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        "author": "PFN & NTT",
        "author_email": "jubatus-team@googlegroups.com",
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            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
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        "description": "|Travis|_ |Coveralls|_ |PyPi|_\n\n.. |Travis| image:: https://api.travis-ci.org/jubatus/jubakit.svg?branch=master\n.. _Travis: https://travis-ci.org/jubatus/jubakit\n\n.. |Coveralls| image:: https://coveralls.io/repos/jubatus/jubakit/badge.svg?branch=master&service=github\n.. _Coveralls: https://coveralls.io/r/jubatus/jubakit\n\n.. |PyPi| image:: https://badge.fury.io/py/jubakit.svg\n.. _PyPi: https://badge.fury.io/py/jubakit\n\njubakit: Jubatus Toolkit\n========================\n\njubakit is a Python module to access Jubatus features easily.\njubakit can be used in conjunction with `scikit-learn <http://scikit-learn.org/>`_ so that you can use powerful features like cross validation and model evaluation.\nSee the `Jubakit Documentation <http://jubat.us/en/jubakit>`_ for the detailed description.\n\nCurrently jubakit supports\n`Classifier <http://jubat.us/en/api/api_classifier.html>`_,\n`Regression <http://jubat.us/en/api/api_regression.html>`_,\n`Anomaly <http://jubat.us/en/api/api_anomaly.html>`_,\n`Recommender <http://jubat.us/en/api/api_recommender.html>`_,\n`NearestNeighbor <http://jubat.us/en/api/api_nearest_neighbor.html>`_,\n`Clustering <http://jubat.us/en/api/api_clustering.html>`_,\n`Burst <http://jubat.us/en/api/api_burst.html>`_,\n`Bandit <http://jubat.us/en/api/api_bandit.html>`_ and\n`Weight <http://jubat.us/en/api/api_weight.html>`_ engines.\n\nInstall\n-------\n\n::\n\n  pip install jubakit\n\nRequirements\n------------\n\n* Python 2.7, 3.3, 3.4 or 3.5.\n* `Jubatus <http://jubat.us/en/quickstart.html>`_ needs to be installed.\n* Although not mandatory, `installing scikit-learn <http://scikit-learn.org/stable/install.html>`_ is required to use some features like K-fold cross validation.\n\nQuick Start\n-----------\n\nThe following example shows how to perform train/classify using CSV dataset.\n\n.. code:: python\n\n  from jubakit.classifier import Classifier, Schema, Dataset, Config\n  from jubakit.loader.csv import CSVLoader\n\n  # Load a CSV file.\n  loader = CSVLoader('iris.csv')\n\n  # Define types for each column in the CSV file.\n  schema = Schema({\n    'Species': Schema.LABEL,\n  }, Schema.NUMBER)\n\n  # Get the shuffled dataset.\n  dataset = Dataset(loader, schema).shuffle()\n\n  # Run the classifier service (`jubaclassifier` process).\n  classifier = Classifier.run(Config())\n\n  # Train the classifier.\n  for _ in classifier.train(dataset): pass\n\n  # Classify using the trained classifier.\n  for (idx, label, result) in classifier.classify(dataset):\n    print(\"true label: {0}, estimated label: {1}\".format(label, result[0][0]))\n\nExamples by Topics\n------------------\n\nSee the `example <https://github.com/jubatus/jubakit/tree/master/example>`_ directory for working examples.\n\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| Example                           | Topics                                        | Requires scikit-learn |\n+===================================+===============================================+=======================+\n| classifier_csv.py                 | Handling CSV file and numeric features        |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| classifier_shogun.py              | Handling CSV file and string features         |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| classifier_digits.py              | Handling toy dataset (digits)                 | \u2713                     |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| classifier_libsvm.py              | Handling LIBSVM file                          | \u2713                     |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| classifier_kfold.py               | K-fold cross validation and metrics           | \u2713                     |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| classifier_parameter.py           | Finding best hyper parameter                  | \u2713                     |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| classifier_hyperopt_tuning.py     | Finding best hyper parameter using hyperopt   | \u2713                     |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| classifier_bulk.py                | Bulk Train-Test Classifier                    |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| classifier_twitter.py             | Handling Twitter Streams                      |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| classifier_model_extract.py       | Extract contents of Classfier model file      |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| classifier_sklearn_wrapper.py     | Classification using scikit-learn wrapper     | \u2713                     |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| classifier_sklearn_grid_search.py | Grid Search example using scikit-learn wrapper| \u2713                     |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| classifier_tensorboard.py         | Visualize a training process using TensorBoard| \u2713                     |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| regression_boston.py              | Regression with toy dataset (boston)          | \u2713                     |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| regression_csv.py                 | Regression with CSV file                      |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| regression_sklearn_wrapper.py     | Regression using scikit-learn wrapper         | \u2713                     |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| anomaly_auc.py                    | Anomaly detection and metrics                 |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| recommender_npb.py                | Recommend similar items                       |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| nearest_neighbor_aaai.py          | Search neighbor items                         |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| clustering_2d.py                  | Clustering 2-dimensional dataset              |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| burst_dummy_stream.py             | Burst detection with stream data              |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| bandit_slot.py                    | Multi-armed bandit with slot machine example  |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| weight_shogun.py                  | Tracing fv_converter behavior using Weight    |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n| weight_model_extract.py           | Extract contents of Weight model file         |                       |\n+-----------------------------------+-----------------------------------------------+-----------------------+\n\nLicense\n-------\n\nMIT License\n",
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