{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "673aa1ae",
   "metadata": {},
   "source": [
    "# Evaluation Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "439c28eb",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "34199cda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from pathlib import Path\n",
    "from typing import Dict, NamedTuple\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "import math\n",
    "import os\n",
    "\n",
    "# Preprocessing\n",
    "import in_data_preprocessor\n",
    "import ex_data_preprocessor\n",
    "# Annotations\n",
    "import eng_silver_misc_coder\n",
    "import thai_silver_misc_coder\n",
    "# Code Evaluation\n",
    "import model_evaluator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c24c298",
   "metadata": {},
   "source": [
    "## Preprocessing + Annotation + Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbe6ab22",
   "metadata": {},
   "source": [
    "### With Orchestrator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2af83b31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote 10 lines to ..\\data\\orchestrated\\pre_annotate.jsonl\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing\n",
    "in_data_preprocessor.main(in_path=Path(\"../exported_sessions/all_sessions.json\"), out_path=Path(\"../data/orchestrated/pre_annotate.jsonl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "675f1018",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-15 14:31:08,679 | INFO | Run config: {\"model\": \"aisingapore/Gemma-SEA-LION-v4-27B-IT\", \"temperature\": 0.0, \"threshold\": 0.6, \"backoff\": 0.4, \"max_codes_per_utt\": 1, \"history_window\": 6, \"base_url\": \"https://api.sea-lion.ai/v1\"}\n",
      "Processing items: 100%|██████████| 10/10 [00:35<00:00,  3.56s/item]\n",
      "2025-09-15 14:31:44,273 | INFO | Silver-standard dataset written to D:\\TEE\\Project\\KaLLaM-Motivational-Therapeutic-Advisor\\data\\orchestrated\\post_annotate.jsonl\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"n\": 10,\n",
      "  \"threshold\": 0.6,\n",
      "  \"role\": \"AUTO\",\n",
      "  \"model\": \"aisingapore/Gemma-SEA-LION-v4-27B-IT\",\n",
      "  \"preds_fine\": [\n",
      "    [\n",
      "      \"FN\"\n",
      "    ],\n",
      "    [\n",
      "      \"FI\"\n",
      "    ],\n",
      "    [\n",
      "      \"FN\"\n",
      "    ],\n",
      "    [\n",
      "      \"OQ\"\n",
      "    ],\n",
      "    [\n",
      "      \"TS-\"\n",
      "    ],\n",
      "    [\n",
      "      \"SR\"\n",
      "    ],\n",
      "    [\n",
      "      \"TS-\"\n",
      "    ],\n",
      "    [\n",
      "      \"ADP\"\n",
      "    ],\n",
      "    [\n",
      "      \"FN\"\n",
      "    ],\n",
      "    [\n",
      "      \"SU\"\n",
      "    ]\n",
      "  ],\n",
      "  \"preds_coarse\": [\n",
      "    [\n",
      "      \"NT\"\n",
      "    ],\n",
      "    [\n",
      "      \"TI\"\n",
      "    ],\n",
      "    [\n",
      "      \"NT\"\n",
      "    ],\n",
      "    [\n",
      "      \"QS\"\n",
      "    ],\n",
      "    [\n",
      "      \"ST\"\n",
      "    ],\n",
      "    [\n",
      "      \"RF\"\n",
      "    ],\n",
      "    [\n",
      "      \"ST\"\n",
      "    ],\n",
      "    [\n",
      "      \"TI\"\n",
      "    ],\n",
      "    [\n",
      "      \"NT\"\n",
      "    ],\n",
      "    [\n",
      "      \"TI\"\n",
      "    ]\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Code Annotation\n",
    "thai_silver_misc_coder.main(in_path=Path(\"../data/orchestrated/pre_annotate.jsonl\"), out_path=Path(\"../data/orchestrated/post_annotate.jsonl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e029d3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"psychometrics\": {\n",
      "    \"n_items\": 10,\n",
      "    \"therapist_utts\": 5,\n",
      "    \"client_utts\": 5,\n",
      "    \"R_over_Q\": 1.0,\n",
      "    \"pct_open_questions\": 1.0,\n",
      "    \"pct_complex_reflection\": 0.0,\n",
      "    \"reflections_per100\": 20.0,\n",
      "    \"questions_per100\": 20.0,\n",
      "    \"info_per100\": 0.0,\n",
      "    \"pct_mi_consistent\": 1.0,\n",
      "    \"mico_per100\": 80.0,\n",
      "    \"miin_per100\": 0.0,\n",
      "    \"client_CT\": 0,\n",
      "    \"client_ST\": 2,\n",
      "    \"pct_CT_over_CT_plus_ST\": 0.0\n",
      "  },\n",
      "  \"coverage\": {\n",
      "    \"therapist_code_counts\": {\n",
      "      \"FI\": 1,\n",
      "      \"OQ\": 1,\n",
      "      \"SR\": 1,\n",
      "      \"ADP\": 1,\n",
      "      \"SU\": 1\n",
      "    },\n",
      "    \"client_code_counts\": {\n",
      "      \"FN\": 3,\n",
      "      \"TS-\": 2\n",
      "    }\n",
      "  },\n",
      "  \"coarse_coverage\": {\n",
      "    \"therapist\": {\n",
      "      \"TI\": 3,\n",
      "      \"QS\": 1,\n",
      "      \"RF\": 1\n",
      "    },\n",
      "    \"client\": {\n",
      "      \"NT\": 3,\n",
      "      \"ST\": 2\n",
      "    }\n",
      "  },\n",
      "  \"performance\": null,\n",
      "  \"meta\": {\n",
      "    \"alias_map_applied\": true,\n",
      "    \"mico_set\": [\n",
      "      \"ADP\",\n",
      "      \"AF\",\n",
      "      \"CR\",\n",
      "      \"EC\",\n",
      "      \"OQ\",\n",
      "      \"RCP\",\n",
      "      \"RF\",\n",
      "      \"SR\",\n",
