{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install plotly kaleido datasets nbformat -U -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aymeric/venv/gaia/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import datasets\n",
    "import pandas as pd\n",
    "from dotenv import load_dotenv\n",
    "from huggingface_hub import login\n",
    "\n",
    "\n",
    "load_dotenv(override=True)\n",
    "login(os.getenv(\"HF_TOKEN\"))\n",
    "\n",
    "pd.set_option(\"max_colwidth\", None)\n",
    "\n",
    "OUTPUT_DIR = \"output\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_ds = datasets.load_dataset(\"gaia-benchmark/GAIA\", \"2023_all\")[\"validation\"]\n",
    "eval_ds = eval_ds.rename_columns({\"Question\": \"question\", \"Final answer\": \"true_answer\", \"Level\": \"task\"})\n",
    "eval_df = pd.DataFrame(eval_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    86\n",
       "1    53\n",
       "3    26\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(eval_ds[\"task\"]).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load all results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "\n",
    "results = []\n",
    "for f in glob.glob(f\"{OUTPUT_DIR}/validation/*.jsonl\"):\n",
    "    df = pd.read_json(f, lines=True)\n",
    "    df[\"agent_name\"] = f.split(\"/\")[-1].split(\".\")[0]\n",
    "    results.append(df)\n",
    "\n",
    "result_df = pd.concat(results)\n",
    "result_df = result_df.drop(columns=[\"start_time\", \"end_time\"])\n",
    "result_df[\"prediction\"] = result_df[\"prediction\"].fillna(\"No prediction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "String  cannot be normalized to number str.\n",
      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 2 High fantasy A Song of Ice and Fire cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String 94 CFM for Cheater cannot be normalized to number str.\n",
      "String  93 CFM for Cheater beater cannot be normalized to number str.\n",
      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 3 or 4 cannot be normalized to number str.\n",
      "String No year cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 250 for Cheater cannot be normalized to number str.\n",
      "String  220 for Cheater beater cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String 776 ft/min for Cheater cannot be normalized to number str.\n",
      "String  768 ft/min for Cheater beater cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String CFM number for Cheater: not listed cannot be normalized to number str.\n",
      "String  CFM number for Cheater beater: 665 ft/min cannot be normalized to number str.\n",
      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 1.46 Å cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String August 1: 0 August 2: 0 August 3: 0 August 4: 0 August 5: 0 August 6: 0 August 7: 0 August 8: 0 August 9: 0 August 10: 0 August 11: 0 August 12: 0 August 13: 0 August 14: 0 August 15: 0 August 16: 0 August 17: 0 August 18: 0 August 19: 0 August 20: 0 August 21: 0 August 22: 0 August 23: 0 August 24: 0 August 25: 0 August 26: 0 August 27: 0 August 28: 0 August 29: 0 August 30: 0 August 31: 0 cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String 120 for Cheater cannot be normalized to number str.\n",
      "String  103 for Cheater beater cannot be normalized to number str.\n",
      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 120.28 for Cheater cannot be normalized to number str.\n",
      "String  119.04 for Cheater beater cannot be normalized to number str.\n",
      "String 3 or 4 cannot be normalized to number str.\n",
      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String 2017 Komo Mai Drive sold for 900000 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 2730-2740 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 89706.00 USD cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String No prediction cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 6 The Lord of the Rings (book) J. R. R. Tolkien Author American literature Fantasy literature Publishers A Song of Ice and Fire cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 1.46 Å cannot be normalized to number str.\n",
      "String  cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 94.5 for Cheater cannot be normalized to number str.\n",
      "String  93.5 for Cheater beater cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 2017 Komo Mai Drive 900000 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 776 for Cheater cannot be normalized to number str.\n",
      "String  Not specified for Cheater Beater cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 5.75 for Cheater cannot be normalized to number str.\n",
      "String  5.22 for Cheater Beater cannot be normalized to number str.\n",
