needed again, it would be far faster to use a single
iteration of Dijkstra’s algorithm from <tt class="docutils literal"><span class="pre">graph_shortest_path</span></tt>.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">graph.graph_laplacian</span></tt>:
(used in <tt class="xref py py-func docutils literal"><span class="pre">sklearn.cluster.spectral.spectral_embedding</span></tt>)
Return the Laplacian of a given graph. There is specialized code for
both dense and sparse connectivity matrices.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">graph_shortest_path.graph_shortest_path</span></tt>:
(used in :class:<tt class="docutils literal"><span class="pre">sklearn.manifold.Isomap</span></tt>)
Return the shortest path between all pairs of connected points on a directed
or undirected graph. Both the Floyd-Warshall algorithm and Dijkstra’s
algorithm are available. The algorithm is most efficient when the
connectivity matrix is a <tt class="docutils literal"><span class="pre">scipy.sparse.csr_matrix</span></tt>.</li>
</ul>
</div>
<div class="section" id="backports">
<h2>Backports<a class="headerlink" href="#backports" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><tt class="xref py py-func docutils literal"><span class="pre">fixes.expit</span></tt>: Logistic sigmoid function. Replacement for SciPy 0.10’s
<tt class="docutils literal"><span class="pre">scipy.special.expit</span></tt>.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">sparsetools.connected_components</span></tt>
(backported from <tt class="docutils literal"><span class="pre">scipy.sparse.connected_components</span></tt> in scipy 0.12).
Used in <tt class="docutils literal"><span class="pre">sklearn.cluster.hierarchical</span></tt>, as well as in tests for
<a class="reference internal" href="../modules/classes.html#module-sklearn.feature_extraction" title="sklearn.feature_extraction"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.feature_extraction</span></tt></a>.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">fixes.isclose</span></tt>
(backported from <tt class="docutils literal"><span class="pre">numpy.isclose</span></tt> in numpy 1.8.1).
In versions before 1.7, this function was not available in
numpy. Used in <tt class="docutils literal"><span class="pre">sklearn.metrics</span></tt>.</li>
</ul>
<div class="section" id="arpack">
<h3>ARPACK<a class="headerlink" href="#arpack" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><tt class="xref py py-func docutils literal"><span class="pre">arpack.eigs</span></tt>
(backported from <tt class="docutils literal"><span class="pre">scipy.sparse.linalg.eigs</span></tt> in scipy 0.10)
Sparse non-symmetric eigenvalue decomposition using the Arnoldi
method. A limited version of <tt class="docutils literal"><span class="pre">eigs</span></tt> is available in earlier
scipy versions.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">arpack.eigsh</span></tt>
(backported from <tt class="docutils literal"><span class="pre">scipy.sparse.linalg.eigsh</span></tt> in scipy 0.10)
Sparse non-symmetric eigenvalue decomposition using the Arnoldi
method. A limited version of <tt class="docutils literal"><span class="pre">eigsh</span></tt> is available in earlier
scipy versions.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">arpack.svds</span></tt>
(backported from <tt class="docutils literal"><span class="pre">scipy.sparse.linalg.svds</span></tt> in scipy 0.10)
Sparse non-symmetric eigenvalue decomposition using the Arnoldi
method. A limited version of <tt class="docutils literal"><span class="pre">svds</span></tt> is available in earlier
scipy versions.</li>
</ul>
</div>
<div class="section" id="benchmarking">
<h3>Benchmarking<a class="headerlink" href="#benchmarking" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><tt class="xref py py-func docutils literal"><span class="pre">bench.total_seconds</span></tt> (back-ported from <tt class="docutils literal"><span class="pre">timedelta.total_seconds</span></tt>
in Python 2.7). Used in <tt class="docutils literal"><span class="pre">benchmarks/bench_glm.py</span></tt>.</li>
</ul>
</div>
</div>
<div class="section" id="testing-functions">
<h2>Testing Functions<a class="headerlink" href="#testing-functions" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><tt class="xref py py-func docutils literal"><span class="pre">testing.assert_in</span></tt>, <tt class="xref py py-func docutils literal"><span class="pre">testing.assert_not_in</span></tt>: Assertions for
container membership. Designed for forward compatibility with Nose 1.0.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">testing.assert_raise_message</span></tt>: Assertions for checking the
