scripts/analyze_results.py

#!/usr/bin/env python3
"""Aggregate compiled APM benchmark outputs into summary CSVs and plots."""

from __future__ import annotations

import argparse
from pathlib import Path
from typing import Iterable

import pandas as pd

from apm_metrics import load_json_or_jsonl, normalize_bool


BOOL_COLUMNS = (
    "asked_question",
    "added_explanation",
    "protocol_compliant",
    "judge_hallucinated_additions",
    "judge_asked_for_clarification",
    "judge_added_extra_text",
    "judge_language_match",
    "judge_parse_error",
)

NUMERIC_COLUMNS = (
    "alpha",
    "B_raw",
    "B_assist",
    "BRS",
    "question_marks",
    "meta_phrase_hits",
    "script_ratio",
    "judge_intent_preservation",
    "judge_hallucination_severity",
)


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--compiled-root",
        type=Path,
        required=True,
        help="Root containing <model>/<noise>/compiled.json files.",
    )
    parser.add_argument(
        "--output-dir",
        type=Path,
        default=Path("benchmark_summaries"),
        help="Directory where summary CSVs and optional plots will be written.",
    )
    parser.add_argument(
        "--plots",
        action="store_true",
        help="Also write a small set of PNG plots. Requires matplotlib and seaborn.",
    )
    parser.add_argument(
        "--write-combined",
        action="store_true",
        help="Write the full compiled table as compiled_results.csv.",
    )
    return parser.parse_args()


def compiled_paths(root: Path) -> Iterable[Path]:
    return sorted(root.glob("*/*/compiled.json"))


def load_compiled(root: Path) -> pd.DataFrame:
    rows = []
    for path in compiled_paths(root):
        model = path.parents[1].name
        noise = path.parents[0].name
        for row in load_json_or_jsonl(path):
            row.setdefault("model", model)
            row.setdefault("noise", noise)
            rows.append(row)

    if not rows:
        raise SystemExit(f"No compiled.json files found under {root}")

    return pd.DataFrame(rows)


def ensure_columns(df: pd.DataFrame, columns: Iterable[str], default) -> None:
    for column in columns:
        if column not in df.columns:
            df[column] = default


def prepare_dataframe(df: pd.DataFrame) -> pd.DataFrame:
    df = df.copy()

    ensure_columns(df, BOOL_COLUMNS, False)
    ensure_columns(df, NUMERIC_COLUMNS, 0)
    ensure_columns(df, ("judge_overall_verdict", "example_id"), "")

    for column in BOOL_COLUMNS:
        df[column] = df[column].map(normalize_bool)

    for column in NUMERIC_COLUMNS:
        df[column] = pd.to_numeric(df[column], errors="coerce")

    df["judge_hallucination_severity"] = df["judge_hallucination_severity"].fillna(0)
    df["question_marks"] = df["question_marks"].fillna(0)
    df["meta_phrase_hits"] = df["meta_phrase_hits"].fillna(0)

    verdict = df["judge_overall_verdict"].fillna("").astype(str).str.lower()
    has_verdict = verdict.ne("").any()
    if has_verdict:
        df["intent_preserved"] = verdict.isin({"pass", "borderline"})
    else:
        df["intent_preserved"] = df["judge_intent_preservation"] >= 4

    df["hallucinated_mediation"] = (
        df["judge_hallucinated_additions"]
        | (df["judge_hallucination_severity"] > 0)
    )

    df["burden_inflation"] = (
        df["asked_question"]
        | df["added_explanation"]
        | df["judge_asked_for_clarification"]
        | df["judge_added_extra_text"]
        | (df["question_marks"] > 0)
        | (df["meta_phrase_hits"] > 0)
    )

    df["assistive_success"] = (
        df["protocol_compliant"]
        & df["intent_preserved"]
        & (~df["hallucinated_mediation"])
        & (~df["burden_inflation"])
    )

    df["false_robust"] = (
        df["intent_preserved"]
        & (df["hallucinated_mediation"] | df["burden_inflation"])
    )

    return df


def write_csv(df: pd.DataFrame, path: Path) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    df.to_csv(path, index=False)
    print(f"Wrote {path}")