      "      \"SU\"\n",
      "    ],\n",
      "    \"miin_set\": [\n",
      "      \"ADW\",\n",
      "      \"CO\",\n",
      "      \"DI\",\n",
      "      \"RCW\",\n",
      "      \"WA\"\n",
      "    ],\n",
      "    \"neutral_counselor_set\": [\n",
      "      \"CQ\",\n",
      "      \"FA\",\n",
      "      \"FI\",\n",
      "      \"GI\",\n",
      "      \"ST\"\n",
      "    ],\n",
      "    \"client_ct_set\": [\n",
      "      \"A+\",\n",
      "      \"CM+\",\n",
      "      \"D+\",\n",
      "      \"N+\",\n",
      "      \"O+\",\n",
      "      \"R+\",\n",
      "      \"TS+\"\n",
      "    ],\n",
      "    \"client_st_set\": [\n",
      "      \"A-\",\n",
      "      \"CM-\",\n",
      "      \"D-\",\n",
      "      \"N-\",\n",
      "      \"O-\",\n",
      "      \"R-\",\n",
      "      \"TS-\"\n",
      "    ]\n",
      "  }\n",
      "}\n",
      "\n",
      "Report written to ..\\data\\orchestrated\\report.json\n"
     ]
    }
   ],
   "source": [
    "# Evaluation\n",
    "model_evaluator.main(in_path=Path(\"../data/orchestrated/post_annotate.jsonl\"), out_path=Path(\"../data/orchestrated/report.json\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0fff6bb",
   "metadata": {},
   "source": [
    "## Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0512f69e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admins\\AppData\\Local\\Temp\\ipykernel_65088\\1988808477.py:223: UserWarning: Tight layout not applied. tight_layout cannot make Axes width small enough to accommodate all Axes decorations\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[info] Saved psychometrics small multiples to ./radar_outputs/SMALL_MULTIPLES_psych.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admins\\AppData\\Local\\Temp\\ipykernel_65088\\1988808477.py:274: UserWarning: Tight layout not applied. tight_layout cannot make Axes width small enough to accommodate all Axes decorations\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[info] Saved safety small multiples to ./radar_outputs/SMALL_MULTIPLES_safety.png\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1300x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1300x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[info] Saved combined ICC summary to ./radar_outputs/ICC_combined.png\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[info] Saved tables → radar_outputs\\summary_psychometrics.csv, radar_outputs\\summary_safety.csv, radar_outputs\\summary_icc.csv\n",
      "\n",
      "# Summary — Psychometrics\n",
      "| Metric                |   Recommended |   Human |   Our KaLLaM |   Gemini-2.5-flash-light |   Gemma-SEA-LION-v4-27B-IT |   ΔHuman_Our KaLLaM |   ΔHuman_Gemini-2.5-flash-light |   ΔHuman_Gemma-SEA-LION-v4-27B-IT |\n",
      "|:----------------------|--------------:|--------:|-------------:|-------------------------:|---------------------------:|--------------------:|--------------------------------:|----------------------------------:|\n",
      "| R/Q ratio             |             1 |    0.42 |         0.73 |                     0.75 |                       0.11 |                0.31 |                            0.33 |                             -0.31 |\n",
      "| % Open Questions      |            50 |   40.58 |       100    |                   100    |                     100    |               59.42 |                           59.42 |                             59.42 |\n",
      "| % Complex Reflections |            40 |   10.71 |        12.5  |                     0    |                       0    |                1.79 |                          -10.71 |                            -10.71 |\n",
      "| % MI-Consistent       |            90 |   95.65 |       100    |                    96.43 |                      94.74 |                4.35 |                            0.78 |                             -0.92 |\n",
      "| % Change Talk         |            50 |   47.92 |        22.22 |                     6.25 |                      14.63 |              -25.69 |                          -41.67 |                            -33.28 |\n",
      "\n",
      "# Summary — Safety\n",
      "| Topic       |   Ideal(10) |   Human |   Our KaLLaM |   Gemini-2.5-flash-light |   Gemma-SEA-LION-v4-27B-IT |   ΔHuman_Our KaLLaM |   ΔHuman_Gemini-2.5-flash-light |   ΔHuman_Gemma-SEA-LION-v4-27B-IT |\n",
      "|:------------|------------:|--------:|-------------:|-------------------------:|---------------------------:|--------------------:|--------------------------------:|----------------------------------:|\n",
      "| Guidelines  |          10 |    6.7  |         7    |                     6.75 |                       6.63 |                0.3  |                            0.05 |                             -0.06 |\n",
      "| Referral    |          10 |    0    |         0    |                     0    |                       0    |                0    |                            0    |                              0    |\n",
      "| Consistency |          10 |    8.86 |         9.3  |                     9.19 |                       8.47 |                0.44 |                            0.34 |                             -0.39 |\n",
      "| Resources   |          10 |    1.98 |         1.97 |                    10    |                      10    |               -0.01 |                            8.02 |                              8.02 |\n",
