SciKit-Learn Laboratory
-----------------------
.. image:: https://gitlab.com/EducationalTestingService/skll/badges/main/pipeline.svg
:target: https://gitlab.com/EducationalTestingService/skll/-/pipelines
:alt: Gitlab CI status
.. image:: https://dev.azure.com/EducationalTestingService/SKLL/_apis/build/status/EducationalTestingService.skll
:target: https://dev.azure.com/EducationalTestingService/SKLL/_build?view=runs
:alt: Azure Pipelines status
.. image:: https://codecov.io/gh/EducationalTestingService/skll/branch/main/graph/badge.svg
:target: https://codecov.io/gh/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/ets/skll.svg
:target: https://anaconda.org/ets/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
.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/EducationalTestingService/skll/main?filepath=examples%2FTutorial.ipynb
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.
Installation
~~~~~~~~~~~~
You can install using either ``pip`` or ``conda``. See details `here <https://skll.readthedocs.io/en/latest/getting_started.html>`__.
Requirements
~~~~~~~~~~~~
- Python 3.8, 3.9, or 3.10
- `beautifulsoup4 <http://www.crummy.com/software/BeautifulSoup/>`__
- `gridmap <https://pypi.org/project/gridmap/>`__ (only required if you plan
to run things in parallel on a DRMAA-compatible cluster)
- `joblib <https://pypi.org/project/joblib/>`__
- `pandas <http://pandas.pydata.org>`__
- `ruamel.yaml <http://yaml.readthedocs.io/en/latest/overview.html>`__
- `scikit-learn <http://scikit-learn.org/stable/>`__
- `seaborn <http://seaborn.pydata.org>`__
- `tabulate <https://pypi.org/project/tabulate/>`__
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
featuresets = [["family.csv", "misc.csv", "socioeconomic.csv", "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 also be absolute paths
logs = output
results = output
predictions = output
probability = true
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>`__.
You can also follow this `interactive Jupyter tutorial <https://mybinder.org/v2/gh/AVajpayeeJr/skll/feature/448-interactive-binder?filepath=examples>`__.
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)
- `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.
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>`__)
Citing
~~~~~~
If you are using SKLL in your work, you can cite it as follows: "We used scikit-learn (Pedragosa et al, 2011) via the SKLL toolkit (https://github.com/EducationalTestingService/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>`__.
Contribute
~~~~~~~~~~
Thank you for your interest in contributing to SKLL! See `CONTRIBUTING.md <https://github.com/EducationalTestingService/skll/blob/main/CONTRIBUTING.md>`__ for instructions on how to get started.
Raw data
{
"_id": null,
"home_page": "http://github.com/EducationalTestingService/skll",
"name": "skll",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "learning scikit-learn",
"author": "Nitin Madnani",
"author_email": "nmadnani@ets.org",
"download_url": "https://files.pythonhosted.org/packages/2f/40/970365e6ca7ba4a960d06739f53aabeec43d87b2dff0ff9f66a2643529ce/skll-3.2.0.tar.gz",
"platform": null,
"description": "SciKit-Learn Laboratory\n-----------------------\n\n.. image:: https://gitlab.com/EducationalTestingService/skll/badges/main/pipeline.svg\n :target: https://gitlab.com/EducationalTestingService/skll/-/pipelines\n :alt: Gitlab CI status\n\n.. image:: https://dev.azure.com/EducationalTestingService/SKLL/_apis/build/status/EducationalTestingService.skll\n :target: https://dev.azure.com/EducationalTestingService/SKLL/_build?view=runs\n :alt: Azure Pipelines status\n\n.. image:: https://codecov.io/gh/EducationalTestingService/skll/branch/main/graph/badge.svg\n :target: https://codecov.io/gh/EducationalTestingService/skll\n\n.. image:: https://img.shields.io/pypi/v/skll.svg\n :target: https://pypi.org/project/skll/\n :alt: Latest version on PyPI\n\n.. image:: https://img.shields.io/pypi/l/skll.svg\n :alt: License\n\n.. image:: https://img.shields.io/conda/v/ets/skll.svg\n :target: https://anaconda.org/ets/skll\n :alt: Conda package for SKLL\n\n.. image:: https://img.shields.io/pypi/pyversions/skll.svg\n :target: https://pypi.org/project/skll/\n :alt: Supported python versions for SKLL\n\n.. image:: https://img.shields.io/badge/DOI-10.5281%2Fzenodo.12825-blue.svg\n :target: http://dx.doi.org/10.5281/zenodo.12825\n :alt: DOI for citing SKLL 1.0.0\n\n.. image:: https://mybinder.org/badge_logo.svg\n :target: https://mybinder.org/v2/gh/EducationalTestingService/skll/main?filepath=examples%2FTutorial.ipynb\n\n\nThis Python package provides command-line utilities to make it easier to run\nmachine learning experiments with scikit-learn. One of the primary goals of\nour project is to make it so that you can run scikit-learn experiments without\nactually needing to write any code other than what you used to generate/extract\nthe features.\n\nInstallation\n~~~~~~~~~~~~\n\nYou can install using either ``pip`` or ``conda``. See details `here <https://skll.readthedocs.io/en/latest/getting_started.html>`__.