add simplified prompts

experiment_runner/analysis/statistical_game_analysis.py

@@ -0,0 +1,214 @@
+"""
+experiment_runner.analysis.statistical_game_analysis
+----------------------------------------------------
+
+Runs the Statistical Game Analyzer to create per-run / combined CSVs,
+then produces a suite of PNG plots:
+
+analysis/
+└── statistical_game_analysis/
+    ├── individual/
+    │   ├── run_00000_game_analysis.csv
+    │   └── …
+    └── plots/
+        ├── game/
+        │   ├── final_supply_centers_owned.png
+        │   └── …
+        ├── game_summary_heatmap.png
+        └── phase/
+            ├── supply_centers_owned_count.png
+            └── …
+
+Complies with experiment-runner’s plug-in contract:
+    run(experiment_dir: pathlib.Path, ctx: dict) -> None
+"""
+from __future__ import annotations
+
+import logging
+import re
+from pathlib import Path
+from typing import List
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+
+# third-party analyser that creates the CSVs
+from analysis.statistical_game_analysis import StatisticalGameAnalyzer  # type: ignore
+
+log = logging.getLogger(__name__)
+
+# ───────────────────────── helpers ──────────────────────────
+_SEASON_ORDER = {"S": 0, "F": 1, "W": 2, "A": 3}
+
+
+def _sanitize(name: str) -> str:
+    return re.sub(r"[^\w\-\.]", "_", name)
+
+
+def _discover_csvs(individual_dir: Path, pattern: str) -> List[Path]:
+    return sorted(individual_dir.glob(pattern))
+
+
+def _numeric_columns(df: pd.DataFrame, extra_exclude: set[str] | None = None) -> List[str]:
+    exclude = {
+        "game_id",
+        "llm_model",
+        "power_name",
+        "game_phase",
+        "analyzed_response_type",
+    }
+    if extra_exclude:
+        exclude |= extra_exclude
+    return [c for c in df.select_dtypes("number").columns if c not in exclude]
+
+
+def _phase_sort_key(ph: str) -> tuple[int, int]:
+    """Convert 'S1901M' → (1901, 0)."""
+    year = int(ph[1:5]) if len(ph) >= 5 and ph[1:5].isdigit() else 0
+    season = _SEASON_ORDER.get(ph[0], 9)
+    return year, season
+
+
+def _phase_index(series: pd.Series) -> pd.Series:
+    uniq = sorted(series.unique(), key=_phase_sort_key)
+    mapping = {ph: i for i, ph in enumerate(uniq)}
+    return series.map(mapping)
+
+
+# ───────────────────────── plots ────────────────────────────
+def _plot_game_level(all_games: pd.DataFrame, plot_dir: Path) -> None:
+    """
+    • Box-plots per metric (hue = power, legend removed).
+    • Z-score heat-map: powers × metrics, colour-coded by relative standing.