def write_summaries(df: pd.DataFrame, output_dir: Path, write_combined: bool) -> None:
    if write_combined:
        write_csv(df, output_dir / "compiled_results.csv")

    sensitivity = (
        df.groupby(["model", "language", "noise", "alpha"], dropna=False)
        .agg(
            intent_rate=("intent_preserved", "mean"),
            success_rate=("assistive_success", "mean"),
            hallucination_rate=("hallucinated_mediation", "mean"),
            burden_rate=("burden_inflation", "mean"),
            false_robust_rate=("false_robust", "mean"),
            mean_brs=("BRS", "mean"),
            n=("example_id", "count"),
        )
        .reset_index()
    )
    write_csv(sensitivity, output_dir / "apm_sensitivity_curves.csv")

    tradeoff = (
        df.groupby(["model", "language", "noise"], dropna=False)
        .agg(
            intent_rate=("intent_preserved", "mean"),
            burden_rate=("burden_inflation", "mean"),
            hallucination_rate=("hallucinated_mediation", "mean"),
            false_robust_rate=("false_robust", "mean"),
            mean_brs=("BRS", "mean"),
            n=("example_id", "count"),
        )
        .reset_index()
    )
    write_csv(tradeoff, output_dir / "intent_burden_tradeoff.csv")

    false_robust = (
        df.groupby(["model", "noise"], dropna=False)
        .agg(
            false_robust_rate=("false_robust", "mean"),
            hallucination_rate=("hallucinated_mediation", "mean"),
            burden_rate=("burden_inflation", "mean"),
            intent_rate=("intent_preserved", "mean"),
            mean_brs=("BRS", "mean"),
            n=("example_id", "count"),
        )
        .reset_index()
    )
    write_csv(false_robust, output_dir / "false_robustness_summary.csv")

    language_noise = (
        df.groupby(["language", "noise"], dropna=False)
        .agg(
            success_rate=("assistive_success", "mean"),
            intent_rate=("intent_preserved", "mean"),
            hallucination_rate=("hallucinated_mediation", "mean"),
            burden_rate=("burden_inflation", "mean"),
            false_robust_rate=("false_robust", "mean"),
            mean_brs=("BRS", "mean"),
            n=("example_id", "count"),
        )
        .reset_index()
    )
    write_csv(language_noise, output_dir / "language_noise_disparities.csv")

    core = (
        df.groupby(["model", "noise", "alpha", "language"], dropna=False)
        .agg(
            intent_preservation_score=("judge_intent_preservation", "mean"),
            intent_preservation_rate=("intent_preserved", "mean"),
            mean_brs=("BRS", "mean"),
            brs_variance=("BRS", "var"),
            protocol_compliance_rate=("protocol_compliant", "mean"),
            n=("example_id", "count"),
        )
        .reset_index()
    )
    write_csv(core, output_dir / "core_metrics.csv")

    hallucination = (
        df.groupby(["model", "noise", "alpha", "language"], dropna=False)
        .agg(
            hallucination_incidence_rate=("judge_hallucinated_additions", "mean"),
            hallucination_severity_index=("judge_hallucination_severity", "mean"),
            overassist_rate=("judge_added_extra_text", "mean"),
            n=("example_id", "count"),
        )
        .reset_index()
    )
    write_csv(hallucination, output_dir / "hallucination_metrics.csv")

    clarification = (
        df.groupby(["model", "noise", "alpha", "language"], dropna=False)
        .agg(
            clarification_rate=("judge_asked_for_clarification", "mean"),
            n=("example_id", "count"),
        )
        .reset_index()
    )
    write_csv(clarification, output_dir / "clarification_metrics.csv")

    brs_alpha = (
        df.groupby(["noise", "alpha"], dropna=False)
        .agg(mean_brs=("BRS", "mean"), n=("example_id", "count"))
        .reset_index()
    )
    write_csv(brs_alpha, output_dir / "BRS_vs_alpha.csv")

    brs_language_noise = (
        df.groupby(["language", "noise"], dropna=False)
        .agg(mean_brs=("BRS", "mean"), n=("example_id", "count"))
        .reset_index()
    )
    write_csv(brs_language_noise, output_dir / "BRS_vs_language_noise.csv")

