Metadata-Version: 1.1
Name: skll
Version: 0.9.10
Summary: SciKit-Learn Laboratory makes it easier to run machinelearning experiments with scikit-learn.
Home-page: http://github.com/EducationalTestingService/skll
Author: Daniel Blanchard
Author-email: dblanchard@ets.org
License: GPL
Description: SciKit-Learn Laboratory
        -----------------------
        
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        This Python package provides utilities to make it easier to run
        machine learning experiments with scikit-learn.
        
        Command-line Interface
        ~~~~~~~~~~~~~~~~~~~~~~
        
        ``run_experiment`` is a command-line utility for running a series of learners on
        datasets specified in a configuration file. For more information about using
        run_experiment (including a quick example), go
        `here <https://skll.readthedocs.org/en/latest/run_experiment.html>`__.
        
        Python API
        ~~~~~~~~~~
        
        If you just want to avoid writing a lot of boilerplate learning code, you can
        use our simple Python API. The main way you'll want to use the API is through
        the ``load_examples`` function and the ``Learner`` class. For more details on
        how to simply train, test, cross-validate, and run grid search on a variety of
        scikit-learn models see
        `the documentation <https://skll.readthedocs.org/en/latest/index.html>`__.
        
        A Note on Pronunciation
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        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://pypi.python.org/pypi/six>`__
        -  `PrettyTable <http://pypi.python.org/pypi/PrettyTable>`__
        -  `BeautifulSoup 4 <http://www.crummy.com/software/BeautifulSoup/>`__
        -  `Grid Map <http://pypi.python.org/pypi/gridmap>`__ (only required if you plan
           to run things in parallel on a DRMAA-compatible cluster)
        
        Changelog
        ~~~~~~~~~
        
        -  v0.9.10
        
           +  Fixed bug introduced in v0.9.9 that broke "predict" mode.
        
        -  v0.9.9
        
           +  Automatically generate a result summary file with all results for
              experiment in one TSV.
           +  Fixed bug where printing predictions to file would cause a crash with some
              learners.
           +  Run unit tests for Python 3.3 as well as 2.7.
           +  More unit tests for increased coverage.
        
        -  v0.9.8
        
           +  Fixed crash due to trying to print name of grid objective which is now a
              str and not a function.
           +  Added --version option to shell scripts.
        
        -  v0.9.7
        
           +  Can now use any objective function scikit-learn supports for tuning (i.e.,
              any valid argument for scorer when instantiating GridSearchCV) in addition
              to those we define.
           +  Removed ml_metrics dependency and we now support custom weights for kappa
              (through the API only so far).
           +  Require's scikit-learn 0.14+.
           +  `accuracy`, `quadratic_weighted_kappa`, `unweighted_kappa`,
              `f1_score_micro`, and `f1_score_macro` functions are no longer available
              under `skll.metrics`. The accuracy and f1 score ones are no longer needed
              because we just use the built-in ones. As for quadratic_weighted_kappa and
              unweighted_kappa, they've been superseded by the kappa function that takes
              a weights argument.
           +  Fixed issue where you couldn't write prediction files if you were
              classifying using numeric classes.
        
        -  v0.9.6
        
           +  Fixes issue with setup.py importing from package when trying to install
              it (for real this time).
        
        -  v0.9.5
        
           +  You can now include feature files that don't have class labels in your
              featuresets. At least one feature file has to have a label though,
              because we only support supervised learning so far.
           +  **Important:** If you're using TSV files in your experiments, you should
              either name the class label column 'y' or use the new `tsv_label` option
              in your configuration file to specify the name of the label column. This
              was necessary to support feature files without labels.
           +  Fixed an issue with how version number was being imported in setup.py that
              would prevent installation if you didn't already have the prereqs
              installed on your machine.
           +  Made random seeds smaller to fix crash on 32-bit machines. This means that
              experiments run with previous versions of skll will yield slightly
              different results if you re-run them with v0.9.5+.
           +  Added `megam_to_csv` for converting .megam files to CSV/TSV files.
           +  Fixed a potential rounding problem with `csv_to_megam` that could slightly
              change feature values in conversion process.
           +  Cleaned up test_skll.py a little bit.
           +  Updated documentation to include missing fields that can be specified in
              config files.
        
        -  v0.9.4
        
           +  Documentation fixes
           +  Added requirements.txt to manifest to fix broken PyPI release tarball.
        
        -  v0.9.3
        
           +  Fixed bug with merging feature sets that used to cause a crash.
           +  If you're running scikit-learn 0.14+, we use their StandardScaler, since
              the bug fix we include in FixedStandardScaler is in there.
           +  Unit tests all pass again
           +  Lots of little things related to using travis (which do not affect users)
        
        -  v0.9.2
        
           +  Fixed example.cfg path issue. Updated some documentation.
           +  Made path in make_example_iris_data.py consistent with the updated one
              in example.cfg
        
        -  v0.9.1
        
           +  Fixed bug where classification experiments would raise an error about class
              labels not being floats
           +  Updated documentation to include quick example for run_experiment.
        
        
        
Keywords: learning scikit-learn
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
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 :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
