{"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":"fb379040-2acb-5abd-b341-9a7e2e48dd03","task_key":"default--v0~2e1~2e0~5fhf--fb379040-2acb-5abd-b341-9a7e2e48dd03","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf fb379040-2acb-5abd-b341-9a7e2e48dd03","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.feature_selection import SelectKBest, f_classif\\nimport seaborn as sns\\ndef task_func(df1, df2):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.feature_selection import SelectKBest, f_classif\\nimport seaborn as sns\\n\\n\\ndef task_func(df1, df2):\\n    \\\"\\\"\\\"Perform the feature selection with SelectKBest (k=2) and return a heatmap of the feature correlations.\\n\\n    Parameters:\\n    - df1 (pd.DataFrame): The dataframe containing features.\\n    - df2 (pd.DataFrame): The dataframe containing the target variable. Must have an 'id' column corresponding to df1.\\n\\n    Returns:\\n    - tuple: A tuple containing:\\n        - list: A list of the selected features.\\n        - Axes: A heatmap showing the correlation between the selected features.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn.feature_selection.SelectKBest\\n    - sklearn.feature_selection.f_classif\\n    - seaborn\\n\\n    Example:\\n    >>> df1 = pd.DataFrame({'id': [1, 2, 3], 'feature1': [1.2, 3.4, 5.6], 'feature2': [2.3, 4.5, 6.7], 'feature3': [3.4, 5.6, 7.8]})\\n    >>> df2 = pd.DataFrame({'id': [1, 2, 3], 'target': [4.5, 6.7, 8.9]})\\n    >>> selected_features, heatmap = task_func(df1, df2)\\n    >>> heatmap\\n    <Axes: >\\n    >>> selected_features\\n    ['feature2', 'feature3']\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Perform the feature selection with SelectKBest (k=2) and return a heatmap of the feature correlations.\\nThe function should output with:\\n    tuple: A tuple containing:\\n    list: A list of the selected features.\\n    Axes: A heatmap showing the correlation between the selected features.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.feature_selection import SelectKBest, f_classif\\nimport seaborn as sns\\ndef task_func(df1, df2):\\n```\",\"libs\":\"['pandas', 'sklearn', 'seaborn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}