我使用bigml.com生成了虹膜数据集的决策树模型。我已将此决策树模型下载为PMML，并希望将其用于本地计算机中的预测。
来自bigml的PMML模型<?xml version="1.0" encoding="utf-8"?>
<PMML version="4.2" xmlns="http://www.dmg.org/PMML-4_2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<Header description="Generated by BigML"/>
<DataDictionary>
<DataField dataType="double" displayName="Sepal length" name="000001" optype="continuous"/>
<DataField dataType="double" displayName="Sepal width" name="000002" optype="continuous"/>
<DataField dataType="double" displayName="Petal length" name="000003" optype="continuous"/>
<DataField dataType="double" displayName="Petal width" name="000004" optype="continuous"/>
<DataField dataType="string" displayName="Species" name="000005" optype="categorical">
<Value value="Iris-setosa"/>
<Value value="Iris-versicolor"/>
<Value value="Iris-virginica"/>
</DataField>
</DataDictionary>
<TreeModel algorithmName="mtree" functionName="classification" modelName="">
<MiningSchema>
<MiningField name="000001"/>
<MiningField name="000002"/>
<MiningField name="000003"/>
<MiningField name="000004"/>
<MiningField name="000005" usageType="target"/>
</MiningSchema>
<Node recordCount="150" score="Iris-setosa">
<True/>
<ScoreDistribution recordCount="50" value="Iris-setosa"/>
<ScoreDistribution recordCount="50" value="Iris-versicolor"/>
<ScoreDistribution recordCount="50" value="Iris-virginica"/>
<Node recordCount="100" score="Iris-versicolor">
<SimplePredicate field="000003" operator="greaterThan" value="2.45"/>
<ScoreDistribution recordCount="50" value="Iris-versicolor"/>
<ScoreDistribution recordCount="50" value="Iris-virginica"/>
<Node recordCount="46" score="Iris-virginica">
<SimplePredicate field="000004" operator="greaterThan" value="1.75"/>
<ScoreDistribution recordCount="45" value="Iris-virginica"/>
<ScoreDistribution recordCount="1" value="Iris-versicolor"/>
<Node recordCount="43" score="Iris-virginica">
<SimplePredicate field="000003" operator="greaterThan" value="4.85"/>
<ScoreDistribution recordCount="43" value="Iris-virginica"/>
</Node>
<Node recordCount="3" score="Iris-virginica">
<SimplePredicate field="000003" operator="lessOrEqual" value="4.85"/>
<ScoreDistribution recordCount="2" value="Iris-virginica"/>
<ScoreDistribution recordCount="1" value="Iris-versicolor"/>
<Node recordCount="1" score="Iris-versicolor">
<SimplePredicate field="000002" operator="greaterThan" value="3.1"/>
<ScoreDistribution recordCount="1" value="Iris-versicolor"/>
</Node>
<Node recordCount="2" score="Iris-virginica">
<SimplePredicate field="000002" operator="lessOrEqual" value="3.1"/>
<ScoreDistribution recordCount="2" value="Iris-virginica"/>
</Node>
</Node>
</Node>
<Node recordCount="54" score="Iris-versicolor">
<SimplePredicate field="000004" operator="lessOrEqual" value="1.75"/>
<ScoreDistribution recordCount="49" value="Iris-versicolor"/>
<ScoreDistribution recordCount="5" value="Iris-virginica"/>
<Node recordCount="6" score="Iris-virginica">
<SimplePredicate field="000003" operator="greaterThan" value="4.95"/>
<ScoreDistribution recordCount="4" value="Iris-virginica"/>
<ScoreDistribution recordCount="2" value="Iris-versicolor"/>
<Node recordCount="3" score="Iris-versicolor">
<SimplePredicate field="000004" operator="greaterThan" value="1.55"/>
<ScoreDistribution recordCount="2" value="Iris-versicolor"/>
<ScoreDistribution recordCount="1" value="Iris-virginica"/>
<Node recordCount="1" score="Iris-virginica">
<SimplePredicate field="000003" operator="greaterThan" value="5.45"/>
<ScoreDistribution recordCount="1" value="Iris-virginica"/>