+    """
+    plot_dir.mkdir(parents=True, exist_ok=True)
+    num_cols = _numeric_columns(all_games)
+
+    # ── per-metric box-plots ──────────────────────────────────────────
+    for col in num_cols:
+        fig, ax = plt.subplots(figsize=(8, 6))
+        sns.boxplot(
+            data=all_games,
+            x="power_name",
+            y=col,
+            hue="power_name",
+            palette="pastel",
+            dodge=False,
+            ax=ax,
+        )
+        leg = ax.get_legend()
+        if leg is not None:
+            leg.remove()
+        ax.set_title(col.replace("_", " ").title())
+        fig.tight_layout()
+        fig.savefig(plot_dir / f"{_sanitize(col)}.png", dpi=140)
+        plt.close(fig)
+
+    # ── summary heat-map (column-wise z-scores) ───────────────────────
+    #   1) mean across runs  2) z-score each column
+    summary = all_games.groupby("power_name")[num_cols].mean().sort_index()
+    zscores = summary.apply(lambda col: (col - col.mean()) / col.std(ddof=0), axis=0)
+
+    fig_w = max(6, len(num_cols) * 0.45 + 2)
+    fig, ax = plt.subplots(figsize=(fig_w, 6))
+    sns.heatmap(
+        zscores,
+        cmap="coolwarm",
+        center=0,
+        linewidths=0.4,
+        annot=True,
+        fmt=".2f",
+        ax=ax,
+    )
+    ax.set_title("Relative Standing (column-wise z-score)")
+    ax.set_ylabel("Power")
+    fig.tight_layout()
+    fig.savefig(plot_dir.parent / "game_summary_zscore_heatmap.png", dpi=160)
+    plt.close(fig)
+
+
+
+
+def _plot_phase_level(all_phase: pd.DataFrame, plot_dir: Path) -> None:
+    if all_phase.empty:
+        return
+    plot_dir.mkdir(parents=True, exist_ok=True)
+
+    if "phase_index" not in all_phase.columns:
+        all_phase["phase_index"] = _phase_index(all_phase["game_phase"])
+
+    num_cols = _numeric_columns(all_phase)
+
+    agg = (
+        all_phase
+        .groupby(["phase_index", "game_phase", "power_name"], as_index=False)[num_cols]
+        .mean()
+    )
+
+    n_phases = agg["phase_index"].nunique()
+    fig_base_width = max(8, n_phases * 0.1 + 4)  # 0.45 in per label + padding
+
+    for col in num_cols:
+        plt.figure(figsize=(fig_base_width, 6))
+        sns.lineplot(
+            data=agg,
+            x="phase_index",
+            y=col,
+            hue="power_name",
+            marker="o",
+        )
+
+        phases_sorted = (
+            agg.drop_duplicates("phase_index")
+            .sort_values("phase_index")[["phase_index", "game_phase"]]
+        )
+        plt.xticks(
+            phases_sorted["phase_index"],
+            phases_sorted["game_phase"],
+            rotation=90,
+            fontsize=8,
+        )
+        plt.xlabel("Game Phase")
+        plt.title(col.replace("_", " ").title())
+        plt.tight_layout()
+        plt.savefig(plot_dir / f"{_sanitize(col)}.png", dpi=140)
+        plt.close()
+
+
+# ───────────────────────── entry-point ─────────────────────────
+def run(experiment_dir: Path, ctx: dict) -> None:  # pylint: disable=unused-argument
+    root = experiment_dir / "analysis" / "statistical_game_analysis"
+    indiv_dir = root / "individual"
+    plots_root = root / "plots"
+
+    # 1. (re)generate CSVs
+    try:
+        StatisticalGameAnalyzer().analyze_multiple_folders(
+            str(experiment_dir / "runs"), str(root)
+        )
+        log.info("statistical_game_analysis: CSV generation complete")
+    except Exception as exc:  # noqa: broad-except
+        log.exception("statistical_game_analysis: CSV generation failed – %s", exc)
+        return
+
+    # 2. load CSVs
+    game_csvs = _discover_csvs(indiv_dir, "*_game_analysis.csv")
+    phase_csvs = _discover_csvs(indiv_dir, "*_phase_analysis.csv")
+
+    if not game_csvs:
+        log.warning("statistical_game_analysis: no *_game_analysis.csv found")
+        return
+
+    all_game_df = pd.concat((pd.read_csv(p) for p in game_csvs), ignore_index=True)
+    all_phase_df = (
+        pd.concat((pd.read_csv(p) for p in phase_csvs), ignore_index=True)
+        if phase_csvs else pd.DataFrame()
+    )
+
+    # 3. plots
+    sns.set_theme(style="whitegrid")
+    _plot_game_level(all_game_df, plots_root / "game")
+    _plot_phase_level(all_phase_df, plots_root / "phase")
+
+    log.info("statistical_game_analysis: plots written → %s", plots_root)