{ "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" }, { "data": { "text/html": [ "
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is_correct
agent_name
code_gpt4o_03_february_goodoldtext-unbroken0.384
code_gpt4o_03_february_magenticbrowser0.352
code_gpt4o_03_february_magenticbrowser20.365
code_gpt4o_03_february_text0.376
code_llama-30.078
code_o1_01_february_text0.491
code_o1_03_february_ablation-toolcalling-manager0.327
code_o1_03_february_fix-print-outputs0.518
code_o1_03_february_fix-print-outputs20.558
code_o1_03_february_goodoldtext-unbroken0.534
code_o1_03_february_remove-navigational0.537
code_o1_03_february_text_high-reasoning-effort0.485
code_o1_04_february_submission0.494
code_o1_04_february_submission-medium0.488
code_o1_04_february_submission30.490
code_o1_04_february_submission40.500
code_o1_04_february_submission50.552
code_o1_22-01_managedagent-summary_planning0.418
code_o1_25-01_visioon0.340
code_o1_29-01_text0.390
code_o3-mini_03_february_remove-navigational0.291
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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)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "is_correct=False
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"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": "Average count" } } } } }, "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\": \"Model\",\n", " \"task\": \"Level\",\n", " \"aggregate_score\": \"Performance\",\n", " \"value\": \"Average count\",\n", " \"eval_score_GPT4\": \"Score\",\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": [ "
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is_correctcount_stepsquestion
attachment_type
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pdb0.0000004.44444418
pdf0.5882354.13725551
png0.2167834.412587143
pptx0.8823534.05882417
py1.0000004.26666715
txt0.7058824.76470617
xlsx0.6127454.823529204
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" ], "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": [] } ], "metadata": { "kernelspec": { "display_name": "gaia", "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": "3.12.0" } }, "nbformat": 4, "nbformat_minor": 2 }