pypmml 0.9.6

Python PMML scoring library

PyPMML

PyPMML is a Python PMML scoring library, it really is the Python API for PMML4S.

Prerequisites

• Java >= 1.8
• Python 2.7 or >= 3.5

Dependencies

• Py4J
• Pandas (optional)

Installation

pip install pypmml

Or install the latest version from github:

pip install --upgrade git+https://github.com/autodeployai/pypmml.git

Usage

1. Load model from various sources, e.g. readable, file path, string, or an array of bytes.

   from pypmml import Model
   
   # The model is from http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml
   model = Model.load('single_iris_dectree.xml')
   

2. Call predict(data) to predict new values that can be in different types, e.g. dict, json, Series or DataFrame of Pandas.

   # data in dict
   result = model.predict({'sepal_length': 5.1, 'sepal_width': 3.5, 'petal_length': 1.4, 'petal_width': 0.2})
    >>> print(result)
   {'probability': 1.0, 'node_id': '1', 'probability_Iris-virginica': 0.0, 'probability_Iris-setosa': 1.0, 'probability_Iris-versicolor': 0.0, 'predicted_class': 'Iris-setosa'}
   
   # data in 'records' json
   result = model.predict('[{"sepal_length": 5.1, "sepal_width": 3.5, "petal_length": 1.4, "petal_width": 0.2}]')
    >>> print(result)
   [{"probability":1.0,"probability_Iris-versicolor":0.0,"probability_Iris-setosa":1.0,"probability_Iris-virginica":0.0,"predicted_class":"Iris-setosa","node_id":"1"}]
   
   # data in 'split' json
   result = model.predict('{"columns": ["sepal_length", "sepal_width", "petal_length", "petal_width"], "data": [[5.1, 3.5, 1.4, 0.2]]}')
    >>> print(result)
   {"columns":["predicted_class","probability","probability_Iris-setosa","probability_Iris-versicolor","probability_Iris-virginica","node_id"],"data":[["Iris-setosa",1.0,1.0,0.0,0.0,"1"]]}
   

   How to work with Pandas

   import pandas as pd
   
   # data in Series
   result = model.predict(pd.Series({'sepal_length': 5.1, 'sepal_width': 3.5, 'petal_length': 1.4, 'petal_width': 0.2}))
   >>> print(result)
   node_id                                   1
   predicted_class                 Iris-setosa
   probability                               1
   probability_Iris-setosa                   1
   probability_Iris-versicolor               0
   probability_Iris-virginica                0
   Name: 0, dtype: object
   
   # The data is from here: http://dmg.org/pmml/pmml_examples/Iris.csv
   data = pd.read_csv('Iris.csv')
   
   # data in DataFrame
   result = model.predict(data)
    >>> print(result)
       node_id   predicted_class  probability  probability_Iris-setosa  probability_Iris-versicolor  probability_Iris-virginica
   0         1       Iris-setosa     1.000000                      1.0                     0.000000                    0.000000
   1         1       Iris-setosa     1.000000                      1.0                     0.000000                    0.000000
   ..      ...               ...          ...                      ...                          ...                         ...
   148      10    Iris-virginica     0.978261                      0.0                     0.021739                    0.978261
   149      10    Iris-virginica     0.978261                      0.0                     0.021739                    0.978261
   
   [150 rows x 6 columns]
   

Use PMML in Scala or Java

See the PMML4S project. PMML4S is a PMML scoring library for Scala. It provides both Scala and Java Evaluator API for PMML.

Use PMML in Spark

See the PMML4S-Spark project. PMML4S-Spark is a PMML scoring library for Spark as SparkML Transformer.

Use PMML in PySpark

See the PyPMML-Spark project. PyPMML-Spark is a Python PMML scoring library for PySpark as SparkML Transformer, it really is the Python API for PMML4s-Spark.

Deploy PMML as REST API

See the AI-Serving project. AI-Serving is serving AI/ML models in the open standard formats PMML and ONNX with both HTTP and gRPC endpoints.

Deploy and Manage AI/ML models at scale

See the DaaS system that deploys AI/ML models in production at scale on Kubernetes.

Support

If you have any questions about the PyPMML library, please open issues on this repository.

Feedback and contributions to the project, no matter what kind, are always very welcome.

License

PyPMML is licensed under APL 2.0.