      "String 2017 Komo Mai Drive sold for 900000 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String 33101 28557 cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "String Unable to determine cannot be normalized to number str.\n",
      "Close call: Alfonso Cardinal Visconti vs Alfonso Visconti\n",
      "Close call: Rockhopper Penguins vs Rockhopper penguin\n",
      "Close call: INT. THE CASTLE vs THE CASTLE\n",
      "Close call: to be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune or to take arms against a sea of troubles and by opposing end them vs To be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune\n",
      "Close call: The World of the Twenty First Century 1994 vs The World of the Twenty First Century\n",
      "Close call: Alfonso Cardinal Visconti vs Alfonso Visconti\n",
      "Close call: Wes Craven's A Nightmare on Elm Street vs A Nightmare on Elm Street\n",
      "Close call: God said let there be dragons vs Here be dragons\n",
      "Close call: rockhopper penguins vs Rockhopper penguin\n",
      "Close call: Harbinger, This Fire, Tidal vs Harbinger, Tidal\n",
      "Close call: EC 3.1.3.1;EC 1.11.1.7 vs 3.1.3.1; 1.11.1.7\n",
      "Close call: to be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune or to take arms against a sea of troubles and by opposing end them vs To be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune\n",
      "Close call: Alfonso Cardinal Visconti vs Alfonso Visconti\n",
      "Close call: to be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune or to take arms against a sea of troubles and by opposing end them vs To be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune\n",
      "Close call: Out of the Silent Planet by C.S. Lewis vs Out of the Silent Planet\n",
      "Close call: broccoli, celery, fresh basil, green beans, lettuce, sweet potatoes vs broccoli, celery, fresh basil, lettuce, sweet potatoes\n",
      "Close call: To be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune or to take arms against a sea of troubles and by opposing end them vs To be or not to be that is the question whether tis nobler in the mind to suffer the slings and arrows of outrageous fortune\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aymeric/Documents/Code/smolagents/examples/open_deep_research/scripts/gaia_scorer.py:52: UserWarning: Answer lists have different lengths, returning False.\n",
      "  warnings.warn(\"Answer lists have different lengths, returning False.\", UserWarning)\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "from collections import Counter\n",
    "\n",
    "from scripts.gaia_scorer import check_close_call, question_scorer\n",
    "\n",
    "\n",
    "result_df[\"is_correct\"] = result_df.apply(lambda x: question_scorer(x[\"prediction\"], x[\"true_answer\"]), axis=1)\n",
    "result_df[\"is_near_correct\"] = result_df.apply(\n",
    "    lambda x: check_close_call(x[\"prediction\"], x[\"true_answer\"], x[\"is_correct\"]),\n",
    "    axis=1,\n",
    ")\n",
    "\n",
    "result_df[\"count_steps\"] = result_df[\"intermediate_steps\"].apply(len)\n",
    "\n",
    "\n",
    "def find_attachment(question):\n",
    "    matches = eval_df.loc[eval_df[\"question\"].apply(lambda x: x in question), \"file_name\"]\n",
    "\n",
    "    if len(matches) == 0:\n",
    "        return \"Not found\"\n",
    "    file_path = matches.values[0]\n",
    "\n",
    "    if isinstance(file_path, str) and len(file_path) > 0:\n",
    "        return file_path.split(\".\")[-1]\n",
    "    else:\n",
    "        return \"None\"\n",
    "\n",
    "\n",
    "result_df[\"attachment_type\"] = result_df[\"question\"].apply(find_attachment)\n",
    "\n",
    "\n",
    "def extract_tool_calls(code):\n",
    "    regex = r\"\\b(\\w+)\\(\"\n",
    "    function_calls = [el for el in re.findall(regex, code) if el.islower()]\n",
    "\n",
    "    function_call_counter = Counter(function_calls)\n",
    "    return function_call_counter\n",
    "\n",
    "\n",
    "def sum_tool_calls(steps):\n",
    "    total_count = Counter()\n",
    "    for step in steps:\n",
    "        if \"llm_output\" in step:\n",
    "            total_count += extract_tool_calls(step[\"llm_output\"])\n",
    "\n",
    "    return total_count\n",
    "\n",
    "\n",
    "# result_df[\"tool_calls\"] = result_df[\"intermediate_steps\"].apply(sum_tool_calls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_thoughts(x):\n",
    "    try:\n",
    "        output = x[0][\"task\"]\n",
    "        for y in x[1:]:\n",
    "            try:\n",
    "                if \"observation\" in y:\n",
    "                    output += y[\"llm_output\"] + \"\\nObservation:\" + y[\"observation\"]\n",
    "                else:\n",
    "                    output += y[\"llm_output\"] + r\"\\Error:\" + str(y[\"error\"])\n",
    "            except Exception:\n",
