Test your modifications

Warning

Data for testing must be manually downloaded (see below). Make sure you have installed pytest & Co (pip install -r https://raw.githubusercontent.com/aramis-lab/clinicadl/dev/requirements-dev.txt) inside your development environment.

Main functionalities of ClinicaDL can be tested using the functions provided in the tests folder (this folder is not included in the package, but it can be cloned from the main repository).

The tests run for every commit/PR in our Continuous Integration setup. To complete them all, it should take around 10 min. The following tests are launched, in the following order:

• command line interface test (test_cli.py): it verifies main arguments on the CLI interface.
• generate test (test_generate.py): it creates trivial and random datasets based on 4 preprocessed MRIs obtained from OASIS dataset (testing dataset). The latter one can be downloaded here and uncompressed into the .clinicadl/test/data/dataset/ folder.
• predict test (test_predict.py): this test predicts synthetic (random) MRI obtained in previous test. You can preprocess the dataset obtained during the generate test (clinica run deeplearning-prepare-data ./dataset/random_example t1-linear image) or you can download it here. This test verifies that the output files exist.
• train test (test_train.py): it runs training over the synthetic dataset and verifies that output folder structure was created. It needs to download and uncompress this file into the .clinicadl/test/data/dataset/ folder and also the RandomCaps dataset downloaded in previous item.
• Several tsvtool functionalities (test_tsvtool.py). This test needs no data download as it is provided in the repo (clinicadl/tests/data/tsvtool). It checks that:
  • the same label lists are found on a de-identified version of ADNI (getlabels),
  • data splits do not lead to data leakage and can correctly be found by loading functions (split and kfold),
  • the analysis tool runs and gives the same result as before on a de-identified version of ADNI (analysis).

To run each of these tests, a folder called .clinicadl/test/data/ contains the files used during the test execution. As mentioned above, some tests need to download extra data. Testing datasets must be extracted inside a folder named .clinicadl/test/data/dataset/. Trained models must be uncompressed inside a folder called .clinicadl/test/data/models/.

Finally, be sure to have installed the pytest library in order to run the test suite (it's not a requirement of the main package). Once everything is on place, each of these tests can be run using the following command:

pytest  --verbose test_cli.py

to launch the command line interface test. A similar command is used to launch the other tests. If you don't run them in order, be sure of downloading the necessary artifacts for the test.