{"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":"4e8b3cf4-6e43-5451-a7cb-6fce878d4104","task_key":"default--v0~2e1~2e0~5fhf--4e8b3cf4-6e43-5451-a7cb-6fce878d4104","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 4e8b3cf4-6e43-5451-a7cb-6fce878d4104","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom sklearn.decomposition import PCA\\ndef task_func(x, y, labels):\\n\",\"complete_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom sklearn.decomposition import PCA\\n\\n\\ndef task_func(x, y, labels):\\n    \\\"\\\"\\\" \\n    Perform Principal Component Analysis (PCA) on \\\"x\\\" as x-values and \\\"y\\\" as y-values and record the results with labels.\\n\\n    Parameters:\\n    x (list): List of numpy arrays representing the x-values of the data points.\\n    y (list): List of numpy arrays representing the y-values of the data points.\\n    labels (list): List of strings representing the labels for the chemical compounds.\\n\\n    Returns:\\n    fig: Matplotlib figure object.\\n\\n    Requirements:\\n    - numpy\\n    - matplotlib.pyplot\\n    - sklearn.decomposition\\n\\n    Example:\\n    >>> x = [np.array([1,2,3]), np.array([4,5,6]), np.array([7,8,9])]\\n    >>> y = [np.array([4,5,6]), np.array([7,8,9]), np.array([10,11,12])]\\n    >>> labels = ['H₂O', 'O₂', 'CO₂']\\n    >>> fig = task_func(x, y, labels)\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Perform Principal Component Analysis (PCA) on \\\"x\\\" as x-values and \\\"y\\\" as y-values and record the results with labels.\\nThe function should output with:\\n    fig: Matplotlib figure object.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom sklearn.decomposition import PCA\\ndef task_func(x, y, labels):\\n```\",\"libs\":\"['numpy', '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":[]}