{"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":"2e0f36fe-7158-5201-9352-e7d25b262fe5","task_key":"default--v0~2e1~2e0~5fhf--2e0f36fe-7158-5201-9352-e7d25b262fe5","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 2e0f36fe-7158-5201-9352-e7d25b262fe5","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport numpy as np\\nfrom statsmodels.tsa.seasonal import seasonal_decompose\\ndef task_func(start_date='2016-01-01', periods=24, freq='M', model='additive'):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport numpy as np\\nfrom statsmodels.tsa.seasonal import seasonal_decompose\\n\\ndef task_func(start_date='2016-01-01', periods=24, freq='M', model='additive'):\\n    \\\"\\\"\\\"\\n    Generate a sales time-series and decompose it into trend, seasonal, and residual components.\\n    \\n    Parameters:\\n    - start_date (str): The start date of the time-series in the format 'YYYY-MM-DD'. Default is '2016-01-01'.\\n    - periods (int): The number of periods to generate for the time-series. Default is 24.\\n    - freq (str): The frequency of the time-series data. Default is 'M' (Monthly End).\\n    - model (str): The type of seasonal decomposition ('additive' or 'multiplicative'). Default is 'additive'.\\n\\n    Returns:\\n    - A dictionary containing 'trend', 'seasonal', and 'residual' components as Pandas Series.\\n    \\n    Requirements:\\n    - numpy\\n    - pandas\\n    - statsmodels\\n    \\n    Examples:\\n    >>> result = task_func('2016-01-01', 24, 'M')\\n    >>> all(key in result for key in ['trend', 'seasonal', 'residual'])\\n    True\\n\\n    >>> result = task_func('2020-01-01', 24, 'M', 'multiplicative')\\n    >>> len(result['seasonal'])\\n    24\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate a sales time-series and decompose it into trend, seasonal, and residual components. >>> result = task_func('2020-01-01', 24, 'M', 'multiplicative') >>> len(result['seasonal']) 24\\nThe function should output with:\\n    A dictionary containing 'trend', 'seasonal', and 'residual' components as Pandas Series.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport numpy as np\\nfrom statsmodels.tsa.seasonal import seasonal_decompose\\ndef task_func(start_date='2016-01-01', periods=24, freq='M', model='additive'):\\n```\",\"libs\":\"['pandas', 'numpy', 'statsmodels']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}