{ "tool_name": "terminal", "tool_arguments": { "code": "cat /usr/local/bin/agent/src/poppy/examples/open_deep_research/analysis.ipynb" }, "tool_response": "Stdout:\n{\n\n \"cells\": [\n\n {\n\n \"cell_type\": \"code\",\n\n \"execution_count\": 1,\n\n \"metadata\": {},\n\n \"outputs\": [],\n\n \"source\": [\n\n \"# !pip install plotly kaleido datasets nbformat -U -q\"\n\n ]\n\n },\n\n {\n\n \"cell_type\": \"code\",\n\n \"execution_count\": 2,\n\n \"metadata\": {},\n\n \"outputs\": [\n\n {\n\n \"name\": \"stderr\",\n\n \"output_type\": \"stream\",\n\n \"text\": [\n\n \"/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\",\n\n \" from .autonotebook import tqdm as notebook_tqdm\\n\",\n\n \"Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\\n\"\n\n ]\n\n }\n\n ],\n\n \"source\": [\n\n \"import os\\n\",\n\n \"\\n\",\n\n \"import datasets\\n\",\n\n \"import pandas as pd\\n\",\n\n \"from dotenv import load_dotenv\\n\",\n\n \"from huggingface_hub import login\\n\",\n\n \"\\n\",\n\n \"\\n\",\n\n \"load_dotenv(override=True)\\n\",\n\n \"login(os.getenv(\\\"HF_TOKEN\\\"))\\n\",\n\n \"\\n\",\n\n \"pd.set_option(\\\"max_colwidth\\\", None)\\n\",\n\n \"\\n\",\n\n \"OUTPUT_DIR = \\\"output\\\"\"\n\n ]\n\n },\n\n {\n\n \"cell_type\": \"code\",\n\n \"execution_count\": 3,\n\n \"metadata\": {},\n\n \"outputs\": [],\n\n \"source\": [\n\n \"eval_ds = datasets.load_dataset(\\\"gaia-benchmark/GAIA\\\", \\\"2023_all\\\")[\\\"validation\\\"]\\n\",\n\n \"eval_ds = eval_ds.rename_columns({\\\"Question\\\": \\\"question\\\", \\\"Final answer\\\": \\\"true_answer\\\", \\\"Level\\\": \\\"task\\\"})\\n\",\n\n \"eval_df = pd.DataFrame(eval_ds)\"\n\n ]\n\n },\n\n {\n\n \"cell_type\": \"code\",\n\n \"execution_count\": 4,\n\n \"metadata\": {},\n\n \"outputs\": [\n\n {\n\n \"data\": {\n\n \"text/plain\": [\n\n \"2 86\\n\",\n\n \"1 53\\n\",\n\n \"3 26\\n\",\n\n \"Name: count, dtype: int64\"\n\n ]\n\n },\n\n \"execution_count\": 4,\n\n \"metadata\": {},\n\n \"output_type\": \"execute_result\"\n\n }\n\n ],\n\n \"source\": [\n\n \"pd.Series(eval_ds[\\\"task\\\"]).value_counts()\"\n\n ]\n\n },\n\n {\n\n \"cell_type\": \"markdown\",\n\n \"metadata\": {},\n\n \"source\": [\n\n \"# 1. Load all results\"\n\n ]\n\n },\n\n {\n\n \"cell_type\": \"code\",\n\n \"execution_count\": 5,\n\n \"metadata\": {},\n\n \"outputs\": [],\n\n \"source\": [\n\n \"import glob\\n\",\n\n \"\\n\",\n\n \"\\n\",\n\n \"results = []\\n\",\n\n \"for f in glob.glob(f\\\"{OUTPUT_DIR}/validation/*.jsonl\\\"):\\n\",\n\n \" df = pd.read_json(f, lines=True)\\n\",\n\n \" df[\\\"agent_name\\\"] = f.split(\\\"/\\\")[-1].split(\\\".\\\")[0]\\n\",\n\n \" results.append(df)\\n\",\n\n \"\\n\",\n\n \"result_df = pd.concat(results)\\n\",\n\n \"result_df = result_df.drop(columns=[\\\"start_time\\\", \\\"end_time\\\"])\\n\",\n\n \"result_df[\\\"prediction\\\"] = result_df[\\\"prediction\\\"].fillna(\\\"No prediction\\\")\"\n\n ]\n\n },\n\n {\n\n \"cell_type\": \"code\",\n\n \"execution_count\": 6,\n\n \"metadata\": {},\n\n \"outputs\": [\n\n {\n\n \"name\": \"stdout\",\n\n \"output_type\": \"stream\",\n\n \"text\": [\n\n \"String cannot be normalized to number str.