将python scikit学习模型导出到pmml [英] Export python scikit learn models into pmml
问题描述
我想将python scikit-learn模型导出到PMML中.
I want to export python scikit-learn models into PMML.
哪种python软件包最合适?
What python package is best suited?
我阅读了有关 Augustus 的信息，但我无法使用scikit-learn模型找到任何示例.
I read about Augustus, but I was not able to find any example using scikit-learn models.
推荐答案
围绕JPMML-SkLearn命令行应用程序的薄包装.有关受支持的Scikit-Learn Estimator和Transformer类型的列表，请参阅JPMML-SkLearn项目的文档.
a thin wrapper around the JPMML-SkLearn command-line application. For a list of supported Scikit-Learn Estimator and Transformer types, please refer to the documentation of the JPMML-SkLearn project.
正如@ user1808924所指出的，它支持Python 2.7或3.4+.它还需要Java 1.7 +
As @user1808924 notes, it supports Python 2.7 or 3.4+. It also requires Java 1.7+
通过以下方式安装:(需要 git )
Installed via: (requires git)
pip install git+https://github.com/jpmml/sklearn2pmml.git
如何将分类器树导出到PMML的示例.首先生长该树:
# example tree & viz from http://scikit-learn.org/stable/modules/tree.html
from sklearn import datasets, tree
iris = datasets.load_iris()
clf = tree.DecisionTreeClassifier()
clf = clf.fit(iris.data, iris.target)
SkLearn2PMML转换有两个部分，一个是估计器(我们的clf)，另一个是映射器(用于离散化或PCA等预处理步骤).我们的映射器非常基础，因为我们没有进行任何转换.
There are two parts to an SkLearn2PMML conversion, an estimator (our clf) and a mapper (for preprocessing steps such as discretization or PCA). Our mapper is pretty basic, since we are not doing any transformations.
from sklearn_pandas import DataFrameMapper
default_mapper = DataFrameMapper([(i, None) for i in iris.feature_names + ['Species']])
from sklearn2pmml import sklearn2pmml
sklearn2pmml(estimator=clf,
mapper=default_mapper,
pmml="D:/workspace/IrisClassificationTree.pmml")
可以(虽然没有记录)通过mapper=None，但是您会看到预测变量名称丢失了(返回x1而不是sepal length等).
It is possible (though not documented) to pass mapper=None, but you will see that the predictor names get lost (returning x1 not sepal length etc.).
让我们看一下.pmml文件:
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<PMML xmlns="http://www.dmg.org/PMML-4_3" version="4.3">
<Header>
<Application name="JPMML-SkLearn" version="1.1.1"/>
<Timestamp>2016-09-26T19:21:43Z</Timestamp>
</Header>
<DataDictionary>
<DataField name="sepal length (cm)" optype="continuous" dataType="float"/>
<DataField name="sepal width (cm)" optype="continuous" dataType="float"/>
<DataField name="petal length (cm)" optype="continuous" dataType="float"/>
<DataField name="petal width (cm)" optype="continuous" dataType="float"/>
<DataField name="Species" optype="categorical" dataType="string">
<Value value="setosa"/>
<Value value="versicolor"/>
<Value value="virginica"/>
</DataField>
</DataDictionary>
<TreeModel functionName="classification" splitCharacteristic="binarySplit">
<MiningSchema>
<MiningField name="Species" usageType="target"/>
<MiningField name="sepal length (cm)"/>
<MiningField name="sepal width (cm)"/>
<MiningField name="petal length (cm)"/>
<MiningField name="petal width (cm)"/>
</MiningSchema>
<Output>
<OutputField name="probability_setosa" dataType="double" feature="probability" value="setosa"/>
<OutputField name="probability_versicolor" dataType="double" feature="probability" value="versicolor"/>
<OutputField name="probability_virginica" dataType="double" feature="probability" value="virginica"/>
</Output>
<Node id="1">
<True/>
<Node id="2" score="setosa" recordCount="50.0">
<SimplePredicate field="petal width (cm)" operator="lessOrEqual" value="0.8"/>
<ScoreDistribution value="setosa" recordCount="50.0"/>
<ScoreDistribution value="versicolor" recordCount="0.0"/>
<ScoreDistribution value="virginica" recordCount="0.0"/>
</Node>
<Node id="3">
<SimplePredicate field="petal width (cm)" operator="greaterThan" value="0.8"/>
<Node id="4">
<SimplePredicate field="petal width (cm)" operator="lessOrEqual" value="1.75"/>
<Node id="5">
