{"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":"b62c6f78-8655-57d1-8f28-b7bfb4d017d8","task_key":"default--v0~2e1~2e0~5fhf--b62c6f78-8655-57d1-8f28-b7bfb4d017d8","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf b62c6f78-8655-57d1-8f28-b7bfb4d017d8","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from sklearn.decomposition import PCA\\nimport matplotlib.pyplot as plt\\ndef task_func(l):\\n\",\"complete_prompt\":\"from sklearn.decomposition import PCA\\nimport matplotlib.pyplot as plt\\n\\ndef task_func(l):\\n    \\\"\\\"\\\"\\n    Perform Principal Component Analysis (PCA) on the given array and record the first two main components.\\n\\n    Parameters:\\n    l (numpy array): The input array.\\n\\n    Returns:\\n    ax (matplotlib.axes._axes.Axes): Axes object of the generated plot\\n\\n    Note:\\n    - This function use \\\"PCA Result\\\" as the title of the plot.\\n    - This function use \\\"First Principal Component\\\" and \\\"Second Principal Component\\\" as the xlabel \\n    and ylabel of the plot, respectively.\\n\\n    Requirements:\\n    - sklearn.decomposition.PCA\\n    - matplotlib.pyplot\\n\\n    Example:\\n    >>> import numpy as np\\n    >>> l = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])\\n    >>> ax = task_func(l)\\n    >>> len(ax.collections[0].get_offsets())\\n    4\\n    >>> print(ax.get_title())\\n    PCA Result\\n    >>> plt.close()\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Perform Principal Component Analysis (PCA) on the given array and record the first two main components.\\nNote that: This function use \\\"PCA Result\\\" as the title of the plot. This function use \\\"First Principal Component\\\" and \\\"Second Principal Component\\\" as the xlabel and ylabel of the plot, respectively.\\nThe function should output with:\\n    ax (matplotlib.axes._axes.Axes): Axes object of the generated plot\\nYou should write self-contained code starting with:\\n```\\nfrom sklearn.decomposition import PCA\\nimport matplotlib.pyplot as plt\\ndef task_func(l):\\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":[]}