Improve tests to make them run on variously typed data using the `global_dtype` fixture · scikit-learn/scikit-learn#22881

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Descrizione

Context: the new `global_dtype` fixture and `SKLEARN_RUN_FLOAT32_TESTS` environment variable

Introduction of low-level computational routines for 32bit motivated an extension of tests to run them on 32bit.

In this regards, https://github.com/scikit-learn/scikit-learn/pull/22690 introduced a new global_dtype fixture as well has the SKLEARN_RUN_FLOAT32_TESTS env. variable to make it possible to run the test on 32bit data.

Running test on 32bit can be done using SKLEARN_RUN_FLOAT32_TESTS=1.

For instance, this run the first global_dtype-parametrised test:

SKLEARN_RUN_FLOAT32_TESTS=1 pytest sklearn/feature_selection/tests/test_mutual_info.py -k test_compute_mi_cc

This allows running tests on 32bit dataset on some CI job, and currently a single CI job is used to run tests on 32bit.

More details about the fixture in the online dev doc for the SKLEARN_TESTS_GLOBAL_RANDOM_SEED env variable:

https://scikit-learn.org/dev/computing/parallelism.html#environment-variables

Guidelines to convert existing tests

Not all scikit-learn tests must use this fixture. We must parametrise tests that actually assert closeness of results using assert_allclose. For instance, tests that check for the exception messages raised when passing invalid inputs must not be converted.
Tests using np.testing.assert_allclose must now use sklearn.utils._testing.assert_allclose as a drop-in replacement.
Check that the dtype of fitted attributes or return values that depend on the dtype of the input datastructure actually have the expected dtype: typically when all inputs are continuous values in float32, it is often (but not always) the case that scikit-learn should carry all arithmetic operations at that precision level and return output arrays with the same precision level. There can be exceptions, in which case they could be made explicit with an inline comment in the test, possibly with a TODO marker when one thing that the current behavior should change (see the related: #11000 and #22682 for instance).
To avoid having to review huge PRs that impact many files at once and can lead to conflicts, let's open PRs that edit at most one test file at a time. For instance use a title such as:

TST use global_dtype in sklearn/_loss/tests/test_glm_distribution.py

Please reference #22881 (i.e. this issue) in the description of the PR and put the full filename of the test file you edit in the title of the PR.
To convert an existing test with a fixed dtype, the general pattern is to rewrite a function such as:

from numpy.testing import assert_allclose

def test_some_function():
    # ...
    rng = np.random.RandomState(0)
    X = rng.randon.rand(n_samples, n_features)
    y = rng.randon.rand(n_samples).astype(global_dtype)
    model.fit(X, y)
    # ...
    y_pred = model.predict(X)
    assert_allclose(y_pred, y_true)

to:

from sklearn.utils._testing import assert_allclose

def test_some_function(global_dtype):
    # ...
    rng = np.random.RandomState(0)
    X = rng.randon.rand(n_samples, n_features).astype(global_dtype, copy=False)
    y = rng.randon.rand(n_samples).astype(global_dtype, copy=False)
    model.fit(X, y)
    # ...
    assert model.fitted_param_.dtype == global_dtype
    y_pred = model.predict(X)
    assert y_pred.dtype == global_dtype
    assert_allclose(y_pred, y_true)

and then check that the test is passing on 32bit datasets

SKLEARN_RUN_FLOAT32_TESTS=1 pytest sklearn/some_module/test/test_some_module.py -k test_some_function

Failures are to be handle on a case-by-case basis.

List of test modules to upgrade

find sklearn -name "test_*.py"

Note that some of those files might not have any test to update.

Guida contributor

Direzione di ricerca: Scegli un file di test non spuntato dalla checklist nell'issue, ad esempio sklearn/ loss/tests/test glm distribution.py. Segui le linee guida dettagliate per aggiungere il parametro fixture `global dtype`, convertire i dati di input in quel dtype, utilizzare `assert allclose` da `sklearn.utils. testing` e verificare il dtype degli attributi fittati. Esegui il test con `SKLEARN RUN FLOAT32 TESTS=1` per verificare che passi. Invia una PR a file singolo che fa riferimento a questo issue, includendo il nome del file nel titolo.
Tech stack: python
Dominio: machine learningtesting
Tipo issue: Test
Difficoltà: 2
Tempo stimato: 1-3 ore
Stato attività: Attiva
Chiarezza: Chiara
Prerequisiti: PythonGit
Adatta ai principianti: 75