\\n\",\n\n \"String 2017 Komo Mai Drive 900000 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cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String 5.75 for Cheater cannot be normalized to number str.\\n\",\n\n \"String 5.22 for Cheater Beater cannot be normalized to number str.\\n\",\n\n \"String 2017 Komo Mai Drive sold for 900000 cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String 33101 28557 cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"String Unable to determine cannot be normalized to number str.\\n\",\n\n \"Close call: Alfonso Cardinal Visconti vs Alfonso Visconti\\n\",\n\n \"Close call: Rockhopper Penguins vs Rockhopper penguin\\n\",\n\n \"Close call: INT. THE CASTLE vs THE CASTLE\\n\",\n\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\",\n\n \"Close call: The World of the Twenty First Century 1994 vs The World of the Twenty First Century\\n\",\n\n \"Close call: Alfonso Cardinal Visconti vs Alfonso Visconti\\n\",\n\n \"Close call: Wes Craven's A Nightmare on Elm Street vs A Nightmare on Elm Street\\n\",\n\n \"Close call: God said let there be dragons vs Here be dragons\\n\",\n\n \"Close call: rockhopper penguins vs Rockhopper penguin\\n\",\n\n \"Close call: Harbinger, This Fire, Tidal vs Harbinger, Tidal\\n\",\n\n \"Close call: EC 3.1.3.1;EC 1.11.1.7 vs 3.1.3.1; 1.11.1.7\\n\",\n\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\",\n\n \"Close call: Alfonso Cardinal Visconti vs Alfonso Visconti\\n\",\n\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\",\n\n \"Close call: Out of the Silent Planet by C.S. Lewis vs Out of the Silent Planet\\n\",\n\n \"Close call: broccoli, celery, fresh basil, green beans, lettuce, sweet potatoes vs broccoli, celery, fresh basil, lettuce, sweet potatoes\\n\",\n\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\"\n\n ]\n\n },\n\n {\n\n \"name\": \"stderr\",\n\n \"output_type\": \"stream\",\n\n \"text\": [\n\n \"/Users/aymeric/Documents/Code/smolagents/examples/open_deep_research/scripts/gaia_scorer.py:52: UserWarning: Answer lists have different lengths, returning False.\\n\",\n\n \" warnings.warn(\\\"Answer lists have different lengths, returning False.\\\", UserWarning)\\n\"\n\n ]\n\n }\n\n ],\n\n \"source\": [\n\n \"import re\\n\",\n\n \"from collections import Counter\\n\",\n\n \"\\n\",\n\n \"from scripts.gaia_scorer import check_close_call, question_scorer\\n\",\n\n \"\\n\",\n\n \"\\n\",\n\n \"result_df[\\\"is_correct\\\"] = result_df.apply(lambda x: question_scorer(x[\\\"prediction\\\"], x[\\\"true_answer\\\"]), axis=1)\\n\",\n\n \"result_df[\\\"is_near_correct\\\"] = result_df.apply(\\n\",\n\n \" lambda x: check_close_call(x[\\\"prediction\\\"], x[\\\"true_answer\\\"], x[\\\"is_correct\\\"]),\\n\",\n\n \" axis=1,\\n\",\n\n \")\\n\",\n\n \"\\n\",\n\n \"result_df[\\\"count_steps\\\"] = result_df[\\\"intermediate_steps\\\"].apply(len)\\n\",\n\n \"\\n\",\n\n \"\\n\",\n\n \"def find_attachment(question):\\n\",\n\n \" matches = eval_df.loc[eval_df[\\\"question\\\"].apply(lambda x: x in question), \\\"file_name\\\"]\\n\",\n\n \"\\n\",\n\n \" if len(matches) == 0:\\n\",\n\n \" return \\\"Not found\\\"\\n\",\n\n \" file_path = matches.values[0]\\n\",\n\n \"\\n\",\n\n \" if isinstance(file_path, str) and len(file_path) > 0:\\n\",\n\n \" return file_path.split(\\\".