error raise message.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">testing.mock_mldata_urlopen</span></tt>: Mocks the urlopen function to fake
requests to mldata.org. Used in tests of <a class="reference internal" href="../modules/classes.html#module-sklearn.datasets" title="sklearn.datasets"><tt class="xref py py-mod docutils literal"><span class="pre">sklearn.datasets</span></tt></a>.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">testing.all_estimators</span></tt> : returns a list of all estimators in
sklearn to test for consistent behavior and interfaces.</li>
</ul>
</div>
<div class="section" id="multiclass-and-multilabel-utility-function">
<h2>Multiclass and multilabel utility function<a class="headerlink" href="#multiclass-and-multilabel-utility-function" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><tt class="xref py py-func docutils literal"><span class="pre">multiclass.is_multilabel</span></tt>: Helper function to check if the task
is a multi-label classification one.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">multiclass.is_label_indicator_matrix</span></tt>: Helper function to check if
a classification output is in label indicator matrix format.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">multiclass.unique_labels</span></tt>: Helper function to extract an ordered
array of unique labels from different formats of target.</li>
</ul>
</div>
<div class="section" id="helper-functions">
<h2>Helper Functions<a class="headerlink" href="#helper-functions" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><tt class="xref py py-class docutils literal"><span class="pre">gen_even_slices</span></tt>: generator to create <tt class="docutils literal"><span class="pre">n</span></tt>-packs of slices going up
to <tt class="docutils literal"><span class="pre">n</span></tt>. Used in <tt class="docutils literal"><span class="pre">sklearn.decomposition.dict_learning</span></tt> and
<tt class="docutils literal"><span class="pre">sklearn.cluster.k_means</span></tt>.</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">safe_mask</span></tt>: Helper function to convert a mask to the format expected
by the numpy array or scipy sparse matrix on which to use it (sparse
matrices support integer indices only while numpy arrays support both
boolean masks and integer indices).</li>
<li><tt class="xref py py-func docutils literal"><span class="pre">safe_sqr</span></tt>: Helper function for unified squaring (<tt class="docutils literal"><span class="pre">**2</span></tt>) of
array-likes, matrices and sparse matrices.</li>
</ul>
</div>
<div class="section" id="hash-functions">
<h2>Hash Functions<a class="headerlink" href="#hash-functions" title="Permalink to this headline">¶</a></h2>
<ul>
<li><p class="first"><tt class="xref py py-func docutils literal"><span class="pre">murmurhash3_32</span></tt> provides a python wrapper for the
<tt class="docutils literal"><span class="pre">MurmurHash3_x86_32</span></tt> C++ non cryptographic hash function. This hash
function is suitable for implementing lookup tables, Bloom filters,
Count Min Sketch, feature hashing and implicitly defined sparse
random projections:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">murmurhash3_32</span>
<span class="gp">>>> </span><span class="n">murmurhash3_32</span><span class="p">(</span><span class="s">"some feature"</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">==</span> <span class="o">-</span><span class="mi">384616559</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">murmurhash3_32</span><span class="p">(</span><span class="s">"some feature"</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">positive</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3910350737</span>
<span class="go">True</span>
</pre></div>
</div>
<p>The <tt class="docutils literal"><span class="pre">sklearn.utils.murmurhash</span></tt> module can also be “cimported” from
other cython modules so as to benefit from the high performance of
MurmurHash while skipping the overhead of the Python interpreter.</p>
</li>
</ul>
</div>
<div class="section" id="warnings-and-exceptions">
<h2>Warnings and Exceptions<a class="headerlink" href="#warnings-and-exceptions" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><tt class="xref py py-class docutils literal"><span class="pre">deprecated</span></tt>: Decorator to mark a function or class as deprecated.</li>
<li><tt class="xref py py-class docutils literal"><span class="pre">ConvergenceWarning</span></tt>: Custom warning to catch convergence problems.
Used in <tt class="docutils literal"><span class="pre">sklearn.covariance.graph_lasso</span></tt>.</li>
</ul>
</div>
</div>
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