File size: 4,717 Bytes
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 | from __future__ import annotations
import json
from dataclasses import asdict
from pathlib import Path
from sepsis_mcp.gossis_experiment import GossisRunConfig
from sepsis_mcp.mimic4_experiment import Mimic4RunConfig
from sepsis_mcp.modeling import ProbabilityEstimator, QuantileRegressionEstimator, RegressionEstimator
def _pipeline_terminal_params(estimator) -> dict[str, object]:
model = getattr(estimator, "model", estimator)
if hasattr(model, "named_steps"):
terminal = list(model.named_steps.values())[-1]
return terminal.get_params()
return model.get_params()
def render_implementation_details() -> str:
gossis_defaults = asdict(GossisRunConfig(data_root=Path("."), output_dir=Path("outputs/gossis-hospital")))
mimic_defaults = asdict(Mimic4RunConfig(csv_path=Path("mimic.csv"), output_dir=Path("outputs/mimic4-validation")))
classifier_defaults = {
"logistic_regression": _pipeline_terminal_params(ProbabilityEstimator(model_type="logistic_regression")),
"mlp": _pipeline_terminal_params(ProbabilityEstimator(model_type="mlp")),
"xgboost": _pipeline_terminal_params(ProbabilityEstimator(model_type="xgboost")),
}
regression_defaults = {
"ridge_regression": _pipeline_terminal_params(RegressionEstimator(model_type="ridge_regression")),
"sklearn_gbdt": _pipeline_terminal_params(RegressionEstimator(model_type="sklearn_gbdt")),
"cqr_xgboost": _pipeline_terminal_params(QuantileRegressionEstimator(model_type="xgboost", quantile=0.95)),
}
return "\n".join(
[
"# Implementation Details",
"",
"## GOSSIS Classification Defaults",
"",
"```json",
json.dumps(gossis_defaults, indent=2, default=str),
"```",
"",
"## MIMIC-IV Validation Defaults",
"",
"```json",
json.dumps(mimic_defaults, indent=2, default=str),
"```",
"",
"## Base Model Hyperparameters",
"",
"### Classification",
"",
"```json",
json.dumps(classifier_defaults, indent=2, default=str),
"```",
"",
"### Regression / CQR",
"",
"```json",
json.dumps(regression_defaults, indent=2, default=str),
"```",
"",
"## Split and Seed Protocol",
"",
"- GOSSIS uses hospital-disjoint splits with optional selection hospitals when `selection_fraction > 0`.",
"- MIMIC-IV uses leave-one-care-unit-out assignments with `rotations` train/selection/calibration rotations per held-out unit.",
"- Random seeds are passed through the run config and sweep grids; appendix sweeps report per-seed outputs and repeated summaries.",
"",
"## Conformity Scores",
"",
"- Classification uses binary nonconformity scores: `p_hat(y!=y_true)` via `binary_nonconformity_scores`.",
"- Standard CP uses a single global conformal threshold.",
"- Missingness-aware CP uses subgroup-specific thresholds when subgroup calibration support exceeds `min_group_size`, otherwise it falls back to the global threshold.",
"- CPMDA exact uses exact mask-signature matching on selected missingness features and falls back to the global threshold when `min_match` is not met.",
"- CQR uses symmetric or normalized residual scores with optional trimming via `trim_fraction`.",
"",
"## Preprocessing and Missingness Handling",
"",
"- Tabular classification models use one-hot encoding plus constant-value imputation for missing values where required by the estimator pipeline.",
"- Structured missingness grouping is selected from candidate variables whose missing fractions lie between `min_variable_missing_fraction` and `max_variable_missing_fraction`.",
"- The default appendix fallback threshold rule is explicit: undersupported groups revert to the marginal calibration threshold rather than forcing unstable subgroup quantiles.",
]
)
def write_implementation_details(*, output_path: Path, manifest_path: Path) -> dict[str, Path]:
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(render_implementation_details(), encoding="utf-8")
manifest_path.write_text(
json.dumps({"implementation_details": str(output_path)}, indent=2, sort_keys=True),
encoding="utf-8",
)
return {"implementation_details": output_path, "manifest": manifest_path}
|