\n\nRequirements\n~~~~~~~~~~~~\n\n- Python 3.8, 3.9, or 3.10\n- `beautifulsoup4 <http://www.crummy.com/software/BeautifulSoup/>`__\n- `gridmap <https://pypi.org/project/gridmap/>`__ (only required if you plan\n to run things in parallel on a DRMAA-compatible cluster)\n- `joblib <https://pypi.org/project/joblib/>`__\n- `pandas <http://pandas.pydata.org>`__\n- `ruamel.yaml <http://yaml.readthedocs.io/en/latest/overview.html>`__\n- `scikit-learn <http://scikit-learn.org/stable/>`__\n- `seaborn <http://seaborn.pydata.org>`__\n- `tabulate <https://pypi.org/project/tabulate/>`__\n\nCommand-line Interface\n~~~~~~~~~~~~~~~~~~~~~~\n\nThe main utility we provide is called ``run_experiment`` and it can be used to\neasily run a series of learners on datasets specified in a configuration file\nlike:\n\n.. code:: ini\n\n [General]\n experiment_name = Titanic_Evaluate_Tuned\n # valid tasks: cross_validate, evaluate, predict, train\n task = evaluate\n\n [Input]\n # these directories could also be absolute paths\n # (and must be if you're not running things in local mode)\n train_directory = train\n test_directory = dev\n # Can specify multiple sets of feature files that are merged together automatically\n featuresets = [[\"family.csv\", \"misc.csv\", \"socioeconomic.csv\", \"vitals.csv\"]]\n # List of scikit-learn learners to use\n learners = [\"RandomForestClassifier\", \"DecisionTreeClassifier\", \"SVC\", \"MultinomialNB\"]\n # Column in CSV containing labels to predict\n label_col = Survived\n # Column in CSV containing instance IDs (if any)\n id_col = PassengerId\n\n [Tuning]\n # Should we tune parameters of all learners by searching provided parameter grids?\n grid_search = true\n # Function to maximize when performing grid search\n objectives = ['accuracy']\n\n [Output]\n # Also compute the area under the ROC curve as an additional metric\n metrics = ['roc_auc']\n # The following can also be absolute paths\n logs = output\n results = output\n predictions = output\n probability = true\n models = output\n\nFor more information about getting started with ``run_experiment``, please check\nout `our tutorial <https://skll.readthedocs.org/en/latest/tutorial.html>`__, or\n`our config file specs <https://skll.readthedocs.org/en/latest/run_experiment.html>`__.\n\nYou can also follow this `interactive Jupyter tutorial <https://mybinder.org/v2/gh/AVajpayeeJr/skll/feature/448-interactive-binder?filepath=examples>`__.\n\nWe also provide utilities for:\n\n- `converting between machine learning toolkit formats <https://skll.readthedocs.org/en/latest/utilities.html#skll-convert>`__\n (e.g., ARFF, CSV)\n- `filtering feature files <https://skll.readthedocs.org/en/latest/utilities.html#filter-features>`__\n- `joining feature files <https://skll.readthedocs.org/en/latest/utilities.html#join-features>`__\n- `other common tasks <https://skll.readthedocs.org/en/latest/utilities.html>`__\n\n\nPython API\n~~~~~~~~~~\n\nIf you just want to avoid writing a lot of boilerplate learning code, you can\nalso use our simple Python API which also supports pandas DataFrames.\nThe main way you'll want to use the API is through\nthe ``Learner`` and ``Reader`` classes. For more details on our API, see\n`the documentation <https://skll.readthedocs.org/en/latest/api.html>`__.\n\nWhile our API can be broadly useful, it should be noted that the command-line\nutilities are intended as the primary way of using SKLL. The API is just a nice\nside-effect of our developing the utilities.\n\n\nA Note on Pronunciation\n~~~~~~~~~~~~~~~~~~~~~~~\n\n.. image:: doc/skll.png\n :alt: SKLL logo\n :align: right\n\n.. container:: clear\n\n .. image:: doc/spacer.png\n\nSciKit-Learn Laboratory (SKLL) is pronounced \"skull\": that's where the learning\nhappens.\n\nTalks\n~~~~~\n\n- *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>`__)\n- *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>`__)\n\nCiting\n~~~~~~\nIf you are using SKLL in your work, you can cite it as follows: \"We used scikit-learn (Pedragosa et al, 2011) via the SKLL toolkit (https://github.com/EducationalTestingService/skll).\"\n\nBooks\n~~~~~\n\nSKLL is featured in `Data Science at the Command Line <http://datascienceatthecommandline.com>`__\nby `Jeroen Janssens <http://jeroenjanssens.com>`__.\n\nChangelog\n~~~~~~~~~\n\nSee `GitHub releases <https://github.com/EducationalTestingService/skll/releases>`__.\n\nContribute\n~~~~~~~~~~\n\nThank you for your interest in contributing to SKLL! See `CONTRIBUTING.md <https://github.com/EducationalTestingService/skll/blob/main/CONTRIBUTING.md>`__ for instructions on how to get started.\n",
"bugtrack_url": null,
"license": "BSD 3 clause",
"summary": "SciKit-Learn Laboratory makes it easier to run machine learning experiments with scikit-learn.",
"version": "3.2.0",
"split_keywords": [
"learning",
"scikit-learn"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "2f40970365e6ca7ba4a960d06739f53aabeec43d87b2dff0ff9f66a2643529ce",
"md5": "9efb94e347af5ee702211a459f5dccce",
"sha256": "e18e45493360b3d1efef1e9d208c5e4fda4dae7f7b9ef3442b738bd5126555db"
},
"downloads": -1,
"filename": "skll-3.2.0.tar.gz",
"has_sig": false,
"md5_digest": "9efb94e347af5ee702211a459f5dccce",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 110286,
"upload_time": "2023-01-19T17:26:51",
"upload_time_iso_8601": "2023-01-19T17:26:51.141251Z",
"url": "https://files.pythonhosted.org/packages/2f/40/970365e6ca7ba4a960d06739f53aabeec43d87b2dff0ff9f66a2643529ce/skll-3.2.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-01-19 17:26:51",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "EducationalTestingService",
"github_project": "skll",
"travis_ci": false,
"coveralls": true,
"github_actions": false,
"requirements": [],
"lcname": "skll"
}
JSON