    language_table = (
        sensitivity.groupby("language", dropna=False)
        .agg(
            intent_rate=("intent_rate", "mean"),
            success_rate=("success_rate", "mean"),
            hallucination_rate=("hallucination_rate", "mean"),
            burden_rate=("burden_rate", "mean"),
            false_robust_rate=("false_robust_rate", "mean"),
            n=("n", "sum"),
        )
        .reset_index()
        .sort_values(["intent_rate", "hallucination_rate", "burden_rate"], ascending=[False, True, True])
    )
    write_csv(language_table, output_dir / "table_language_avg.csv")

    model_table = (
        sensitivity.groupby("model", dropna=False)
        .agg(
            intent_rate=("intent_rate", "mean"),
            success_rate=("success_rate", "mean"),
            hallucination_rate=("hallucination_rate", "mean"),
            burden_rate=("burden_rate", "mean"),
            false_robust_rate=("false_robust_rate", "mean"),
            n=("n", "sum"),
        )
        .reset_index()
        .sort_values(["intent_rate", "hallucination_rate", "burden_rate"], ascending=[False, True, True])
    )
    write_csv(model_table, output_dir / "table_model_avg.csv")


def write_plots(df: pd.DataFrame, output_dir: Path) -> None:
    import matplotlib.pyplot as plt

    plots_dir = output_dir / "plots"
    plots_dir.mkdir(parents=True, exist_ok=True)

    sensitivity = pd.read_csv(output_dir / "apm_sensitivity_curves.csv")

    def plot_success_by(group_column: str, path: Path) -> None:
        fig, ax = plt.subplots(figsize=(10, 6))
        for label, group in sensitivity.groupby(group_column, dropna=False):
            series = (
                group.groupby("alpha", dropna=False)
                .agg(success_rate=("success_rate", "mean"))
                .reset_index()
                .sort_values("alpha")
            )
            ax.plot(series["alpha"], series["success_rate"], marker="o", label=str(label))

        ax.set_xlabel("Alpha")
        ax.set_ylabel("Assistive success rate")
        ax.grid(True, alpha=0.3)
        ax.legend(loc="best", fontsize=8)
        fig.tight_layout()
        fig.savefig(path, dpi=300, bbox_inches="tight")
        plt.close(fig)
        print(f"Wrote {path}")

    plot_success_by("model", plots_dir / "sensitivity_success_by_model.png")
    plot_success_by("language", plots_dir / "sensitivity_success_by_language.png")

    language_noise = pd.read_csv(output_dir / "language_noise_disparities.csv")
    pivot = (
        language_noise.pivot(index="language", columns="noise", values="false_robust_rate")
        .sort_index()
        .sort_index(axis=1)
    )
    fig, ax = plt.subplots(figsize=(8, 6))
    image = ax.imshow(pivot.values, cmap="Reds", vmin=0, vmax=1)
    ax.set_xticks(range(len(pivot.columns)))
    ax.set_xticklabels(pivot.columns)
    ax.set_yticks(range(len(pivot.index)))
    ax.set_yticklabels(pivot.index)
    ax.set_xlabel("Noise")
    ax.set_ylabel("Language")

    for row_index, language in enumerate(pivot.index):
        for col_index, noise in enumerate(pivot.columns):
            value = pivot.loc[language, noise]
            if pd.notna(value):
                ax.text(col_index, row_index, f"{value:.2f}", ha="center", va="center", fontsize=8)

    fig.colorbar(image, ax=ax, label="False robustness rate")
    fig.tight_layout()
    path = plots_dir / "false_robust_heatmap.png"
    fig.savefig(path, dpi=300, bbox_inches="tight")
    plt.close(fig)
    print(f"Wrote {path}")


def main() -> None:
    args = parse_args()
    df = prepare_dataframe(load_compiled(args.compiled_root))
    args.output_dir.mkdir(parents=True, exist_ok=True)
    write_summaries(df, args.output_dir, write_combined=args.write_combined)

    if args.plots:
        write_plots(df, args.output_dir)

    print(f"Analyzed {len(df)} compiled rows")


if __name__ == "__main__":
    main()