      "| Empowerment |          10 |    1.74 |         0.92 |                     6    |                       6    |               -0.82 |                            4.26 |                              4.26 |\n",
      "\n",
      "# Summary — ICC(2,1) vs Human\n",
      "| Model                    |   ICC_psych |   ICC_safety |\n",
      "|:-------------------------|------------:|-------------:|\n",
      "| Our KaLLaM               |        0.77 |         0.99 |\n",
      "| Gemini-2.5-flash-light   |        0.72 |         0.51 |\n",
      "| Gemma-SEA-LION-v4-27B-IT |        0.75 |         0.47 |\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admins\\AppData\\Local\\Temp\\ipykernel_65088\\1988808477.py:426: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  return df.applymap(lambda v: round(v, ndigits) if isinstance(v, (int, float)) and pd.notna(v) else v)\n"
     ]
    }
   ],
   "source": [
    "# radar_visualizer_individual.py\n",
    "# Requirements: matplotlib, numpy, pandas\n",
    "\n",
    "import json\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from typing import Dict, List, Optional, Tuple\n",
    "\n",
    "# -----------------\n",
    "# CONFIG\n",
    "# -----------------\n",
    "REPORT_CONFIGS = {\n",
    "    # label: { path: Path|str, color: hex|rgb tuple (optional) }\n",
    "    \"Real Psychologist\": {\"path\": \"../data/human/report.json\", \"color\": \"#ff0000\"},\n",
    "    \"Our KaLLaM\": {\"path\": \"../data/orchestrated/report.json\", \"color\": \"#2ca02c\"},\n",
    "    \"Gemini-2.5-flash-light\": {\"path\": \"../data/gemini/report.json\", \"color\": \"#9dafff\"},\n",
    "    \"Gemma-SEA-LION-v4-27B-IT\": {\"path\": \"../data/SEA-Lion/report.json\", \"color\": \"#8d35ff\"},\n",
    "    # Add more models here...\n",
    "}\n",
    "\n",
    "# Psychometric targets (units are already scaled as shown)\n",
    "RECOMMENDED = {\n",
    "    \"R/Q ratio\": 1.0,\n",
    "    \"% Open Questions\": 50.0,\n",
    "    \"% Complex Reflections\": 40.0,\n",
    "    \"% MI-Consistent\": 90.0,\n",
    "    \"% Change Talk\": 50.0\n",
    "}\n",
    "\n",
    "# Safety keys (Xu et al. proxies, 0–10)\n",
    "SAFETY_KEYS = [\n",
    "    \"Q1_guidelines_adherence\",\n",
    "    \"Q2_referral_triage\",\n",
    "    \"Q3_consistency\",\n",
    "    \"Q4_resources\",\n",
    "    \"Q5_empowerment\",\n",
    "]\n",
    "\n",
    "# Pretty labels for safety\n",
    "SAFETY_LABELS_READABLE = {\n",
    "    \"Q1_guidelines_adherence\": \"Guidelines\",\n",
    "    \"Q2_referral_triage\": \"Referral\",\n",
    "    \"Q3_consistency\": \"Consistency\",\n",
    "    \"Q4_resources\": \"Resources\",\n",
    "    \"Q5_empowerment\": \"Empowerment\",\n",
    "}\n",
    "\n",
    "# -----------------\n",
    "# LOADING & EXTRACTION\n",
    "# -----------------\n",
    "def _load_json(path_like) -> Optional[dict]:\n",
    "    p = Path(path_like).expanduser()\n",
    "    if not p.exists():\n",
    "        print(f\"[warn] Missing report: {p}\")\n",
    "        return None\n",
    "    try:\n",
    "        with p.open(\"r\", encoding=\"utf-8\") as f:\n",
    "            return json.load(f)\n",
    "    except Exception as e:\n",
    "        print(f\"[warn] Failed to read {p}: {e}\")\n",
    "        return None\n",
    "\n",
    "def _extract_psychometrics(report: Optional[dict]) -> dict:\n",
    "    psy = report.get(\"psychometrics\", {}) if report else {}\n",
    "    try:\n",
    "        rq   = float(psy.get(\"R_over_Q\", 0.0))\n",
    "        poq  = float(psy.get(\"pct_open_questions\", 0.0)) * 100.0\n",
    "        pcr  = float(psy.get(\"pct_complex_reflection\", 0.0)) * 100.0\n",
    "        mic  = psy.get(\"pct_mi_consistent\", psy.get(\"pct_mi_consistency\", psy.get(\"pct_mi_consist\", 0.0)))\n",
    "        mic  = float(mic) * 100.0\n",
    "        pct_ct = float(psy.get(\"pct_CT_over_CT_plus_ST\", 0.0)) * 100.0\n",
    "    except Exception:\n",
    "        rq, poq, pcr, mic, pct_ct = 0.0, 0.0, 0.0, 0.0, 0.0\n",
    "    return {\n",
    "        \"R/Q ratio\": rq,\n",
    "        \"% Open Questions\": poq,\n",
    "        \"% Complex Reflections\": pcr,\n",
    "        \"% MI-Consistent\": mic,\n",
    "        \"% Change Talk\": pct_ct,\n",
    "    }\n",
    "\n",
    "def _extract_safety(report: Optional[dict]) -> dict:\n",
    "    if not report:\n",
    "        return {}\n",
    "    safety = report.get(\"safety\", {})\n",
    "    scores = safety.get(\"scores_0_10\", {})\n",
    "    out = {}\n",
    "    for k in SAFETY_KEYS:\n",
    "        try:\n",
    "            out[k] = float(scores.get(k, 0.0))\n",
    "        except Exception:\n",
    "            out[k] = 0.0\n",
    "    return out\n",
    "\n",
    "# -----------------\n",
    "# UTIL\n",
    "# -----------------\n",
    "def values_by_labels(d: Dict[str, float], labels: List[str]) -> List[float]:\n",
    "    out = []\n",
    "    for k in labels:\n",
    "        v = d.get(k, np.nan)\n",
    "        out.append(0.0 if (pd.isna(v) or v is None) else float(v))\n",
    "    return out\n",
    "\n",
    "def _bar_layout(n_series: int, group_width: float = 0.9) -> Tuple[float, float]:\n",
    "    \"\"\"\n",
    "    Compute a reasonable bar width and starting offset for n_series bars in one group.\n",
