Metadata-Version: 2.1
Name: sk-dist
Version: 0.1.5
Summary: Distributed scikit-learn meta-estimators with PySpark
Home-page: UNKNOWN
Author: Ibotta Inc.
Author-email: machine_learning@ibotta.com
License: Apache 2.0
Download-URL: https://pypi.org/project/sk-dist/#files
Project-URL: Source Code, https://github.com/Ibotta/sk-dist
Description: 
        sk-dist: Distributed scikit-learn meta-estimators in PySpark
        ============================================================
        
        |License| |Build Status| |PyPI Package|
        
        What is it?
        -----------
        
        ``sk-dist`` is a Python package for machine learning built on top of
        `scikit-learn <https://scikit-learn.org/stable/index.html>`__ and is
        distributed under the `Apache 2.0 software
        license <https://github.com/Ibotta/sk-dist/blob/master/LICENSE>`__. The
        ``sk-dist`` module can be thought of as "distributed scikit-learn" as
        its core functionality is to extend the ``scikit-learn`` built-in
        ``joblib`` parallelization of meta-estimator training to
        `spark <https://spark.apache.org/>`__. A popular use case is the 
        parallelization of grid search as shown here:
        
        
        Check out the `blog post <https://medium.com/building-ibotta/train-sklearn-100x-faster-bec530fc1f45>`__ 
        for more information on the motivation and use cases of ``sk-dist``.
        
        Main Features
        -------------
        
        -  **Distributed Training** - ``sk-dist`` parallelizes the training of
           ``scikit-learn`` meta-estimators with PySpark. This allows
           distributed training of these estimators without any constraint on
           the physical resources of any one machine. In all cases, spark
           artifacts are automatically stripped from the fitted estimator. These
           estimators can then be pickled and un-pickled for prediction tasks,
           operating identically at predict time to their ``scikit-learn``
           counterparts. Supported tasks are:
        
           -  *Grid Search*: `Hyperparameter optimization
              techniques <https://scikit-learn.org/stable/modules/grid_search.html>`__,
              particularly
              `GridSearchCV <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV>`__
              and
              `RandomizedSeachCV <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV>`__,
              are distributed such that each parameter set candidate is trained
              in parallel.
           -  *Multiclass Strategies*: `Multiclass classification
              strategies <https://scikit-learn.org/stable/modules/multiclass.html>`__,
              particularly
              `OneVsRestClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier>`__
              and
              `OneVsOneClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsOneClassifier.html#sklearn.multiclass.OneVsOneClassifier>`__,
              are distributed such that each binary probelm is trained in
              parallel.
           -  *Tree Ensembles*: `Decision tree
              ensembles <https://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees>`__
              for classification and regression, particularly
              `RandomForest <https://scikit-learn.org/stable/modules/ensemble.html#random-forests>`__
              and
              `ExtraTrees <https://scikit-learn.org/stable/modules/ensemble.html#extremely-randomized-trees>`__,
              are distributed such that each tree is trained in parallel.
        
        -  **Distributed Prediction** - ``sk-dist`` provides a prediction module
           which builds `vectorized
           UDFs <https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html#pandas-udfs-aka-vectorized-udfs>`__
           for
           `PySpark <https://spark.apache.org/docs/latest/api/python/index.html>`__
           `DataFrames <https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame>`__
           using fitted ``scikit-learn`` estimators. This distributes the
           ``predict`` and ``predict_proba`` methods of ``scikit-learn``
           estimators, enabling large scale prediction with ``scikit-learn``.
        -  **Feature Encoding** - ``sk-dist`` provides a flexible feature
           encoding utility called ``Encoderizer`` which encodes mix-typed
           feature spaces using either default behavior or user defined
           customizable settings. It is particularly aimed at text features, but
           it additionally handles numeric and dictionary type feature spaces.
        
