path: root/python-scikit-spark.spec

%global _empty_manifest_terminate_build 0
Name:		python-scikit-spark
Version:	0.4.0
Release:	1
Summary:	Spark acceleration for Scikit-Learn cross validation techniques
License:	Apache 2.0
URL:		https://github.com/scikit-spark/scikit-spark
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/79/a6/376f8dc174655538d50f8b19441bf82b6c7f327dbb8261a54e6affc5433a/scikit-spark-0.4.0.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-six

%description
# Spark acceleration for Scikit-Learn

This project is a major re-write of the 
[spark-sklearn](https://github.com/databricks/spark-sklearn) project, which 
seems to no longer be under development. It focuses specifically on the 
acceleration of Scikit-Learn's cross validation functionality using PySpark.

### Improvements over spark-sklearn
`scikit-spark` supports `scikit-learn` versions past 0.19, `spark-sklearn` [have stated that they are probably not 
going to support newer versions](https://github.com/databricks/spark-sklearn/issues/113).

The functionality in `scikit-spark` is based on `sklearn.model_selection` module rather than the 
deprecated and soon to be removed `sklearn.grid_search`. The new `model_selection` versions 
contain several nicer features and `scikit-spark` maintains full compatibility.

## Installation
The package can be installed through pip:
```bash
pip install scikit-spark
```

It has so far only been tested with Spark 2.2.0 and up, but may work with 
older versions. 

### Supported scikit-learn versions
- 0.18 untested, likely doesn't work
- 0.19 supported
- 0.20 supported
- 0.21 supported (Python 3 only)
- 0.22 supported (Python 3 only)

## Usage

The functionality here is meant to as closely resemble using Scikit-Learn as 
possible. By default (with `spark=True`) the `SparkSession` is obtained
internally by calling `SparkSession.builder.getOrCreate()`, so the instantiation
and calling of the functions is the same (You will preferably have already 
created a `SparkSession`). 

This example is adapted from the Scikit-Learn documentation. It instantiates
a local `SparkSession`, and distributes the cross validation folds and 
iterations using this. In actual use, to get the benefit of this package it 
should be used distributed across several machines with Spark as running it 
locally is slower than the `Scikit-Learn` parallelisation implementation.

```python
from sklearn import svm, datasets
from pyspark.sql import SparkSession

iris = datasets.load_iris()
parameters = {'kernel':('linear', 'rbf'), 'C':[0.01, 0.1, 1, 10, 100]}
svc = svm.SVC()

spark = SparkSession.builder\
    .master("local[*]")\
    .appName("skspark-grid-search-doctests")\
    .getOrCreate()

# How to run grid search
from skspark.model_selection import GridSearchCV

gs = GridSearchCV(svc, parameters)
gs.fit(iris.data, iris.target)

# How to run random search
from skspark.model_selection import RandomizedSearchCV

rs = RandomizedSearchCV(spark, svc, parameters)
rs.fit(iris.data, iris.target)
```

## Current and upcoming functionality
- Current
    - model_selection.RandomizedSearchCV
    - model_selection.GridSearchCV
- Upcoming
    - model_selection.cross_val_predict
    - model_selection.cross_val_score

*The docstrings are modifications of the Scikit-Learn ones and are still being
converted to specifically refer to this project.* 

## Performance optimisations 

### Reducing RAM usage 
*Coming soon*





%package -n python3-scikit-spark
Summary:	Spark acceleration for Scikit-Learn cross validation techniques
Provides:	python-scikit-spark
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-scikit-spark
# Spark acceleration for Scikit-Learn

This project is a major re-write of the 
[spark-sklearn](https://github.com/databricks/spark-sklearn) project, which 
seems to no longer be under development. It focuses specifically on the 
acceleration of Scikit-Learn's cross validation functionality using PySpark.

### Improvements over spark-sklearn
`scikit-spark` supports `scikit-learn` versions past 0.19, `spark-sklearn` [have stated that they are probably not 
going to support newer versions](https://github.com/databricks/spark-sklearn/issues/113).

The functionality in `scikit-spark` is based on `sklearn.model_selection` module rather than the 
deprecated and soon to be removed `sklearn.grid_search`. The new `model_selection` versions 
contain several nicer features and `scikit-spark` maintains full compatibility.

## Installation
The package can be installed through pip:
```bash
pip install scikit-spark
```

It has so far only been tested with Spark 2.2.0 and up, but may work with 
older versions. 

### Supported scikit-learn versions
- 0.18 untested, likely doesn't work
- 0.19 supported
- 0.20 supported
- 0.21 supported (Python 3 only)
- 0.22 supported (Python 3 only)

## Usage

The functionality here is meant to as closely resemble using Scikit-Learn as 
possible. By default (with `spark=True`) the `SparkSession` is obtained
internally by calling `SparkSession.builder.getOrCreate()`, so the instantiation
and calling of the functions is the same (You will preferably have already 
created a `SparkSession`). 

