Running unit tests

Setup

Before you run the tests, you need to fire up the MongoDB container. This can be done via the below command:

PORT=1234 docker-compose up -d mongo

The PORT environment variable will not actually be used, it's just docker-compose does not realize this variable is not needed if you're only starting the MongoDB service.

Your MongoDB service will be running, and can be reached at port 27018.

Make sure you have a MongoDB service up and running!

Otherwise all the unit tests will fail due to model algorithm timing out!

Run the test

Via command line

poe test

This will run all the unit tests within tests/ directory, which are the files that contain a test_ prefix in their names.

Via VSCode

  1. Press ctrl+shift+p, or go to Help > Show All Commands
  2. Type in Python: Configure Tests, select that and then press enter
  3. Select pytest
  4. Select tests
  5. Go to the testing panel

If no tests were run before the green ticks will not be there.

To run the tests, right click on the exodus_model_template symbol, and select Debug Test.

Write your own test

Most likely you want to make sure your model algorithm can run with some dataset you already have. To test whether your model algorithm can train and predict upon that dataset:

  1. Create a directory in tests/datasets. Let's say tests/datasets/foo/
  2. Move your training CSV file into tests/datasets/foo/, and rename it to tests/datasets/foo/train.csv
  3. Move your prediction CSV file into tests/datasets/foo/, and rename it to tests/datasets/foo/prediction.csv
  4. If you have a holdout dataset, move that file into tests/datasets/foo/, and rename it to tests/datasets/foo/holdout.csv
  5. Create a new file called tests/datasets/foo/meta.json, which should contain the following fields:
    • "target_column_name": the name of the target column
    • "features": a dict from the column names to the column types. The types should all be either one of "string" or "double"
  6. Write the test method by extending the tests/test_ml.py files: 
    def test_foo():
    train_predict_delete("./tests/datasets/foo")
    It really is as simple as that!