        Installation
        ------------
        
        Dependencies
        ~~~~~~~~~~~~
        
        ``sk-dist`` requires:
        
        -  `Python <https://www.python.org/>`__ (>= 3.5)
        -  `pandas <https://pandas.pydata.org/>`__ (>=0.19.0)
        -  `numpy <https://www.numpy.org/>`__ (>=1.17.0)
        -  `scipy <https://www.scipy.org/>`__ (>=1.3.1)
        -  `scikit-learn <https://scikit-learn.org/stable/>`__ (>=0.21.3)
        -  `joblib <https://joblib.readthedocs.io/en/latest/>`__ (>=0.11)
        
        sk-dist does not support Python 2
        
        Spark Dependencies
        ~~~~~~~~~~~~~~~~~~
        
        Most ``sk-dist`` functionality requires a spark installation as well as
        PySpark. Some functionality can run without spark, so spark related
        dependencies are not required. The connection between sk-dist and spark
        relies solely on a ``sparkContext`` as an argument to various
        ``sk-dist`` classes upon instantiation.
        
        A variety of spark configurations and setups will work. It is left up to
        the user to configure their own spark setup. The testing suite runs
        ``spark 2.3`` and ``spark 2.4``, though any ``spark 2.0+`` versions 
        are expected to work.
        
        Additional spark related dependecies are ``pyarrow``, which is used only
        for ``skdist.predict`` functions. This uses vectorized pandas UDFs which
        require ``pyarrow>=0.8.0``. Depending on the spark version, it may be
        necessary to set
        ``spark.conf.set("spark.sql.execution.arrow.enabled", "true")`` in the
        spark configuration.
        
        User Installation
        ~~~~~~~~~~~~~~~~~
        
        The easiest way to install ``sk-dist`` is with ``pip``:
        
        ::
        
            pip install --upgrade sk-dist
        
        You can also download the source code:
        
        ::
        
            git clone https://github.com/Ibotta/sk-dist.git
        
        Testing
        ~~~~~~~
        
        With ``pytest`` installed, you can run tests locally:
        
        ::
        
            pytest sk-dist
        
        Examples
        --------
        
        The package contains numerous 
        `examples <https://github.com/Ibotta/sk-dist/tree/master/examples>`__ 
        on how to use ``sk-dist`` in practice. Examples of note are:
        
        -  `Grid Search with XGBoost <https://github.com/Ibotta/sk-dist/blob/master/examples/search/xgb.py>`__
        -  `Spark ML Benchmark Comparison <https://github.com/Ibotta/sk-dist/blob/master/examples/search/spark_ml.py>`__
        -  `Encoderizer with 20 Newsgroups <https://github.com/Ibotta/sk-dist/blob/master/examples/encoder/basic_usage.py>`__
        -  `One-Vs-Rest vs One-Vs-One <https://github.com/Ibotta/sk-dist/blob/master/examples/multiclass/basic_usage.py>`__
        -  `Large Scale Sklearn Prediction with PySpark UDFs <https://github.com/Ibotta/sk-dist/blob/master/examples/predict/basic_usage.py>`_
        
        Gradient Boosting
        -----------------
        
        ``sk-dist`` has been tested with a number of popular gradient boosting packages that conform to the ``scikit-learn`` API. This 
        includes ``xgboost`` and ``catboost``. These will need to be installed in addition to ``sk-dist`` on all nodes of the spark 
        cluster via a node bootstrap script. Version compatibility is left up to the user.
        
        Support for ``lightgbm`` is not guaranteed, as it requires `additional installations <https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html#linux>`__ on all 
        nodes of the spark cluster. This may work given proper installation but has not beed tested with ``sk-dist``.
        
        Background
        ----------
        
        The project was started at `Ibotta
        Inc. <https://medium.com/building-ibotta>`__ on the machine learning
        team and open sourced in 2019.
        
        It is currently maintained by the machine learning team at Ibotta. Special
        thanks to those who contributed to ``sk-dist`` while it was initially
        in development at Ibotta:
        
        -  `Evan Harris <https://github.com/denver1117>`__
        -  `Nicole Woytarowicz <https://github.com/nicolele>`__
        -  `Mike Lewis <https://github.com/Mikelew88>`__
        -  `Bobby Crimi <https://github.com/rpcrimi>`__
        
        
        .. |License| image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg
           :target: https://opensource.org/licenses/Apache-2.0
        .. |Build Status| image:: https://travis-ci.org/Ibotta/sk-dist.png?branch=master
           :target: https://travis-ci.org/Ibotta/sk-dist
        .. |PyPI Package| image:: https://badge.fury.io/py/sk-dist.svg
           :target: https://pypi.org/project/sk-dist/
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.5
Provides-Extra: tests
