Spaces:
Sleeping
Sleeping
File size: 3,958 Bytes
d81f51d | 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 | """Run the pipeline across multiple OULAD presentations."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import pandas as pd
from . import pipeline
from .config import DEMO_CACHE, TABLES, ensure_dirs, raw_data_dir
DEFAULT_PRESENTATIONS = ["AAA_2013J", "AAA_2014J", "BBB_2014J", "CCC_2014J"]
def available_presentations() -> list[str]:
info = pd.read_csv(raw_data_dir() / "studentInfo.csv", usecols=["code_module", "code_presentation"])
pairs = info.drop_duplicates().sort_values(["code_module", "code_presentation"])
return [f"{row.code_module}_{row.code_presentation}" for row in pairs.itertuples(index=False)]
def _load_meta() -> dict:
path = DEMO_CACHE / "meta.json"
if not path.exists():
return {}
return json.loads(path.read_text(encoding="utf-8"))
def run_many(
presentations: list[str],
bootstrap_b: int,
sample_n: int | None,
n_jobs: int,
random_baselines: int,
) -> pd.DataFrame:
ensure_dirs()
rows = []
for presentation in presentations:
print(f"\n=== {presentation} ===")
result = pipeline.run(
presentation=presentation,
bootstrap_b=bootstrap_b,
sample_n=sample_n,
n_jobs=n_jobs,
random_baselines=random_baselines,
)
winner = result["winner"]
group_metrics = result["group_metrics"].set_index("strategy")
meta = _load_meta()
mode_b = group_metrics.loc["mode_b"].to_dict() if "mode_b" in group_metrics.index else {}
rows.append(
{
"presentation": presentation,
"n_learners": meta.get("n_learners"),
"n_features": meta.get("n_features"),
"winner_config": winner.get("config_id"),
"winner_reducer": winner.get("reducer"),
"winner_clusterer": winner.get("clusterer"),
"winner_k": winner.get("k"),
"winner_bootstrap_ari": winner.get("bootstrap_ari_mean"),
"mode_b_complementarity": mode_b.get("complementarity"),
"mode_b_cluster_coverage": mode_b.get("cluster_coverage"),
"mode_b_outcome_diversity": mode_b.get("outcome_diversity"),
"mode_b_high_risk_group_rate": mode_b.get("high_risk_group_rate"),
}
)
summary = pd.DataFrame(rows)
summary.to_csv(TABLES / "multi_presentation_summary.csv", index=False)
summary.to_parquet(TABLES / "multi_presentation_summary.parquet", index=False)
return summary
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="Run pipeline across multiple OULAD presentations")
parser.add_argument(
"--presentations",
nargs="*",
default=None,
help="Presentations such as AAA_2014J BBB_2014J. Defaults to a small representative set.",
)
parser.add_argument("--list", action="store_true", help="List available presentations and exit")
parser.add_argument("--bootstrap-b", type=int, default=5)
parser.add_argument("--sample-n", type=int, default=300)
parser.add_argument("--n-jobs", type=int, default=1)
parser.add_argument("--random-baselines", type=int, default=20)
return parser
def main() -> None:
parser = build_parser()
args = parser.parse_args()
available = available_presentations()
if args.list:
print("\n".join(available))
return
presentations = args.presentations or [p for p in DEFAULT_PRESENTATIONS if p in available]
if not presentations:
presentations = available[:4]
summary = run_many(
presentations,
bootstrap_b=args.bootstrap_b,
sample_n=args.sample_n,
n_jobs=args.n_jobs,
random_baselines=args.random_baselines,
)
print("\nSummary")
print(summary.to_string(index=False))
if __name__ == "__main__":
main()
|