| """Tests de salida binaria de modelos sklearn.""" |
|
|
| import numpy as np |
| import pytest |
|
|
| from src.models.baseline import build_model |
|
|
| X_TRAIN = [ |
| "the quick brown fox is nice", |
| "the lazy dog sleeps well", |
| "the fox and dog are friends", |
| "another calm peaceful day today", |
| "you are stupid and worthless idiot", |
| "kill them all right now attack", |
| ] |
| Y_TRAIN = [0, 0, 0, 0, 1, 1] |
| X_PRED = ["the fox is calm", "you idiot fool"] |
|
|
|
|
| @pytest.fixture(scope="module") |
| def trained_lr(models_config: str, features_config: str, best_params_config: str): |
| model = build_model( |
| "lr", |
| config_path=models_config, |
| feat_config_path=features_config, |
| best_params_path=best_params_config, |
| ) |
| model.fit(X_TRAIN, Y_TRAIN) |
| return model |
|
|
|
|
| def test_predict_binary_labels(trained_lr): |
| preds = trained_lr.predict(X_PRED) |
|
|
| assert preds.shape == (len(X_PRED),) |
| assert set(np.unique(preds)).issubset({0, 1}) |
|
|
|
|
| def test_predict_proba_valid_binary_distribution(trained_lr): |
| proba = trained_lr.predict_proba(X_PRED) |
|
|
| assert proba.shape == (len(X_PRED), 2) |
| assert np.all(proba >= 0.0) |
| assert np.all(proba <= 1.0) |
| np.testing.assert_allclose(proba.sum(axis=1), 1.0, rtol=1e-5) |
|
|