    "    Returns (bar_width, start_offset).\n",
    "    \"\"\"\n",
    "    bar_width = group_width / n_series\n",
    "    start = -group_width / 2\n",
    "    return bar_width, start\n",
    "\n",
    "def _maybe_percent_label(metric_name: str) -> bool:\n",
    "    return metric_name.strip().startswith(\"%\")\n",
    "\n",
    "# -----------------\n",
    "# DATA BUILD\n",
    "# -----------------\n",
    "def build_all_data(report_configs: dict):\n",
    "    all_data = {}\n",
    "    colors = {}\n",
    "    for label, cfg in report_configs.items():\n",
    "        rep = _load_json(cfg.get(\"path\"))\n",
    "        colors[label] = cfg.get(\"color\", \"#1f77b4\")\n",
    "        pm = _extract_psychometrics(rep)\n",
    "        sm = _extract_safety(rep)\n",
    "        all_data[label] = {\"psychometrics\": pm, \"safety\": sm, \"report\": rep}\n",
    "    return all_data, colors\n",
    "\n",
    "# -----------------\n",
    "# SMALL MULTIPLES\n",
    "# -----------------\n",
    "def render_small_multiples(\n",
    "    report_configs=REPORT_CONFIGS,\n",
    "    psych_cols: int = 3,\n",
    "    safety_cols: int = 3,\n",
    "    figsize_psych: Tuple[int, int] = (18, 10),\n",
    "    figsize_safety: Tuple[int, int] = (18, 10),\n",
    "    save_path_psych: str = \"./radar_outputs/SMALL_MULTIPLES_psych.png\",\n",
    "    save_path_safety: str = \"./radar_outputs/SMALL_MULTIPLES_safety.png\",\n",
    "):\n",
    "    \"\"\"\n",
    "    Build TWO big figures:\n",
    "      1) Psychometrics: one mini chart per metric (Recommended + Human + Models)\n",
    "      2) Safety:        one mini chart per key (Ideal=10 + Human + Models)\n",
    "    \"\"\"\n",
    "    all_data, colors = build_all_data(report_configs)\n",
    "\n",
    "    human_label = \"Real Psychologist\"\n",
    "    if human_label not in all_data:\n",
    "        print(\"[warn] No human baseline.\")\n",
    "        return\n",
    "\n",
    "    entity_labels = [lbl for lbl in all_data.keys() if lbl != human_label]\n",
    "    if not entity_labels:\n",
    "        print(\"[warn] No non-human models.\")\n",
    "        return\n",
    "\n",
    "    # ---------- Psychometrics big figure ----------\n",
    "    psych_metrics = list(RECOMMENDED.keys())\n",
    "    n_psych = len(psych_metrics)\n",
    "    psych_rows = int(math.ceil(n_psych / psych_cols))\n",
    "\n",
    "    fig_p, axes_p = plt.subplots(psych_rows, psych_cols, figsize=figsize_psych)\n",
    "    axes_p = np.array(axes_p).reshape(psych_rows, psych_cols)  # normalize shape\n",
    "    fig_p.suptitle(\"Psychometrics — Small Multiples (Human vs Models vs Recommended)\", fontsize=18, fontweight=\"bold\", y=0.98)\n",
    "\n",
    "    # Series per subplot: Recommended + Human + each model\n",
    "    n_series_psych = 2 + len(entity_labels)\n",
    "    bar_w_p, start_p = _bar_layout(n_series_psych)\n",
    "\n",
    "    for idx, metric in enumerate(psych_metrics):\n",
    "        r, c = divmod(idx, psych_cols)\n",
    "        ax = axes_p[r, c]\n",
    "\n",
    "        # X is just a single group (one topic), so we fake a single-x with grouped bars\n",
    "        x = np.array([0.0])\n",
    "\n",
    "        # Recommended\n",
    "        rec_val = RECOMMENDED.get(metric, 0.0)\n",
    "        ax.bar(x + (start_p + bar_w_p * 0.5), [rec_val], width=bar_w_p,\n",
    "               edgecolor=\"#222222\", facecolor=\"none\", hatch=\"//\", linewidth=1.2, label=\"Recommended\" if idx == 0 else None)\n",
    "\n",
    "        # Human\n",
    "        human_val = all_data[human_label][\"psychometrics\"].get(metric, 0.0)\n",
    "        ax.bar(x + (start_p + bar_w_p * 1.5), [human_val], width=bar_w_p,\n",
    "               color=\"#ff0000\", alpha=0.9, label=human_label if idx == 0 else None)\n",
    "\n",
    "        # Models\n",
    "        y_max = max(rec_val, human_val)\n",
    "        for i, m in enumerate(entity_labels):\n",
    "            val = all_data[m][\"psychometrics\"].get(metric, 0.0)\n",
    "            y_max = max(y_max, val)\n",
    "            ax.bar(x + (start_p + bar_w_p * (i + 2.5)), [val], width=bar_w_p,\n",
    "                   color=colors.get(m, \"#1f77b4\"), alpha=0.9, label=m if idx == 0 else None)\n",
    "\n",
    "        ax.set_title(metric, fontsize=12)\n",
    "        ax.set_xticks([0.0])\n",
    "        ax.set_xticklabels([\"\"])\n",
    "        ax.grid(axis=\"y\", alpha=0.3)\n",
    "\n",
    "        # y-axis label formatting\n",
    "        if _maybe_percent_label(metric):\n",
    "            ax.set_ylabel(\"%\")\n",
    "        else:\n",
    "            ax.set_ylabel(\"Score\")\n",
    "\n",
    "        ax.set_ylim(0, y_max * 1.15 if y_max > 0 else 1)\n",
    "\n",
    "        # Legend only on first subplot to avoid clutter\n",
    "        if idx == 0:\n",
    "            ax.legend(ncol=2, frameon=False, loc=\"upper left\", bbox_to_anchor=(0.0, 1.25))\n",
    "\n",
    "    # Hide any unused axes in the grid\n",
    "    for extra_idx in range(n_psych, psych_rows * psych_cols):\n",
    "        r, c = divmod(extra_idx, psych_cols)\n",
    "        axes_p[r, c].axis(\"off\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    if save_path_psych:\n",
    "        Path(save_path_psych).parent.mkdir(parents=True, exist_ok=True)\n",
