Sub-module in Apache Ignite ML: Spark ML model parser
----------------------------

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

This module supports the loading machine learning models generated by Spark ML.

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 Vector Assmebler
        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")

This listing of code was used to get the Spark ML models for examples in spark-model-parser.

This module supports the all common models in Ignite ML and Spark ML:

- LogisticRegression
- LinearRegression
- LinearSVC
- DecisionTreeClassifier
- RandomForestClassifier
- GBTClassifier
- KMeans
- DecisionTreeRegressor
- RandomForestRegressor
- GBTRegressor

To load in Ignite ML you should use SparkModelParser class via method parse() call like in https://github.com/apache/ignite/tree/master/examples/src/main/java/org/apache/ignite/examples/ml/inference/spark/modelparser

NOTE: it doesn't support loading from PipelineModel in Spark.