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32f5a65 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | from __future__ import annotations
import json
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
import pytest
def _write_dummy_png(path: Path) -> Path:
path.parent.mkdir(parents=True, exist_ok=True)
fig, ax = plt.subplots(figsize=(1.5, 1.0))
ax.plot([0, 1], [0, 1])
fig.savefig(path, dpi=100)
plt.close(fig)
return path
def test_run_appendix_artifacts_writes_bundle(tmp_path: Path) -> None:
from sepsis_mcp.neurips_appendix_artifacts import run_appendix_artifacts
score_dir = tmp_path / "score"
eta_dir = tmp_path / "eta"
calsize_dir = tmp_path / "calsize"
model_swap_dir = tmp_path / "swap"
stress_dir = tmp_path / "stress"
output_dir = tmp_path / "artifacts"
score_dir.mkdir()
eta_dir.mkdir()
calsize_dir.mkdir()
model_swap_dir.mkdir()
stress_dir.mkdir()
pd.DataFrame(
[
{"dataset": "gossis", "model_type": "xgboost", "seed": 0, "split_kind": "calibration", "group": 0, "group_label": "low", "score": 0.1},
{"dataset": "gossis", "model_type": "xgboost", "seed": 0, "split_kind": "calibration", "group": 1, "group_label": "high", "score": 0.8},
{"dataset": "gossis", "model_type": "xgboost", "seed": 0, "split_kind": "test", "group": 0, "group_label": "low", "score": 0.2},
{"dataset": "gossis", "model_type": "xgboost", "seed": 0, "split_kind": "test", "group": 1, "group_label": "high", "score": 0.9},
]
).to_csv(score_dir / "score_records.csv", index=False)
pd.DataFrame(
[
{
"model_type": "xgboost",
"group_label": "low",
"mean_eta": 0.20,
"std_eta": 0.01,
"mean_delta": 0.05,
"std_delta": 0.01,
"mean_cost": 0.02,
"advantage_rate": 1.0,
}
]
).to_csv(eta_dir / "eta_aggregate.csv", index=False)
pd.DataFrame(
[
{"cal_size": 250, "cal_size_label": "250", "method": "standard", "coverage_mean": 0.88, "max_gap_mean": 0.06, "set_size_mean": 0.92},
{"cal_size": 250, "cal_size_label": "250", "method": "missingness_aware", "coverage_mean": 0.90, "max_gap_mean": 0.03, "set_size_mean": 0.94},
]
).to_csv(calsize_dir / "calsize_summary.csv", index=False)
_write_dummy_png(calsize_dir / "calsize_panels.png")
pd.DataFrame(
[
{
"calibrate_model": "logistic_regression",
"deploy_model": "xgboost",
"grouping": "coverage_gap_variable",
"mean_gap": 0.04,
"mean_coverage": 0.90,
"mean_test_group_overlap_vs_self": 1.0,
"mean_coverage_drop_vs_self": 0.01,
"mean_gap_increase_vs_self": 0.00,
},
{
"calibrate_model": "xgboost",
"deploy_model": "xgboost",
"grouping": "coverage_gap_variable",
"mean_gap": 0.02,
"mean_coverage": 0.91,
"mean_test_group_overlap_vs_self": 1.0,
"mean_coverage_drop_vs_self": 0.00,
"mean_gap_increase_vs_self": 0.00,
},
]
).to_csv(model_swap_dir / "swap_matrix.csv", index=False)
pd.DataFrame(
[
{
"comparison": "coverage_gap_variable_vs_predicted_risk_tercile_frozen",
"win_grouping": "coverage_gap_variable",
"win_count": 3,
"mean_gap_difference": 0.02,
}
]
).to_csv(model_swap_dir / "swap_win_summary.csv", index=False)
pd.DataFrame(
[
{"perturbation_type": "mar_dropout", "perturbation_strength": 0.0, "drop_rate": 0.0, "method": "standard", "mean_gap": 0.03, "std_gap": 0.0, "run_count": 1},
{"perturbation_type": "mar_dropout", "perturbation_strength": 0.3, "drop_rate": 0.3, "method": "standard", "mean_gap": 0.06, "std_gap": 0.0, "run_count": 1},
{"perturbation_type": "mar_dropout", "perturbation_strength": 0.0, "drop_rate": 0.0, "method": "missingness_aware", "mean_gap": 0.02, "std_gap": 0.0, "run_count": 1},
{"perturbation_type": "mar_dropout", "perturbation_strength": 0.3, "drop_rate": 0.3, "method": "missingness_aware", "mean_gap": 0.03, "std_gap": 0.0, "run_count": 1},
]
).to_csv(stress_dir / "stress_aggregate.csv", index=False)
paths = run_appendix_artifacts(
score_dir=score_dir,
eta_dir=eta_dir,
calsize_dir=calsize_dir,
model_swap_dir=model_swap_dir,
stress_dir=stress_dir,
output_dir=output_dir,
)
assert Path(paths["score_figure"]).exists()
assert Path(paths["eta_figure"]).exists()
assert Path(paths["swap_figure"]).exists()
assert Path(paths["stress_figure"]).exists()
manifest = json.loads((output_dir / "manifest.json").read_text(encoding="utf-8"))
assert "score_figure" in manifest
assert "provenance" in manifest
assert (output_dir / "source_tables" / "score_records.csv").exists()
assert (output_dir / "source_tables" / "appendix_eta_focus_table.csv").exists()
eta_focus = pd.read_csv(output_dir / "source_tables" / "appendix_eta_focus_table.csv")
assert "mean_eta_minus_cost" in eta_focus.columns
assert "mean_eta_minus_delta" not in eta_focus.columns
assert eta_focus.loc[0, "mean_eta_minus_cost"] == pytest.approx(0.18)
assert (output_dir / "appendix" / "calsize_panels.png").exists()
provenance = json.loads((output_dir / "provenance.json").read_text(encoding="utf-8"))
assert "git" in provenance
assert "inputs" in provenance
assert "artifacts" in provenance
assert "appendix_score_ecdfs.png" in provenance["artifacts"]
score_info = provenance["artifacts"]["appendix_score_ecdfs.png"]
assert score_info["derived_from"] == ["score_records.csv"]
assert (output_dir / "TRACEABILITY.md").exists()
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