from __future__ import annotations

import json
from pathlib import Path

import pandas as pd


def test_run_weighted_baselines_analysis_writes_summary_and_subgroup_outputs(tmp_path: Path) -> None:
    from sepsis_mcp.appendix_weighted_baselines_analysis import (
        WeightedBaselinesAnalysisConfig,
        run_weighted_baselines_analysis,
    )

    sweep_dir = tmp_path / "sweep"
    output_dir = tmp_path / "analysis"
    sweep_dir.mkdir(parents=True, exist_ok=True)

    pd.DataFrame(
        [
            {
                "experiment": "hospital_disjoint",
                "method": "standard",
                "min_hospital_admissions": 500,
                "alpha": 0.1,
                "selection_fraction": 0.1,
                "model_type": "xgboost",
                "missingness_grouping_strategy": "coverage_gap_variable",
                "mask_strategy": "none",
                "mask_rate": 0.0,
                "selective_feature_group": "labs",
                "weighted_shrinkage_lambda": 0.5,
                "run_count": 3,
                "empirical_coverage_mean": 0.90,
                "max_group_coverage_gap_mean": 0.05,
                "average_set_size_mean": 0.95,
            },
            {
                "experiment": "hospital_disjoint",
                "method": "weighted_propensity_logistic",
                "min_hospital_admissions": 500,
                "alpha": 0.1,
                "selection_fraction": 0.1,
                "model_type": "xgboost",
                "missingness_grouping_strategy": "coverage_gap_variable",
                "mask_strategy": "none",
                "mask_rate": 0.0,
                "selective_feature_group": "labs",
                "weighted_shrinkage_lambda": 0.5,
                "run_count": 3,
                "empirical_coverage_mean": 0.91,
                "max_group_coverage_gap_mean": 0.03,
                "average_set_size_mean": 0.96,
            },
        ]
    ).to_csv(sweep_dir / "repeated_summary.csv", index=False)

    pd.DataFrame(
        [
            {
                "run_id": "seed0",
                "method": "standard",
                "model_type": "xgboost",
                "group_label": "low",
                "count": 100,
                "coverage": 0.85,
                "coverage_gap": -0.05,
                "average_set_size": 0.95,
            },
            {
                "run_id": "seed0",
                "method": "weighted_propensity_logistic",
                "model_type": "xgboost",
                "group_label": "low",
                "count": 100,
                "coverage": 0.89,
                "coverage_gap": -0.01,
                "average_set_size": 0.96,
            },
        ]
    ).to_csv(sweep_dir / "subgroup_summary.csv", index=False)

    paths = run_weighted_baselines_analysis(
        WeightedBaselinesAnalysisConfig(
            sweep_dir=sweep_dir,
            output_dir=output_dir,
        )
    )

    summary = pd.read_csv(paths["weighted_baselines_summary"])
    subgroups = pd.read_csv(paths["weighted_baselines_subgroups"])
    manifest = json.loads(paths["manifest"].read_text(encoding="utf-8"))

    assert "gap_reduction_vs_standard_mean" in summary.columns
    assert summary.loc[summary["method"] == "weighted_propensity_logistic", "gap_reduction_vs_standard_mean"].iloc[0] == 0.02
    assert set(subgroups["method"]) == {"standard", "weighted_propensity_logistic"}
    assert Path(manifest["weighted_baselines_summary"]).exists()