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        "author": "Aditya Kelvianto Sidharta",
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        "description": "# scikit-rest\n\n![logo](logo.png)\nAutomatically deploy your ML model as a REST API\n\nOften times, deploying your favorite Scikit-learn / XGBoost / Pytorch / Tensorflow model as a REST API might take a lot of time. There are a lot of boilerplate codes to be written. `scikit-rest` is a package designed to alleviate most of the pain points within this process.\n## Prerequisites\n\nThis package officially supports Python 3\n\n## Installing\n```\npip install scikit_rest\n```\n\n## Usage\nThe main function offered in this package is `serve`, with the following syntax:\n```\n    serve(\n        col_list: List[str],\n        col_types: Dict[str, Union[List, type]],\n        transform_fn: Callable,\n        predict_fn: Union[Callable, sklearn.base.BaseEstimator],\n        port: int,\n        is_nullable: bool ,\n        name: str,\n    )\n```\n\n### col_list\n\nList of Column names, where the order of the values will dictate the order within the pandas DataFrame\n```\ncol_list = ['class', 'sex', 'age', 'embarked', 'date', 'is_englishman']\n```\n\n### col_types\n\nDictionary of Column Names and the type of the variable, used for input Validation. If the values\nof the dictionary is instead a list, We assume that any input for the variable can only be any of\n the ones listed within the list\n```\ncol_types = {\n    'class' : int,\n    'sex' : str,\n    'age' : float,\n    'embarked': ['C', 'S', 'Q'],\n    'date': datetime.datetime,\n    'is_englishman': bool\n}\n```\n \n \n### transform_fn\n\nFunction which convert the input dataframe into test dataframe, we can call model.predict upon to get the final result\n```\ndef transform_fn(input_df):\n    df = input_df.copy()\n    df['sex'] = df['sex'].apply(lambda x : transform_sex(x))\n    df['embarked'] = df['embarked'].apply(lambda x : transform_embarked(x))\n    df['date'] = df['date'].dt.year\n    df = df.fillna(0.)\n    return df\n```\n\n### predict_fn\nFunction which convert the test dataframe into result. If a ML model instance is passed in, we will instead try to call model.predict_proba / model.predict to get the result\n```\ndef predict_fn(input_df):\n    df = input_df.copy()\n    return model.predict(df).item()\n```\n\n### port\nPort Number where the REST API should be served upon\n\n### is_nullable\nWhether input API can be nullable\n\n### name\nName of the program\n\n\n## Example\nExample of Usage can be found at [example](example) folder\n\n\n## Contributing\nPlease read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct,\n and the process for submitting pull requests to us.\n\n## Authors\n[Aditya Kelvianto Sidharta][https://adityasidharta.com]\n\n\n## License\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details\n\n",
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