    "                pass\n",
    "        return output\n",
    "    except Exception:\n",
    "        return None\n",
    "\n",
    "\n",
    "result_df[\"thoughts\"] = result_df[\"intermediate_steps\"].apply(lambda x: get_thoughts(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agent_name\n",
       "code_gpt4o_03_february_text                         165\n",
       "code_o1_03_february_ablation-toolcalling-manager    165\n",
       "code_o1_01_february_text                            165\n",
       "code_o3-mini_03_february_remove-navigational        165\n",
       "code_o1_04_february_submission5                     165\n",
       "code_o1_03_february_text_high-reasoning-effort      165\n",
       "code_o1_03_february_remove-navigational             164\n",
       "code_o1_03_february_fix-print-outputs               164\n",
       "code_o1_04_february_submission                      162\n",
       "code_o1_03_february_goodoldtext-unbroken            161\n",
       "code_gpt4o_03_february_goodoldtext-unbroken         159\n",
       "code_gpt4o_03_february_magenticbrowser              159\n",
       "code_o1_03_february_fix-print-outputs2              156\n",
       "code_gpt4o_03_february_magenticbrowser2             156\n",
       "code_o1_04_february_submission-medium               125\n",
       "code_o1_29-01_text                                  105\n",
       "code_llama-3                                         90\n",
       "code_o1_22-01_managedagent-summary_planning          67\n",
       "code_o1_25-01_visioon                                53\n",
       "code_o1_04_february_submission3                      49\n",
       "code_qwen-coder-32B_03_february_text                 43\n",
       "code_o1_04_february_submission4                       6\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df[\"agent_name\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Inspect specific runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agent_name\n",
       "code_gpt4o_03_february_text                         165\n",
       "code_o1_03_february_ablation-toolcalling-manager    165\n",
       "code_o1_01_february_text                            165\n",
       "code_o3-mini_03_february_remove-navigational        165\n",
       "code_o1_04_february_submission5                     165\n",
       "code_o1_03_february_text_high-reasoning-effort      165\n",
       "code_o1_03_february_remove-navigational             164\n",
       "code_o1_03_february_fix-print-outputs               164\n",
       "code_o1_04_february_submission                      162\n",
       "code_o1_03_february_goodoldtext-unbroken            161\n",
       "code_gpt4o_03_february_goodoldtext-unbroken         159\n",
       "code_gpt4o_03_february_magenticbrowser              159\n",
       "code_o1_03_february_fix-print-outputs2              156\n",
       "code_gpt4o_03_february_magenticbrowser2             156\n",
       "code_o1_04_february_submission-medium               125\n",
       "code_o1_29-01_text                                  105\n",
       "code_llama-3                                         90\n",
       "code_o1_22-01_managedagent-summary_planning          67\n",
       "code_o1_25-01_visioon                                53\n",
       "code_o1_04_february_submission3                      49\n",
       "code_qwen-coder-32B_03_february_text                 43\n",
       "code_o1_04_february_submission4                       6\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "agent_name                                    task\n",
       "code_gpt4o_03_february_goodoldtext-unbroken   2       84\n",
       "                                              1       53\n",
       "                                              3       22\n",
       "code_gpt4o_03_february_magenticbrowser        2       83\n",
       "                                              1       52\n",
       "                                                      ..\n",
       "code_o3-mini_03_february_remove-navigational  1       53\n",
       "                                              3       26\n",
       "code_qwen-coder-32B_03_february_text          2       22\n",
       "                                              1       14\n",
       "                                              3        7\n",
       "Name: count, Length: 65, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total length: 2809 - is complete: False\n"
     ]
    }
   ],
   "source": [
    "o1_vision = \"code_o1_25-01_visioon\"\n",
    "o1_next = \"code_o1_29-01_text\"\n",
    "o1 = \"code_o1_01_february_text\"\n",
    "\n",
    "list_versions = [o1, o1_vision, o1_next]\n",
    "\n",
    "# submission_selection_name = \"react_code_llama3-70b_02-05_full-gaia-validation-code\"\n",
    "sel_df = result_df\n",
    "# sel_df = sel_df.loc[\n",