This example is adapted from the Scikit-Learn documentation. It instantiates
a local `SparkSession`, and distributes the cross validation folds and 
iterations using this. In actual use, to get the benefit of this package it 
should be used distributed across several machines with Spark as running it 
locally is slower than the `Scikit-Learn` parallelisation implementation.

```python
from sklearn import svm, datasets
from pyspark.sql import SparkSession

iris = datasets.load_iris()
parameters = {'kernel':('linear', 'rbf'), 'C':[0.01, 0.1, 1, 10, 100]}
svc = svm.SVC()

spark = SparkSession.builder\
    .master("local[*]")\
    .appName("skspark-grid-search-doctests")\
    .getOrCreate()

# How to run grid search
from skspark.model_selection import GridSearchCV

gs = GridSearchCV(svc, parameters)
gs.fit(iris.data, iris.target)

# How to run random search
from skspark.model_selection import RandomizedSearchCV

rs = RandomizedSearchCV(spark, svc, parameters)
rs.fit(iris.data, iris.target)
```

## Current and upcoming functionality
- Current
    - model_selection.RandomizedSearchCV
    - model_selection.GridSearchCV
- Upcoming
    - model_selection.cross_val_predict
    - model_selection.cross_val_score

*The docstrings are modifications of the Scikit-Learn ones and are still being
converted to specifically refer to this project.* 

## Performance optimisations 

### Reducing RAM usage 
*Coming soon*





%package help
Summary:	Development documents and examples for scikit-spark
Provides:	python3-scikit-spark-doc
%description help
# Spark acceleration for Scikit-Learn

This project is a major re-write of the 
[spark-sklearn](https://github.com/databricks/spark-sklearn) project, which 
seems to no longer be under development. It focuses specifically on the 
acceleration of Scikit-Learn's cross validation functionality using PySpark.

### Improvements over spark-sklearn
`scikit-spark` supports `scikit-learn` versions past 0.19, `spark-sklearn` [have stated that they are probably not 
going to support newer versions](https://github.com/databricks/spark-sklearn/issues/113).

The functionality in `scikit-spark` is based on `sklearn.model_selection` module rather than the 
deprecated and soon to be removed `sklearn.grid_search`. The new `model_selection` versions 
contain several nicer features and `scikit-spark` maintains full compatibility.

## Installation
The package can be installed through pip:
```bash
pip install scikit-spark
```

It has so far only been tested with Spark 2.2.0 and up, but may work with 
older versions. 

### Supported scikit-learn versions
- 0.18 untested, likely doesn't work
- 0.19 supported
- 0.20 supported
- 0.21 supported (Python 3 only)
- 0.22 supported (Python 3 only)

## Usage

The functionality here is meant to as closely resemble using Scikit-Learn as 
possible. By default (with `spark=True`) the `SparkSession` is obtained
internally by calling `SparkSession.builder.getOrCreate()`, so the instantiation
and calling of the functions is the same (You will preferably have already 
created a `SparkSession`). 

This example is adapted from the Scikit-Learn documentation. It instantiates
a local `SparkSession`, and distributes the cross validation folds and 
iterations using this. In actual use, to get the benefit of this package it 
should be used distributed across several machines with Spark as running it 
locally is slower than the `Scikit-Learn` parallelisation implementation.

```python
from sklearn import svm, datasets
from pyspark.sql import SparkSession

iris = datasets.load_iris()
parameters = {'kernel':('linear', 'rbf'), 'C':[0.01, 0.1, 1, 10, 100]}
svc = svm.SVC()

spark = SparkSession.builder\
    .master("local[*]")\
    .appName("skspark-grid-search-doctests")\
    .getOrCreate()

# How to run grid search
from skspark.model_selection import GridSearchCV

gs = GridSearchCV(svc, parameters)
gs.fit(iris.data, iris.target)

# How to run random search
from skspark.model_selection import RandomizedSearchCV

rs = RandomizedSearchCV(spark, svc, parameters)
rs.fit(iris.data, iris.target)
```

## Current and upcoming functionality
- Current
    - model_selection.RandomizedSearchCV
    - model_selection.GridSearchCV
- Upcoming
    - model_selection.cross_val_predict
    - model_selection.cross_val_score

*The docstrings are modifications of the Scikit-Learn ones and are still being
converted to specifically refer to this project.* 

## Performance optimisations 

### Reducing RAM usage 
*Coming soon*





%prep
%autosetup -n scikit-spark-0.4.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-scikit-spark -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.0-1
- Package Spec generated