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 joblibsparkThe 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)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
ndarrayor 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).
This package is available on PYPI:
pip install spark-sklearnThis 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/pysparkYou can directly run tests:
cd python && ./run-tests.shThis requires the environment variable SPARK_HOME to point to your local copy of Spark.
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.
API documentation is currently hosted on Github pages. To build the docs yourself, see the instructions in docs/.
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