    "#     (result_df[\"agent_name\"].isin(list_versions))\n",
    "#     # & (~result_df[\"question\"].isin(UNSOLVED_QUESTIONS))\n",
    "# ]\n",
    "sel_df = sel_df.reset_index(drop=True)\n",
    "display(sel_df[\"agent_name\"].value_counts())\n",
    "sel_df = sel_df.drop_duplicates(subset=[\"agent_name\", \"question\"])\n",
    "display(sel_df.groupby(\"agent_name\")[[\"task\"]].value_counts())\n",
    "print(\"Total length:\", len(sel_df), \"- is complete:\", len(sel_df) == 165)\n",
    "# assert sel_df[\"question\"].value_counts().max() == len(list_versions), \"Some questions are duplicate!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Average score:'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "      <th></th>\n",
       "      <th>is_correct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agent_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>code_gpt4o_03_february_goodoldtext-unbroken</th>\n",
       "      <td>0.384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code_gpt4o_03_february_magenticbrowser</th>\n",
       "      <td>0.352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code_gpt4o_03_february_magenticbrowser2</th>\n",
       "      <td>0.365</td>\n",
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       "      <th>code_gpt4o_03_february_text</th>\n",
       "      <td>0.376</td>\n",
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       "      <th>code_llama-3</th>\n",
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       "      <td>0.518</td>\n",
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       "    <tr>\n",
       "      <th>code_o1_03_february_fix-print-outputs2</th>\n",
       "      <td>0.558</td>\n",
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       "    <tr>\n",
       "      <th>code_o1_03_february_goodoldtext-unbroken</th>\n",
       "      <td>0.534</td>\n",
       "    </tr>\n",
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       "      <th>code_o1_03_february_remove-navigational</th>\n",
       "      <td>0.537</td>\n",
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       "      <th>code_o1_03_february_text_high-reasoning-effort</th>\n",
       "      <td>0.485</td>\n",
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       "      <td>0.494</td>\n",
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       "      <td>0.500</td>\n",
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       "      <td>0.552</td>\n",
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       "      <th>code_o1_22-01_managedagent-summary_planning</th>\n",
       "      <td>0.418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code_o1_25-01_visioon</th>\n",
       "      <td>0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code_o1_29-01_text</th>\n",
       "      <td>0.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code_o3-mini_03_february_remove-navigational</th>\n",
       "      <td>0.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code_qwen-coder-32B_03_february_text</th>\n",
       "      <td>0.209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                                  is_correct\n",
       "agent_name                                                  \n",
       "code_gpt4o_03_february_goodoldtext-unbroken            0.384\n",
       "code_gpt4o_03_february_magenticbrowser                 0.352\n",
       "code_gpt4o_03_february_magenticbrowser2                0.365\n",
       "code_gpt4o_03_february_text                            0.376\n",
       "code_llama-3                                           0.078\n",
       "code_o1_01_february_text                               0.491\n",
       "code_o1_03_february_ablation-toolcalling-manager       0.327\n",
       "code_o1_03_february_fix-print-outputs                  0.518\n",
       "code_o1_03_february_fix-print-outputs2                 0.558\n",
       "code_o1_03_february_goodoldtext-unbroken               0.534\n",
       "code_o1_03_february_remove-navigational                0.537\n",
       "code_o1_03_february_text_high-reasoning-effort         0.485\n",
       "code_o1_04_february_submission                         0.494\n",
       "code_o1_04_february_submission-medium                  0.488\n",
       "code_o1_04_february_submission3                        0.490\n",
       "code_o1_04_february_submission4                        0.500\n",
       "code_o1_04_february_submission5                        0.552\n",
       "code_o1_22-01_managedagent-summary_planning            0.418\n",
       "code_o1_25-01_visioon                                  0.340\n",
       "code_o1_29-01_text                                     0.390\n",
       "code_o3-mini_03_february_remove-navigational           0.291\n",
       "code_qwen-coder-32B_03_february_text                   0.209"
