{"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":"eb34c4da-8ba3-5d6c-a07c-5ca70aff194a","task_key":"default--v0~2e1~2e0~5fhf--eb34c4da-8ba3-5d6c-a07c-5ca70aff194a","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf eb34c4da-8ba3-5d6c-a07c-5ca70aff194a","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import matplotlib.pyplot as plt\\nimport numpy as np\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(x, y, labels):\\n\",\"complete_prompt\":\"import matplotlib.pyplot as plt\\nimport numpy as np\\nfrom sklearn.preprocessing import StandardScaler\\n\\n\\ndef task_func(x, y, labels):\\n    \\\"\\\"\\\"\\n    Scale the \\\"x\\\" and \\\"y\\\" arrays using the standard scaler of sklearn and plot them with given labels.\\n    Each pair of x and y arrays are scaled independently and plotted as a separate series with a label.\\n\\n    Parameters:\\n    - x (list of np.ndarray): List of numpy arrays representing the x-values of the data points.\\n    - y (list of np.ndarray): List of numpy arrays representing the y-values of the data points.\\n    - labels (list of str): List of strings representing the labels for each data series.\\n\\n    Returns:\\n    - matplotlib.figure.Figure: The figure object containing the plot.\\n\\n    Requirements:\\n    - numpy\\n    - matplotlib.pyplot\\n    - sklearn.preprocessing\\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 = ['A', 'B', 'C']\\n    >>> fig = task_func(x, y, labels)\\n    >>> plt.show()\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Scale the \\\"x\\\" and \\\"y\\\" arrays using the standard scaler of sklearn and plot them with given labels. Each pair of x and y arrays are scaled independently and plotted as a separate series with a label.\\nThe function should output with:\\n    matplotlib.figure.Figure: The figure object containing the plot.\\nYou should write self-contained code starting with:\\n```\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nfrom sklearn.preprocessing import StandardScaler\\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":[]}