Source code for revscoring

This library contains a set of facilities for constructing and applying
:class:`~revscoring.ScorerModel` s to MediaWiki revisions. This library
eases the training and testing of Machine Learning-based scoring

* See the :ref:`API reference <api-reference>` for detailed information

Key Features

Scorer Models
:class:`~revscoring.ScorerModel` are the core of
the `revscoring` system.  Provide a simple interface with complex
internals.  Most commonly, a :class:`revscoring.scorer_models.MLScorerModel`
(Machine Learned) is
:meth:`~revscoring.scorer_models.MLScorerModel.train`'d and
:meth:`~revscoring.scorer_models.MLScorerModel.test`'d on
labeled data to provide a basis for scoring.
We currently support
:mod:`Support Vector Classifier <revscoring.scorer_models.svc>`,
:mod:`Random Forest <revscoring.scorer_models.rf>`, and
:mod:`Naive Bayes <revscoring.scorer_models.nb>`
type models. See :mod:`revscoring.scorer_models`

    >>> import mwapi
    >>> from revscoring import ScorerModel
    >>> from revscoring.extractors import api
    >>> with open("models/enwiki.damaging.linear_svc.model") as f:
    ...     model = ScorerModel.load(f)
    >>> extractor = api.Extractor(mwapi.Session(host="",
    ...                                         user_agent="revscoring demo"))
    >>> values = extractor.extract(123456789, model.features)
    >>> print(model.score(values))
    {'prediction': True,
     'probability': {False: 0.4694409344514984,
                     True: 0.5305590655485017}}

Feature extraction
Revscoring provides a dependency-injection-based feature extraction
framework that allows new features to be built on top of old.  This allows
a powerful means to expressing new features and a simple way to address
efficiency concerns. See :mod:`revscoring.features`,
:mod:`revscoring.datasources`, and :mod:`revscoring.extractors`


    >>> from mwapi import Session
    >>> from revscoring.extractors import api
    >>> from revscoring.features import temporal, wikitext
    >>> session = Session("", user_agent="test")
    >>> api_extractor = api.Extractor(session)
    >>> features = [temporal.revision.day_of_week,
    ...             temporal.revision.hour_of_day,
    ...             wikitext.revision.parent.headings_by_level(2)]
    >>> values = api_extractor.extract(624577024, features)
    >>> for feature, value in zip(features, values):
    ...     print("\t{0}: {1}".format(feature, repr(value)))
        <temporal.revision.day_of_week>: 6
        <temporal.revision.hour_of_day>: 19
        <wikitext.revision.parent.headings_by_level(2)>: 5

Language support
Many features require language specific utilities to be available to
support feature extraction.  In order to support this, we provide a
collection of language feature sets that work like other features except
that they are language-specific.  Language-specific feature sets are
available for the following languages:
:data:`~revscoring.languages.ukrainian`, and
See :mod:`revscoring.languages`


    >>> from revscoring.datasources.revision_oriented import revision
    >>> from revscoring.dependencies import solve
    >>> from revscoring.languages import english, spanish
    >>> features = [english.informals.revision.matches,
    ...              spanish.informals.revision.matches]
    >>> values = solve(features, cache={revision.text: "I think it is stupid."})
    >>> for feature, value in zip(features, values):
    ...     print("\t{0}: {1}".format(feature, repr(value)))
        <len(<english.informals.revision.matches>)>: 2
        <len(<spanish.informals.revision.matches>)>: 0
"""  # noqa
from .datasources import Datasource
from .dependencies import Dependent, DependentSet
from .extractors import Extractor
from .features import Feature
from .scorer_models import ScorerModel

__version__ = "1.0.0rc2"  # Change in

__all__ = [Datasource, Dependent, DependentSet, Extractor, Feature,

Revision Scoring