\\\")[-1]\\n\",\n\n \" else:\\n\",\n\n \" return \\\"None\\\"\\n\",\n\n \"\\n\",\n\n \"\\n\",\n\n \"result_df[\\\"attachment_type\\\"] = result_df[\\\"question\\\"].apply(find_attachment)\\n\",\n\n \"\\n\",\n\n \"\\n\",\n\n \"def extract_tool_calls(code):\\n\",\n\n \" regex = r\\\"\\\\b(\\\\w+)\\\\(\\\"\\n\",\n\n \" function_calls = [el for el in re.findall(regex, code) if el.islower()]\\n\",\n\n \"\\n\",\n\n \" function_call_counter = Counter(function_calls)\\n\",\n\n \" return function_call_counter\\n\",\n\n \"\\n\",\n\n \"\\n\",\n\n \"def sum_tool_calls(steps):\\n\",\n\n \" total_count = Counter()\\n\",\n\n \" for step in steps:\\n\",\n\n \" if \\\"llm_output\\\" in step:\\n\",\n\n \" total_count += extract_tool_calls(step[\\\"llm_output\\\"])\\n\",\n\n \"\\n\",\n\n \" return total_count\\n\",\n\n \"\\n\",\n\n \"\\n\",\n\n \"# result_df[\\\"tool_calls\\\"] = result_df[\\\"intermediate_steps\\\"].apply(sum_tool_calls)\"\n\n ]\n\n },\n\n {\n\n \"cell_type\": \"code\",\n\n \"execution_count\": 7,\n\n \"metadata\": {},\n\n \"outputs\": [],\n\n \"source\": [\n\n \"def get_thoughts(x):\\n\",\n\n \" try:\\n\",\n\n \" output = x[0][\\\"task\\\"]\\n\",\n\n \" for y in x[1:]:\\n\",\n\n \" try:\\n\",\n\n \" if \\\"observation\\\" in y:\\n\",\n\n \" output += y[\\\"llm_output\\\"] + \\\"\\\\nObservation:\\\" + y[\\\"observation\\\"]\\n\",\n\n \" else:\\n\",\n\n \" output += y[\\\"llm_output\\\"] + r\\\"\\\\Error:\\\" + str(y[\\\"error\\\"])\\n\",\n\n \" except Exception:\\n\",\n\n \" pass\\n\",\n\n \" return output\\n\",\n\n \" except Exception:\\n\",\n\n \" return None\\n\",\n\n \"\\n\",\n\n \"\\n\",\n\n \"result_df[\\\"thoughts\\\"] = result_df[\\\"intermediate_steps\\\"].apply(lambda x: get_thoughts(x))\"\n\n ]\n\n },\n\n {\n\n \"cell_type\": \"code\",\n\n \"execution_count\": 8,\n\n \"metadata\": {},\n\n \"outputs\": [\n\n {\n\n \"data\": {\n\n \"text/plain\": [\n\n \"agent_name\\n\",\n\n \"code_gpt4o_03_february_text 165\\n\",\n\n \"code_o1_03_february_ablation-toolcalling-manager 165\\n\",\n\n \"code_o1_01_february_text 165\\n\",\n\n \"code_o3-mini_03_february_remove-navigational 165\\n\",\n\n \"code_o1_04_february_submission5 165\\n\",\n\n \"code_o1_03_february_text_high-reasoning-effort 165\\n\",\n\n \"code_o1_03_february_remove-navigational 164\\n\",\n\n \"code_o1_03_february_fix-print-outputs 164\\n\",\n\n \"code_o1_04_february_submission 162\\n\",\n\n \"code_o1_03_february_goodoldtext-unbroken 161\\n\",\n\n \"code_gpt4o_03_february_goodoldtext-unbroken 159\\n\",\n\n \"code_gpt4o_03_february_magenticbrowser 159\\n\",\n\n \"code_o1_03_february_fix-print-outputs2 156\\n\",\n\n \"code_gpt4o_03_february_magenticbrowser2 156\\n\",\n\n \"code_o1_04_february_submission-medium 125\\n\",\n\n \"code_o1_29-01_text 105\\n\",\n\n \"code_llama-3 90\\n\",\n\n \"code_o1_22-01_managedagent-summary_planning 67\\n\",\n\n \"code_o1_25-01_visioon 53\\n\",\n\n \"code_o1_04_february_submission3 49\\n\",\n\n \"code_qwen-coder-32B_03_february_text 43\\n\",\n\n \"code_o1_04_february_submission4 6\\n\",\n\n \"Name: count, dtype: int64\"\n\n ]\n\n },\n\n \"execution_count\": 8,\n\n \"metadata\": {},\n\n \"output_type\": \"execute_result\"\n\n }\n\n ],\n\n \"source\": [\n\n \"result_df[\\\"agent_name\\\"].value_counts()\"\n\n ]\n\n },\n\n {\n\n \"cell_type\": \"markdown\",\n\n \"metadata\": {},\n\n \"source\": [\n\n \"# 2. Inspect specific runs\"\n\n ]\n\n },\n\n {\n\n \"cell_type\": \"code\",\n\n \"execution_count\": 9,\n\n \"metadata\": {},\n\n \"outputs\": [\n\n {\n\n \"data\": {\n\n \"text/plain\": [\n\n \"agent_name\\n\",\n\n \"code_gpt4o_03_february_text 165\\n\",\n\n \"code_o1_03_february_ablation-toolcalling-manager 165\\n\",\n\n \"code_o1_01_february_text 165\\n\",\n\n \"code_o3-mini_03_february_remove-navigational 165\\n\",\n\n \"code_o1_04_february_submission5 165\\n\",\n\n \"code_o1_03_february_text_high-reasoning-effort 165\\n\",\n\n \"code_o1_03_february_remove-navigational 164\\n\",\n\n \"code_o1_03_february_fix-print-outputs 164\\n\",\n\n \"code_o1_04_february_submission 162\\n\",\n\n \"code_o1_03_february_goodoldtext-unbroken 161\\n\",\n\n \"code_gpt4o_03_february_goodoldtext-unbroken 159\\n\",\n\n \"code_gpt4o_03_february_magenticbrowser 159\\n\",\n\n \"code_o1_03_february_fix-print-outputs2 156\\n\",\n\n \"code_gpt4o_03_february_magenticbrowser2 156\\n\",\n\n \"code_o1_04_february_submission-medium 125\\n\",\n\n \"code_o1_29-01_text 105\\n\",\n\n \"code_llama-3 90\\n\",\n\n \"code_o1_22-01_managedagent-summary_planning 67\\n\",\n\n \"code_o1_25-01_visioon 53\\n\",\n\n \"code_o1_04_february_submission3 49\\n\",\n\n \"code_qwen-coder-32B_03_february_text 43\\n\",\n\n \"code_o1_04_february_submission4 6\\n\",\n\n \"Name: count, dtype: int64\"\n\n ]\n\n },\n\n \"metadata\": {},\n\n \"output_type\": \"display_data\"\n\n },\n\n {\n\n \"data\": {\n\n \"text/plain\": [\n\n \"agent_name task\\n\",\n\n \"code_gpt4o_03_february_goodoldtext-unbroken 2 84\\n\",\n\n \" 1 53\\n\",\n\n \" 3 22\\n\",\n\n \"code_gpt4o_03_february_magenticbrowser 2 83\\n\",\n\n \" 1 52\\n\",\n\n \" ..\\n\",\n\n \"code_o3-mini_03_february_remove-navigational 1 53\\n\",\n\n \" 3 26\\n\",\n\n \"code_qwen-coder-32B_03_february_text 2 22\\n\",\n\n \" 1 14\\n\",\n\n \" 3 7\\n\",\n\n \"Name: count, Length: 65, dtype: int64\"\n\n ]\n\n },\n\n \"metadata\": {},\n\n \"output_type\": \"display_data\"\n\n },\n\n {\n\n \"name\": \"stdout\",\n\n \"output_type\": \"stream\",\n\n \"text\": [\n\n \"Total length: 2809 - is complete: False\\n\"\n\n ]\n\n }\n\n ],\n\n \"source\": [\n\n \"o1_vision = \\\"code_o1_25-01_visioon\\\"\\n\",\n\n \"o1_next = \\\"code_o1_29-01_text\\\"\\n\",\n\n \"o1 = \\\"code_o1_01_february_text\\\"\\n\",\n\n \"\\n\",\n\n \"list_versions = [o1, o1_vision, o1_next]\\n\",\n\n \"\\n\",\n\n \"# submission_selection_name = \\\"react_code_llama3-70b_02-05_full-gaia-validation-code\\\"\\n\",\n\n \"sel_df = result_df\\n\",\n\n \"# sel_df = sel_df.loc[\\n\",\n\n \"# (result_df[\\\"agent_name\\\"].isin(list_versions))\\n\",\n\n \"# # & (~result_df[\\\"question\\\"].isin(UNSOLVED_QUESTIONS))\\n\",\n\n \"# ]\\n\",\n\n \"sel_df = sel_df.reset_index(drop=True)\\n\",\n\n \"display(sel_df[\\\"agent_name\\\"].value_counts())\\n\",\n\n \"sel_df = sel_df.drop_duplicates(subset=[\\\"agent_name\\\", \\\"question\\\"])\\n\",\n\n \"display(sel_df.groupby(\\\"agent_name\\\")[[\\\"task\\\"]].value_counts())\\n\",\n\n \"print(\\\"Total length:\\\", len(sel_df), \\\"- is complete:\\\", len(sel_df) == 165)\\n\",\n\n \"# assert sel_df[\\\"question\\\"].value_counts().max() == len(list_versions), \\\"Some questions are duplicate!\\\"\"\n\n ]\n\n },\n\n {\n\n \"cell_type\": \"code\",\n\n \"execution_count\": 10,\n\n \"metadata\": {},\n\n \"outputs\": [\n\n {\n\n \"data\": {\n\n \"text/plain\": [\n\n \"'Average score:'\"\n\n ]\n\n },\n\n \"metadata\": {},\n\n \"output_type\": \"display_data\"\n\n },\n\n {\n\n \"data\": {\n\n \"text/html\": [\n\n \"

\\n\",\n\n \"\\n\",\n\n \"\\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \" \\n\",\n\n \"

	is_correct
agent_name
code_gpt4o_03_february_goodoldtext-unbroken	0.384
code_gpt4o_03_february_magenticbrowser	0.352
code_gpt4o_03_february_magenticbrowser2	0.365
code_gpt4o_03_february_text	0.376
code_llama-3	0.078
code_o1_01_february_text	0.491
code_o1_03_february_ablation-toolcalling-manager	0.327
code_o1_03_february_fix-print-outputs	0.518
code_o1_03_february_fix-print-outputs2	0.558
code_o1_03_february_goodoldtext-unbroken	0.534
code_o1_03_february_remove-navigational	0.537
code_o1_03_february_text_high-reasoning-effort	0.485
code_o1_04_february_submission	0.494
code_o1_04_february_submission-medium	0.488
code_o1_04_february_submission3	0.490
code_o1_04_february_submission4	0.500
code_o1_04_february_submission5	0.552
code_o1_22-01_managedagent-summary_planning	0.418
code_o1_25-01_visioon	0.340
code_o1_29-01_text	0.390
code_o3-mini_03_february_remove-navigational	0.291
code_qwen-coder-32B_03_february_text	0.209

\\n\",\n\n \"

\"\n\n ],\n\n \"text/plain\": [\n\n \" is_correct\\n\",\n\n \"agent_name \\n\",\n\n \"code_gpt4o_03_february_goodoldtext-unbroken 0.384\\n\",\n\n \"code_gpt4o_03_february_magenticbrowser 0.352\\n\",\n\n \"code_gpt4o_03_february_magenticbrowser2 0.365\\n\",\n\n \"code_gpt4o_03_february_text 0.376\\n\",\n\n \"code_llama-3 0.078\\n\",\n\n \"code_o1_01_february_text 0.491\\n\",\n\n \"code_o1_03_february_ablation-toolcalling-manager 0.327\\n\",\n\n \"code_o1_03_february_fix-print-outputs 0.518\\n\",\n\n \"code_o1_03_february_fix-print-outputs2 0.558\\n\",\n\n \"code_o1_03_february_goodoldtext-unbroken 0.534\\n\",\n\n \"code_o1_03_february_remove-navigational 0.537\\n\",\n\n \"code_o1_03_february_text_high-reasoning-effort 0.485\\n\",\n\n \"code_o1_04_february_submission 0.494\\n\",\n\n \"code_o1_04_february_submission-medium 0.488\\n\",\n\n \"code_o1_04_february_submission3 0.490\\n\",\n\n \"code_o1_04_february_submission4 0.500\\n\",\n\n \"code_o1_04_february_submission5 0.552\\n\",\n\n \"code_o1_22-01_managedagent-summary_planning 0.418\\n\",\n\n \"code_o1_25-01_visioon 0.340\\n\",\n\n \"code_o1_29-01_text 0.390\\n\",\n\n \"code_o3-mini_03_february_remove-navigational 0.291\\n\",\n\n \"code_qwen-coder-32B_03_february_text 0.209\"\n\n ]\n\n },\n\n \"metadata\": {},\n\n \"output_type\": \"display_data\"\n\n },\n\n {\n\n \"data\": {\n\n \"text/html\": [\n\n \"

		is_correct	is_near_correct	count_steps	count
agent_name	task
code_gpt4o_03_february_goodoldtext-unbroken	1	0.452830	0.452830	7.000000	53
2	0.380952	0.392857	8.511905	84
3	0.227273	0.227273	10.409091	22
code_gpt4o_03_february_magenticbrowser	1	0.480769	0.480769	7.153846	52
2	0.349398	0.361446	8.168675	83
...	...	...	...	...	...
code_o3-mini_03_february_remove-navigational	2	0.232558	0.244186	4.976744	86
3	0.153846	0.153846	6.615385	26
code_qwen-coder-32B_03_february_text	1	0.357143	0.357143	5.428571	14
2	0.136364	0.136364	6.409091	22
3	0.142857	0.142857	6.571429	7

\\n\",\n\n \"

65 rows \u00d7 4 columns

\\n\",\n\n \"

\"\n\n ],\n\n \"text/plain\": [\n\n \" is_correct \\\\\\n\",\n\n \"agent_name task \\n\",\n\n \"code_gpt4o_03_february_goodoldtext-unbroken 1 0.452830 \\n\",\n\n \" 2 0.380952 \\n\",\n\n \" 3 0.227273 \\n\",\n\n \"code_gpt4o_03_february_magenticbrowser 1 0.480769 \\n\",\n\n \" 2 0.349398 \\n\",\n\n \"... ... \\n\",\n\n \"code_o3-mini_03_february_remove-navigational 2 0.232558 \\n\",\n\n \" 3 0.153846 \\n\",\n\n \"code_qwen-coder-32B_03_february_text 1 0.357143 \\n\",\n\n \" 2 0.136364 \\n\",\n\n \" 3 0.142857 \\n\",\n\n \"\\n\",\n\n \" is_near_correct \\\\\\n\",\n\n \"agent_name task \\n\",\n\n \"code_gpt4o_03_february_goodoldtext-unbroken 1 0.452830 \\n\",\n\n \" 2 0.392857 \\n\",\n\n \" 3 0.227273 \\n\",\n\n \"code_gpt4o_03_february_magenticbrowser 1 0.480769 \\n\",\n\n \" 2 0.361446 \\n\",\n\n \"... ... \\n\",\n\n \"code_o3-mini_03_february_remove-navigational 2 0.244186 \\n\",\n\n \" 3 0.153846 \\n\",\n\n \"code_qwen-coder-32B_03_february_text 1 0.357143 \\n\",\n\n \" 2 0.136364 \\n\",\n\n \" 3 0.142857 \\n\",\n\n \"\\n\",\n\n \" count_steps count \\n\",\n\n \"agent_name task \\n\",\n\n \"code_gpt4o_03_february_goodoldtext-unbroken 1 7.000000 53 \\n\",\n\n \" 2 8.511905 84 \\n\",\n\n \" 3 10.409091 22 \\n\",\n\n \"code_gpt4o_03_february_magenticbrowser 1 7.153846 52 \\n\",\n\n \" 2 8.168675 83 \\n\",\n\n \"... ... ... \\n\",\n\n \"code_o3-mini_03_february_remove-navigational 2 4.976744 86 \\n\",\n\n \" 3 6.615385 26 \\n\",\n\n \"code_qwen-coder-32B_03_february_text 1 5.428571 14 \\n\",\n\n \" 2 6.409091 22 \\n\",\n\n \" 3 6.571429 7 \\n\",\n\n \"\\n\",\n\n \"[65 rows x 4 columns]\"\n\n ]\n\n },\n\n \"metadata\": {},\n\n \"output_type\": \"display_data\"\n\n }\n\n ],\n\n \"source\": [\n\n \"display(\\\"Average score:\\\", sel_df.groupby(\\\"agent_name\\\")[[\\\"is_correct\\\"]].mean().round(3))\\n\",\n\n \"display(\\n\",\n\n \" sel_df.groupby([\\\"agent_name\\\", \\\"task\\\"])[[\\\"is_correct\\\", \\\"is_near_correct\\\", \\\"count_steps\\\", \\\"question\\\"]]\\n\",\n\n \" .agg(\\n\",\n\n \" {\\n\",\n\n \" \\\"is_correct\\\": \\\"mean\\\",\\n\",\n\n \" \\\"is_near_correct\\\": \\\"mean\\\",\\n\",\n\n \" \\\"count_steps\\\": \\\"mean\\\",\\n\",\n\n \" \\\"question\\\": \\\"count\\\",\\n\",\n\n \" }\\n\",\n\n \" )\\n\",\n\n \" .rename(columns={\\\"question\\\": \\\"count\\\"})\\n\",\n\n \")\"\n\n ]\n\n },\n\n {\n\n \"cell_type\": \"code\",\n\n \"execution_count\": 11,\n\n \"metadata\": {},\n\n \"outputs\": [\n\n {\n\n \"data\": {\n\n \"application/vnd.plotly.v1+json\": {\n\n \"config\": {\n\n \"plotlyServerURL\": \"https://plot.ly\"\n\n },\n\n \"data\": [\n\n {\n\n \"customdata\": [\n\n [\n\n \"The attached spreadsheet shows the inventory for a\"\n\n ],\n\n [\n\n \"How many studio albums were published by Mercedes \"\n\n ],\n\n [\n\n \"In Unlambda, what exact charcter or text needs to \"\n\n ],\n\n [\n\n \"If we assume all articles published by Nature in 2\"\n\n ],\n\n [\n\n \"Here's a fun riddle that I think you'll enjoy.\\n\\nYo\"\n\n ],\n\n [\n\n \"If Eliud Kipchoge could maintain his record-making\"\n\n ],\n\n [\n\n \"The object in the British Museum's collection with\"\n\n ],\n\n [\n\n \"A paper about AI regulation that was originally su\"\n\n ],\n\n [\n\n \"What's the last line of the rhyme under the flavor\"\n\n ],\n\n [\n\n \"According to github, when was Regression added to \"\n\n ],\n\n [\n\n \"I\u2019m researching species that became invasive after\"\n\n ],\n\n [\n\n \".rewsna eht sa \\\"tfel\\\" drow eht fo etisoppo eht eti\"\n\n ],\n\n [\n\n \"My family reunion is this week, and I was assigned\"\n\n ],\n\n [\n\n \"An office held a Secret Santa gift exchange where \"\n\n ],\n\n [\n\n \"When you take the average of the standard populati\"\n\n ],\n\n [\n\n \"I need to fact-check a citation. This is the citat\"\n\n ],\n\n [\n\n \"\u00ac(A \u2227 B) \u2194 (\u00acA \u2228 \u00acB)\\n\u00ac(A \u2228 B) \u2194 (\u00acA \u2227 \u00acB)\\n(A \u2192 B) \"\n\n ],\n\n [\n\n \"In the fictional language of Tizin, basic sentence\"\n\n ],\n\n [\n\n \"What was the volume in m^3 of the fish bag that wa\"\n\n ],\n\n [\n\n \"In April of 1977, who was the Prime Minister of th\"\n\n ],\n\n [\n\n \"Compute the check digit the Tropicos ID for the Or\"\n\n ],\n\n [\n\n \"Assuming scientists in the famous youtube video Th\"\n\n ],\n\n [\n\n \"In Series 9, Episode 11 of Doctor Who, the Doctor \"\n\n ],\n\n [\n\n \"Use density measures from the chemistry materials \"\n\n ],\n\n [\n\n \"What two-word type of model did Manash Pratim Kash\"\n\n ],\n\n [\n\n \"Review the chess position provided in the image. 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