{"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":"5450f24b-37ff-50ae-b8bd-6413a3a2d5ae","task_key":"default--v0~2e1~2e0~5fhf--5450f24b-37ff-50ae-b8bd-6413a3a2d5ae","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 5450f24b-37ff-50ae-b8bd-6413a3a2d5ae","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from datetime import datetime\\nimport random\\nimport matplotlib.pyplot as plt\\ndef task_func(\\n    epoch_milliseconds,\\n    teams=[\\\"Team1\\\", \\\"Team2\\\", \\\"Team3\\\", \\\"Team4\\\", \\\"Team5\\\"],\\n    random_seed=0,\\n):\\n\",\"complete_prompt\":\"from datetime import datetime\\nimport random\\nimport matplotlib.pyplot as plt\\n\\n\\ndef task_func(\\n    epoch_milliseconds,\\n    teams=[\\\"Team1\\\", \\\"Team2\\\", \\\"Team3\\\", \\\"Team4\\\", \\\"Team5\\\"],\\n    random_seed=0,\\n):\\n    \\\"\\\"\\\"\\n    Generate and plot a performance trend for different teams from a given epoch timestamp to the current time.\\n\\n    The performance data is generated by creating a series of random values for each day from the starting timestamp\\n    to the present day. Each team's performance is simulated as a random float between 0.1 and 1 for each day.\\n    The plot shows days since the start date on the x-axis and performance on the y-axis.\\n\\n    Parameters:\\n    epoch_milliseconds (int): The epoch milliseconds from where to start the generation. Must not be in the future.\\n    teams (list of str, optional): Team names. If not provided, defaults to ['Team1', 'Team2', 'Team3', 'Team4', 'Team5'].\\n    random_seed (int, optional): Seed for random number generation to ensure reproducibility. Defaults to 0.\\n\\n    Returns:\\n    dict: A dictionary containing performance data for each team, with days as indices and performance as float values.\\n    matplotlib.figure.Figure: A figure object showing the performance trend of each team over the days.\\n\\n    Requirements:\\n    - datetime.datetime\\n    - random\\n    - matplotlib\\n\\n    Example:\\n    >>> results, ax = task_func(1236472051807)\\n    >>> results.keys()\\n    dict_keys(['Team1', 'Team2', 'Team3', 'Team4', 'Team5'])\\n    >>> type(ax)\\n    <class 'matplotlib.figure.Figure'>\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate and plot a performance trend for different teams from a given epoch timestamp to the current time. The performance data is generated by creating a series of random values for each day from the starting timestamp to the present day. Each team's performance is simulated as a random float between 0.1 and 1 for each day. The plot shows days since the start date on the x-axis and performance on the y-axis.\\nThe function should output with:\\n    dict: A dictionary containing performance data for each team, with days as indices and performance as float values.\\n    matplotlib.figure.Figure: A figure object showing the performance trend of each team over the days.\\nYou should write self-contained code starting with:\\n```\\nfrom datetime import datetime\\nimport random\\nimport matplotlib.pyplot as plt\\ndef task_func(\\n    epoch_milliseconds,\\n    teams=[\\\"Team1\\\", \\\"Team2\\\", \\\"Team3\\\", \\\"Team4\\\", \\\"Team5\\\"],\\n    random_seed=0,\\n):\\n```\",\"libs\":\"['datetime', 'random', 'matplotlib']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}