<SimplePredicate field="petal length (cm)" operator="lessOrEqual" value="4.95"/>
<Node id="6" score="versicolor" recordCount="47.0">
<SimplePredicate field="petal width (cm)" operator="lessOrEqual" value="1.6500001"/>
<ScoreDistribution value="setosa" recordCount="0.0"/>
<ScoreDistribution value="versicolor" recordCount="47.0"/>
<ScoreDistribution value="virginica" recordCount="0.0"/>
</Node>
<Node id="7" score="virginica" recordCount="1.0">
<SimplePredicate field="petal width (cm)" operator="greaterThan" value="1.6500001"/>
<ScoreDistribution value="setosa" recordCount="0.0"/>
<ScoreDistribution value="versicolor" recordCount="0.0"/>
<ScoreDistribution value="virginica" recordCount="1.0"/>
</Node>
</Node>
<Node id="8">
<SimplePredicate field="petal length (cm)" operator="greaterThan" value="4.95"/>
<Node id="9" score="virginica" recordCount="3.0">
<SimplePredicate field="petal width (cm)" operator="lessOrEqual" value="1.55"/>
<ScoreDistribution value="setosa" recordCount="0.0"/>
<ScoreDistribution value="versicolor" recordCount="0.0"/>
<ScoreDistribution value="virginica" recordCount="3.0"/>
</Node>
<Node id="10">
<SimplePredicate field="petal width (cm)" operator="greaterThan" value="1.55"/>
<Node id="11" score="versicolor" recordCount="2.0">
<SimplePredicate field="sepal length (cm)" operator="lessOrEqual" value="6.95"/>
<ScoreDistribution value="setosa" recordCount="0.0"/>
<ScoreDistribution value="versicolor" recordCount="2.0"/>
<ScoreDistribution value="virginica" recordCount="0.0"/>
</Node>
<Node id="12" score="virginica" recordCount="1.0">
<SimplePredicate field="sepal length (cm)" operator="greaterThan" value="6.95"/>
<ScoreDistribution value="setosa" recordCount="0.0"/>
<ScoreDistribution value="versicolor" recordCount="0.0"/>
<ScoreDistribution value="virginica" recordCount="1.0"/>
</Node>
</Node>
</Node>
</Node>
<Node id="13">
<SimplePredicate field="petal width (cm)" operator="greaterThan" value="1.75"/>
<Node id="14">
<SimplePredicate field="petal length (cm)" operator="lessOrEqual" value="4.8500004"/>
<Node id="15" score="virginica" recordCount="2.0">
<SimplePredicate field="sepal width (cm)" operator="lessOrEqual" value="3.1"/>
<ScoreDistribution value="setosa" recordCount="0.0"/>
<ScoreDistribution value="versicolor" recordCount="0.0"/>
<ScoreDistribution value="virginica" recordCount="2.0"/>
</Node>
<Node id="16" score="versicolor" recordCount="1.0">
<SimplePredicate field="sepal width (cm)" operator="greaterThan" value="3.1"/>
<ScoreDistribution value="setosa" recordCount="0.0"/>
<ScoreDistribution value="versicolor" recordCount="1.0"/>
<ScoreDistribution value="virginica" recordCount="0.0"/>
</Node>
</Node>
<Node id="17" score="virginica" recordCount="43.0">
<SimplePredicate field="petal length (cm)" operator="greaterThan" value="4.8500004"/>
<ScoreDistribution value="setosa" recordCount="0.0"/>
<ScoreDistribution value="versicolor" recordCount="0.0"/>
<ScoreDistribution value="virginica" recordCount="43.0"/>
</Node>
</Node>
</Node>
</Node>
</TreeModel>
</PMML>
第一个分割(节点1)的花瓣宽度为0.8.节点2(花瓣宽度<== 0.8)捕获了所有刚毛，没有其他东西.
The first split (Node 1) is on petal width at 0.8. Node 2 (petal width <= 0.8) captures all of the setosa, with nothing else.
您可以将pmml输出与graphviz输出进行比较:
You can compare the pmml output to the graphviz output:
from sklearn.externals.six import StringIO
import pydotplus # this might be pydot for python 2.7
dot_data = StringIO()
tree.export_graphviz(clf,
out_file=dot_data,
feature_names=iris.feature_names,
class_names=iris.target_names,
filled=True, rounded=True,
special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("D:/workspace/iris.pdf")
# for in-line display, you can also do:
# from IPython.display import Image
# Image(graph.create_png())
这篇关于将python scikit学习模型导出到pmml的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
