|
| 1 | +import os |
| 2 | + |
| 3 | +import numpy as np |
| 4 | +import pytest |
| 5 | + |
| 6 | +from pySEQTarget import SEQopts, SEQuential |
| 7 | +from pySEQTarget.data import load_data |
| 8 | + |
| 9 | + |
| 10 | +def _make_seq(seed, **extra_opts): |
| 11 | + data = load_data("SEQdata") |
| 12 | + return SEQuential( |
| 13 | + data, |
| 14 | + id_col="ID", |
| 15 | + time_col="time", |
| 16 | + eligible_col="eligible", |
| 17 | + treatment_col="tx_init", |
| 18 | + outcome_col="outcome", |
| 19 | + time_varying_cols=["N", "L", "P"], |
| 20 | + fixed_cols=["sex"], |
| 21 | + method="ITT", |
| 22 | + parameters=SEQopts(seed=seed, **extra_opts), |
| 23 | + ) |
| 24 | + |
| 25 | + |
| 26 | +def test_hazard_reproducible_with_seed(): |
| 27 | + results = [] |
| 28 | + for _ in range(2): |
| 29 | + s = _make_seq(seed=42, hazard_estimate=True) |
| 30 | + s.expand() |
| 31 | + s.fit() |
| 32 | + s.hazard() |
| 33 | + results.append(s.hazard_ratio) |
| 34 | + |
| 35 | + assert results[0]["Hazard ratio"][0] == results[1]["Hazard ratio"][0] |
| 36 | + |
| 37 | + |
| 38 | +def test_hazard_bootstrap_se_reproducible_with_seed(): |
| 39 | + results = [] |
| 40 | + for _ in range(2): |
| 41 | + s = _make_seq(seed=42, hazard_estimate=True, bootstrap_nboot=3) |
| 42 | + s.expand() |
| 43 | + s.bootstrap() |
| 44 | + s.fit() |
| 45 | + s.hazard() |
| 46 | + results.append(s.hazard_ratio) |
| 47 | + |
| 48 | + assert results[0]["Hazard ratio"][0] == results[1]["Hazard ratio"][0] |
| 49 | + assert results[0]["LCI"][0] == results[1]["LCI"][0] |
| 50 | + assert results[0]["UCI"][0] == results[1]["UCI"][0] |
| 51 | + |
| 52 | + |
| 53 | +@pytest.mark.skipif( |
| 54 | + os.getenv("CI") == "true", reason="Bootstrap reproducibility test hangs in CI" |
| 55 | +) |
| 56 | +def test_hazard_bootstrap_percentile_reproducible_with_seed(): |
| 57 | + results = [] |
| 58 | + for _ in range(2): |
| 59 | + s = _make_seq( |
| 60 | + seed=42, |
| 61 | + hazard_estimate=True, |
| 62 | + bootstrap_nboot=3, |
| 63 | + bootstrap_CI_method="percentile", |
| 64 | + ) |
| 65 | + s.expand() |
| 66 | + s.bootstrap() |
| 67 | + s.fit() |
| 68 | + s.hazard() |
| 69 | + results.append(s.hazard_ratio) |
| 70 | + |
| 71 | + assert results[0]["Hazard ratio"][0] == results[1]["Hazard ratio"][0] |
| 72 | + assert results[0]["LCI"][0] == results[1]["LCI"][0] |
| 73 | + assert results[0]["UCI"][0] == results[1]["UCI"][0] |
| 74 | + |
| 75 | + |
| 76 | +@pytest.mark.skipif( |
| 77 | + os.getenv("CI") == "true", reason="Reproducibility test hangs in CI" |
| 78 | +) |
| 79 | +def test_survival_reproducible_with_seed(): |
| 80 | + results = [] |
| 81 | + for _ in range(2): |
| 82 | + s = _make_seq(seed=42, km_curves=True) |
| 83 | + s.expand() |
| 84 | + s.fit() |
| 85 | + s.survival() |
| 86 | + results.append(s.km_data) |
| 87 | + |
| 88 | + np.testing.assert_allclose( |
| 89 | + results[0]["pred"].to_numpy(), results[1]["pred"].to_numpy(), atol=1e-14 |
| 90 | + ) |
| 91 | + |
| 92 | + |
| 93 | +@pytest.mark.skipif( |
| 94 | + os.getenv("CI") == "true", reason="Bootstrap reproducibility test hangs in CI" |
| 95 | +) |
| 96 | +def test_survival_bootstrap_reproducible_with_seed(): |
| 97 | + results = [] |
| 98 | + for _ in range(2): |
| 99 | + s = _make_seq(seed=42, km_curves=True, bootstrap_nboot=3) |
| 100 | + s.expand() |
| 101 | + s.bootstrap() |
| 102 | + s.fit() |
| 103 | + s.survival() |
| 104 | + results.append(s.km_data) |
| 105 | + |
| 106 | + for col in ["pred", "SE", "LCI", "UCI"]: |
| 107 | + np.testing.assert_allclose( |
| 108 | + results[0][col].to_numpy(), results[1][col].to_numpy(), atol=1e-14 |
| 109 | + ) |
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