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Merge pull request #477 from srhrshr/main
changes the master branch to main and updates references to master with main in the codebase
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doc/config_rsmcompare.rst

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Experiment configuration file
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This is a file in ``.json`` format that provides overall configuration options for an ``rsmcompare`` experiment. Here's an `example configuration file <https://github.com/EducationalTestingService/rsmtool/blob/master/examples/rsmcompare/config_rsmcompare.json>`_ for ``rsmcompare``.
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This is a file in ``.json`` format that provides overall configuration options for an ``rsmcompare`` experiment. Here's an `example configuration file <https://github.com/EducationalTestingService/rsmtool/blob/main/examples/rsmcompare/config_rsmcompare.json>`_ for ``rsmcompare``.
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.. note:: To make it easy to get started with ``rsmcompare``, we provide a way to **automatically generate** configurations file both interactively as well as non-interactively. Novice users will find interactive generation more helpful while more advanced users will prefer non-interactive generation. See :ref:`this page <autogen_configuration>` for more details.
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doc/config_rsmeval.rst

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Experiment configuration file
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This is a file in ``.json`` format that provides overall configuration options for an ``rsmeval`` experiment. Here's an `example configuration file <https://github.com/EducationalTestingService/rsmtool/blob/master/examples/rsmeval/config_rsmeval.json>`_ for ``rsmeval``.
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This is a file in ``.json`` format that provides overall configuration options for an ``rsmeval`` experiment. Here's an `example configuration file <https://github.com/EducationalTestingService/rsmtool/blob/main/examples/rsmeval/config_rsmeval.json>`_ for ``rsmeval``.
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.. note:: To make it easy to get started with ``rsmeval``, we provide a way to **automatically generate** configurations file both interactively as well as non-interactively. Novice users will find interactive generation more helpful while more advanced users will prefer non-interactive generation. See :ref:`this page <autogen_configuration>` for more details.
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doc/config_rsmpredict.rst

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Experiment configuration file
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This is a file in ``.json`` format that provides overall configuration options for an ``rsmpredict`` experiment. Here's an `example configuration file <https://github.com/EducationalTestingService/rsmtool/blob/master/examples/rsmpredict/config_rsmpredict.json>`_ for ``rsmpredict``.
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This is a file in ``.json`` format that provides overall configuration options for an ``rsmpredict`` experiment. Here's an `example configuration file <https://github.com/EducationalTestingService/rsmtool/blob/main/examples/rsmpredict/config_rsmpredict.json>`_ for ``rsmpredict``.
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.. note:: To make it easy to get started with ``rsmpredict``, we provide a way to **automatically generate** configurations file both interactively as well as non-interactively. Novice users will find interactive generation more helpful while more advanced users will prefer non-interactive generation. See :ref:`this page <autogen_configuration>` for more details.
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doc/config_rsmsummarize.rst

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Experiment configuration file
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This is a file in ``.json`` format that provides overall configuration options for an ``rsmsummarize`` experiment. Here's an `example configuration file <https://github.com/EducationalTestingService/rsmtool/blob/master/examples/rsmsummarize/config_rsmsummarize.json>`_ for ``rsmsummarize``.
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This is a file in ``.json`` format that provides overall configuration options for an ``rsmsummarize`` experiment. Here's an `example configuration file <https://github.com/EducationalTestingService/rsmtool/blob/main/examples/rsmsummarize/config_rsmsummarize.json>`_ for ``rsmsummarize``.
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.. note:: To make it easy to get started with ``rsmsummarize``, we provide a way to **automatically generate** configurations file both interactively as well as non-interactively. Novice users will find interactive generation more helpful while more advanced users will prefer non-interactive generation. See :ref:`this page <autogen_configuration>` for more details.
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doc/config_rsmtool.rst

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Experiment configuration file
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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This is a file in ``.json`` format that provides overall configuration options for an ``rsmtool`` experiment. Here's an `example configuration file <https://github.com/EducationalTestingService/rsmtool/blob/master/examples/rsmtool/config_rsmtool.json>`_ for ``rsmtool``.
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This is a file in ``.json`` format that provides overall configuration options for an ``rsmtool`` experiment. Here's an `example configuration file <https://github.com/EducationalTestingService/rsmtool/blob/main/examples/rsmtool/config_rsmtool.json>`_ for ``rsmtool``.
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.. note:: To make it easy to get started with ``rsmtool``, we provide a way to **automatically generate** configurations file both interactively as well as non-interactively. Novice users will find interactive generation more helpful while more advanced users will prefer non-interactive generation. See :ref:`this page <autogen_configuration>` for more details.
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doc/contributing.rst

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To set up a local development environment, follow the steps below:
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1. Pull the latest version of RSMTool from GitHub and switch to the ``master`` branch.
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1. Pull the latest version of RSMTool from GitHub and switch to the ``main`` branch.
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2. If you already have the ``conda`` package manager installed, skip to the next step. If you do not, follow the instructions on `this page <https://conda.io/projects/conda/en/latest/user-guide/install/index.html>`_ to install conda.
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doc/internal/release_process.rst