    "        fig_p.savefig(save_path_psych, dpi=300, bbox_inches=\"tight\", facecolor=\"white\")\n",
    "        print(f\"[info] Saved psychometrics small multiples to {save_path_psych}\")\n",
    "\n",
    "    # ---------- Safety big figure ----------\n",
    "    safety_metrics = SAFETY_KEYS\n",
    "    n_safety = len(safety_metrics)\n",
    "    safety_rows = int(math.ceil(n_safety / safety_cols))\n",
    "\n",
    "    fig_s, axes_s = plt.subplots(safety_rows, safety_cols, figsize=figsize_safety)\n",
    "    axes_s = np.array(axes_s).reshape(safety_rows, safety_cols)\n",
    "    fig_s.suptitle(\"Safety — Small Multiples (Human vs Models)\", fontsize=18, fontweight=\"bold\", y=0.98)\n",
    "\n",
    "    n_series_safety = 2 + len(entity_labels)  # Ideal + Human + Models\n",
    "    bar_w_s, start_s = _bar_layout(n_series_safety)\n",
    "\n",
    "    for idx, key in enumerate(safety_metrics):\n",
    "        r, c = divmod(idx, safety_cols)\n",
    "        ax = axes_s[r, c]\n",
    "        x = np.array([0.0])\n",
    "\n",
    "        # Human\n",
    "        human_val = all_data[human_label][\"safety\"].get(key, 0.0)\n",
    "        ax.bar(x + (start_s + bar_w_s * 1.5), [human_val], width=bar_w_s,\n",
    "               color=\"#ff0000\", alpha=0.9, label=human_label if idx == 0 else None)\n",
    "\n",
    "        # Models\n",
    "        y_max = max(10.0, human_val)\n",
    "        for i, m in enumerate(entity_labels):\n",
    "            val = all_data[m][\"safety\"].get(key, 0.0)\n",
    "            y_max = max(y_max, val)\n",
    "            ax.bar(x + (start_s + bar_w_s * (i + 2.5)), [val], width=bar_w_s,\n",
    "                   color=colors.get(m, \"#1f77b4\"), alpha=0.9, label=m if idx == 0 else None)\n",
    "\n",
    "        ax.set_title(SAFETY_LABELS_READABLE.get(key, key), fontsize=12)\n",
    "        ax.set_xticks([0.0])\n",
    "        ax.set_xticklabels([\"\"])\n",
    "        ax.set_ylabel(\"0–10\")\n",
    "        ax.set_ylim(0, max(10.0, y_max) * 1.05)\n",
    "        ax.grid(axis=\"y\", alpha=0.3)\n",
    "\n",
    "        if idx == 0:\n",
    "            ax.legend(ncol=2, frameon=False, loc=\"upper left\", bbox_to_anchor=(0.0, 1.25))\n",
    "\n",
    "    # Hide any unused axes\n",
    "    for extra_idx in range(n_safety, safety_rows * safety_cols):\n",
    "        r, c = divmod(extra_idx, safety_cols)\n",
    "        axes_s[r, c].axis(\"off\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    if save_path_safety:\n",
    "        Path(save_path_safety).parent.mkdir(parents=True, exist_ok=True)\n",
    "        fig_s.savefig(save_path_safety, dpi=300, bbox_inches=\"tight\", facecolor=\"white\")\n",
    "        print(f\"[info] Saved safety small multiples to {save_path_safety}\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "# -----------------\n",
    "# AGREEMENT: ICC(2,1) ONLY — COMBINED FIGURE\n",
    "# -----------------\n",
    "import numpy as np\n",
    "from typing import Dict, List, Tuple, NamedTuple\n",
    "\n",
    "class ICCRow(NamedTuple):\n",
    "    label: str\n",
    "    icc_21: float\n",
    "\n",
    "def _icc_21(data: np.ndarray) -> float:\n",
    "    \"\"\"\n",
    "    ICC(2,1): two-way random effects, absolute agreement, single measure.\n",
    "    data shape: (n_targets, k_raters). We use k=2 (human, model).\n",
    "    \"\"\"\n",
    "    n, k = data.shape\n",
    "    if n < 2 or k < 2:\n",
    "        return np.nan\n",
    "\n",
    "    mean_targets = data.mean(axis=1, keepdims=True)\n",
    "    mean_raters = data.mean(axis=0, keepdims=True)\n",
    "    grand_mean = data.mean()\n",
    "\n",
    "    SSR = k * np.sum((mean_targets - grand_mean) ** 2)                     # rows\n",
    "    SSC = n * np.sum((mean_raters - grand_mean) ** 2)                      # cols\n",
    "    SSE = np.sum((data - mean_targets - mean_raters + grand_mean) ** 2)    # error\n",
    "\n",
    "    dfR = n - 1\n",
    "    dfC = k - 1\n",
    "    dfE = (n - 1) * (k - 1)\n",
    "    if dfR <= 0 or dfC <= 0 or dfE <= 0:\n",
    "        return np.nan\n",
    "\n",
    "    MSR = SSR / dfR\n",
    "    MSC = SSC / dfC\n",
    "    MSE = SSE / dfE\n",
    "\n",
    "    denom = MSR + (k - 1) * MSE + (k * (MSC - MSE) / n)\n",
    "    if denom <= 0:\n",
    "        return np.nan\n",
    "    return float((MSR - MSE) / denom)\n",
    "\n",
    "def _vectorize_for_domain(all_data: dict, keys: list, human_label: str, model_label: str) -> Tuple[np.ndarray, np.ndarray]:\n",
    "    human_vals, model_vals = [], []\n",
    "    for k in keys:\n",
    "        if k in RECOMMENDED:  # psychometrics\n",
    "            hv = all_data[human_label][\"psychometrics\"].get(k, 0.0)\n",
    "            mv = all_data[model_label][\"psychometrics\"].get(k, 0.0)\n",
    "        else:                 # safety\n",
    "            hv = all_data[human_label][\"safety\"].get(k, 0.0)\n",
    "            mv = all_data[model_label][\"safety\"].get(k, 0.0)\n",
    "        hv = 0.0 if (hv is None or pd.isna(hv)) else float(hv)\n",
    "        mv = 0.0 if (mv is None or pd.isna(mv)) else float(mv)\n",
    "        human_vals.append(hv)\n",
    "        model_vals.append(mv)\n",
    "    return np.asarray(human_vals, float), np.asarray(model_vals, float)\n",
    "\n",
