PyPMML is a Python PMML scoring library, it really is the Python API for PMML4S.
- Java >= 1.8
- Python 2.7 or >= 3.5
- Py4J
- Pandas (optional)
pip install pypmml
Or install the latest version from github:
pip install --upgrade git+https://github.com/autodeployai/pypmml.git
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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')
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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]
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Shutdown the gateway of Py4J to free resources.
Model.close()
See the PyPMML-Spark project.
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.
PyPMML is licensed under APL 2.0.