{"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":"71898bf0-60b2-51b0-b603-aa17c3634319","task_key":"default--v0~2e1~2e0~5fhf--71898bf0-60b2-51b0-b603-aa17c3634319","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 71898bf0-60b2-51b0-b603-aa17c3634319","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nfrom sklearn import datasets\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.linear_model import LinearRegression\\ndef task_func(n_samples=100, n_features=10, random_seed=None):\\n\",\"complete_prompt\":\"import numpy as np\\nfrom sklearn import datasets\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.linear_model import LinearRegression\\n\\n\\ndef task_func(n_samples=100, n_features=10, random_seed=None):\\n    \\\"\\\"\\\"\\n    Generate synthetic data using a simple regression model, fit a linear regression model to the data,\\n    and return the predicted values along with the coefficients and intercept of the model.\\n\\n    Parameters:\\n    - n_samples (int): The number of samples for the synthetic data. Default is 100.\\n    - n_features (int): The number of features for the synthetic data. Default is 10.\\n    - random_seed (int, optional): The seed for reproducibility. Default is None.\\n\\n    Returns:\\n    - tuple: A tuple containing:\\n        - predictions (numpy.ndarray): The predicted values of the test set.\\n        - coefficients (numpy.ndarray): Coefficients of the linear regression model.\\n        - intercept (float): Intercept of the linear regression model.\\n        - mse (float): Mean squared error of the model predictions.\\n\\n    Requirements:\\n    - numpy\\n    - sklearn.datasets.make_regression\\n    - sklearn.model_selection.train_test_split\\n    - sklearn.linear_model.LinearRegression\\n    \\n    Example:\\n    >>> predictions, coefficients, intercept, mse = task_func(100, 5, random_seed=42)\\n    >>> predictions[:3]\\n    array([ 180.79207843, -295.0210232 ,  118.23799221])\\n    >>> round(mse, 4)\\n    0.0113\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate synthetic data using a simple regression model, fit a linear regression model to the data, and return the predicted values along with the coefficients and intercept of the model.\\nThe function should output with:\\n    tuple: A tuple containing:\\n    predictions (numpy.ndarray): The predicted values of the test set.\\n    coefficients (numpy.ndarray): Coefficients of the linear regression model.\\n    intercept (float): Intercept of the linear regression model.\\n    mse (float): Mean squared error of the model predictions.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nfrom sklearn import datasets\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.linear_model import LinearRegression\\ndef task_func(n_samples=100, n_features=10, random_seed=None):\\n```\",\"libs\":\"['numpy', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}