Adding Custom Pytest Marks

When you're debugging you're usually not interested in running all of your tests. Typically you'd only like to run a subset. That's why pytest allows you to add tags to your tests by the pytest.mark decorator.

We'll explore this in the code below, where we will make a slow running test.

import time
import pytest
import numpy as np

def normalize(X):
    return (X - X.min())/(X.max() - X.min())

def threshold(X, min_val=-1, max_val=1):
    result = np.where(X <= min_val, min_val, X)
    return np.where(result >= max_val, max_val, result)

@pytest.fixture(params=[(1,1), (2,2), (3,3), (4,4)], ids=lambda d: f"rows: {d[0]} cols: {d[1]}")
def random_numpy_array(request):
    return np.random.normal(request.param)

# We will now add the `slow` mark to the test.
@pytest.mark.slow
@pytest.mark.parametrize("func", [normalize, threshold], ids=lambda d: d.__name__)
def test_shape_same(func, random_numpy_array):
    time.sleep(1)
    X_norm = func(random_numpy_array)
    assert random_numpy_array.shape == X_norm.shape

def test_min_max_normalise(random_numpy_array):
    X_norm = normalize(random_numpy_array)
    assert X_norm.min() == 0.0
    assert X_norm.max() == 1.0

@pytest.mark.parametrize("min_val", [-3, -2, -1], ids=lambda x: f"min_val:{x}")
@pytest.mark.parametrize("max_val", [3, 2, 1], ids=lambda x: f"max_val:{x}")
def test_min_max_threshold(random_numpy_array, min_val, max_val):
    X_norm = threshold(random_numpy_array, min_val, max_val)
    assert X_norm.min() >= min_val
    assert X_norm.max() <= max_val

Given that we've now added a custom mark to our test, we can select it from the command line.

pytest -m slow
pytest -m "not slow"