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    "info": {
        "author": "Luca Cappelletti",
        "author_email": "cappelletti.luca94@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Information Technology",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Information Analysis"
        ],
        "description": "Plot Keras History\n=========================================================================================\n|travis| |sonar_quality| |sonar_maintainability| |codacy| |pip| |downloads|\n\nA python package to print a `Keras model training history <https://keras.io/callbacks/#history>`_\n\nHow do I install this package?\n----------------------------------------------\nAs usual, just download it using pip:\n\n.. code:: shell\n\n    pip install plot_keras_history\n\nTests Coverage\n----------------------------------------------\nSince some software handling coverages sometime get slightly different results, here's three of them:\n\n|coveralls| |sonar_coverage| |code_climate_coverage|\n\nUsage\n------------------------------------------------\nLet's say you have a model generated by the function my_keras_model:\n\nPlotting a training history\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nIn the following example we will see how to plot and either show or save the training history:\n\n|standard|\n\n.. code:: python\n\n    from plot_keras_history import plot_history\n    import matplotlib.pyplot as plt\n\n    model = my_keras_model()\n    history = model.fit(...).history\n    plot_history(history)\n    plt.show()\n    plot_history(history, path=\"standard.png\")\n    plt.close()\n\nPlotting into separate graphs\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nBy default, the graphs are all in one big image, but for various reasons you might need them one by one:\n\n.. code:: python\n\n    from plot_keras_history import plot_history\n    import matplotlib.pyplot as plt\n\n    model = my_keras_model()\n    history = model.fit(...).history\n    plot_history(history, path=\"singleton\", single_graphs=True)\n    plt.close()\n\nReducing the history noise with Savgol Filters\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nIn some occasion it is necessary to be able to see the progress of the history to interpolate the results to remove a bit of noise. A parameter is offered to automatically apply a Savgol filter:\n\n|interpolated|\n\n.. code:: python\n\n    from plot_keras_history import plot_history\n    import matplotlib.pyplot as plt\n\n    model = my_keras_model()\n    history = model.fit(...).history\n    plot_history(history, path=\"interpolated.png\", interpolate=True)\n    plt.close()\n\nAutomatic aliases\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nA number of metrics are automatically converted from the default ones to more talking ones, for example \"lr\" becomes \"Learning Rate\", or \"acc\" becomes \"Accuracy\".\n\nAll the available options\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n    def plot_history(\n        history, # Either the history object or a pandas DataFrame. When using a dataframe, the index name is used as abscissae label.\n        style:str=\"-\", # The style of the lines.\n        interpolate: bool = False, # Wethever to interpolate or not the graphs datapoints.\n        side: float = 5, # Dimension of the graphs side.\n        graphs_per_row: int = 4, # Number of graphs for each row.\n        customization_callback: Callable = None, # Callback for customizing the graphs.\n        path: str = None, # Path where to store the resulting image or images (in the case of single_graphs)\n        single_graphs: bool = False #  Wethever to save the graphs as single of multiples.\n    )\n\nChaining histories\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nIt's common to stop and restart a model's training, and this would break the history object into two: for this reason the method `chain_histories <https://github.com/LucaCappelletti94/plot_keras_history/blob/dd590ce7f89b2a52236f231a9a6377b3e1d76489/plot_keras_history/utils.py#L3-L8>`_ is available:\n\n.. code:: python\n\n    from plot_keras_history import chain_histories\n\n    model = my_keras_model()\n    history1 = model.fit(...).history\n    history2 = model.fit(...).history\n    history = chain_histories(history1, history2)\n\nExtras\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nNumerous additional metrics are available in `extra_keras_metrics <https://github.com/LucaCappelletti94/extra_keras_metrics>`_\n\n.. |travis| image:: https://travis-ci.org/LucaCappelletti94/plot_keras_history.png\n   :target: https://travis-ci.org/LucaCappelletti94/plot_keras_history\n   :alt: Travis CI build\n\n.. |sonar_quality| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_plot_keras_history&metric=alert_status\n    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_plot_keras_history\n    :alt: SonarCloud Quality\n\n.. |sonar_maintainability| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_plot_keras_history&metric=sqale_rating\n    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_plot_keras_history\n    :alt: SonarCloud Maintainability\n\n.. |sonar_coverage| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_plot_keras_history&metric=coverage\n    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_plot_keras_history\n    :alt: SonarCloud Coverage\n\n.. |coveralls| image:: https://coveralls.io/repos/github/LucaCappelletti94/plot_keras_history/badge.svg?branch=master\n    :target: https://coveralls.io/github/LucaCappelletti94/plot_keras_history?branch=master\n    :alt: Coveralls Coverage\n\n.. |pip| image:: https://badge.fury.io/py/plot-keras-history.svg\n    :target: https://badge.fury.io/py/plot_keras_history\n    :alt: Pypi project\n\n.. |downloads| image:: https://pepy.tech/badge/plot-keras-history\n    :target: https://pepy.tech/badge/plot-keras-history\n    :alt: Pypi total project downloads \n\n.. |codacy|  image:: https://api.codacy.com/project/badge/Grade/4f09666f140a4fc785fecc94b0ed9a6a\n    :target: https://www.codacy.com/app/LucaCappelletti94/plot_keras_history?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=LucaCappelletti94/plot_keras_history&amp;utm_campaign=Badge_Grade\n    :alt: Codacy Maintainability\n\n.. |code_climate_maintainability| image:: https://api.codeclimate.com/v1/badges/5540f8112de448ac3298/maintainability\n    :target: https://codeclimate.com/github/LucaCappelletti94/plot_keras_history/maintainability\n    :alt: Maintainability\n\n.. |code_climate_coverage| image:: https://api.codeclimate.com/v1/badges/5540f8112de448ac3298/test_coverage\n    :target: https://codeclimate.com/github/LucaCappelletti94/plot_keras_history/test_coverage\n    :alt: Code Climate Coverate\n\n.. |standard| image:: https://github.com/LucaCappelletti94/plot_keras_history/blob/master/plots/normal.png?raw=true\n.. |interpolated| image:: https://github.com/LucaCappelletti94/plot_keras_history/blob/master/plots/interpolated.png?raw=true",
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