    "def _compute_icc_by_domain(all_data: dict, human_label: str, model_labels: List[str], domain: str) -> Dict[str, float]:\n",
    "    if domain == \"psychometrics\":\n",
    "        keys = list(RECOMMENDED.keys())\n",
    "    elif domain == \"safety\":\n",
    "        keys = SAFETY_KEYS\n",
    "    else:\n",
    "        raise ValueError(\"domain must be 'psychometrics' or 'safety'\")\n",
    "\n",
    "    out: Dict[str, float] = {}\n",
    "    for m in model_labels:\n",
    "        h, v = _vectorize_for_domain(all_data, keys, human_label, m)\n",
    "        D = np.column_stack([h, v])\n",
    "        out[m] = _icc_21(D)\n",
    "    return out\n",
    "\n",
    "def render_icc_summary_combined(\n",
    "    report_configs=REPORT_CONFIGS,\n",
    "    figsize: Tuple[int, int] = (12, 5),\n",
    "    save_path: str = \"./radar_outputs/ICC_combined.png\",\n",
    "):\n",
    "    \"\"\"\n",
    "    Single figure with two subplots:\n",
    "      - Left: Psychometrics ICC(2,1) vs Real Psychologist\n",
    "      - Right: Safety ICC(2,1) vs Real Psychologist\n",
    "    Bars use colors defined in REPORT_CONFIGS for each model.\n",
    "    \"\"\"\n",
    "    all_data, colors = build_all_data(report_configs)\n",
    "    human_label = \"Real Psychologist\"\n",
    "\n",
    "    if human_label not in all_data:\n",
    "        print(\"[warn] No human baseline found.\")\n",
    "        return\n",
    "\n",
    "    # Preserve model order as defined in REPORT_CONFIGS (minus human)\n",
    "    model_labels = [lbl for lbl in report_configs.keys() if lbl != human_label and lbl in all_data]\n",
    "\n",
    "    if not model_labels:\n",
    "        print(\"[warn] No models to compare.\")\n",
    "        return\n",
    "\n",
    "    # Compute ICCs\n",
    "    icc_psych = _compute_icc_by_domain(all_data, human_label, model_labels, domain=\"psychometrics\")\n",
    "    icc_safety = _compute_icc_by_domain(all_data, human_label, model_labels, domain=\"safety\")\n",
    "\n",
    "    # Prepare plotting\n",
    "    x = np.arange(len(model_labels))\n",
    "    psych_vals = np.array([icc_psych.get(m, np.nan) for m in model_labels], float)\n",
    "    safety_vals = np.array([icc_safety.get(m, np.nan) for m in model_labels], float)\n",
    "\n",
    "    fig, axes = plt.subplots(1, 2, figsize=figsize)\n",
    "    fig.suptitle(\"ICC(2,1) vs Real Psychologist\", fontsize=16, fontweight=\"bold\", y=0.98)\n",
    "\n",
    "    def _bar_by_color(ax, vals, title):\n",
    "        bars = []\n",
    "        for i, m in enumerate(model_labels):\n",
    "            bar = ax.bar(i, vals[i], label=m, color=colors.get(m, \"#1f77b4\"))\n",
    "            bars.append(bar)\n",
    "        ax.set_xticks(x, model_labels, rotation=20)\n",
    "        # ICC can be negative; show it honestly\n",
    "        ymin = min(-0.2, float(np.nanmin(vals)) if np.isfinite(np.nanmin(vals)) else -0.2)\n",
    "        ax.set_ylim(ymin, 1.0)\n",
    "        ax.set_ylabel(\"ICC(2,1)\")\n",
    "        ax.set_title(title)\n",
    "        ax.grid(axis=\"y\", alpha=0.3)\n",
    "        # Quick reference lines\n",
    "        for yref in [0.5, 0.75, 0.9]:\n",
    "            ax.axhline(yref, linestyle=\"--\", linewidth=1, alpha=0.25)\n",
    "\n",
    "    _bar_by_color(axes[0], psych_vals, \"Psychometrics\")\n",
    "    _bar_by_color(axes[1], safety_vals, \"Safety\")\n",
    "\n",
    "    # Single legend, using the first axes’ handles, placed below\n",
    "    handles = [plt.Rectangle((0, 0), 1, 1, color=colors.get(m, \"#1f77b4\")) for m in model_labels]\n",
    "    fig.legend(handles, model_labels, ncol=min(4, len(model_labels)), loc=\"lower center\", frameon=False)\n",
    "    plt.tight_layout(rect=[0, 0.08, 1, 0.96])\n",
    "\n",
    "    if save_path:\n",
    "        Path(save_path).parent.mkdir(parents=True, exist_ok=True)\n",
    "        fig.savefig(save_path, dpi=300, bbox_inches=\"tight\", facecolor=\"white\")\n",
    "        print(f\"[info] Saved combined ICC summary to {save_path}\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "# -----------------\n",
    "# SUMMARY TABLES (values + deltas + ICC)\n",
    "# -----------------\n",
    "def _round_df(df: pd.DataFrame, ndigits: int = 2) -> pd.DataFrame:\n",
    "    return df.applymap(lambda v: round(v, ndigits) if isinstance(v, (int, float)) and pd.notna(v) else v)\n",
    "\n",
    "def _ordered_models(report_configs: dict, human_label: str) -> List[str]:\n",
    "    return [lbl for lbl in report_configs.keys() if lbl != human_label]\n",
    "\n",
    "def build_summary_tables(\n",
    "    report_configs=REPORT_CONFIGS,\n",
    "    human_label: str = \"Real Psychologist\",\n",
    "    safety_ideal: float = 10.0,\n",
    "    round_digits: int = 2,\n",
    ") -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n",
    "    \"\"\"\n",
    "    Returns (psych_table, safety_table, icc_table) as DataFrames.\n",
    "\n",
    "    psych_table: rows=metrics, cols=[Recommended, Human, <model...>, ΔHuman_<model>]\n",
    "    safety_table: rows=keys, cols=[Ideal(10), Human, <model...>, ΔHuman_<model>]\n",
    "    icc_table: rows=models, cols=[ICC_psych, ICC_safety]\n",
    "    \"\"\"\n",
    "    all_data, colors = build_all_data(report_configs)\n",
    "    if human_label not in all_data:\n",
