Metadata-Version: 1.1
Name: jubakit
Version: 0.6.2
Summary: Jubatus Toolkit
Home-page: http://jubat.us
Author: PFN & NTT
Author-email: jubatus-team@googlegroups.com
License: MIT
Description-Content-Type: UNKNOWN
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        jubakit: Jubatus Toolkit
        ========================
        
        jubakit is a Python module to access Jubatus features easily.
        jubakit 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.
        See the `Jubakit Documentation <http://jubat.us/en/jubakit>`_ for the detailed description.
        
        Currently jubakit supports
        `Classifier <http://jubat.us/en/api/api_classifier.html>`_,
        `Regression <http://jubat.us/en/api/api_regression.html>`_,
        `Anomaly <http://jubat.us/en/api/api_anomaly.html>`_,
        `Recommender <http://jubat.us/en/api/api_recommender.html>`_,
        `NearestNeighbor <http://jubat.us/en/api/api_nearest_neighbor.html>`_,
        `Clustering <http://jubat.us/en/api/api_clustering.html>`_,
        `Burst <http://jubat.us/en/api/api_burst.html>`_,
        `Bandit <http://jubat.us/en/api/api_bandit.html>`_ and
        `Weight <http://jubat.us/en/api/api_weight.html>`_ engines.
        
        Install
        -------
        
        ::
        
          pip install jubakit
        
        Requirements
        ------------
        
        * Python 2.7, 3.3, 3.4 or 3.5.
        * `Jubatus <http://jubat.us/en/quickstart.html>`_ needs to be installed.
        * Although not mandatory, `installing scikit-learn <http://scikit-learn.org/stable/install.html>`_ is required to use some features like K-fold cross validation.
        
        Quick Start
        -----------
        
        The following example shows how to perform train/classify using CSV dataset.
        
        .. code:: python
        
          from jubakit.classifier import Classifier, Schema, Dataset, Config
          from jubakit.loader.csv import CSVLoader
        
          # Load a CSV file.
          loader = CSVLoader('iris.csv')
        
          # Define types for each column in the CSV file.
          schema = Schema({
            'Species': Schema.LABEL,
          }, Schema.NUMBER)
        
          # Get the shuffled dataset.
          dataset = Dataset(loader, schema).shuffle()
        
          # Run the classifier service (`jubaclassifier` process).
          classifier = Classifier.run(Config())
        
          # Train the classifier.
          for _ in classifier.train(dataset): pass
        
          # Classify using the trained classifier.
          for (idx, label, result) in classifier.classify(dataset):
            print("true label: {0}, estimated label: {1}".format(label, result[0][0]))
        
        Examples by Topics
        ------------------
        
        See the `example <https://github.com/jubatus/jubakit/tree/master/example>`_ directory for working examples.
        
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | Example                           | Topics                                        | Requires scikit-learn |
        +===================================+===============================================+=======================+
        | classifier_csv.py                 | Handling CSV file and numeric features        |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | classifier_shogun.py              | Handling CSV file and string features         |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | classifier_digits.py              | Handling toy dataset (digits)                 | ✓                     |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | classifier_libsvm.py              | Handling LIBSVM file                          | ✓                     |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | classifier_kfold.py               | K-fold cross validation and metrics           | ✓                     |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | classifier_parameter.py           | Finding best hyper parameter                  | ✓                     |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | classifier_hyperopt_tuning.py     | Finding best hyper parameter using hyperopt   | ✓                     |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | classifier_bulk.py                | Bulk Train-Test Classifier                    |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | classifier_twitter.py             | Handling Twitter Streams                      |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | classifier_model_extract.py       | Extract contents of Classfier model file      |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | classifier_sklearn_wrapper.py     | Classification using scikit-learn wrapper     | ✓                     |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | classifier_sklearn_grid_search.py | Grid Search example using scikit-learn wrapper| ✓                     |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | classifier_tensorboard.py         | Visualize a training process using TensorBoard| ✓                     |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | regression_boston.py              | Regression with toy dataset (boston)          | ✓                     |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | regression_csv.py                 | Regression with CSV file                      |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | regression_sklearn_wrapper.py     | Regression using scikit-learn wrapper         | ✓                     |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | anomaly_auc.py                    | Anomaly detection and metrics                 |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | recommender_npb.py                | Recommend similar items                       |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | nearest_neighbor_aaai.py          | Search neighbor items                         |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | clustering_2d.py                  | Clustering 2-dimensional dataset              |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | burst_dummy_stream.py             | Burst detection with stream data              |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | bandit_slot.py                    | Multi-armed bandit with slot machine example  |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | weight_shogun.py                  | Tracing fv_converter behavior using Weight    |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        | weight_model_extract.py           | Extract contents of Weight model file         |                       |
        +-----------------------------------+-----------------------------------------------+-----------------------+
        
        License
        -------
        
        MIT License
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