      ]
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>is_correct</th>\n",
       "      <th>is_near_correct</th>\n",
       "      <th>count_steps</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agent_name</th>\n",
       "      <th>task</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">code_gpt4o_03_february_goodoldtext-unbroken</th>\n",
       "      <th>1</th>\n",
       "      <td>0.452830</td>\n",
       "      <td>0.452830</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>8.511905</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>10.409091</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">code_gpt4o_03_february_magenticbrowser</th>\n",
       "      <th>1</th>\n",
       "      <td>0.480769</td>\n",
       "      <td>0.480769</td>\n",
       "      <td>7.153846</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.349398</td>\n",
       "      <td>0.361446</td>\n",
       "      <td>8.168675</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">code_o3-mini_03_february_remove-navigational</th>\n",
       "      <th>2</th>\n",
       "      <td>0.232558</td>\n",
       "      <td>0.244186</td>\n",
       "      <td>4.976744</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>6.615385</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">code_qwen-coder-32B_03_february_text</th>\n",
       "      <th>1</th>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>5.428571</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>6.409091</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>6.571429</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   is_correct  \\\n",
       "agent_name                                   task               \n",
       "code_gpt4o_03_february_goodoldtext-unbroken  1       0.452830   \n",
       "                                             2       0.380952   \n",
       "                                             3       0.227273   \n",
       "code_gpt4o_03_february_magenticbrowser       1       0.480769   \n",
       "                                             2       0.349398   \n",
       "...                                                       ...   \n",
       "code_o3-mini_03_february_remove-navigational 2       0.232558   \n",
       "                                             3       0.153846   \n",
       "code_qwen-coder-32B_03_february_text         1       0.357143   \n",
       "                                             2       0.136364   \n",
       "                                             3       0.142857   \n",
       "\n",
       "                                                   is_near_correct  \\\n",
       "agent_name                                   task                    \n",
       "code_gpt4o_03_february_goodoldtext-unbroken  1            0.452830   \n",
       "                                             2            0.392857   \n",
       "                                             3            0.227273   \n",
       "code_gpt4o_03_february_magenticbrowser       1            0.480769   \n",
       "                                             2            0.361446   \n",
       "...                                                            ...   \n",
       "code_o3-mini_03_february_remove-navigational 2            0.244186   \n",
       "                                             3            0.153846   \n",
       "code_qwen-coder-32B_03_february_text         1            0.357143   \n",
       "                                             2            0.136364   \n",
       "                                             3            0.142857   \n",
       "\n",
       "                                                   count_steps  count  \n",
       "agent_name                                   task                      \n",
       "code_gpt4o_03_february_goodoldtext-unbroken  1        7.000000     53  \n",
       "                                             2        8.511905     84  \n",
       "                                             3       10.409091     22  \n",
       "code_gpt4o_03_february_magenticbrowser       1        7.153846     52  \n",
       "                                             2        8.168675     83  \n",
       "...                                                        ...    ...  \n",
       "code_o3-mini_03_february_remove-navigational 2        4.976744     86  \n",
       "                                             3        6.615385     26  \n",
       "code_qwen-coder-32B_03_february_text         1        5.428571     14  \n",
       "                                             2        6.409091     22  \n",
       "                                             3        6.571429      7  \n",
       "\n",
       "[65 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\"Average score:\", sel_df.groupby(\"agent_name\")[[\"is_correct\"]].mean().round(3))\n",