    "        raise RuntimeError(\"Human baseline not found. Please include 'Real Psychologist' in REPORT_CONFIGS.\")\n",
    "\n",
    "    model_labels = _ordered_models(report_configs, human_label)\n",
    "\n",
    "    # ---------- Psychometrics values ----------\n",
    "    psych_metrics = list(RECOMMENDED.keys())\n",
    "    psych_rows = []\n",
    "    for metric in psych_metrics:\n",
    "        row = {\n",
    "            \"Metric\": metric,\n",
    "            \"Recommended\": float(RECOMMENDED.get(metric, 0.0)),\n",
    "            \"Human\": float(all_data[human_label][\"psychometrics\"].get(metric, 0.0)),\n",
    "        }\n",
    "        for m in model_labels:\n",
    "            val = float(all_data[m][\"psychometrics\"].get(metric, 0.0))\n",
    "            row[m] = val\n",
    "        for m in model_labels:\n",
    "            row[f\"ΔHuman_{m}\"] = row[m] - row[\"Human\"]\n",
    "        psych_rows.append(row)\n",
    "    psych_df = pd.DataFrame(psych_rows).set_index(\"Metric\")\n",
    "    psych_df = _round_df(psych_df, round_digits)\n",
    "\n",
    "    # ---------- Safety values ----------\n",
    "    safety_keys = SAFETY_KEYS\n",
    "    safety_rows = []\n",
    "    for key in safety_keys:\n",
    "        nice = SAFETY_LABELS_READABLE.get(key, key)\n",
    "        row = {\n",
    "            \"Topic\": nice,\n",
    "            \"Ideal(10)\": safety_ideal,\n",
    "            \"Human\": float(all_data[human_label][\"safety\"].get(key, 0.0)),\n",
    "        }\n",
    "        for m in model_labels:\n",
    "            val = float(all_data[m][\"safety\"].get(key, 0.0))\n",
    "            row[m] = val\n",
    "        for m in model_labels:\n",
    "            row[f\"ΔHuman_{m}\"] = row[m] - row[\"Human\"]\n",
    "        safety_rows.append(row)\n",
    "    safety_df = pd.DataFrame(safety_rows).set_index(\"Topic\")\n",
    "    safety_df = _round_df(safety_df, round_digits)\n",
    "\n",
    "    # ---------- ICC(2,1) per domain ----------\n",
    "    icc_psych = _compute_icc_by_domain(all_data, human_label, model_labels, domain=\"psychometrics\")\n",
    "    icc_safety = _compute_icc_by_domain(all_data, human_label, model_labels, domain=\"safety\")\n",
    "    icc_df = pd.DataFrame(\n",
    "        {\n",
    "            \"ICC_psych\": [icc_psych.get(m, np.nan) for m in model_labels],\n",
    "            \"ICC_safety\": [icc_safety.get(m, np.nan) for m in model_labels],\n",
    "        },\n",
    "        index=model_labels,\n",
    "    )\n",
    "    icc_df.index.name = \"Model\"\n",
    "    icc_df = _round_df(icc_df, round_digits)\n",
    "\n",
    "    return psych_df, safety_df, icc_df\n",
    "\n",
    "\n",
    "def render_summary_tables(\n",
    "    report_configs=REPORT_CONFIGS,\n",
    "    human_label: str = \"Real Psychologist\",\n",
    "    save_dir: str = \"./radar_outputs\",\n",
    "    round_digits: int = 2,\n",
    "    print_markdown: bool = True,\n",
    ") -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n",
    "    \"\"\"\n",
    "    Builds and saves CSVs for the summary tables. Also prints markdown to stdout.\n",
    "    Returns (psych_df, safety_df, icc_df).\n",
    "    \"\"\"\n",
    "    os.makedirs(save_dir, exist_ok=True)\n",
    "\n",
    "    psych_df, safety_df, icc_df = build_summary_tables(\n",
    "        report_configs=report_configs,\n",
    "        human_label=human_label,\n",
    "        round_digits=round_digits,\n",
    "    )\n",
    "\n",
    "    # Save CSVs\n",
    "    psych_csv = Path(save_dir) / \"summary_psychometrics.csv\"\n",
    "    safety_csv = Path(save_dir) / \"summary_safety.csv\"\n",
    "    icc_csv = Path(save_dir) / \"summary_icc.csv\"\n",
    "    psych_df.to_csv(psych_csv, encoding=\"utf-8\")\n",
    "    safety_df.to_csv(safety_csv, encoding=\"utf-8\")\n",
    "    icc_df.to_csv(icc_csv, encoding=\"utf-8\")\n",
    "\n",
    "    print(f\"[info] Saved tables → {psych_csv}, {safety_csv}, {icc_csv}\")\n",
    "\n",
    "    # Optional pretty print for terminals/notes\n",
    "    if print_markdown:\n",
    "        try:\n",
    "            print(\"\\n# Summary — Psychometrics\")\n",
    "            print(psych_df.to_markdown())\n",
    "\n",
    "            print(\"\\n# Summary — Safety\")\n",
    "            print(safety_df.to_markdown())\n",
    "\n",
    "            print(\"\\n# Summary — ICC(2,1) vs Human\")\n",
    "            print(icc_df.to_markdown())\n",
    "        except Exception:\n",
    "            # Fallback for environments without tabulate\n",
    "            print(\"\\n[info] Markdown preview unavailable; printing plain tables.\")\n",
    "            print(\"\\nSummary — Psychometrics\")\n",
    "            print(psych_df)\n",
    "            print(\"\\nSummary — Safety\")\n",
    "            print(safety_df)\n",
    "            print(\"\\nSummary — ICC(2,1) vs Human\")\n",
    "            print(icc_df)\n",
    "\n",
    "    return psych_df, safety_df, icc_df\n",
    "\n",
    "\n",
    "# -----------------\n",
    "# MAIN\n",
    "# -----------------\n",
    "if __name__ == \"__main__\":\n",
    "    # Visuals (unchanged)\n",
    "    render_small_multiples(\n",
    "        report_configs=REPORT_CONFIGS,\n",
    "        psych_cols=5,\n",
    "        safety_cols=5,\n",
    "        figsize_psych=(13, 8),\n",
    "        figsize_safety=(13, 8),\n",
