{"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":"6fe90a00-5065-5ec3-bd14-9d6309fdb19f","task_key":"default--v0~2e1~2e0~5fhf--6fe90a00-5065-5ec3-bd14-9d6309fdb19f","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 6fe90a00-5065-5ec3-bd14-9d6309fdb19f","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\ndef task_func(data: np.ndarray) -> plt.Axes:\\n\",\"complete_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\n\\n\\ndef task_func(data: np.ndarray) -> plt.Axes:\\n    \\\"\\\"\\\"\\n    Plots the cumulative probability distribution of a given NumPy array of numbers,\\n    representing how the cumulative probability increases with the sorted data indexes.\\n\\n    Parameters:\\n    - data (numpy.ndarray): The input NumPy array of non-negative numbers.\\n\\n    Returns:\\n    - matplotlib.pyplot.Axes: The plot of cumulative probabilities.\\n\\n    Requirements:\\n    - numpy\\n    - matplotlib\\n\\n    Raises:\\n    - ValueError: If the input array contains negative numbers or NaNs.\\n    - TypeError: If the input array contains non-numeric inputs.\\n\\n    Note:\\n    - In case of an all-zeros input, the cumulative probability remains at 0 across all indexes.\\n    - The plot uses marker ('o') and a solid line ('-') for the cumulative probability curve.\\n    - The plot is titled \\\"Cumulative Probability Plot\\\", with \\\"Index\\\" on the x-axis and\\n      \\\"Cumulative Probability\\\" on the y-axis.\\n\\n    Example:\\n    >>> ax = task_func(np.array([1, 2, 3, 4, 5]))\\n    >>> ax.get_title()\\n    'Cumulative Probability Plot'\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Plots the cumulative probability distribution of a given NumPy array of numbers, representing how the cumulative probability increases with the sorted data indexes.\\nNote that: In case of an all-zeros input, the cumulative probability remains at 0 across all indexes. The plot uses marker ('o') and a solid line ('-') for the cumulative probability curve. The plot is titled \\\"Cumulative Probability Plot\\\", with \\\"Index\\\" on the x-axis and \\\"Cumulative Probability\\\" on the y-axis.\\nThe function should raise the exception for: ValueError: If the input array contains negative numbers or NaNs. TypeError: If the input array contains non-numeric inputs.\\nThe function should output with:\\n    matplotlib.pyplot.Axes: The plot of cumulative probabilities.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\ndef task_func(data: np.ndarray) -> plt.Axes:\\n```\",\"libs\":\"['numpy', 'matplotlib']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}