pypmml 0.9.1

PMML4S Python API

PyPMML

PyPMML 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. filename, string, or 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.fromFile('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, 'PredictedValue': '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,"PredictedValue":"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":["PredictedValue","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
   PredictedValue                 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   PredictedValue  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]
   

3. Shutdown the gateway of Py4J to free resources.

   Model.close()
   

Use in PySpark

See the PyPMML-Spark project.

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.