{
    "info": {
        "author": "Marcus McCurdy",
        "author_email": "marcus.mccurdy@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
        ],
        "description": "\n==========================\nKeras Consistency Callback\n==========================\n\nA Keras callback that calculates your model's consistency during training at\neach epoch. The callback prints the consistency and also adds the consistency at\nthe end of each epoch to the training history under the ``consistency`` key.\n\nUsage\n-----\n\nHere is a usage example::\n\n    import pandas as pd\n    from numeraicb import Consistency\n    from keras.models import Sequential\n    from keras.layers.core import Dense\n\n    train = pd.read_csv('numerai_training_data.csv')\n    tourn = pd.read_csv('numerai_tournament_data.csv')\n\n    validation = tourn[tourn.data_type == 'validation']\n\n    features = ['feature{}'.format(i) for i in range(1, 51)]\n\n    X = train[features].values\n    Y = train.target.values\n\n    X_validation = validation[features].values\n    Y_validation = validation.target.values\n\n    model = Sequential()\n    model.add(Dense(30, kernel_initializer='uniform', input_dim=X.shape[1], activation='relu'))\n    model.add(Dense(1, activation='sigmoid'))\n    model.compile(optimizer='adamax', loss='binary_crossentropy')\n\n    cb = Consistency(tourn)\n\n    # Now your models consistency will be printed at each epoch\n    history = model.fit(X, Y, callbacks=[cb], validation_data=(X_validation, Y_validation))\n\n    # Consistency is stored in the history as well\n    for epoch, consistency in enumerate(history.history['consistency']):\n        print('consistency at epoch {}: {:.2%}'.format(epoch, consistency))\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
        "description_content_type": "",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/volker48/numeraicb",
        "keywords": "",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "numeraicb",
        "package_url": "https://pypi.org/project/numeraicb/",
        "platform": "",
        "project_url": "https://pypi.org/project/numeraicb/",
        "project_urls": {
            "Homepage": "https://github.com/volker48/numeraicb"
        },
        "release_url": "https://pypi.org/project/numeraicb/1.0.1/",
        "requires_dist": [
            "Keras (>=2.0.0)",
            "pandas (>=0.18.1)",
            "numpy (>=1.9.1)",
            "scikit-learn (>=0.18)"
        ],
        "requires_python": "",
        "summary": "Keras Callback to track Numerai consistency",
        "version": "1.0.1"
    },
    "last_serial": 3956004,
    "releases": {
        "1.0.0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "2ef37bf3abbf68a6c1051a3b6b654216",
                    "sha256": "893e8370c247a6d0c9c646fda229be8853d3e507b816457eca3e6059e28e6e44"
                },
                "downloads": -1,
                "filename": "numeraicb-1.0.0-py2.py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "2ef37bf3abbf68a6c1051a3b6b654216",
                "packagetype": "bdist_wheel",
                "python_version": "py2.py3",
                "requires_python": null,
                "size": 4159,
                "upload_time": "2017-11-11T01:35:55",
                "url": "https://files.pythonhosted.org/packages/64/02/4caacab2077957ad291ee361306e881cdb6258a0c70ad2bdc85c813e24ce/numeraicb-1.0.0-py2.py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "f1c549842bb9f6b97d578c5146457453",
                    "sha256": "8e70ad04b9c0c754621b2028972d9fc02ce9dfeab0fce20d94462b1c263bb051"
                },
                "downloads": -1,
                "filename": "numeraicb-1.0.0.tar.gz",
                "has_sig": false,
                "md5_digest": "f1c549842bb9f6b97d578c5146457453",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 15464,
                "upload_time": "2017-11-11T01:35:56",
                "url": "https://files.pythonhosted.org/packages/08/0b/7c9e06ac0bf17bdba2a3d2a5b238a7b0fe2ecd4e72e1f7b7b7a8feb440a3/numeraicb-1.0.0.tar.gz"
            }
        ],
        "1.0.1": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "22a7d9b7ae635b3aff8a39d7dd47fba0",
                    "sha256": "6cbbf5b911f42468eaa310527dc618a236d4df05cf773cfa1359a089e86428a3"
                },
                "downloads": -1,
                "filename": "numeraicb-1.0.1-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "22a7d9b7ae635b3aff8a39d7dd47fba0",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": null,
                "size": 4157,
                "upload_time": "2018-06-13T01:24:41",
                "url": "https://files.pythonhosted.org/packages/18/b2/84a1b756aa1418d2f1579a7399056448720cce1f103c6b30518b82734f72/numeraicb-1.0.1-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "3ca9473169ac5f19fa43f072ce3cee3f",
                    "sha256": "0aaeab3b860581d7cb347357e407a9fe9b1278e0c372184d0699d7090ffaf9b3"
                },
                "downloads": -1,
                "filename": "numeraicb-1.0.1.tar.gz",
                "has_sig": false,
                "md5_digest": "3ca9473169ac5f19fa43f072ce3cee3f",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 15478,
                "upload_time": "2018-06-13T01:24:42",
                "url": "https://files.pythonhosted.org/packages/7f/8a/bf5f60a161f3fcbd1424ea5304d2077c0120e66c83f0d14a3ac357261ccf/numeraicb-1.0.1.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "22a7d9b7ae635b3aff8a39d7dd47fba0",
                "sha256": "6cbbf5b911f42468eaa310527dc618a236d4df05cf773cfa1359a089e86428a3"
            },
            "downloads": -1,
            "filename": "numeraicb-1.0.1-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "22a7d9b7ae635b3aff8a39d7dd47fba0",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": null,
            "size": 4157,
            "upload_time": "2018-06-13T01:24:41",
            "url": "https://files.pythonhosted.org/packages/18/b2/84a1b756aa1418d2f1579a7399056448720cce1f103c6b30518b82734f72/numeraicb-1.0.1-py3-none-any.whl"
        },
        {
            "comment_text": "",
            "digests": {
                "md5": "3ca9473169ac5f19fa43f072ce3cee3f",
                "sha256": "0aaeab3b860581d7cb347357e407a9fe9b1278e0c372184d0699d7090ffaf9b3"
            },
            "downloads": -1,
            "filename": "numeraicb-1.0.1.tar.gz",
            "has_sig": false,
            "md5_digest": "3ca9473169ac5f19fa43f072ce3cee3f",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 15478,
            "upload_time": "2018-06-13T01:24:42",
            "url": "https://files.pythonhosted.org/packages/7f/8a/bf5f60a161f3fcbd1424ea5304d2077c0120e66c83f0d14a3ac357261ccf/numeraicb-1.0.1.tar.gz"
        }
    ]
}