{"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":"1b0d8ecd-81eb-5fe3-b2af-d04adff9ef07","task_key":"default--v0~2e1~2e0~5fhf--1b0d8ecd-81eb-5fe3-b2af-d04adff9ef07","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 1b0d8ecd-81eb-5fe3-b2af-d04adff9ef07","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import matplotlib.pyplot as plt\\nfrom sklearn.datasets import make_blobs\\ndef task_func(n_samples=100, centers=3, n_features=2, random_seed=42):\\n\",\"complete_prompt\":\"import matplotlib.pyplot as plt\\nfrom sklearn.datasets import make_blobs\\n\\n\\ndef task_func(n_samples=100, centers=3, n_features=2, random_seed=42):\\n    \\\"\\\"\\\"\\n    Create isotropic Gaussian blobs to form clusters and visualize them.\\n\\n    Parameters:\\n    - n_samples (int): The total number of points divided among clusters.\\n    - centers (int): The number of centers to generate.\\n    - n_features (int): The number of features for each sample.\\n    - random_seed (int): The seed for the random number generator.\\n\\n    Returns:\\n    tuple: A tuple containing:\\n        - X (numpy.ndarray): The matrix of blob points.\\n        - y (numpy.ndarray): The vector of blob labels.\\n        - ax (matplotlib.axes.Axes): The Axes object with the scatter plot.\\n\\n    Requirements:\\n    - matplotlib.pyplot\\n    - sklearn\\n\\n    Example:\\n    >>> X, y, ax = task_func(n_samples=500, centers=5, random_seed=0)\\n    >>> type(X), type(y), type(ax)\\n    (<class 'numpy.ndarray'>, <class 'numpy.ndarray'>, <class 'matplotlib.axes._axes.Axes'>)\\n    >>> ax\\n    <Axes: >\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Create isotropic Gaussian blobs to form clusters and visualize them.\\nThe function should output with:\\n    tuple: A tuple containing:\\n    X (numpy.ndarray): The matrix of blob points.\\n    y (numpy.ndarray): The vector of blob labels.\\n    ax (matplotlib.axes.Axes): The Axes object with the scatter plot.\\nYou should write self-contained code starting with:\\n```\\nimport matplotlib.pyplot as plt\\nfrom sklearn.datasets import make_blobs\\ndef task_func(n_samples=100, centers=3, n_features=2, random_seed=42):\\n```\",\"libs\":\"['matplotlib', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}