{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"bigcodebench","formal_name":"BigCodeBench","introduction":"BigCodeBench poses 1,140 function-level tasks drawn across 139 libraries. It tests whether a model can compose several real APIs correctly rather than write one self-contained function.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://bigcode-bench.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"c94498ab-2aee-5c80-8019-7c7676de0805","task_key":"default--v0~2e1~2e0~5fhf--c94498ab-2aee-5c80-8019-7c7676de0805","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf c94498ab-2aee-5c80-8019-7c7676de0805","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from random import randint, seed\\nimport pandas as pd\\nfrom sklearn.linear_model import LinearRegression\\n# Constants\\nTEAMS = ['Team A', 'Team B', 'Team C', 'Team D', 'Team E']\\nPENALTY_COST = 1000  # in dollars\\ndef task_func(goals, penalties, rng_seed=None):\\n\",\"complete_prompt\":\"from random import randint, seed\\nimport pandas as pd\\nfrom sklearn.linear_model import LinearRegression\\n\\n\\n# Constants\\nTEAMS = ['Team A', 'Team B', 'Team C', 'Team D', 'Team E']\\nPENALTY_COST = 1000  # in dollars\\n\\n\\ndef task_func(goals, penalties, rng_seed=None):\\n    \\\"\\\"\\\"\\n    Simulates football match results with random goals and penalties for multiple teams,\\n    and trains a linear regression model to predict penalty costs from goals.\\n\\n    Parameters:\\n    - goals (int): Maximum number of goals a team can score in a match.\\n    - penalties (int): Maximum number of penalties a team can receive in a match.\\n    - rng_seed (int, optional): Seed for the random number generator to ensure reproducibility. Defaults to None.\\n\\n    Returns:\\n    - tuple:\\n        - pd.DataFrame: Contains 'Team', 'Goals', and 'Penalty Cost' columns.\\n        - LinearRegression: Trained model to predict 'Penalty Cost' based on 'Goals'.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn.linear_model\\n    - random\\n\\n    Example:\\n    >>> df, model = task_func(5, 3, rng_seed=42)\\n    >>> predictions = model.predict([[2], [3]])\\n    >>> print(predictions)\\n    [706.89655172 439.65517241]\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Simulates football match results with random goals and penalties for multiple teams, and trains a linear regression model to predict penalty costs from goals.\\nThe function should output with:\\n    tuple:\\n    pd.DataFrame: Contains 'Team', 'Goals', and 'Penalty Cost' columns.\\n    LinearRegression: Trained model to predict 'Penalty Cost' based on 'Goals'.\\nYou should write self-contained code starting with:\\n```\\nfrom random import randint, seed\\nimport pandas as pd\\nfrom sklearn.linear_model import LinearRegression\\n# Constants\\nTEAMS = ['Team A', 'Team B', 'Team C', 'Team D', 'Team E']\\nPENALTY_COST = 1000  # in dollars\\ndef task_func(goals, penalties, rng_seed=None):\\n```\",\"libs\":\"['pandas', 'random', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}