{"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":"0e17f1be-7a42-5cc6-9b17-df8c0d45f2bb","task_key":"default--v0~2e1~2e0~5fhf--0e17f1be-7a42-5cc6-9b17-df8c0d45f2bb","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 0e17f1be-7a42-5cc6-9b17-df8c0d45f2bb","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport random\\nfrom sklearn.model_selection import train_test_split\\ndef task_func(n_data_points=10000, min_value=0.0, max_value=10.0, test_size=0.2):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport random\\nfrom sklearn.model_selection import train_test_split\\n\\ndef task_func(n_data_points=10000, min_value=0.0, max_value=10.0, test_size=0.2):\\n    '''\\n    Generate a random set of floating-point numbers within a specified range, truncate each value to 3 decimal places,\\n    and divide the data into train and test sets based on a given test size.\\n\\n    Parameters:\\n    - n_data_points (int): Number of data points to generate. Default is 10000.\\n    - min_value (float): Minimum value of the generated data points. Default is 0.0.\\n    - max_value (float): Maximum value of the generated data points. Default is 10.0.\\n    - test_size (float): Proportion of the dataset to include in the test split. Default is 0.2.\\n\\n    Returns:\\n    tuple: A tuple with two pandas DataFrames (train set, test set).\\n\\n    Requirements:\\n    - pandas\\n    - random\\n    - sklearn.model_selection\\n\\n    Note:\\n    - The function use \\\"Value\\\" for the column name in the DataFrames (train set, test set) that being returned.\\n\\n    Example:\\n    >>> random.seed(0)\\n    >>> train_data, test_data = task_func()\\n    >>> print(train_data.shape[0])\\n    8000\\n    >>> print(test_data.shape[0])\\n    2000\\n    >>> random.seed(0)\\n    >>> train_data, test_data = task_func(n_data_points=500, min_value=1.0, max_value=1.0, test_size=0.3)\\n    >>> print(train_data.shape[0])\\n    350\\n    >>> print(test_data.shape[0])\\n    150\\n    >>> print(test_data.iloc[0]['Value'])\\n    1.0\\n    '''\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate a random set of floating-point numbers within a specified range, truncate each value to 3 decimal places, and divide the data into train and test sets based on a given test size.\\nNote that: The function use \\\"Value\\\" for the column name in the DataFrames (train set, test set) that being returned.\\nThe function should output with:\\n    tuple: A tuple with two pandas DataFrames (train set, test set).\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport random\\nfrom sklearn.model_selection import train_test_split\\ndef task_func(n_data_points=10000, min_value=0.0, max_value=10.0, test_size=0.2):\\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":[]}