Import Model from Apache Spark

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This is a legacy Apache Ignite documentation

The new documentation is hosted here: https://ignite.apache.org/docs/latest/

Model import from Apache Spark via Parquet files

Starting with Ignite 2.8, it's possible to import the following models of Apache Spark ML:

• Logistic regression (org.apache.spark.ml.classification.LogisticRegressionModel)
• Linear regression (org.apache.spark.ml.classification.LogisticRegressionModel)
• Decision tree (org.apache.spark.ml.classification.DecisionTreeClassificationModel)
• Support Vector Machine (org.apache.spark.ml.classification.LinearSVCModel)
• Random forest (org.apache.spark.ml.classification.RandomForestClassificationModel)
• K-Means (org.apache.spark.ml.clustering.KMeansModel)
• Decision tree regression (org.apache.spark.ml.regression.DecisionTreeRegressionModel)
• Random forest regression (org.apache.spark.ml.regression.RandomForestRegressionModel)
• Gradient boosted trees regression (org.apache.spark.ml.regression.GBTRegressionModel)
• Gradient boosted trees (org.apache.spark.ml.classification.GBTClassificationModel)

This feature works with models saved in snappy.parquet files.

Supported and tested Spark version: 2.3.0
Possibly might work with next Spark versions: 2.1, 2.2, 2.3, 2.4

To get the model from Spark ML you should save the model built as a result of training in Spark ML to the parquet file like in example below:

val spark: SparkSession = TitanicUtils.getSparkSession

val passengers = TitanicUtils.readPassengersWithCasting(spark)
    .select("survived", "pclass", "sibsp", "parch", "sex", "embarked", "age")

// Step - 1: Make Vectors from dataframe's columns using special VectorAssmebler
val assembler = new VectorAssembler()
    .setInputCols(Array("pclass", "sibsp", "parch", "survived"))
    .setOutputCol("features")

// Step - 2: Transform dataframe to vectorized dataframe with dropping rows
val output = assembler.transform(
    passengers.na.drop(Array("pclass", "sibsp", "parch", "survived", "age"))
).select("features", "age")

  
val lr = new LinearRegression()
    .setMaxIter(100)
    .setRegParam(0.1)
    .setElasticNetParam(0.1)
    .setLabelCol("age")
    .setFeaturesCol("features")
  
// Fit the model
val model = lr.fit(output)
model.write.overwrite().save("/home/models/titanic/linreg")

To load in Ignite ML you should use SparkModelParser class via method parse() call

DecisionTreeNode mdl = (DecisionTreeNode)SparkModelParser.parse(
   SPARK_MDL_PATH,
   SupportedSparkModels.DECISION_TREE
);

You can see more examples of using this API in the examples module in the package: org.apache.ignite.examples.ml.inference.spark.modelparser

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NOTE

It does not support loading from PipelineModel in Spark.
It does not support intermediate feature transformers from Spark due to different nature of preprocessing on Ignite and Spark side.