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Integration tools for running scikit-learn on Spark

Project description

#Spark-sklearn - Spark integration with scikit-learn and numpy.

This package contains some tools to integrate the [Spark computing framework]( with the popular [scikit-learn machine library]( Among other tools:
- 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`s or sparse matrices.
- (experimental) distribute Scipy's sparse matrices as a dataset of sparse vectors.

Spark-sklearn focuses on problems that have a small amount of data and that can be run in parallel.
- for small datasets, spark-sklearn 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](

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

**Difference with the [sparkit-learn project](** The sparkit-learn project aims at a comprehensive integration between Spark and scikit-learn. In particular, it adds some primitives to distribute numerical data using Spark, and it reimplements some of the most common algorithms found in scikit-learn.

## License

This package is released under the Apache 2.0 license. See the LICENSE file.

## Installation

This package has the following requirements:
- a recent version of scikit-learn. Version 0.17 has been tested, older versions may work too.
- Spark >= 2.0. Spark may be downloaded from the
[Spark official website]( In order to use spark-sklearn, you need to use the pyspark interpreter or another Spark-compliant python interpreter. See the [Spark guide]( for more details.
- [nose]( (testing dependency only)

This package is available on PYPI:

pip install spark-sklearn

This project is also available as as [Spark package](

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:


__Running tests__ You can directly run tests:

cd python && ./

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 spark-sklearn.

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

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

## Documentation

More extensive documentation (generated with Sphinx) is available in the `python/doc_gen/index.html` file.

## Changelog

- 2015-12-10 First public release (0.1)
- 2016-08-16 Minor release:
1. the official Spark target is Spark 0.2
2. support for keyed models

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