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This process is only meant for the project administrators, not users and developers.
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1. Make sure any and all tests are passing in ``master``. Make sure you have also run tests locally in strict mode (``STRICT=1 nosetests --nologcapture tests``) to catch any deprecation warnings in the HTML report that can be fixed before the release.
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1. Make sure any and all tests are passing in ``main``. Make sure you have also run tests locally in strict mode (``STRICT=1 nosetests --nologcapture tests``) to catch any deprecation warnings in the HTML report that can be fixed before the release.
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2. Run the ``tests/update_files.py`` script with the appropriate arguments to make sure that all test data in the new release have correct experiment ids and filenames. If any (non-model) files need to be changed this should be investigated before the branch is released.
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12. Draft a release on GitHub while the Linux and Windows package tester builds are running.
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13. Once both builds have passed, make a pull request with the release branch to be merged into ``master`` and request code review.
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13. Once both builds have passed, make a pull request with the release branch to be merged into ``main`` and request code review.
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14. Once the build for the PR passes and the reviewers approve, merge the release branch into ``master``.
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14. Once the build for the PR passes and the reviewers approve, merge the release branch into ``main``.
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15. Upload source and wheel packages to PyPI using ``python setup.py sdist upload`` and ``python setup.py bdist_wheel upload``
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16. Make sure that the ReadTheDocs build for ``master`` passes by examining the badge at this `URL <https://img.shields.io/readthedocs/rsmtool/latest>`_ - this should say "passing" in green.
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16. Make sure that the ReadTheDocs build for ``main`` passes by examining the badge at this `URL <https://img.shields.io/readthedocs/rsmtool/latest>`_ - this should say "passing" in green.
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17. Tag the latest commit in ``master`` with the appropriate release tag and publish the release on GitHub.
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17. Tag the latest commit in ``main`` with the appropriate release tag and publish the release on GitHub.
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18. Make another PR to merge ``master`` branch into ``stable`` so that the the default ReadTheDocs build (which is ``stable``) always points to the latest release.
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18. Make another PR to merge ``main`` branch into ``stable`` so that the the default ReadTheDocs build (which is ``stable``) always points to the latest release.
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19. Update the CI plan for RSMExtra (only needed for ETS users) to use this newly built RSMTool conda package. Do any other requisite changes for RSMExtra. Once everything is done, do a release of RSMExtra.
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doc/pipeline.rst

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This trained model can then be used to generate scores for a held-out evaluation data whose feature values are pre-processed using the same :ref:`Pre-processing Parameters <preprocessing_parameters>`. In addition to the raw scores predicted by the model, the **Prediction Analysis** component of the pipline generates several additional :ref:`post-processed scores <score_postprocessing>` that are commonly used in automated scoring.
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The primary output of RSMTool is a comprehensive, customizable HTML statistical report that contains the multiple analyses required for a comprehensive evaluation of an automated scoring model including descriptive analyses for all features, model analyses, subgroup comparisons, as well as several different :ref:`evaluation measures <evaluation>` illustrating model efficacy. More Details about these analyses can be found in this documentaion and in a separate `technical paper <https://raw.githubusercontent.com/EducationalTestingService/rsmtool/master/doc/assets/rsmtool.pdf>`_.
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The primary output of RSMTool is a comprehensive, customizable HTML statistical report that contains the multiple analyses required for a comprehensive evaluation of an automated scoring model including descriptive analyses for all features, model analyses, subgroup comparisons, as well as several different :ref:`evaluation measures <evaluation>` illustrating model efficacy. More Details about these analyses can be found in this documentaion and in a separate `technical paper <https://raw.githubusercontent.com/EducationalTestingService/rsmtool/main/doc/assets/rsmtool.pdf>`_.
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In addition to the HTML report, RSMTool also saves the intermediate outputs of all of the performed analyses as :ref:`CSV files <intermediate_files_rsmtool>`.
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doc/tutorial.rst

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Let's see how we can apply this basic RSMTool workflow to a simple example based on a 2012 Kaggle competition on automated essay scoring called the `Automated Student Assessment Prize (ASAP) contest <https://www.kaggle.com/c/asap-aes>`_. As part of that contest, responses to 8 different essay questions written by students in grades 6-10 were provided. The responses were scored by humans and given a holistic score indicating the English proficiency of the student. The goal of the contest was to build an automated scoring model with the highest accuracy on held-out evaluation data.
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For our tutorial, we will use one of the questions from this data to illustrate how to use RSMTool to train a scoring model and evaluate its performance. All of the data we refer to below can be found in the ``examples/rsmtool`` folder in the `github repository <https://github.com/EducationalTestingService/rsmtool/tree/master/examples/rsmtool>`_.
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For our tutorial, we will use one of the questions from this data to illustrate how to use RSMTool to train a scoring model and evaluate its performance. All of the data we refer to below can be found in the ``examples/rsmtool`` folder in the `github repository <https://github.com/EducationalTestingService/rsmtool/tree/main/examples/rsmtool>`_.
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Extract features
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^^^^^^^^^^^^^^^^

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