(global_random_seed)
| 371 | |
| 372 | |
| 373 | def test_preprocess_data(global_random_seed): |
| 374 | rng = np.random.RandomState(global_random_seed) |
| 375 | n_samples = 200 |
| 376 | n_features = 2 |
| 377 | X = rng.rand(n_samples, n_features) |
| 378 | y = rng.rand(n_samples) |
| 379 | expected_X_mean = np.mean(X, axis=0) |
| 380 | expected_y_mean = np.mean(y, axis=0) |
| 381 | |
| 382 | Xt, yt, X_mean, y_mean, X_scale, sqrt_sw = _preprocess_data( |
| 383 | X, y, fit_intercept=False |
| 384 | ) |
| 385 | assert_array_almost_equal(X_mean, np.zeros(n_features)) |
| 386 | assert_array_almost_equal(y_mean, 0) |
| 387 | assert_array_almost_equal(X_scale, np.ones(n_features)) |
| 388 | assert sqrt_sw is None |
| 389 | assert_array_almost_equal(Xt, X) |
| 390 | assert_array_almost_equal(yt, y) |
| 391 | |
| 392 | Xt, yt, X_mean, y_mean, X_scale, sqrt_sw = _preprocess_data( |
| 393 | X, y, fit_intercept=True |
| 394 | ) |
| 395 | assert_array_almost_equal(X_mean, expected_X_mean) |
| 396 | assert_array_almost_equal(y_mean, expected_y_mean) |
| 397 | assert_array_almost_equal(X_scale, np.ones(n_features)) |
| 398 | assert sqrt_sw is None |
| 399 | assert_array_almost_equal(Xt, X - expected_X_mean) |
| 400 | assert_array_almost_equal(yt, y - expected_y_mean) |
| 401 | |
| 402 | |
| 403 | @pytest.mark.parametrize("sparse_container", [None] + CSC_CONTAINERS) |
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