# Contributing to RSMTool¶

Contributions to RSMTool are very welcome. You can use the instructions below to get started on developing new features or functionality for RSMTool. When contributing to RSMTool, all of your contributions must come with updates to documentations as well as tests.

## Setting up¶

To set up a local development environment, follow the steps below:

1. Pull the latest version of RSMTool from GitHub and switch to the master branch.

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 to install conda.

3. Create a new conda environment (say, rsmdev) and install the packages specified in the requirements.txt file by running:

conda create -n rsmdev -c conda-forge -c ets --file requirements.txt

4. Activate the environment using conda activate rsmdev. [1]

5. Run pip install -e . to install rsmtool into the environment in editable mode which is what we need for development.

6. Create a new git branch with a useful and descriptive name.

7. Make your changes and add tests. See the next section for more on writing new tests.

8. Run nosetests -v --nologcapture tests to run the tests. We use the --nologcapture switch, since otherwise test failures for some tests tend to produce very long Jupyter notebook traces.

## Documentation¶

Note that the file doc/requirements.txt is meant specifically for the ReadTheDocs documentation build process and should not be used locally. To build the documentation locally, you must use the same conda environment created above.

If you are on macOS and use the Dash app, follow steps 1 and 2 here to build the RSMTool Dash docset locally.

## RSMTool tests¶

Existing tests for RSMTool are spread across the various test_*.py files under the tests directory after you check out the RSMTool source code from GitHub.

There are two kinds of existing tests in RSMTool:

1. The first type of tests are unit tests, i.e., very specific tests for which you have a single example (usually embedded in the test itself) and you compare the generated output with known or expected output. These tests should have a very narrow and well defined scope. To see examples of such unit tests, see the test functions in the file tests/test_utils.py.
2. The second type of tests are functional tests which are generally written from the users’ perspective to test that RSMTool is doing things that users would expect it to. In RSMTool, most (if not all) functional tests are written in the form of “experiment tests”, i.e., we first define an experimental configuration using an rsmtool (or rsmeval/rsmpredict/rsmcompare/rsmsummarize) configuration file, then we run the experiment, and then compare the generated output files to expected output files to make sure that RSMTool components are operating as expected. To see examples of such tests, you can look at any of the tests/test_experiment_*.py files.

Note

RSMTool functional tests are parameterized, i.e., since most are identical other than the configuration file that needs to be run, the basic functionality of the test has been factored out into utility functions. Each line starting with param in any of the test_experiment_*.py files represents a specific functional test.

Any new contributions to RSMTool, no matter how small or trivial, must be accompanied by updates to documentations as well as new unit and/or functional tests. Adding new unit tests is fairly straightforward. However, adding new functional tests is a little more involved.

## Writing new functional tests¶

To write a new experiment test for RSMTool (or any of the other tools):

1. Create a new directory under tests/data/experiments using a descriptive name.
2. Create a JSON configuration file under that directory with the various fields appropriately set for what you want to test. Feel free to use multiple words separated by hyphens to come up with a name that describes the testing condition. The name of the configuration file should be the same as the value of the experiment_id field in your JSON file. By convention, that’s usually the same as the name of the directory you created but with underscores instead of hyphens. If you are creating a new test for rsmcompare or rsmsummarize, copy over one or more of the existing rsmtool or rsmeval test experiments as input(s) and keep the same name. This will ensure that these inputs will be regularly updated and remain consistent with the current outputs generated by these tools. If you must create a test for a scenario not covered by a current tool, create a new rsmtool/rsmeval test first following the instructions on this page.
3. Next, you need to add the test to the list of parameterized tests in the appropriate test file based on the tool for which you are adding the test, e.g., rsmeval tests should be added to tests/test_experiment_rsmeval.py, rsmpredict tests to tests/test_experiment_rsmpredict.py, and so on. Tests for rsmtool can be added to any of the four files. The arguments for the param() call can be found in the Table 1 below.
4. In some rare cases, you might want to use a non-parameterized experiment test if you are doing something very different. These should be few and far between. Examples of these can also be seen in various tests/test_experiment_*.py files.
5. Another rare scenario is the need to create an entirely new tests/test_experiment_X.py file instead of using one of the existing ones. This should not be necessary unless you are trying to test a newly added tool or component.
 Writing test(s) for rsmtool First positional argument is the name of the test directory you created. Second positional argument is the experiment ID from the JSON configuration file. Use consistency=True if you have set second_human_score_column in the configuration file. Use skll=True if you are writing a test for a SKLL model. Set subgroups keyword argument to the same list that you specified in the configuration file. Set file_format="tsv" (or "xlsx") if you specified the same field in the configuration file. Writing test(s) for rsmeval Same arguments as RSMTool except the skll keyword argument is not applicable. Writing test(s) for rsmpredict The only positional argument is the name of the test directory you created. Use excluded=True if you want to check the excluded responses file as part of the test. Set file_format="tsv" (or "xlsx") if you specified the same field in the configuration file. Writing test(s) for rsmcompare First positional argument is the name of the test directory you created. Second positional argument is the comparison ID from the JSON configuration file. Writing test(s) for rsmsummarize The only positional argument is the name of the test directory you created. Set file_format="tsv" (or "xlsx") if you specified the same field in the configuration file.

