extensionmodelnode properties

Last updated: Jan 17, 2024

Extension Model node icon With the Extension Model node, you can run R or Python for Spark scripts to build and score results.

Note that many of the properties and much of the information on this page is only applicable to SPSS Modeler Desktop streams.

Python for Spark example

import modeler.api
stream = modeler.script.stream()
node = stream.create("extension_build", "extension_build")
node.setPropertyValue("syntax_type", "Python")

build_script = """
import json
import spss.pyspark.runtime
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.linalg import DenseVector
from pyspark.mllib.tree import DecisionTree

cxt = spss.pyspark.runtime.getContext()
df = cxt.getSparkInputData()
schema = df.dtypes[:]

target = "Drug"
predictors = ["Age","BP","Sex","Cholesterol","Na","K"]

def metaMap(row,schema):
    col = 0
    meta = []
    for (cname, ctype) in schema:
        if ctype == 'string':
            meta.append(set([row[col]]))
        else:
            meta.append((row[col],row[col]))
        col += 1        
    return meta

def metaReduce(meta1,meta2,schema):
    col = 0
    meta = []
    for (cname, ctype) in schema:
        if ctype == 'string':
            meta.append(meta1[col].union(meta2[col]))
        else:
            meta.append((min(meta1[col][0],meta2[col][0]),max(meta1[col][1],meta2[col][1])))
        col += 1
    return meta

metadata = df.rdd.map(lambda row: metaMap(row,schema)).reduce(lambda x,y:metaReduce(x,y,schema))

def setToList(v):
    if isinstance(v,set): 
        return list(v)
    return v

metadata = map(lambda x: setToList(x), metadata)
print metadata

lookup = {}
for i in range(0,len(schema)):
    lookup[schema[i][0]] = i

def row2LabeledPoint(dm,lookup,target,predictors,row):
    target_index = lookup[target]
    tval = dm[target_index].index(row[target_index])
    pvals = []
    for predictor in predictors:
        predictor_index = lookup[predictor]
        if isinstance(dm[predictor_index],list):        
            pval = dm[predictor_index].index(row[predictor_index])
        else:
            pval = row[predictor_index]
        pvals.append(pval)
    return LabeledPoint(tval,DenseVector(pvals))

# count number of target classes
predictorClassCount = len(metadata[lookup[target]])

# define function to extract categorical predictor information from datamodel 
def getCategoricalFeatureInfo(dm,lookup,predictors):
    info = {}
    for i in range(0,len(predictors)):
        predictor = predictors[i]
        predictor_index = lookup[predictor]
        if isinstance(dm[predictor_index],list):
            info[i] = len(dm[predictor_index])
    return info

# convert dataframe to an RDD containing LabeledPoint
lps = df.rdd.map(lambda row: row2LabeledPoint(metadata,lookup,target,predictors,row))

treeModel = DecisionTree.trainClassifier(
    lps,
    numClasses=predictorClassCount,
    categoricalFeaturesInfo=getCategoricalFeatureInfo(metadata, lookup, predictors),
    impurity='gini',
    maxDepth=5,
    maxBins=100)

_outputPath = cxt.createTemporaryFolder()
treeModel.save(cxt.getSparkContext(), _outputPath)
cxt.setModelContentFromPath("TreeModel", _outputPath)
cxt.setModelContentFromString("model.dm",json.dumps(metadata), mimeType="application/json")\
   .setModelContentFromString("model.structure",treeModel.toDebugString())

"""

node.setPropertyValue("python_build_syntax", build_script)

R example

node.setPropertyValue("syntax_type", "R")
node.setPropertyValue("r_build_syntax", """modelerModel <- lm(modelerData$Na~modelerData$K,modelerData)
modelerDataModel
modelerModel 
""")

Table 1. extensionmodelnode properties
`extensionmodelnode` Properties	Values	Property description
`syntax_type`	R Python	Specify which script runs: R or Python (R is the default).
`r_build_syntax`	string	The R scripting syntax for model building.
`r_score_syntax`	string	The R scripting syntax for model scoring.
`python_build_syntax`	string	The Python scripting syntax for model building.
`python_score_syntax`	string	The Python scripting syntax for model scoring.
`convert_flags`	`StringsAndDoubles` `LogicalValues`	Option to convert flag fields.
`convert_missing`	flag	Option to convert missing values to R `NA` value.
`convert_datetime`	flag	Option to convert variables with date or datetime formats to R date/time formats.
`convert_datetime_class`	`POSIXct` `POSIXlt`	Options to specify to what format variables with date or datetime formats are converted.
`output_html`	flag	Option to display graphs in the R model nugget.
`output_text`	flag	Option to write R console text output to the R model nugget.