{"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":"1a0a42e0-b4a9-59b8-97b7-23b5cce35035","task_key":"default--v0~2e1~2e0~5fhf--1a0a42e0-b4a9-59b8-97b7-23b5cce35035","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 1a0a42e0-b4a9-59b8-97b7-23b5cce35035","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from sklearn.model_selection import train_test_split\\nfrom sklearn.metrics import precision_recall_curve\\nfrom tensorflow import keras\\nimport matplotlib.pyplot as plt\\ndef task_func(X, Y):\\n\",\"complete_prompt\":\"from sklearn.model_selection import train_test_split\\nfrom sklearn.metrics import precision_recall_curve\\nfrom tensorflow import keras\\nimport matplotlib.pyplot as plt\\n\\ndef task_func(X, Y):\\n    \\\"\\\"\\\"\\n    This function should:\\n    - Splits the input data into training (70%) and test (30%) sets.\\n    - Constructs a Keras Sequential model with one hidden dense layer and sigmoid activation.\\n      The input dimension is determined based on the first feature set of X.\\n    - Compiles the model using binary cross-entropy loss and SGD optimizer.\\n    - Fits the model to the training data in a non-verbose mode.\\n    - Plots the Precision-Recall curve for the model based on the test set data.\\n\\n    Parameters:\\n    X (np.ndarray): Input data for the model. Must have at least one feature.\\n    Y (np.ndarray): Target labels for the model.\\n\\n    Returns:\\n    - keras.models.Sequential: The trained Keras model.\\n    - matplotlib.axes._axes.Axes: The matplotlib Axes object for the Precision-Recall curve plot.\\n    \\n    Notes:\\n    - The plot's x-axis is labeled 'Recall', and the y-axis is labeled 'Precision'.\\n    - The title of the axes is set to 'Precision-Recall Curve'.\\n    - The axes object allows for further customization of the plot outside the function.\\n\\n    Requirements:\\n    - tensorflow.keras\\n    - sklearn.model_selection.train_test_split\\n    - sklearn.metrics.precision_recall_curve\\n    - matplotlib.pyplot\\n\\n    Examples:\\n    >>> X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\\n    >>> Y = np.array([[0], [1], [1], [0]])\\n    >>> model, ax = task_func(X, Y)\\n    >>> isinstance(model, Sequential)\\n    True\\n    >>> isinstance(ax, plt.Axes)\\n    True\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"This function should: - Splits the input data into training (70%) and test (30%) sets. - Constructs a Keras Sequential model with one hidden dense layer and sigmoid activation. The input dimension is determined based on the first feature set of X. - Compiles the model using binary cross-entropy loss and SGD optimizer. - Fits the model to the training data in a non-verbose mode. - Plots the Precision-Recall curve for the model based on the test set data.\\nNote that: Notes: The plot's x-axis is labeled 'Recall', and the y-axis is labeled 'Precision'. The title of the axes is set to 'Precision-Recall Curve'. The axes object allows for further customization of the plot outside the function.\\nThe function should output with:\\n    keras.models.Sequential: The trained Keras model.\\n    matplotlib.axes._axes.Axes: The matplotlib Axes object for the Precision-Recall curve plot.\\nYou should write self-contained code starting with:\\n```\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.metrics import precision_recall_curve\\nfrom tensorflow import keras\\nimport matplotlib.pyplot as plt\\ndef task_func(X, Y):\\n```\",\"libs\":\"['tensorflow', 'matplotlib', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}