Once you have added all new functional tests, commit all of your changes. Next, you should run nosetests --nologcapture to run all the tests. Obviously, the newly added tests will fail since you have not yet generated the expected output for that test.

To do this, you should now run the following:

python tests/update_files.py --tests tests --outputs test_outputs


This will copy over the generated outputs for the newly added tests and show you a report of the files that it added. It will also update the input files form tests for rsmcompare and rsmsummarize. If run correctly, the report should only refer the files affected by the functionality you implemented. If you run nosetests again, your newly added tests should now pass.

At this point, you should inspect all of the new test files added by the above command to make sure that the outputs are as expected. You can find these files under tests/data/experiments/<test>/output where <test> refers to the test(s) that you added.

However, if your changes resulted in updates to the inputs to rsmsummarize or rsmcompare tests, you will first need to re-run the tests for these two tools and then re-run the update_files.py to update the outputs.

Once you are satisified that the outputs are as expected, you can commit them.

The two examples below might help make this process easier to understand:

Example 1: You made a code change to better handle an edge case that only affects one test.

1. Run nosetests --nologcapture tests/*.py. The affected test failed.
2. Run python tests/update_files.py --tests tests --outputs test_outputs to update test outputs. You will see the total number of deleted, updated and missing files. There should be no deleted files and no missing files. Only the files for your new test should be updated. There are no warnings in the output.
3. If this is the case, you are now ready to commit your change and the updated test outputs.

Example 2: You made a code change that changes the output of many tests. For example, you renamed one of the evaluation metrics.

1. Run nosetests --nologcapture tests/*.py. Many tests will now fail since the output produced by the tool(s) has changed.
2. Run python tests/update_files.py --tests tests --outputs test_outputs to update test outputs. The files affected by your change are shown as added/deleted. You also see the following warning:
3. This means that the changes you made to the code changed the outputs for one or more rsmtool/rsmeval tests that served as inputs to one or more rsmcompare/rsmsummarize tests. Therefore, it is likely that the current test outputs no longer match the expected output and the tests for those two tools must be be re-run.
4. Run nosetests --nologcapture tests/*rsmsummarize*.py and nosetests --nologcapture tests/*rsmcompare*.py. If you see any failures, make sure they are related to the changes you made since those are expected.
1. Next, re-run python tests/update_files.py --tests tests --outputs test_outputs which should only update the outputs for the rsmcompare/rsmsummarize tests.
2. If this is the case, you are now ready to commit your changes.

1. To run a specific test function in a specific test file, simply use nosetests --nologcapture tests/test_X.py:Y where test_X.py is the name of the test file, and Y is the test functions. Note that this will not work for parameterized tests. If you want to run a specific parameterized test, you can comment out all of the other param() calls and run the test_run_experiment_parameterized() function as above.
2. If you make any changes to the code that can change the output that the tests are expected to produce, you must re-run all of the tests and then update the expected test outputs using the update_files.py command as shown above.
3. In the rare case that you do need to create an entirely new tests/test_experiment_X.py file instead of using one of the existing ones, you can choose whether to exclude the tests contained in this file from updating their expected outputs when update_files.py is run by setting _AUTO_UPDATE=False at the top of the file. This should only be necessary if you are absolutely sure that your tests will never need updating.
4. The --pdb-errors and --pdb-failures options for nosetests are your friends. If you encounter test errors or test failures where the cause may not be immediately clear, re-run the nosetests command with the appropriate option. Doing so will drop you into an interactive PDB session as soon as a error (or failure) is encountered and then you inspect the variables at that point (or use “u” and “d” to go up and down the call stack). This may be particularly useful for tests in tests/test_cli.py that use subprocess.run(). If these tests are erroring out, use --pdb-errors and inspect the “stderr” variable in the resulting PDB session to see what the error is.
 [1] For older versions of conda, you may have to do source activate rsmtool on Linux/macOS and activate rsmtool on Windows.