Metadata-Version: 2.1
Name: pypmml-spark
Version: 0.9.5
Summary: Python PMML scoring library for PySpark as SparkML Transformer
Home-page: https://github.com/autodeployai/pypmml-spark
Author: AutoDeployAI
Author-email: autodeploy.ai@gmail.com
License: Apache License 2.0
Download-URL: https://github.com/autodeployai/pypmml-spark/archive/v0.9.5.tar.gz
Description: # PyPMML-Spark
        
        _PyPMML-Spark_ is a Python PMML scoring library for PySpark as SparkML Transformer, it really is the Python API for [PMML4S-Spark](https://github.com/autodeployai/pmml4s-spark).
        
        ## Prerequisites
         - Java >= 1.8
         - Python 2.7 or >= 3.5
        
        ## Dependencies
          - PySpark >= 2.4.0
          
        ## Installation
        
        ```bash
        pip install pypmml-spark
        ```
        
        Or install the latest version from github:
        
        ```bash
        pip install --upgrade git+https://github.com/autodeployai/pypmml-spark.git
        ```
        
        After that, you need to do more to use it in Spark that must know those jars in the package `pypmml_spark.jars`. There are several ways to do that:
        
        1. The easiest way is to run the script `link_pmml4s_jars_into_spark.py` that is delivered with `pypmml-spark`:
        
            ```bash
            link_pmml4s_jars_into_spark.py
            ```
            
        2. Use those config options to specify dependent jars properly. e.g. `--jars`, or `spark.executor.extraClassPath` and `spark.executor.extraClassPath`. See [Spark](http://spark.apache.org/docs/latest/configuration.html) for details about those parameters.
        
        ## Usage
        
        1. Load model from various sources, e.g. filename, string, or array of bytes.
        
            ```python
            from pypmml_spark import ScoreModel
            
            # The model is from http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml
            model = ScoreModel.fromFile('single_iris_dectree.xml')
            ```
        
        2. Call `transform(dataset)` to run a batch score against an input dataset.
        
            ```python
            # The data is from http://dmg.org/pmml/pmml_examples/Iris.csv
            df = spark.read.csv('Iris.csv', header='true')
            score_df = model.transform(df)
            ```
        
        ## Use PMML in Scala or Java
        See the [PMML4S](https://github.com/autodeployai/pmml4s) project. _PMML4S_ is a PMML scoring library for Scala. It provides both Scala and Java Evaluator API for PMML.
        
        ## Use PMML in Python
        See the [PyPMML](https://github.com/autodeployai/pypmml) project. _PyPMML_ is a Python PMML scoring library, it really is the Python API for PMML4S.
        
        ## Use PMML in Spark
        See the [PMML4S-Spark](https://github.com/autodeployai/pmml4s-spark) project. _PMML4S-Spark_ is a PMML scoring library for Spark as SparkML Transformer.
        
        ## Deploy PMML as REST API
        See the [DaaS](https://www.autodeploy.ai/) system that deploys AI & ML models in production at scale on Kubernetes.
        
        ## Support
        If you have any questions about the _PyPMML-Spark_ library, please open issues on this repository.
        
        Feedback and contributions to the project, no matter what kind, are always very welcome. 
        
        ## License
        _PyPMML-Spark_ is licensed under [APL 2.0](http://www.apache.org/licenses/LICENSE-2.0).
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
