.. _developers-utils:

========================
Utilities for Developers
========================

Scikit-learn contains a number of utilities to help with development.  These are
located in :mod:`sklearn.utils`, and include tools in a number of categories.
All the following functions and classes are in the module :mod:`sklearn.utils`.

.. warning ::

   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.


.. currentmodule:: sklearn.utils

Validation Tools
================

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.

- :func:`assert_all_finite`: Throw an error if array contains NaNs or Infs.

- :func:`safe_asarray`: Convert input to array or sparse matrix.  Equivalent
  to ``np.asarray``, but sparse matrices are passed through.

- :func:`as_float_array`: convert input to an array of floats.  If a sparse
  matrix is passed, a sparse matrix will be returned.

- :func:`array2d`: equivalent to ``np.atleast_2d``, but the ``order`` and
  ``dtype`` of the input are maintained.

- :func:`atleast2d_or_csr`: equivalent to ``array2d``, but if a sparse matrix
  is passed, will convert to csr format.  Also calls ``assert_all_finite``.

- :func:`check_arrays`: check that all input arrays have consistent first
  dimensions.  This will work for an arbitrary number of arrays.

- :func:`warn_if_not_float`: Warn if input is not a floating-point value.
  the input ``X`` is assumed to have ``X.dtype``.

If your code relies on a random number generator, it should never use
functions like ``numpy.random.random`` or ``numpy.random.normal``.  This
approach can lead to repeatability issues in unit tests.  Instead, a
``numpy.random.RandomState`` object should be used, which is built from
a ``random_state`` argument passed to the class or function.  The function
:func:`check_random_state`, below, can then be used to create a random
number generator object.

- :func:`check_random_state`: create a ``np.random.RandomState`` object from
  a parameter ``random_state``.

  - If ``random_state`` is ``None`` or ``np.random``, then a
    randomly-initialized ``RandomState`` object is returned.
  - If ``random_state`` is an integer, then it is used to seed a new
    ``RandomState`` object.
  - If ``random_state`` is a ``RandomState`` object, then it is passed through.

For example::

    >>> from sklearn.utils import check_random_state
    >>> random_state = 0
    >>> random_state = check_random_state(random_state)
    >>> random_state.rand(4)
    array([ 0.5488135 ,  0.71518937,  0.60276338,  0.54488318])


Efficient Linear Algebra & Array Operations
===========================================

- :func:`extmath.randomized_range_finder`: construct an orthonormal matrix
  whose range approximates the range of the input.  This is used in
  :func:`extmath.randomized_svd`, below.

- :func:`extmath.randomized_svd`: 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.

- :func:`arrayfuncs.cholesky_delete`:
  (used in :func:`sklearn.linear_model.least_angle.lars_path`)  Remove an
  item from a cholesky factorization.

- :func:`arrayfuncs.min_pos`: (used in ``sklearn.linear_model.least_angle``)
  Find the minimum of the positive values within an array.

- :func:`extmath.norm`: computes Euclidean (L2) vector norm
  by directly calling the BLAS
  ``nrm2`` function.  This is more stable than ``scipy.linalg.norm``.  See
  `Fabian's blog post
  <http://fseoane.net/blog/2011/computing-the-vector-norm/>`_ for a discussion.

- :func:`extmath.fast_logdet`: efficiently compute the log of the determinant
  of a matrix.

- :func:`extmath.density`: efficiently compute the density of a sparse vector

- :func:`extmath.safe_sparse_dot`: dot product which will correctly handle
  ``scipy.sparse`` inputs.  If the inputs are dense, it is equivalent to
  ``numpy.dot``.

- :func:`extmath.logsumexp`: compute the sum of X assuming X is in the log
  domain. This is equivalent to calling ``np.log(np.sum(np.exp(X)))``, but is
  robust to overflow/underflow errors.  Note that there is similar
  functionality in ``np.logaddexp.reduce``, but because of the pairwise nature
  of this routine, it is slower for large arrays.
  Scipy has a similar routine in ``scipy.misc.logsumexp`` (In scipy versions
  < 0.10, this is found in ``scipy.maxentropy.logsumexp``),
  but the scipy version does not accept an ``axis`` keyword.

- :func:`extmath.weighted_mode`: an extension of ``scipy.stats.mode`` which
  allows each item to have a real-valued weight.

- :func:`resample`: Resample arrays or sparse matrices in a consistent way.
  used in :func:`shuffle`, below.

- :func:`shuffle`: Shuffle arrays or sparse matrices in a consistent way.
  Used in ``sklearn.cluster.k_means``.


Efficient Routines for Sparse Matrices
======================================

The ``sklearn.utils.sparsefuncs`` cython module hosts compiled extensions to
efficiently process ``scipy.sparse`` data.

- :func:`sparsefuncs.mean_variance_axis0`: compute the means and
  variances along axis 0 of a CSR matrix.
  Used for normalizing the tolerance stopping criterion in
  :class:`sklearn.cluster.k_means_.KMeans`.

