Setting up development environment locally¶
Follow our installation instructions and set up a suitable environment to build statsmodels from source. We recommend that you develop using a development install of statsmodels in a venv by running:
python -m venv .venv
python -m pip install -e ".[develop]"
from the root directory of the git repository. The flag
-e is for editable.
This command compiles the C code and add statsmodels to your activate python
environment by creating links from your python environment’s libraries
to the statsmodels source code. Therefore, changes to pure python code will
be immediately available to the user without a re-install. Changes to C code or
Cython code require rerunning
python -m pip install -e ".[develop]" before these changes are
Test Driven Development¶
We strive to follow a Test Driven Development (TDD) pattern. All models or statistical functions that are added to the main code base are to have tests versus an existing statistical package, if possible.
Introduction to pytest¶
Like many packages, statsmodels uses the pytest testing system and the convenient extensions in numpy.testing. Pytest will find any file, directory, function, or class name that starts with
Test (classes only). Test function should start with
test, test classes should start with
Test. These functions and classes should be placed in files with names beginning with
test in a directory called
Test are run from the command line by calling
pytest. Directly running tests using
pytest requires that statsmodels is installed using
python -m pip install -e ".[develop]" as described
Tests can be run at different levels of granularity:
Project level, which runs all tests. Running the entire test suite is slow and normally this would only be needed if making deep changes to statsmodels.
Folder level, which runs all tests below a folder
File level, which runs all tests in a file
Class level, which runs all tests in a class
Test level, which runs a single test. The first example runs a test in a class. The second runs a stand alone test.
How To Write A Test¶
NumPy provides a good introduction to unit testing with pytest and NumPy extensions here. It is worth a read for some more details. Here, we will document a few conventions we follow that are worth mentioning. Often we want to test a whole model at once rather than just one function, for example. The following is a pared down version test_discrete.py. In this case, several different models with different options need to be tested. The tests look something like
from numpy.testing import assert_almost_equal
import statsmodels.api as sm
from results.results_discrete import Spector
res2 are the results. res1 are the values from statsmodels
assert_almost_equal(self.res1.params, self.res2.params, 4)
decimal_tvalues = 4
assert_almost_equal(self.res1.params, self.res2.params, self.decimal_tvalues)
# ... as many more tests as there are common results
Tests the Probit model using Newton's method for fitting.
# set up model
data = sm.datasets.spector.load()
data.exog = sm.add_constant(data.exog)
cls.res1 = sm.Probit(data.endog, data.exog).fit(method='newton', disp=0)
# set up results
res2 = Spector.probit
cls.res2 = res2
# set up precision
cls.decimal_tvalues = 3
assert_almost_equal(self.res1.foo, self.res2.foo, 4)
The main workhorse is the CheckDiscreteResults class. Notice that we can set the level of precision
for tvalues to be different than the default in the subclass TestProbitNewton. All of the test
classes have a
setup_class. Otherwise, pytest would reinstantiate the class
before every single test method. If the fitting of the model is time consuming, then this is clearly
undesirable. Finally, we have a script at the bottom so that we can run the tests should be running
the Python file.
The test results are the final piece of the above example. For many tests, especially those for the models, there are many results against which you would like to test. It makes sense then to separate the hard-coded results from the actual tests to make the tests more readable. If there are only a few results it’s not necessary to separate the results. We often take results from some other statistical package. It is important to document where you got the results from and why they might differ from the results that we get. Each tests folder has a results subdirectory. Consider the folder structure for the discrete models:
It is up to you how best to structure the results. In the discrete model example, you will notice that there are result classes based around particular datasets with a method for loading different model results for that dataset. You can also include text files that hold results to be loaded by results classes if it is easier than putting them in the class itself.
Speeding up full runs¶
Running the full test suite is slow. Fortunately it is only necessary to run the full suite when
making low-level changes (e.g., to
statsmodels.base) There are two methods available to
speed up runs of the full test suite when needed.
Use the pytest-xdist package
python -m pip install pytest-xdist
pytest -n auto statsmodels
Skip slow tests using
pytest --skip-slow statsmodels
You can combine these two approaches for faster runs.
export MKL_NUM_THREADS=1 && export OMP_NUM_THREADS=1
pytest -n auto --skip-slow statsmodels
The root of statsmodels and all submodules expose a
test() method which can
be used to run all tests either in the package (
statsmodels.test()) or in
a module (
statsmodels.regression.test()). This method allows tests to be
run from an install copy of statsmodels even it is was not installed using the
editable flag as described above. This method is required for testing wheels in
release builds and is not recommended for development.
Using this method, all tests are run using:
import statsmodels.api as sm
Submodules tests are run using:
Run the test suite