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    "info": {
        "author": "Alejandro Gonz\u00e1lez Tineo",
        "author_email": "alejandrojgt@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "\n**baikal is a graph-based, functional API for building complex machine learning \npipelines of objects that implement the scikit-learn API**. It is mostly inspired \non the excellent `Keras <https://keras.io>`__ API for Deep Learning, and borrows \na  few concepts from the `TensorFlow <https://www.tensorflow.org>`__ framework \nand the (perhaps lesser known) `graphkit <https://github.com/yahoo/graphkit>`__\npackage.\n\n**baikal** aims to provide an API that allows to build complex, non-linear \nmachine learning pipelines that looks like this: \n\n.. image:: https://raw.githubusercontent.com/alegonz/baikal/master/illustrations/multiple_input_nonlinear_pipeline_example_diagram.png\n\nwith code that looks like this:\n\n.. code-block:: python\n\n    x1 = Input()\n    x2 = Input()\n\n    y1 = ExtraTreesClassifier()(x1)\n    y2 = RandomForestClassifier()(x2)\n    z = PowerTransformer()(x2)\n    z = PCA()(z)\n    y3 = LogisticRegression()(z)\n\n    ensemble_features = Stack()([y1, y2, y3])\n    y = SVC()(ensemble_features)\n\n    model = Model([x1, x2], y)\n\n**baikal** is compatible with Python >=3.5 and is distributed under the \nBSD 3-clause license.\n\n\n",
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        "keywords": "",
        "license": "new BSD",
        "maintainer": "",
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        "name": "baikal",
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        "summary": "A graph-based functional API for building complex scikit-learn pipelines.",
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