Metadata-Version: 1.1
Name: skll
Version: 1.5.1
Summary: SciKit-Learn Laboratory makes it easier to run machine learning experiments with scikit-learn.
Home-page: http://github.com/EducationalTestingService/skll
Author: Nitin Madnani
Author-email: nmadnani@ets.org
License: BSD 3 clause
Description-Content-Type: UNKNOWN
Description: SciKit-Learn Laboratory
        -----------------------
        
        .. image:: https://img.shields.io/travis/EducationalTestingService/skll/master.svg
           :alt: Build status
           :target: https://travis-ci.org/EducationalTestingService/skll
        
        .. image:: https://img.shields.io/coveralls/EducationalTestingService/skll/master.svg
            :target: https://coveralls.io/r/EducationalTestingService/skll
        
        .. image:: https://img.shields.io/pypi/v/skll.svg
           :target: https://pypi.org/project/skll/
           :alt: Latest version on PyPI
        
        .. image:: https://img.shields.io/pypi/l/skll.svg
           :alt: License
        
        .. image:: https://img.shields.io/conda/v/desilinguist/skll.svg
           :target: https://anaconda.org/desilinguist/skll
           :alt: Conda package for SKLL
        
        .. image:: https://img.shields.io/pypi/pyversions/skll.svg
           :target: https://pypi.org/project/skll/
           :alt: Supported python versions for SKLL
        
        .. image:: https://img.shields.io/badge/DOI-10.5281%2Fzenodo.12825-blue.svg
           :target: http://dx.doi.org/10.5281/zenodo.12825
           :alt: DOI for citing SKLL 1.0.0
        
        This Python package provides command-line utilities to make it easier to run
        machine learning experiments with scikit-learn.  One of the primary goals of
        our project is to make it so that you can run scikit-learn experiments without
        actually needing to write any code other than what you used to generate/extract
        the features.
        
        Command-line Interface
        ~~~~~~~~~~~~~~~~~~~~~~
        
        The main utility we provide is called ``run_experiment`` and it can be used to
        easily run a series of learners on datasets specified in a configuration file
        like:
        
        .. code:: ini
        
          [General]
          experiment_name = Titanic_Evaluate_Tuned
          # valid tasks: cross_validate, evaluate, predict, train
          task = evaluate
        
          [Input]
          # these directories could also be absolute paths
          # (and must be if you're not running things in local mode)
          train_directory = train
          test_directory = dev
          # Can specify multiple sets of feature files that are merged together automatically
          # (even across formats)
          featuresets = [["family.ndj", "misc.csv", "socioeconomic.arff", "vitals.csv"]]
          # List of scikit-learn learners to use
          learners = ["RandomForestClassifier", "DecisionTreeClassifier", "SVC", "MultinomialNB"]
          # Column in CSV containing labels to predict
          label_col = Survived
          # Column in CSV containing instance IDs (if any)
          id_col = PassengerId
        
          [Tuning]
          # Should we tune parameters of all learners by searching provided parameter grids?
          grid_search = true
          # Function to maximize when performing grid search
          objectives = ['accuracy']
        
          [Output]
          # Also compute the area under the ROC curve as an additional metric
          metrics = ['roc_auc']
          # The following can/should be absolute paths
          log = output
          results = output
          predictions = output
          models = output
        
        
        For more information about getting started with ``run_experiment``, please check
        out `our tutorial <https://skll.readthedocs.org/en/latest/tutorial.html>`__, or
        `our config file specs <https://skll.readthedocs.org/en/latest/run_experiment.html>`__.
        
        We also provide utilities for:
        
        -  `converting between machine learning toolkit formats <https://skll.readthedocs.org/en/latest/utilities.html#skll-convert>`__
           (e.g., ARFF, CSV, MegaM)
        -  `filtering feature files <https://skll.readthedocs.org/en/latest/utilities.html#filter-features>`__
        -  `joining feature files <https://skll.readthedocs.org/en/latest/utilities.html#join-features>`__
        -  `other common tasks <https://skll.readthedocs.org/en/latest/utilities.html>`__
        
        
        Python API
        ~~~~~~~~~~
        
        If you just want to avoid writing a lot of boilerplate learning code, you can
        also use our simple Python API which also supports pandas DataFrames.
        The main way you'll want to use the API is through
        the ``Learner`` and ``Reader`` classes. For more details on our API, see
        `the documentation <https://skll.readthedocs.org/en/latest/api.html>`__.
        
        While our API can be broadly useful, it should be noted that the command-line
        utilities are intended as the primary way of using SKLL.  The API is just a nice
        side-effect of our developing the utilities.
        
        
        A Note on Pronunciation
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        .. image:: doc/skll.png
           :alt: SKLL logo
           :align: right
        
        .. container:: clear
        
          .. image:: doc/spacer.png
        
        SciKit-Learn Laboratory (SKLL) is pronounced "skull": that's where the learning
        happens.
        
        Requirements
        ~~~~~~~~~~~~
        
        -  Python 2.7+
        -  `scikit-learn <http://scikit-learn.org/stable/>`__
        -  `six <https://warehouse.python.org/project/six>`__
        -  `PrettyTable <https://warehouse.python.org/project/PrettyTable>`__
        -  `BeautifulSoup 4 <http://www.crummy.com/software/BeautifulSoup/>`__
        -  `Grid Map <https://warehouse.python.org/project/gridmap>`__ (only required if you plan
           to run things in parallel on a DRMAA-compatible cluster)
        -  `joblib <https://warehouse.python.org/project/joblib>`__
        -  `ruamel.yaml <http://yaml.readthedocs.io/en/latest/overview.html>`__
        -  `configparser <https://warehouse.python.org/project/configparser>`__ (only required for
           Python 2.7)
        -  `logutils <https://warehouse.python.org/project/logutils>`__ (only required for Python 2.7)
        -  `mock <https://warehouse.python.org/project/mock>`__ (only required for Python 2.7)
        
        The following packages can be optionally installed for additional features
        but are not required:
        
        -  `pandas <http://pandas.pydata.org>`__ (optional)
        -  `seaborn <http://seaborn.pydata.org>`__ (optional)
        
        Talks
        ~~~~~
        
        -  *Simpler Machine Learning with SKLL 1.0*, Dan Blanchard, PyData NYC 2014 (`video <https://www.youtube.com/watch?v=VEo2shBuOrc&feature=youtu.be&t=1s>`__ | `slides <http://www.slideshare.net/DanielBlanchard2/py-data-nyc-2014>`__)
        -  *Simpler Machine Learning with SKLL*, Dan Blanchard, PyData NYC 2013 (`video <http://vimeo.com/79511496>`__ | `slides <http://www.slideshare.net/DanielBlanchard2/simple-machine-learning-with-skll>`__)
        
        Books
        ~~~~~
        
        SKLL is featured in `Data Science at the Command Line <http://datascienceatthecommandline.com>`__
        by `Jeroen Janssens <http://jeroenjanssens.com>`__.
        
        Changelog
        ~~~~~~~~~
        
        See `GitHub releases <https://github.com/EducationalTestingService/skll/releases>`__.
        
Keywords: learning scikit-learn
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
