| import os |
| import sys |
|
|
| import numpy as np |
| import pytest |
|
|
| HERE = os.path.dirname(os.path.abspath(__file__)) |
| sys.path.insert(0, HERE) |
|
|
| import stats as S |
|
|
|
|
| def test_rmse_mae_median_ae_hand_computed(): |
| predicted = [1, 2, 3] |
| actual = [1, 2, 4] |
| |
| assert S.rmse(predicted, actual) == np.sqrt(1 / 3) |
| assert S.mae(predicted, actual) == 1 / 3 |
| assert S.median_ae(predicted, actual) == 0.0 |
|
|
|
|
| def test_summarize_errors(): |
| out = S.summarize_errors([1, 2, 3], [1, 2, 4]) |
| assert out["n"] == 3 |
| assert out["rmse"] == pytest.approx(np.sqrt(1 / 3)) |
| assert out["mae"] == pytest.approx(1 / 3) |
| assert out["median_ae"] == 0.0 |
|
|
|
|
| def test_linear_fit_1d_exact_recovery_no_noise(): |
| x = np.linspace(0, 10, 20) |
| y = 2 * x + 1 |
| intercept, slope = S.linear_fit_1d(x, y) |
| assert abs(intercept - 1) < 1e-8 |
| assert abs(slope - 2) < 1e-8 |
|
|
|
|
| def test_ols_fit_matches_polyfit(): |
| rng = np.random.default_rng(0) |
| x = rng.normal(size=200) |
| y = 3 * x - 2 + rng.normal(scale=0.01, size=200) |
| design = np.column_stack([np.ones(len(x)), x]) |
| beta = S.ols_fit(design, y) |
| ref_slope, ref_intercept = np.polyfit(x, y, 1) |
| assert abs(beta[0] - ref_intercept) < 1e-6 |
| assert abs(beta[1] - ref_slope) < 1e-6 |
|
|