    "display(\n",
    "    sel_df.groupby([\"agent_name\", \"task\"])[[\"is_correct\", \"is_near_correct\", \"count_steps\", \"question\"]]\n",
    "    .agg(\n",
    "        {\n",
    "            \"is_correct\": \"mean\",\n",
    "            \"is_near_correct\": \"mean\",\n",
    "            \"count_steps\": \"mean\",\n",
    "            \"question\": \"count\",\n",
    "        }\n",
    "    )\n",
    "    .rename(columns={\"question\": \"count\"})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
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          "hoverlabel": {
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          "mapbox": {
           "style": "light"
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          "paper_bgcolor": "white",
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           "bgcolor": "#E5ECF6",
           "radialaxis": {
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
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          },
          "title": {
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          },
          "xaxis": {
           "automargin": true,
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           "linecolor": "white",
           "ticks": "",
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           "zerolinewidth": 2
          },
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           "automargin": true,
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        "xaxis": {
         "anchor": "y",
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         "title": {
          "text": "index"
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        },
        "yaxis": {
         "anchor": "x",
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          0,
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       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "\n",
    "cumulative_df = (\n",
    "    (\n",
    "        sel_df.groupby(\"agent_name\")[[\"is_correct\", \"is_near_correct\"]]\n",
    "        .expanding(min_periods=1, axis=0, method=\"single\")\n",
    "        .agg({\"is_correct\": \"mean\", \"is_near_correct\": \"count\"})\n",
    "        .reset_index()\n",
    "    )\n",
    "    .copy()\n",
    "    .rename(columns={\"is_near_correct\": \"index\"})\n",
    ")\n",
    "cumulative_df[\"index\"] = cumulative_df[\"index\"].astype(int) - 1\n",
    "\n",
    "\n",
    "def find_question(row):\n",
    "    try:\n",
    "        res = sel_df.loc[sel_df[\"agent_name\"] == row[\"agent_name\"], \"question\"].iloc[row[\"index\"]][:50]\n",
    "        return res\n",
    "    except Exception:\n",
    "        return \"\"\n",
    "\n",
    "\n",
    "cumulative_df[\"question\"] = cumulative_df.apply(find_question, axis=1)\n",
    "# cumulative_df[\"question\"] = [el[:50] for el in sel_df[\"question\"].values]\n",
    "\n",
    "# cumulative_df[\"is_correct\"] = cumulative_df[\"is_correct\"] * (165 - 68) / 165\n",
    "\n",
    "px.line(\n",
    "    cumulative_df,\n",
    "    color=\"agent_name\",\n",
    "    x=\"index\",\n",
    "    y=\"is_correct\",\n",
    "    hover_data=\"question\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Dive deeper into one run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "165\n"
     ]
    }
   ],
   "source": [
    "sel_df = result_df.loc[result_df[\"agent_name\"] == o1]\n",
    "print(len(sel_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6m/9b1tts6d5w960j80wbw9tx3m0000gn/T/ipykernel_25011/2022001392.py:10: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/var/folders/6m/9b1tts6d5w960j80wbw9tx3m0000gn/T/ipykernel_25011/2022001392.py:10: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/var/folders/6m/9b1tts6d5w960j80wbw9tx3m0000gn/T/ipykernel_25011/2022001392.py:10: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/var/folders/6m/9b1tts6d5w960j80wbw9tx3m0000gn/T/ipykernel_25011/2022001392.py:10: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/var/folders/6m/9b1tts6d5w960j80wbw9tx3m0000gn/T/ipykernel_25011/2022001392.py:11: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "error_types = [\n",
    "    \"AgentParsingError\",\n",
    "    \"AgentExecutionError\",\n",
    "    \"AgentMaxIterationsError\",\n",
    "    \"AgentGenerationError\",\n",
    "]\n",
    "sel_df[error_types] = 0\n",
    "sel_df[\"Count steps\"] = np.nan\n",
    "\n",
    "\n",
    "def count_errors(row):\n",
    "    if isinstance(row[\"intermediate_steps\"], list):\n",
    "        row[\"Count steps\"] = len(row[\"intermediate_steps\"])\n",
    "        for step in row[\"intermediate_steps\"]:\n",
    "            if isinstance(step, dict) and \"error\" in step:\n",
    "                try:\n",
    "                    row[str(step[\"error\"][\"error_type\"])] += 1\n",
