Quick and Easy Deployment of Machine Learning (ML) Models

  from sklearn.compose import ColumnTransformer
  from sklearn.linear_model import LogisticRegression
  from sklearn.preprocessing import OneHotEncoder
  from sklearn2pmml import sklearn2pmml
  from sklearn2pmml.pipeline import PMMLPipeline
  
  import pandas
  
  df = pandas.read_csv("Audit.csv")
  
  pipeline = PMMLPipeline([
      ("transformer", ColumnTransformer([
          ("continuous", "passthrough", ["Age", "Hours", "Income"]),
          ("categorical", OneHotEncoder(), ["Employment", "Education", "Marital", "Occupation", "Gender", "Deductions"])
      ])),
      ("classifier", LogisticRegression(multi_class = "ovr"))
  ])
  pipeline.fit(df, df["Adjusted"])
  pipeline.verify(df.sample(10))
  
  sklearn2pmml(pipeline, "LogisticRegression.pmml")

  library("dplyr")
  library("r2pmml")
  
  df = read.csv("Audit.csv")
  df$Adjusted = as.factor(df$Adjusted)
  
  audit.glm = glm("Adjusted ~ .", data = df, family = "binomial")
  audit.glm = verify(audit.glm, newdata = sample_n(df, 10))
  
  r2pmml(audit.glm, "LogisticRegression.pmml")

  import java.io.File
  import org.apache.spark.ml.Pipeline
  import org.apache.spark.ml.classification.LogisticRegression
  import org.apache.spark.ml.feature.RFormula
  import org.jpmml.sparkml.PMMLBuilder
  import org.jpmml.sparkml.model.HasPredictionModelOptions;
  
  val df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("Audit.csv")
  
  val rFormula = new RFormula().setFormula("Adjusted ~ .")
  val lr = new LogisticRegression().setLabelCol(rFormula.getLabelCol).setFeaturesCol(rFormula.getFeaturesCol)
  val pipeline = new Pipeline().setStages(Array(rFormula, lr))
  
  val pipelineModel = pipeline.fit(df)
  
  val pmmlBuilder = new PMMLBuilder(df.schema, pipelineModel).putOption(HasPredictionModelOptions.OPTION_KEEP_PREDICTIONCOL, false).verify(df.sample(false, 0.01).limit(10))
  
  pmmlBuilder.buildFile(new File("LogisticRegression.pmml"))

  $ java -jar openscoring-server-executable-${version}.jar

  $ curl -X PUT --data-binary @LogisticRegression.pmml -H "Content-type: text/xml" http://localhost:8080/openscoring/model/MyAuditModel
  $ curl -X POST --data-binary @Audit.csv -H "Content-type: text/plain; charset=UTF-8" http://localhost:8080/openscoring/model/MyAuditModel/csv > Audit-results.csv
  $ curl -X DELETE http://localhost:8080/openscoring/model/MyAuditModel

  from openscoring import Openscoring
  
  os = Openscoring(base_url = "http://localhost:8080/openscoring")
  
  os.deployFile("MyAuditModel", "LogisticRegression.pmml")
  os.evaluateCsvFile("MyAuditModel", "Audit.csv", "Audit-results.csv")
  os.undeploy("MyAuditModel")