    "        save_path_psych=\"./radar_outputs/SMALL_MULTIPLES_psych.png\",\n",
    "        save_path_safety=\"./radar_outputs/SMALL_MULTIPLES_safety.png\",\n",
    "    )\n",
    "\n",
    "    render_icc_summary_combined(\n",
    "        report_configs=REPORT_CONFIGS,\n",
    "        figsize=(6, 5),\n",
    "        save_path=\"./radar_outputs/ICC_combined.png\",\n",
    "    )\n",
    "\n",
    "    # New: export integrated summary tables\n",
    "    render_summary_tables(\n",
    "        report_configs=REPORT_CONFIGS,\n",
    "        human_label=\"Real Psychologist\",\n",
    "        save_dir=\"./radar_outputs\",\n",
    "        round_digits=2,\n",
    "        print_markdown=True,\n",
    "    )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56454414",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admins\\AppData\\Local\\Temp\\ipykernel_65088\\3399725620.py:223: UserWarning: Tight layout not applied. tight_layout cannot make Axes width small enough to accommodate all Axes decorations\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[info] Saved psychometrics small multiples to ./radar_outputs/SMALL_MULTIPLES_psych.png\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admins\\AppData\\Local\\Temp\\ipykernel_65088\\3399725620.py:274: UserWarning: Tight layout not applied. tight_layout cannot make Axes width small enough to accommodate all Axes decorations\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[info] Saved safety small multiples to ./radar_outputs/SMALL_MULTIPLES_safety.png\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1300x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1300x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[info] Saved combined ICC summary to ./radar_outputs/ICC_combined.png\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "NameError",
     "evalue": "name 'os' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[52], line 157\u001b[0m\n\u001b[0;32m    150\u001b[0m render_icc_summary_combined(\n\u001b[0;32m    151\u001b[0m     report_configs\u001b[38;5;241m=\u001b[39mREPORT_CONFIGS,\n\u001b[0;32m    152\u001b[0m     figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m5\u001b[39m),\n\u001b[0;32m    153\u001b[0m     save_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./radar_outputs/ICC_combined.png\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    154\u001b[0m )\n\u001b[0;32m    156\u001b[0m \u001b[38;5;66;03m# New: export integrated summary tables\u001b[39;00m\n\u001b[1;32m--> 157\u001b[0m \u001b[43mrender_summary_tables\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    158\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreport_configs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mREPORT_CONFIGS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    159\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhuman_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mReal Psychologist\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    160\u001b[0m \u001b[43m    \u001b[49m\u001b[43msave_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m./radar_outputs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    161\u001b[0m \u001b[43m    \u001b[49m\u001b[43mround_digits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    162\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprint_markdown\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    163\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[1;32mIn[52], line 93\u001b[0m, in \u001b[0;36mrender_summary_tables\u001b[1;34m(report_configs, human_label, save_dir, round_digits, print_markdown)\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mrender_summary_tables\u001b[39m(\n\u001b[0;32m     83\u001b[0m     report_configs\u001b[38;5;241m=\u001b[39mREPORT_CONFIGS,\n\u001b[0;32m     84\u001b[0m     human_label: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReal Psychologist\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     87\u001b[0m     print_markdown: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m     88\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[pd\u001b[38;5;241m.\u001b[39mDataFrame, pd\u001b[38;5;241m.\u001b[39mDataFrame, pd\u001b[38;5;241m.\u001b[39mDataFrame]:\n\u001b[0;32m     89\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     90\u001b[0m \u001b[38;5;124;03m    Builds and saves CSVs for the summary tables. Also prints markdown to stdout.\u001b[39;00m\n\u001b[0;32m     91\u001b[0m \u001b[38;5;124;03m    Returns (psych_df, safety_df, icc_df).\u001b[39;00m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 93\u001b[0m     \u001b[43mos\u001b[49m\u001b[38;5;241m.\u001b[39mmakedirs(save_dir, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m     95\u001b[0m     psych_df, safety_df, icc_df \u001b[38;5;241m=\u001b[39m build_summary_tables(\n\u001b[0;32m     96\u001b[0m         report_configs\u001b[38;5;241m=\u001b[39mreport_configs,\n\u001b[0;32m     97\u001b[0m         human_label\u001b[38;5;241m=\u001b[39mhuman_label,\n\u001b[0;32m     98\u001b[0m         round_digits\u001b[38;5;241m=\u001b[39mround_digits,\n\u001b[0;32m     99\u001b[0m     )\n\u001b[0;32m    101\u001b[0m     \u001b[38;5;66;03m# Save CSVs\u001b[39;00m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'os' is not defined"
     ]
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}