    "                except Exception:\n",
    "                    pass\n",
    "    return row\n",
    "\n",
    "\n",
    "sel_df = sel_df.apply(count_errors, axis=1)"
   ]
  },
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   "execution_count": 14,
   "metadata": {},
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             0.2222222222222222,
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             0.3333333333333333,
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             0.4444444444444444,
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             0.5555555555555556,
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             0.6666666666666666,
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             0.7777777777777778,
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             0.8888888888888888,
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           "sequentialminus": [
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             0,
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             0.1111111111111111,
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             0.2222222222222222,
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             0.4444444444444444,
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             0.5555555555555556,
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             0.6666666666666666,
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             0.7777777777777778,
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             0.8888888888888888,
             "#fdca26"
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            [
             1,
             "#f0f921"
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           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "width": 800,
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "variable"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "<b>Average count</b>"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "\n",
    "aggregate_errors = (\n",
    "    sel_df.groupby([\"is_correct\"])[error_types + [\"Count steps\"]].mean().reset_index().melt(id_vars=[\"is_correct\"])\n",
    ")\n",
    "\n",
    "fig = px.bar(\n",
    "    aggregate_errors,\n",
    "    y=\"value\",\n",
    "    x=\"variable\",\n",
    "    color=\"is_correct\",\n",
    "    labels={\n",
    "        \"agent_name\": \"<b>Model</b>\",\n",
    "        \"task\": \"<b>Level</b>\",\n",
    "        \"aggregate_score\": \"<b>Performance</b>\",\n",
    "        \"value\": \"<b>Average count</b>\",\n",
    "        \"eval_score_GPT4\": \"<b>Score</b>\",\n",
    "    },\n",
    ")\n",
    "fig.update_layout(\n",
    "    height=500,\n",
    "    width=800,\n",
    "    barmode=\"group\",\n",
    "    bargroupgap=0.0,\n",
    ")\n",
    "fig.update_traces(textposition=\"outside\")\n",
    "fig.write_image(\"aggregate_errors.png\", scale=3)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect result by file extension type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_correct</th>\n",
       "      <th>count_steps</th>\n",
       "      <th>question</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attachment_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <td>0.423799</td>\n",
       "      <td>4.959725</td>\n",
       "      <td>2185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>csv</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>docx</th>\n",
       "      <td>0.571429</td>\n",
       "      <td>4.904762</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jpg</th>\n",
       "      <td>0.142857</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jsonld</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mp3</th>\n",
       "      <td>0.480000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdb</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.444444</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdf</th>\n",
       "      <td>0.588235</td>\n",
       "      <td>4.137255</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>png</th>\n",
       "      <td>0.216783</td>\n",
       "      <td>4.412587</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pptx</th>\n",
       "      <td>0.882353</td>\n",
       "      <td>4.058824</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>py</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.266667</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>txt</th>\n",
       "      <td>0.705882</td>\n",
       "      <td>4.764706</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xlsx</th>\n",
       "      <td>0.612745</td>\n",
       "      <td>4.823529</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip</th>\n",
       "      <td>0.448276</td>\n",