</Node>
<Node recordCount="2" score="Iris-versicolor">
<SimplePredicate field="000003" operator="lessOrEqual" value="5.45"/>
<ScoreDistribution recordCount="2" value="Iris-versicolor"/>
</Node>
</Node>
<Node recordCount="3" score="Iris-virginica">
<SimplePredicate field="000004" operator="lessOrEqual" value="1.55"/>
<ScoreDistribution recordCount="3" value="Iris-virginica"/>
</Node>
</Node>
<Node recordCount="48" score="Iris-versicolor">
<SimplePredicate field="000003" operator="lessOrEqual" value="4.95"/>
<ScoreDistribution recordCount="47" value="Iris-versicolor"/>
<ScoreDistribution recordCount="1" value="Iris-virginica"/>
<Node recordCount="1" score="Iris-virginica">
<SimplePredicate field="000004" operator="greaterThan" value="1.65"/>
<ScoreDistribution recordCount="1" value="Iris-virginica"/>
</Node>
<Node recordCount="47" score="Iris-versicolor">
<SimplePredicate field="000004" operator="lessOrEqual" value="1.65"/>
<ScoreDistribution recordCount="47" value="Iris-versicolor"/>
</Node>
</Node>
</Node>
</Node>
<Node recordCount="50" score="Iris-setosa">
<SimplePredicate field="000003" operator="lessOrEqual" value="2.45"/>
<ScoreDistribution recordCount="50" value="Iris-setosa"/>
</Node>
</Node>
</TreeModel>
</PMML>
我通常使用R进行机器学习，并希望在我的系统中加载并使用此模型进行预测。 R本身有一个pmml包，但似乎不可能use it for prediction。有没有其他方法我可以使用这个PMML模型在R中进行预测。如果不可能，这个PMML模型可以用于其他语言，如python或weka？如果是，我该怎么做（需要代码）。
来自bigml的python模型
def predict_species(sepal_width=None,
petal_length=None,
petal_width=None):
""" Predictor for Species from
This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic
in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes
of 50 instances each, where each class refers to a type of iris plant.
Source
Iris Data Set[*]
Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository[*]. Irvine, CA: University of California, School of Information and Computer Science.
[*]Iris Data Set: http://archive.ics.uci.edu/ml/datasets/Iris
[*]UCI Machine Learning Repository: http://archive.ics.uci.edu/ml
"""
if (petal_length is None):
return u'Iris-setosa'
if (petal_length > 2.45):
if (petal_width is None):
return u'Iris-versicolor'
if (petal_width > 1.75):
if (petal_length > 4.85):
return u'Iris-virginica'
if (petal_length <= 4.85):
if (sepal_width is None):
return u'Iris-virginica'
if (sepal_width > 3.1):
return u'Iris-versicolor'
if (sepal_width <= 3.1):
return u'Iris-virginica'
if (petal_width <= 1.75):
if (petal_length > 4.95):
if (petal_width > 1.55):
if (petal_length > 5.45):
return u'Iris-virginica'
if (petal_length <= 5.45):
return u'Iris-versicolor'
if (petal_width <= 1.55):
return u'Iris-virginica'
if (petal_length <= 4.95):
if (petal_width > 1.65):
return u'Iris-virginica'
if (petal_width <= 1.65):
return u'Iris-versicolor'
if (petal_length <= 2.45):
return u'Iris-setosa'
答案 0 :(得分：2)
使用BigML执行本地预测的最简单方法是直接通过API调用下载模型（集合，集群，异常检测器等）。
例如，使用BigML's Python Bindings作为分类或回归模型，您可以执行以下操作：
from bigml.model import Model
model = Model('model/570f4b6e84622c5ed10095a9')
model.predict({'feature_1': 1, 'feature_2': 2})
使用本地群集查找最近的质心：
from bigml.cluster import Cluster
cluster = Cluster('cluster/572500b849c4a15c9d00451f')
cluster.centroid({'feature_1': 1, 'feature_2': 2})
使用本地异常检测器为新数据点评分：
from bigml.anomaly import Anomaly
anomaly_detector = Anomaly('anomaly/570f4c333bbd21090101e79f')
anomaly_detector.anomaly_score({'feature_1': 1, 'feature_2': 2})
上面的类（Model，Cluster和Anomaly）将下载定义每个模型的JSON PML代码，并将其重新定义为本地函数（在本例中为python）。由于您可能不希望使用R来实现实际应用程序，因此最好使用您将用于应用程序的语言执行预测：python，node.js，java等.BigML提供open-所有这些的源绑定。