spark-sklearn

Scikit-learn integration package for Apache Spark

This package contains some tools to integrate the Spark computing framework <https://spark.apache.org/>_ with the popular scikit-learn machine library <https://scikit-learn.org/stable/>_. Among other things, it can:

• train and evaluate multiple scikit-learn models in parallel. It is a distributed analog to the multicore implementation <https://pythonhosted.org/joblib/parallel.html>_ included by default in scikit-learn
• convert Spark's Dataframes seamlessly into numpy ndarray or sparse matrices
• (experimental) distribute Scipy's sparse matrices as a dataset of sparse vectors

It focuses on problems that have a small amount of data and that can be run in parallel. For small datasets, it distributes the search for estimator parameters (GridSearchCV in scikit-learn), using Spark. For datasets that do not fit in memory, we recommend using the distributed implementation in Spark MLlib https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html`_.

This package distributes simple tasks like grid-search cross-validation. It does not distribute individual learning algorithms (unlike Spark MLlib).

Installation

This package is available on PYPI:

::

pip install spark-sklearn

This project is also available as Spark package <https://spark-packages.org/package/databricks/spark-sklearn>_.

The developer version has the following requirements:

• scikit-learn 0.18 or 0.19. Later versions may work, but tests currently are incompatible with 0.20.
• Spark >= 2.1.1. Spark may be downloaded from the Spark website <https://spark.apache.org/>. In order to use this package, you need to use the pyspark interpreter or another Spark-compliant python interpreter. See the Spark guide <https://spark.apache.org/docs/latest/programming-guide.html#overview> for more details.
• nose <https://nose.readthedocs.org>_ (testing dependency only)
• pandas, if using the pandas integration or testing. pandas==0.18 has been tested.

If you want to use a developer version, you just need to make sure the python/ subdirectory is in the PYTHONPATH when launching the pyspark interpreter:

::

PYTHONPATH=$PYTHONPATH:./python:$SPARK_HOME/bin/pyspark

You can directly run tests:

::

cd python && ./run-tests.sh

This requires the environment variable SPARK_HOME to point to your local copy of Spark.

Example

Here is a simple example that runs a grid search with Spark. See the Installation <#installation>_ section on how to install the package.

.. code:: python

from sklearn import svm, datasets
from spark_sklearn import GridSearchCV
iris = datasets.load_iris()
parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
svr = svm.SVC(gamma='auto')
clf = GridSearchCV(sc, svr, parameters)
clf.fit(iris.data, iris.target)

This classifier can be used as a drop-in replacement for any scikit-learn classifier, with the same API.

Documentation

API documentation <http://databricks.github.io/spark-sklearn-docs>_ is currently hosted on Github pages. To build the docs yourself, see the instructions in docs/.

.. image:: https://travis-ci.org/databricks/spark-sklearn.svg?branch=master :target: https://travis-ci.org/databricks/spark-sklearn