- :func:`sparsefuncs.inplace_csr_row_normalize_l1` and
  :func:`sparsefuncs.inplace_csr_row_normalize_l2`: can be used to normalize
  individual sparse samples to unit l1 or l2 norm as done in
  :class:`sklearn.preprocessing.Normalizer`.

- :func:`sparsefuncs.inplace_csr_column_scale`: 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
  :class:`sklearn.preprocessing.Scaler`.


Graph Routines
==============

- :func:`graph.single_source_shortest_path_length`:
  (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 ``graph_shortest_path``.

- :func:`graph.graph_laplacian`:
  (used in :func:`sklearn.cluster.spectral.spectral_embedding`)
  Return the Laplacian of a given graph.  There is specialized code for
  both dense and sparse connectivity matrices.

- :func:`graph_shortest_path.graph_shortest_path`:
  (used in :class:``sklearn.manifold.Isomap``)
  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 ``scipy.sparse.csr_matrix``.


Backports
=========

- :class:`fixes.Counter` (partial backport of ``collections.Counter`` from
  Python 2.7) Used in ``sklearn.feature_extraction.text``.

- :func:`fixes.unique`: (backport of ``np.unique`` from numpy 1.4).  Find the
  unique entries in an array.  In numpy versions < 1.4, ``np.unique`` is less
  flexible.  Used in :mod:`sklearn.cross_validation`.

- :func:`fixes.copysign`: (backport of ``np.copysign`` from numpy 1.4).
  Change the sign of ``x1`` to that of ``x2``, element-wise.

- :func:`fixes.in1d`: (backport of ``np.in1d`` from numpy 1.4).
  Test whether each element of an array is in a second array.  Used in
  ``sklearn.datasets.twenty_newsgroups`` and
  ``sklearn.feature_extraction.image``.

- :func:`fixes.savemat` (backport of ``scipy.io.savemat`` from scipy 0.7.2).
  Save an array in MATLAB-format. In earlier versions, the keyword
  ``oned_as`` is not available.

- :func:`fixes.count_nonzero` (backport of ``np.count_nonzero`` from
  numpy 1.6).  Count the nonzero elements of a matrix.  Used in
  tests of :mod:`sklearn.linear_model`.

- :func:`arrayfuncs.solve_triangular`
  (Back-ported from scipy v0.9)  Used in ``sklearn.linear_model.omp``,
  independent back-ports in ``sklearn.mixture.gmm`` and
  :mod:`sklearn.gaussian_process`.

- :func:`sparsetools.cs_graph_components`
  (backported from ``scipy.sparse.cs_graph_components`` in scipy 0.9).
  Used in ``sklearn.cluster.hierarchical``, as well as in tests for
  :mod:`sklearn.feature_extraction`.


ARPACK
------

- :func:`arpack.eigs`
  (backported from ``scipy.sparse.linalg.eigs`` in scipy 0.10)
  Sparse non-symmetric eigenvalue decomposition using the Arnoldi
  method.  A limited version of ``eigs`` is available in earlier
  scipy versions.

- :func:`arpack.eigsh`
  (backported from ``scipy.sparse.linalg.eigsh`` in scipy 0.10)
  Sparse non-symmetric eigenvalue decomposition using the Arnoldi
  method.  A limited version of ``eigsh`` is available in earlier
  scipy versions.

- :func:`arpack.svds`
  (backported from ``scipy.sparse.linalg.svds`` in scipy 0.10)
  Sparse non-symmetric eigenvalue decomposition using the Arnoldi
  method.  A limited version of ``svds`` is available in earlier
  scipy versions.


Benchmarking
------------

- :func:`bench.total_seconds` (back-ported from ``timedelta.total_seconds``
  in Python 2.7).  Used in ``benchmarks/bench_glm.py``.


Testing Functions
=================

- :func:`testing.assert_in`, :func:`testing.assert_not_in`: Assertions for
  container membership. Designed for forward compatibility with Nose 1.0.

- :class:`mock_urllib2`: Object which mocks the urllib2 module to fake
  requests of mldata.  Used in tests of :mod:`sklearn.datasets`.


Helper Functions
================

- :class:`gen_even_slices`: generator to create ``n``-packs of slices going up
  to ``n``.  Used in ``sklearn.decomposition.dict_learning`` and
  ``sklearn.cluster.k_means``.

- :class:`arraybuilder.ArrayBuilder`: Helper class to incrementally build
  a 1-d numpy.ndarray.  Currently used in
  ``sklearn.datasets._svmlight_format.pyx``.

- :func:`safe_mask`: 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).


Hash Functions
==============

- :func:`murmurhash3_32` provides a python wrapper for the
  `MurmurHash3_x86_32` 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::

    >>> from sklearn.utils import murmurhash3_32
    >>> murmurhash3_32("some feature", seed=0)
    -384616559

    >>> murmurhash3_32("some feature", seed=0, positive=True)
    3910350737L

  The ``sklearn.utils.murmurhash`` 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.


Warnings and Exceptions
=======================

- :class:`deprecated`: Decorator to mark a function or class as deprecated.

- :class:`ConvergenceWarning`: Custom warning to catch convergence problems.
  Used in ``sklearn.covariance.graph_lasso``.