from __future__ import annotations

import json
from pathlib import Path

import pandas as pd


def test_run_external_baselines_analysis_writes_overlap_interpretability_and_stress_outputs(tmp_path: Path) -> None:
    from sepsis_mcp.external_baselines_analysis import run_external_baselines_analysis

    run_dir = tmp_path / "run-a"
    run_dir.mkdir()

    pd.DataFrame(
        [
            {
                "split": "test",
                "sample_id": "a",
                "hospital_id": "icu-a",
                "learned_group": 0,
                "learned_group_label": "leaf_0",
                "leaf_id": 3,
                "missingness_group": 0,
                "missingness_group_label": "low",
            },
            {
                "split": "test",
                "sample_id": "b",
                "hospital_id": "icu-a",
                "learned_group": 0,
                "learned_group_label": "leaf_0",
                "leaf_id": 3,
                "missingness_group": 0,
                "missingness_group_label": "low",
            },
            {
                "split": "test",
                "sample_id": "c",
                "hospital_id": "icu-a",
                "learned_group": 1,
                "learned_group_label": "leaf_1",
                "leaf_id": 4,
                "missingness_group": 1,
                "missingness_group_label": "high",
            },
            {
                "split": "test",
                "sample_id": "d",
                "hospital_id": "icu-a",
                "learned_group": 1,
                "learned_group_label": "leaf_1",
                "leaf_id": 4,
                "missingness_group": 1,
                "missingness_group_label": "high",
            },
        ]
    ).to_csv(run_dir / "partition_overlap_assignments.csv", index=False)
    pd.DataFrame(
        [
            {"feature": "creatinine_max_nan", "importance": 0.75, "split_count": 2, "is_missingness_proxy": True},
            {"feature": "heart_rate_max", "importance": 0.25, "split_count": 1, "is_missingness_proxy": False},
        ]
    ).to_csv(run_dir / "partition_feature_importance.csv", index=False)
    pd.DataFrame(
        [
            {"drop_rate": 0.0, "method": "standard", "mean_coverage": 0.95, "mean_gap": 0.05, "mean_set_size": 1.0},
            {"drop_rate": 0.3, "method": "standard", "mean_coverage": 0.92, "mean_gap": 0.08, "mean_set_size": 1.05},
            {"drop_rate": 0.0, "method": "gibbs_missingness", "mean_coverage": 0.90, "mean_gap": 0.10, "mean_set_size": 0.95},
            {"drop_rate": 0.3, "method": "gibbs_missingness", "mean_coverage": 0.72, "mean_gap": 0.30, "mean_set_size": 0.75},
        ]
    ).to_csv(run_dir / "stress_aggregate.csv", index=False)

    output_dir = tmp_path / "analysis"
    paths = run_external_baselines_analysis(run_dirs=(run_dir,), output_dir=output_dir)

    overlap_summary = pd.read_csv(paths["overlap_summary"])
    overlap_details = pd.read_csv(paths["overlap_details"])
    interpretability = pd.read_csv(paths["partition_interpretability"])
    stress_summary = pd.read_csv(paths["stress_degradation_summary"])
    analysis_summary = json.loads(paths["analysis_summary"].read_text(encoding="utf-8"))

    assert overlap_summary.loc[0, "ari"] == 1.0
    assert overlap_summary.loc[0, "nmi"] == 1.0
    assert overlap_details.loc[0, "overlap_count"] > 0
    assert interpretability.loc[0, "share_missingness_proxy_importance"] == 0.75
    gibbs_row = stress_summary.loc[stress_summary["method"] == "gibbs_missingness"].iloc[0]
    assert gibbs_row["coverage_change_at_max_drop"] == -0.18
    assert gibbs_row["gap_change_at_max_drop"] == 0.2
    assert analysis_summary["run_count"] == 1