       "      <td>5.344828</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 is_correct  count_steps  question\n",
       "attachment_type                                   \n",
       "None               0.423799     4.959725      2185\n",
       "csv                0.000000     7.750000        16\n",
       "docx               0.571429     4.904762        21\n",
       "jpg                0.142857     5.750000        28\n",
       "jsonld             0.000000     6.600000        15\n",
       "mp3                0.480000     4.500000        50\n",
       "pdb                0.000000     4.444444        18\n",
       "pdf                0.588235     4.137255        51\n",
       "png                0.216783     4.412587       143\n",
       "pptx               0.882353     4.058824        17\n",
       "py                 1.000000     4.266667        15\n",
       "txt                0.705882     4.764706        17\n",
       "xlsx               0.612745     4.823529       204\n",
       "zip                0.448276     5.344828        29"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    result_df.groupby([\"attachment_type\"])[[\"is_correct\", \"count_steps\", \"question\"]].agg(\n",
    "        {\"is_correct\": \"mean\", \"count_steps\": \"mean\", \"question\": \"count\"}\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Ensembling methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = result_df[\"agent_name\"].value_counts()\n",
    "long_series = result_df.loc[result_df[\"agent_name\"].isin(counts[counts > 140].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agent_name\n",
       "code_gpt4o_03_february_goodoldtext-unbroken         38.36\n",
       "code_gpt4o_03_february_magenticbrowser              35.22\n",
       "code_gpt4o_03_february_magenticbrowser2             36.54\n",
       "code_gpt4o_03_february_text                         37.58\n",
       "code_o1_01_february_text                            49.09\n",
       "code_o1_03_february_ablation-toolcalling-manager    32.73\n",
       "code_o1_03_february_fix-print-outputs               51.83\n",
       "code_o1_03_february_fix-print-outputs2              55.77\n",
       "code_o1_03_february_goodoldtext-unbroken            53.42\n",
       "code_o1_03_february_remove-navigational             53.66\n",
       "code_o1_03_february_text_high-reasoning-effort      48.48\n",
       "code_o1_04_february_submission                      49.38\n",
       "code_o1_04_february_submission5                     55.15\n",
       "code_o3-mini_03_february_remove-navigational        29.09\n",
       "Name: is_correct, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Majority score: 58.18\n",
      "Oracle score: 72.73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6m/9b1tts6d5w960j80wbw9tx3m0000gn/T/ipykernel_25011/3287428472.py:20: DeprecationWarning:\n",
      "\n",
      "DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def majority_vote(df):\n",
    "    df = df[(df[\"prediction\"] != \"Unable to determine\") & (~df[\"prediction\"].isna()) & (df[\"prediction\"] != \"None\")]\n",
    "\n",
    "    answer_modes = df.groupby(\"question\")[\"prediction\"].agg(lambda x: x.mode()[0]).reset_index()\n",
    "    first_occurrences = (\n",
    "        df.groupby([\"question\", \"prediction\"]).agg({\"task\": \"first\", \"is_correct\": \"first\"}).reset_index()\n",
    "    )\n",
    "    result = answer_modes.merge(first_occurrences, on=[\"question\", \"prediction\"], how=\"left\")\n",
    "\n",
    "    return result\n",
    "\n",
    "\n",
    "def oracle(df):\n",
    "    def get_first_correct_or_first_wrong(group):\n",
    "        correct_answers = group[group[\"is_correct\"]]\n",
    "        if len(correct_answers) > 0:\n",
    "            return correct_answers.iloc[0]\n",
    "        return group.iloc[0]\n",
    "\n",
    "    result = df.groupby(\"question\").apply(get_first_correct_or_first_wrong)\n",
    "\n",
    "    return result.reset_index(drop=True)\n",
    "\n",
    "\n",
    "display((long_series.groupby(\"agent_name\")[\"is_correct\"].mean() * 100).round(2))\n",
    "print(f\"Majority score: {majority_vote(long_series)['is_correct'].mean() * 100:.2f}\")\n",
    "print(f\"Oracle score: {oracle(long_series)['is_correct'].mean() * 100:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_run = \"code_o1_04_february_submission5.jsonl\"\n",
    "df = pd.read_json(f\"output/validation/{agent_run}\", lines=True)\n",
    "df = df[[\"task_id\", \"prediction\", \"intermediate_steps\"]]\n",
    "df = df.rename(columns={\"prediction\": \"model_answer\", \"intermediate_steps\": \"reasoning_trace\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_json(\"submission.jsonl\", orient=\"records\", lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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