{"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":"2a6fb183-b752-5259-bc59-d8056573cec3","task_key":"default--v0~2e1~2e0~5fhf--2a6fb183-b752-5259-bc59-d8056573cec3","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 2a6fb183-b752-5259-bc59-d8056573cec3","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.linear_model import LogisticRegression\\nfrom sklearn.metrics import accuracy_score\\ndef task_func(data, columns, target_column):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.linear_model import LogisticRegression\\nfrom sklearn.metrics import accuracy_score\\n\\ndef task_func(data, columns, target_column):\\n    \\\"\\\"\\\"\\n    Perform a logistic regression on a DataFrame to predict a specific target column.\\n    \\n    Parameters:\\n    - data (numpy.array): The input data as a NumPy array.\\n    - columns (list): The list of column names.\\n    - target_column (str): The target column name.\\n\\n    Returns:\\n    - accuracy (float): The accuracy of the logistic regression model.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn\\n    \\n    Example:\\n    >>> import numpy as np\\n    >>> np.random.seed(42)\\n    >>> data = np.random.randint(0, 100, size=(100, 4))  # Using np to generate random data\\n    >>> columns = ['A', 'B', 'C', 'target']\\n    >>> task_func(data, columns, 'target')\\n    0.0\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Perform a logistic regression on a DataFrame to predict a specific target column.\\nThe function should output with:\\n    accuracy (float): The accuracy of the logistic regression model.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.linear_model import LogisticRegression\\nfrom sklearn.metrics import accuracy_score\\ndef task_func(data, columns, target_column):\\n```\",\"libs\":\"['pandas', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}