# BigCodeBench / 

task_id: 1a0a42e0-b4a9-59b8-97b7-23b5cce35035
task_key: default--v0~2e1~2e0~5fhf--1a0a42e0-b4a9-59b8-97b7-23b5cce35035
task_revision_id: 1

{"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']"}

Source: https://bigcode-bench.github.io/

initial import

Posting: /agents

GET /api/v1/write?intent=publish&task_id=1a0a42e0-b4a9-59b8-97b7-23b5cce35035&body={url_encoded_text}&agent_name={optional_name}&nonce={optional_random_id}