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ReStructuredText
152 lines
7.0 KiB
ReStructuredText
.. _faq:
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===========================
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Frequently Asked Questions
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===========================
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Here we try to give some answers to questions that regularly pop up on the mailing list.
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What is the project name (a lot of people get it wrong)?
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--------------------------------------------------------
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scikit-learn, but not scikit or SciKit nor sci-kit learn. Also not scikits.learn or scikits-learn, which where previously used.
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How do you pronounce the project name?
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------------------------------------------
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sy-kit learn. sci stands for science!
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Why scikit?
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------------
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There are multiple scikits, which are scientific toolboxes build around SciPy.
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You can find a list at `<https://scikits.appspot.com/scikits>`_.
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Apart from scikit-learn, another popular one is `scikit-image <http://scikit-image.org/>`_.
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How can I contribute to scikit-learn?
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-----------------------------------------
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See :ref:`contributing`.
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Can I add this new algorithm that I (or someone else) just published?
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-------------------------------------------------------------------------
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No. As a rule we only add well-established algorithms. A rule of thumb is at least
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3 years since publications, 200+ citations and wide use and usefullness. A
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technique that provides a clear-cut improvement (e.g. an enhanced data
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structure or efficient approximation) on a widely-used method will also be
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considered for inclusion.
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Your implementation doesn't need to be in scikit-learn to be used together
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with scikit-learn tools, though. Implement your favorite algorithm
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in a scikit-learn compatible way, upload it to github and we will list
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it under :ref:`related_projects`.
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Also see selectiveness_.
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Can I add this classical algorithm from the 80s?
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---------------------------------------------------
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Depends. If there is a common usecase within the scope of scikit-learn, such
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as classification, regression or clustering, where it outperforms methods
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that are already implemented in scikit-learn, we will consider it.
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.. _selectiveness:
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Why are you so selective on what algorithms you include in scikit-learn?
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------------------------------------------------------------------------
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Code is maintenance cost, and we need to balance the amount of
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code we have with the size of the team (and add to this the fact that
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complexity scales non linearly with the number of features).
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The package relies on core developers using their free time to
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fix bugs, maintain code and review contributions.
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Any algorithm that is added needs future attention by the developers,
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at which point the original author might long have lost interest.
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Also see `this thread on the mailing list
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<http://sourceforge.net/p/scikit-learn/mailman/scikit-learn-general/thread/CAAkaFLWcBG%2BgtsFQzpTLfZoCsHMDv9UG5WaqT0LwUApte0TVzg%40mail.gmail.com/#msg33104380>`_.
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Why did you remove HMMs from scikit-learn?
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--------------------------------------------
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See :ref:`adding_graphical_models`.
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.. _adding_graphical_models:
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Will you add graphical models or sequence prediction to scikit-learn?
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------------------------------------------------------------------------
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Not in the foreseeable future.
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scikit-learn tries to provide a unified API for the basic tasks in machine
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learning, with pipelines and meta-algorithms like grid search to tie
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everything together. The required concepts, APIs, algorithms and
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expertise required for structured learning are different from what
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scikit-learn has to offer. If we started doing arbitrary structured
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learning, we'd need to redesign the whole package and the project
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would likely collapse under its own weight.
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There are two project with API similar to scikit-learn that
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do structured prediction:
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* `pystruct <http://pystruct.github.io/>`_ handles general structured
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learning (focuses on SSVMs on arbitrary graph structures with
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approximate inference; defines the notion of sample as an instance of
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the graph structure)
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* `seqlearn <http://larsmans.github.io/seqlearn/>`_ handles sequences only (focuses on
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exact inference; has HMMs, but mostly for the sake of completeness;
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treats a feature vector as a sample and uses an offset encoding for
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the dependencies between feature vectors)
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Will you add GPU support?
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--------------------------
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No, or at least not in the near future. The main reason is that GPU support will introduce many software dependencies and introduce platform specific issues.
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scikit-learn is designed to be easy to install on a wide variety of platforms.
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Outside of neural networks, GPUs don't play a large role in machine learning today, and much larger gains in speed can often be achieved by a careful choice of algorithms.
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Do you support PyPy?
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--------------------
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In case you didn't know, `PyPy <http://pypy.org/>`_ is the new, fast,
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just-in-time compiling Python implementation. We don't support it.
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When the `NumPy support <http://buildbot.pypy.org/numpy-status/latest.html>`_
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in PyPy is complete or near-complete, and SciPy is ported over as well,
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we can start thinking of a port.
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We use too much of NumPy to work with a partial implementation.
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How do I deal with string data (or trees, graphs...)?
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-----------------------------------------------------
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scikit-learn estimators assume you'll feed them real-valued feature vectors.
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This assumption is hard-coded in pretty much all of the library.
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However, you can feed non-numerical inputs to estimators in several ways.
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If you have text documents, you can use a term frequency features; see
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:ref:`text_feature_extraction` for the built-in *text vectorizers*.
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For more general feature extraction from any kind of data, see
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:ref:`dict_feature_extraction` and :ref:`feature_hashing`.
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Another common case is when you have non-numerical data and a custom distance
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(or similarity) metric on these data. Examples include strings with edit
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distance (aka. Levenshtein distance; e.g., DNA or RNA sequences). These can be
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encoded as numbers, but doing so is painful and error-prone. Working with
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distance metrics on arbitrary data can be done in two ways.
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Firstly, many estimators take precomputed distance/similarity matrices, so if
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the dataset is not too large, you can compute distances for all pairs of inputs.
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If the dataset is large, you can use feature vectors with only one "feature",
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which is an index into a separate data structure, and supply a custom metric
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function that looks up the actual data in this data structure. E.g., to use
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DBSCAN with Levenshtein distances::
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>>> from leven import levenshtein # doctest: +SKIP
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>>> import numpy as np
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>>> from sklearn.cluster import dbscan
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>>> data = ["ACCTCCTAGAAG", "ACCTACTAGAAGTT", "GAATATTAGGCCGA"]
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>>> def lev_metric(x, y):
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... i, j = int(x[0]), int(y[0]) # extract indices
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... return levenshtein(data[i], data[j])
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...
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>>> X = np.arange(len(data)).reshape(-1, 1)
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>>> X
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array([[0],
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[1],
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[2]])
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>>> dbscan(X, metric=lev_metric, eps=5, min_samples=2) # doctest: +SKIP
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([0, 1], array([ 0, 0, -1]))
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(This uses the third-party edit distance package ``leven``.)
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Similar tricks can be used, with some care, for tree kernels, graph kernels,
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etc.
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