Skip to content
This repository has been archived by the owner. It is now read-only.
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
bin
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Deprecation

This project is deprecated. We now recommend using scikit-learn and Joblib Apache Spark Backend to distribute scikit-learn hyperparameter tuning tasks on a Spark cluster:

You need pyspark>=2.4.4 and scikit-learn>=0.21 to use Joblib Apache Spark Backend, which can be installed using pip:

pip install joblibspark

The following example shows how to distributed GridSearchCV on a Spark cluster using joblibspark. Same applies to RandomizedSearchCV.

from sklearn import svm, datasets
from sklearn.model_selection import GridSearchCV
from joblibspark import register_spark
from sklearn.utils import parallel_backend

register_spark() # register spark backend

iris = datasets.load_iris()
parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
svr = svm.SVC(gamma='auto')

clf = GridSearchCV(svr, parameters, cv=5)

with parallel_backend('spark', n_jobs=3):
    clf.fit(iris.data, iris.target)

Scikit-learn integration package for Apache Spark

This package contains some tools to integrate the Spark computing framework with the popular scikit-learn machine library. Among other things, it can:

  • train and evaluate multiple scikit-learn models in parallel. It is a distributed analog to the multicore implementation 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.

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.

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. In order to use this package, you need to use the pyspark interpreter or another Spark-compliant python interpreter. See the Spark guide for more details.
  • nose (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 section on how to install the package.

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 is currently hosted on Github pages. To build the docs yourself, see the instructions in docs/.

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