We use YAPF to format all of the code in DeepChem. Although it sometimes produces slightly awkward formatting, it does have two major benefits. First, it ensures complete consistency throughout the entire codebase. And second, it avoids disagreements about how a piece of code should be formatted.
Whenever you modify a file, run
yapf on it to reformat it before
checking it in.
yapf -i <modified file>
YAPF is run on every pull request to make sure the formatting is correct, so if you forget to do this the continuous integration system will remind you. Because different versions of YAPF can produce different results, it is essential to use the same version that is being run on CI. At present, that is 0.22. We periodically update it to newer versions.
We use Flake8 to check our code syntax. Lint tools basically provide these benefits.
Prevent things like syntax errors or typos
Save our review time (no need to check unused codes or typos)
Whenever you modify a file, run
flake8 on it.
flake8 <modified file> --count
If the command returns 0, it means your code passes the Flake8 check.
All classes and functions should include docstrings describing their purpose and intended usage. When in doubt about how much information to include, always err on the side of including more rather than less. Explain what problem a class is intended to solve, what algorithms it uses, and how to use it correctly. When appropriate, cite the relevant publications.
All docstrings should follow the numpy docstring formatting conventions. To ensure that the code examples in the docstrings are working as expected, run
python -m doctest <modified file>
Having an extensive collection of test cases is essential to ensure the code works correctly. If you haven’t written tests for a feature, that means the feature isn’t finished yet. Untested code is code that probably doesn’t work.
Complex numerical code is sometimes challenging to fully test. When an
algorithm produces a result, it sometimes is not obvious how to tell whether the
result is correct or not. As far as possible, try to find simple examples for
which the correct answer is exactly known. Sometimes we rely on stochastic
tests which will probably pass if the code is correct and probably fail if
the code is broken. This means these tests are expected to fail a small
fraction of the time. Such tests can be marked with the
annotation. If they fail during continuous integration, they will be run a
second time and an error only reported if they fail again.
If possible, each test should run in no more than a few seconds. Occasionally
this is not possible. In that case, mark the test with the
annotation. Slow tests are skipped during continuous integration, so changes
that break them may sometimes slip through and get merged into the repository.
We still try to run them regularly, so hopefully the problem will be discovered
To test your code locally, you will have to setup a symbolic link to your current development directory. To do this, simply run
python setup.py develop
while installing the package from source. This will let you see changes that you make to the source code when you import the package and, in particular, it allows you to import the new classes/methods for unit tests.
Ensure that the tests pass locally! Check this by running
python -m pytest <modified file>
Testing Machine Learning Models¶
Testing the correctness of a machine learning model can be quite tricky to do in practice. When adding a new machine learning model to DeepChem, you should add at least a few basic types of unit tests:
Overfitting test: Create a small synthetic dataset and test that your model can learn this datasest with high accuracy. For regression and classification task, this should correspond to low training error on the dataset. For generative tasks, this should correspond to low training loss on the dataset.
Reloading test: Check that a trained model can be saved to disk and reloaded correctly. This should involve checking that predictions from the saved and reloaded models matching exactly.
Note that unit tests are not sufficient to gauge the real performance of a model. You should benchmark your model on larger datasets as well and report your benchmarking tests in the PR comments.
Type annotations are an important tool for avoiding bugs. All new code should provide type annotations for function arguments and return types. When you make significant changes to existing code that does not have type annotations, please consider adding them at the same time.
We use the mypy static type checker to verify code correctness. It is
automatically run on every pull request. If you want to run it locally to make
sure you are using types correctly before checking in your code,
the top level directory of the repository and execute the command
mypy -p deepchem --ignore-missing-imports
Because Python is such a dynamic language, it sometimes is not obvious what type
to specify. A good rule of thumb is to be permissive about input types and
strict about output types. For example, many functions are documented as taking
a list as an argument, but actually work just as well with a tuple. In those
cases, it is best to specify the input type as
Sequence to accept either
one. But if a function returns a list, specify the type as
we can guarantee the return value will always have that exact type.
Another important case is NumPy arrays. Many functions are documented as taking
an array, but actually can accept any array-like object: a list of numbers, a
list of lists of numbers, a list of arrays, etc. In that case, specify the type
Sequence to accept any of these. On the other hand, if the function
truly requires an array and will fail with any other input, specify it as
deepchem.utils.typing module contains definitions of some types that
appear frequently in the DeepChem API. You may find them useful when annotating