How to annotate BigCodeBench Human Baseline Evaluation

[
  {
    "id": "bcb-001",
    "text": "Write a function that reads a CSV file, groups rows by a specified column, computes the mean of a numeric column for each group, and returns a sorted bar chart as a matplotlib Figure. Handle missing values by dropping them before aggregation. The function should validate that both columns exist in the CSV.",
    "function_signature": "def task_func(csv_path: str, group_col: str, value_col: str) -> matplotlib.figure.Figure:",
    "agent_code": "import pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.figure\n\ndef task_func(csv_path: str, group_col: str, value_col: str) -> matplotlib.figure.Figure:\n    df = pd.read_csv(csv_path)\n    \n    if group_col not in df.columns:\n        raise ValueError(f\"Column '{group_col}' not found in CSV\")\n    if value_col not in df.columns:\n        raise ValueError(f\"Column '{value_col}' not found in CSV\")\n    \n    df = df.dropna(subset=[group_col, value_col])\n    grouped = df.groupby(group_col)[value_col].mean().sort_values(ascending=False)\n    \n    fig, ax = plt.subplots(figsize=(10, 6))\n    grouped.plot(kind='bar', ax=ax)\n    ax.set_xlabel(group_col)\n    ax.set_ylabel(f'Mean {value_col}')\n    ax.set_title(f'Mean {value_col} by {group_col}')\n    plt.tight_layout()\n    \n    return fig",
    "execution_result": "$ python -m pytest test_task_001.py -v\ntest_task_001.py::test_basic_grouping PASSED\ntest_task_001.py::test_missing_values_dropped PASSED\ntest_task_001.py::test_invalid_group_column PASSED\ntest_task_001.py::test_invalid_value_column PASSED\ntest_task_001.py::test_sorted_descending PASSED\ntest_task_001.py::test_returns_figure PASSED\n\n6 passed in 1.23s"
  },
  {
    "id": "bcb-002",
    "text": "Write a function that takes a directory path, recursively finds all Python files, parses each file's AST to extract function definitions, and returns a dictionary mapping file paths to lists of function names. Skip files that have syntax errors. Include nested functions.",
    "function_signature": "def task_func(directory: str) -> dict[str, list[str]]:",
    "agent_code": "import os\nimport ast\nfrom typing import Dict, List\n\ndef task_func(directory: str) -> Dict[str, List[str]]:\n    result = {}\n    \n    for root, dirs, files in os.walk(directory):\n        for fname in files:\n            if not fname.endswith('.py'):\n                continue\n            fpath = os.path.join(root, fname)\n            try:\n                with open(fpath, 'r') as f:\n                    source = f.read()\n                tree = ast.parse(source)\n            except SyntaxError:\n                continue\n            \n            functions = []\n            for node in ast.walk(tree):\n                if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):\n                    functions.append(node.name)\n            \n            if functions:\n                result[fpath] = functions\n    \n    return result",
    "execution_result": "$ python -m pytest test_task_002.py -v\ntest_task_002.py::test_basic_extraction PASSED\ntest_task_002.py::test_nested_functions PASSED\ntest_task_002.py::test_async_functions PASSED\ntest_task_002.py::test_syntax_error_skip PASSED\ntest_task_002.py::test_empty_directory PASSED\ntest_task_002.py::test_no_python_files PASSED\ntest_task_002.py::test_subdirectories PASSED\n\n7 passed in 0.45s"
  }
]

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