File size: 8,541 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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | from __future__ import annotations
import argparse
import json
from dataclasses import asdict, dataclass
from pathlib import Path
from time import perf_counter
import pandas as pd
from sepsis_mcp.conformal import CPMDAExactClassifier, MissingnessAwareConformalClassifier, SplitConformalClassifier
from sepsis_mcp.gossis import build_hospital_disjoint_split_with_selection, load_gossis_dataset
from sepsis_mcp.gossis_experiment import (
GossisRunConfig,
_build_structured_grouping,
_encode_feature_splits,
_missingness_mask_matrix,
)
from sepsis_mcp.modeling import ProbabilityEstimator
@dataclass
class RuntimeAnalysisConfig:
data_root: Path
output_dir: Path
model_type: str = "xgboost"
random_state: int = 0
alpha: float = 0.1
selection_fraction: float = 0.1
min_hospital_admissions: int = 500
min_selection_group_rows: int = 100
def summarize_runtime_records(records: pd.DataFrame) -> pd.DataFrame:
if records.empty:
return pd.DataFrame()
summary = records.copy()
standard = (
summary[summary["method"] == "standard"][["stage", "seconds"]]
.rename(columns={"seconds": "standard_seconds"})
)
summary = summary.merge(standard, on="stage", how="left", validate="many_to_one")
summary["relative_to_standard"] = (summary["seconds"] / summary["standard_seconds"]).round(6)
summary.loc[summary["stage"] == "variable_selection", "relative_to_standard"] = pd.NA
return summary.sort_values(["stage", "method"]).reset_index(drop=True)
def run_runtime_analysis(config: RuntimeAnalysisConfig) -> dict[str, Path]:
config.output_dir.mkdir(parents=True, exist_ok=True)
dataset = load_gossis_dataset(config.data_root, min_hospital_admissions=config.min_hospital_admissions)
split = build_hospital_disjoint_split_with_selection(
dataset.frame,
train_fraction=0.6,
selection_fraction=config.selection_fraction,
calibration_fraction=0.1,
random_state=config.random_state,
)
train_features, calibration_features, test_features = _encode_feature_splits(
split.train_frame,
split.calibration_frame,
split.test_frame,
dataset.feature_columns,
)
selection_features, _, _ = _encode_feature_splits(
split.train_frame,
split.selection_frame,
split.test_frame,
dataset.feature_columns,
)
estimator = ProbabilityEstimator(random_state=config.random_state, model_type=config.model_type)
estimator.fit(train_features, split.train_frame["label"])
selection_probabilities = estimator.predict_positive_proba(selection_features)
calibration_probabilities = estimator.predict_positive_proba(calibration_features)
test_probabilities = estimator.predict_positive_proba(test_features)
records: list[dict[str, float | int | str]] = []
selection_start = perf_counter()
structured_grouping = _build_structured_grouping(
config=GossisRunConfig(
data_root=config.data_root,
alpha=config.alpha,
selection_fraction=config.selection_fraction,
model_type=config.model_type,
random_state=config.random_state,
missingness_grouping_strategy="coverage_gap_variable",
min_selection_group_rows=config.min_selection_group_rows,
),
feature_columns=dataset.feature_columns,
selection_frame=split.selection_frame,
calibration_frame=split.calibration_frame,
test_frame=split.test_frame,
selection_probabilities=selection_probabilities,
calibration_probabilities=calibration_probabilities,
test_probabilities=test_probabilities,
)
records.append({"method": "missingness_grouping", "stage": "variable_selection", "seconds": perf_counter() - selection_start})
method_objects = {
"standard": SplitConformalClassifier(alpha=config.alpha),
"missingness_aware": MissingnessAwareConformalClassifier(
alpha=config.alpha,
min_group_size=max(2, min(10, len(split.calibration_frame) // 5)),
),
"cp_mda_exact": CPMDAExactClassifier(
alpha=config.alpha,
top_k_features=10,
min_match=10,
),
}
calibration_payloads = {
"standard": {
"calibration_labels": split.calibration_frame["label"].tolist(),
"calibration_positive_probabilities": calibration_probabilities.tolist(),
},
"missingness_aware": {
"calibration_labels": split.calibration_frame["label"].tolist(),
"calibration_positive_probabilities": calibration_probabilities.tolist(),
"calibration_group_ids": structured_grouping.calibration_groups["group"].tolist(),
},
"cp_mda_exact": {
"calibration_labels": split.calibration_frame["label"].tolist(),
"calibration_positive_probabilities": calibration_probabilities.tolist(),
"calibration_masks": _missingness_mask_matrix(split.calibration_frame, dataset.feature_columns),
"feature_names": dataset.feature_columns,
},
}
prediction_payloads = {
"standard": {"positive_probabilities": test_probabilities.tolist()},
"missingness_aware": {
"positive_probabilities": test_probabilities.tolist(),
"test_group_ids": structured_grouping.test_groups["group"].tolist(),
},
"cp_mda_exact": {
"positive_probabilities": test_probabilities.tolist(),
"test_masks": _missingness_mask_matrix(split.test_frame, dataset.feature_columns),
},
}
for method_name, method in method_objects.items():
start = perf_counter()
method.fit(**calibration_payloads[method_name])
records.append({"method": method_name, "stage": "calibration", "seconds": perf_counter() - start})
start = perf_counter()
method.predict_sets(**prediction_payloads[method_name])
records.append(
{
"method": method_name,
"stage": "test",
"seconds": perf_counter() - start,
}
)
records_frame = pd.DataFrame(records)
summary = summarize_runtime_records(records_frame)
records_path = config.output_dir / "runtime_records.csv"
summary_path = config.output_dir / "runtime_summary.csv"
config_path = config.output_dir / "config.json"
manifest_path = config.output_dir / "manifest.json"
records_frame.to_csv(records_path, index=False)
summary.to_csv(summary_path, index=False)
config_path.write_text(json.dumps(asdict(config), indent=2, default=str), encoding="utf-8")
manifest_path.write_text(
json.dumps(
{
"runtime_records": str(records_path),
"runtime_summary": str(summary_path),
"config": str(config_path),
},
indent=2,
sort_keys=True,
),
encoding="utf-8",
)
return {
"runtime_records": records_path,
"runtime_summary": summary_path,
"config": config_path,
"manifest": manifest_path,
}
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(prog="sepsis-mcp-appendix-runtime-analysis")
parser.add_argument("--data-root", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--model-type", default="xgboost")
parser.add_argument("--random-state", type=int, default=0)
parser.add_argument("--alpha", type=float, default=0.1)
parser.add_argument("--selection-fraction", type=float, default=0.1)
parser.add_argument("--min-hospital-admissions", type=int, default=500)
parser.add_argument("--min-selection-group-rows", type=int, default=100)
return parser
def main(argv: list[str] | None = None) -> int:
parser = build_parser()
args = parser.parse_args(argv)
run_runtime_analysis(
RuntimeAnalysisConfig(
data_root=args.data_root,
output_dir=args.output_dir,
model_type=args.model_type,
random_state=args.random_state,
alpha=args.alpha,
selection_fraction=args.selection_fraction,
min_hospital_admissions=args.min_hospital_admissions,
min_selection_group_rows=args.min_selection_group_rows,
)
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
|